title: Public health impacts of the International Network of Paediatric Surveillance Units

abstract: For many uncommon but important paediatric conditions and disorders, there is a lack of national data on disease incidence, disease burden, short-term outcomes, or the impact of preventive health measures on incidence and outcomes. Sometimes, even the availability of national data is not sufficient to advance knowledge. ::: ::: The International Network of Paediatric Surveillance Units (INoPSU) , comprising 13 national member countries, was established to address these important information gaps. Many studies have been undertaken simultaneously, allowing for comparison among different geographical regions. Study results have affected public health and clinical care, and provided new and timely data to advise on the development of new policies and legislation. ::: ::: ::: LEARNING POINTS ::: Since its inception in 1998, INoPSU, a network that involves more than 10,000 clinicians seeing more than 50 million children, conducted studies that have provided scientific evidence to support the following public health actions (Table 1). ::: ::: ::: ::: TABLE 1 ::: ::: Studies resulting in public health actions ::: ::: ::: ::: Vaccine-preventable diseases ::: ::: Pertussis infection: International study results have demonstrated the severity of this infection and the possibility of transmission from older family members to children too young to be vaccinated. In several countries, this led to a review of the age for administering the first vaccine, and to a targeted approach for adult and adolescent immunization programs. Furthermore, surveillance in Cyprus quickly identified an outbreak, providing valuable information to public health authorities. ::: ::: ::: Neonatal herpes simplex virus (HSV): Study results demonstrated significant mortality rates in some countries, with HSV-1 as the most prevalent type. The need for an effective HSV-1 and HSV-2 vaccine is evident. ::: ::: ::: Congenital cytomegalovirus infection (CMV): All countries documented the severity of this illness, which is the most common infectious cause of birth anomalies. Following a two-year study in Canada, only severely affected CMV cases were being detected, representing a fraction of the expected numbers. Study results support the need for a new vaccine as well as routine CMV screening. ::: ::: ::: ::: ::: Clinical practice guidelines and health planning services ::: ::: Hemolytic-uremic syndrome: This syndrome peaks in most countries during the summer, with outbreaks due to different strains of Shiga toxin-producing Escherichia coli acquired from water, hamburger meat and petting farms. Surveillance of hemolytic-uremic syndrome in seven different countries described the geographical differences of prevalent strains of E coli. Study results supported legislative measures for safe food production, public water testing and ongoing education on hygiene. ::: ::: ::: Vitamin K deficiency bleeding: Study results demonstrated that most cases are of late onset and related to liver disease, with many patients receiving no or incomplete vitamin K prophylaxis at birth. Results reaffirmed the recommendations for the continued use of vitamin K prophylaxis to prevent hemorrhagic disease of the newborn. Administration by intramuscular injection has been shown to be the most effective. ::: ::: ::: Vitamin D deficiency rickets: Although this disease is not as rare as first anticipated, the majority of cases were found in darker-skinned and exclusively breastfed children. Study results reinforce clinical practice guidelines that vitamin D supplementation should be given to all exclusively breastfed children to prevent nutritional rickets. In Australia, vitamin D deficiency rickets was most common among refugee families, supporting active screening and treatment in this vulnerable group. ::: ::: ::: Early-onset eating disorders: Food avoidance was the most common clinical feature in early-onset eating disorders. Children often presented with significant weight loss, and many had medical problems (such as bradycardia, hypothermia and hypotension) and were hospitalized. In Australia, approximately 25% of reported cases were boys, and in Canada, 14%. Study results demonstrate the need to establish preadolescent diagnostic criteria and early detection through the use of growth charts. ::: ::: ::: ::: ::: Injury prevention ::: ::: Seatbelt syndrome: Studies in both Canada and Australia confirmed the lack of uniform legislation and signalled high morbidity rates. In Canada, 25% of reported children were left paraplegic following a motor vehicle crash. In Australia, children injured while travelling restrained by adult seatbelts sustained serious abdominal and spinal injuries. Data gained from these studies have supported advocacy for age- and size-appropriate use of restraints in motor vehicles, and informed changes to legislation on child restraints. ::: ::: ::: Chemistry set poisoning: Results of surveillance supported changes to the European Union recommendations on safe toy packaging, including warnings and instructions on the package. ::: ::: ::: Accidental lamp oil poisoning: Ingestion of even a very small amount of lamp oil can seriously harm a child. Since 1999, there has been a ban on coloured and perfumed lamp oils in Germany and other countries of the European Union. Results from 2000 to 2009 led to additional restrictions in 2009 and extended to all uncoloured and unscented lamp oils and grill lighter fluids. The quality standard of the unsafe ornamental lamps has also been improved, but they will need to meet a child-safe standard in the future.


title: Distributed Algorithm for High-Utility Subgraph Pattern Mining Over Big Data Platforms

abstract: Frequent subgraph pattern mining (FSM) finds subgraph patterns that occur in a graph database with a frequency that is more than a given threshold. In FSM, the notion of occurrence captures the presence or absence of a node and an edge in a binary fashion and considers relevance of each edge or node as same. However, an edge or a node may have different relevancy score. Therefore, the utility of a pattern should be defined using the relevance score of participating edges or nodes. This paper defines the utility notion of a pattern using this idea and presents algorithms to mine high-utility patterns from a given graph database. A significant issue in high-utility pattern mining is that the antimonotonic property no longer holds contrary to the FSM. Hence pruning of the search space becomes a daunting task. To address this issue, we incorporate a function to estimate an upper-bound utility of a pattern object that also satisfies the anti-monotonic property. This paper presents three optimization heuristics for the solution on a distributed platform, namely, a novel use of bloom filter to avoid exploration of non-candidates, avoidance of sending database information with each pattern, and avoidance of sending pattern embeddings with each pattern. The experimental study on Apache Spark shows the effectiveness of our proposed optimization strategies.


Forecasting tourism demand to Catalonia: neural networks vs. time series models


title: A Taxonomy of Business Process Modelling and Information Systems Modelling Techniques

abstract: Modeling always has been at the core of both organizational design and information systems (IS) development. Models enable decision makers to filter out the irrelevant complexities of the real world, so that efforts can be directed toward the most important parts of the system under study. However, both business analysts and IS professionals may find it difficult to navigate through a maze of theoretical paradigms, methodological approaches, and representational formalisms that have been proposed for both business process modeling (BPM) and information systems modeling (ISM). This paper sets out to put an order to this chaos by proposing an evaluation framework and a novel taxonomy of BPM and ISM techniques. These findings, coupled with a detailed review of BPM and ISM techniques, can assist decision makers in comparatively evaluating and selecting suitable modeling techniques, depending on the characteristics and requirements of individual projects.


title: SQBC: An efficient subgraph matching method over large and dense graphs

abstract: Recent progress in biology and computer science have generated many complicated networks, most of which can be modeled as large and dense graphs. Developing effective and efficient subgraph match methods over these graphs is urgent, meaningful and necessary. Although some excellent exploratory approaches have been proposed these years, they show poor performances when the graphs are large and dense. This paper presents a novel Subgraph Query technique Based on Clique feature, called SQBC, which integrates the carefully designed clique encoding with the existing vertex encoding [40] as the basic index unit to reduce the search space. Furthermore, SQBC optimizes the subgraph isomorphism test based on clique features. Extensive experiments over biological networks, RDF dataset and synthetic graphs have shown that SQBC outperforms the most popular competitors both in effectiveness and efficiency especially when the data graphs are large and dense.


title: Fuzzy Logic Based Automatic Braking System in Trains

abstract: This paper focuses on a new approach to braking system in train, by the using the technique of artificial intelligence applied to it. The intelligence for braking is provided by a fuzzy logic controller. The fuzzy logic controller is simulated using Matlab-Simulink fuzzy logic toolbox. Generally the Indian railways use two persons to operate a train and they employ a manual procedure for stopping the train in each station. The proposed fuzzy logic controller helps in reduction of manpower for the train operation. The simulated fuzzy logic controller gets activated at about 500 m from the station based on its decision with reference to the speed and distance, the train stops at the station smoothly and automatically.


title: Uniquely decodable and directly accessible non-prefix-free codes via wavelet trees

abstract: Unique decodability is the essential feature of any coding scheme, which is naturally provided by prefix-free codes satisfying the Kraft-McMillan inequality. Non-prefix-free codes have received much less attention due to the lack of an efficient method to support this property. In this study we introduce a novel technique that uses wavelet trees to bring unique decodability to non-prefix-free codes. Proposed method also provides direct access to the ith codeword, which can be extended to any variable-length coding scheme. The space overhead required for unique decoding is upper bounded by n·log q bits, where n is the number of symbols, and q is the number of distinct codeword lengths, which is normally expected to be a small number in non-prefix-free codes. Direct access is supported by using an additional o(n · log q) bits. We show that the overhead space requirement is much less than sampling methods using state-of-the-art compact integer representations.


title: Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks

abstract: Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instruments with microelectromechanical system (MEMS) technology. Seismocardiography (SCG) has been considered to be free from the burden of measurement for cardiac activity, but it has been limited in its application in daily life. The most important issues regarding SCG are to overcome the limitations of motion artifacts due to the sensitivity of motion sensor. Although novel adaptive filters for noise cancellation have been developed, they depend on the researcher’s subjective decision. Convolutional neural networks (CNNs) can extract significant features from data automatically without a researcher’s subjective decision, so that signal processing has been recently replaced as CNNs. Thus, this study aimed to develop a novel method to enhance heart rate estimation from thoracic movement by CNNs. Thoracic movement was measured by six-axis accelerometer and gyroscope signals using a wearable sensor that can be worn by simply clipping on clothes. The dataset was collected from 30 participants (15 males, 15 females) using 12 measurement conditions according to two physical conditions (i.e., relaxed and aroused conditions), three body postures (i.e., sitting, standing, and supine), and six movement speeds (i.e., 3.2, 4.5, 5.8, 6.4, 8.5, and 10.3 km/h). The motion data (i.e., six-axis accelerometer and gyroscope) and heart rate (i.e., electrocardiogram (ECG)) were determined as the input data and labels in the dataset, respectively. The CNN model was developed based on VGG Net and optimized by testing according to network depth and data augmentation. The ensemble network of the VGG-16 without data augmentation and the VGG-19 with data augmentation was determined as optimal architecture for generalization. As a result, the proposed method showed higher accuracy than the previous SCG method using signal processing in most measurement conditions. The three main contributions are as follows: (1) the CNN model enhanced heart rate estimation with the benefits of automatic feature extraction from the data; (2) the proposed method was compared with the previous SCG method using signal processing; (3) the method was tested in 12 measurement conditions related to daily motion for a more practical application.


title: Language evolution in the laboratory

abstract: The historical origins of natural language cannot be observed directly. We can, however, study systems that support language and we can also develop models that explore the plausibility of different hypotheses about how language emerged. More recently, evolutionary linguists have begun to conduct language evolution experiments in the laboratory, where the emergence of new languages used by human participants can be observed directly. This enables researchers to study both the cognitive capacities necessary for language and the ways in which languages themselves emerge. One theme that runs through this work is how individual-level behaviours result in population-level linguistic phenomena. A central challenge for the future will be to explore how different forms of information transmission affect this process.


title: An Efficient Deep Reinforcement Learning Model for Urban Traffic Control

abstract: Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult. Since model-based UTC methods may not accurately describe the complex nature of traffic dynamics in all situations, model-free data-driven UTC methods, especially reinforcement learning (RL) based UTC methods, received increasing interests in the last decade. However, existing DL approaches did not propose an efficient algorithm to solve the complicated multiple intersections control problems whose state-action spaces are vast. To solve this problem, we propose a Deep Reinforcement Learning (DRL) algorithm that combines several tricks to master an appropriate control strategy within an acceptable time. This new algorithm relaxes the fixed traffic demand pattern assumption and reduces human invention in parameter tuning. Simulation experiments have shown that our method outperforms traditional rule-based approaches and has the potential to handle more complex traffic problems in the real world.


title: Hydroponic smart farming using cyber physical social system with telegram messenger

abstract: In the Cyber Physical Social System (CPSS), collaborative work between hydroponic farmers is now possible. With this new concept, hydroponic smart farming system that can be monitored online via Telegram Messenger is developed. The design that is created can monitor important parameters in the hydroponics system, such as light intensity, room temperature, humidity, pH, nutrient temperature, and Electrical Conductivity (EC). The prototype is designed using Raspberry Pi 3 that connects directly with sensors such as DHT11 module, LDR, pH sensor module, and EC sensor. Telegram BOT that allows to monitor sensors online via Telegram is also made. With the integration of the Physical System (Raspberry Pi, sensor) and Social System (Telegram Messenger) connected online via internet or cyber, the hydroponic system monitoring becomes more flexible.


Fast and Robust Pyramid-based Image Processing


title: Investigation of Adaptive Filtering for Noise Cancellation inECG signals

abstract: New generation of medical treatment has been supported by computerized processes. Signals recorded from the human body provide valuable information about the biological activities of body organs. The organs' characteristic topologies with temporal and spectral, properties, can be correlated with a normal or pathological function. In response to dynamic changes in the behavior of those organs, the signals exhibit time-varying, non-stationary responses. The signals are always contaminated by a drift and interference caused by several bioelectric phenomena, or by various types of noise, such as intrinsic noise from the recorder and noise from electrode-skin contact. In this paper we utilize adaptive filters for noise cancellation and analysis ECG signals.


title: TreeTalk: Composition and Compression of Trees for Image Descriptions

abstract: We present a new tree based approach to composing expressive image descriptions that makes use of naturally occuring web images with captions. We investigate two related tasks: image caption generalization and generation , where the former is an optional sub-task of the latter. The high-level idea of our approach is to harvest expressive phrases (as tree fragments) from existing image descriptions, then to compose a new description by selectively combining the extracted (and optionally pruned) tree fragments. Key algorithmic components are tree composition and compression , both integrating tree structure with sequence structure. Our proposed system attains significantly better performance than previous approaches for both image caption generalization and generation . In addition, our work is the first to show the empirical benefit of automatically generalized captions for composing natural image descriptions.


title: Flexible diet choice offsets protein costs of pathogen resistance in a caterpillar

abstract: Mounting effective resistance against pathogens is costly in terms of energy and nutrients. However, it remains unexplored whether hosts can offset such costs by adjusting their dietary intake so as to recoup the specific resources involved. We test this possibility by experimentally challenging caterpillars (Spodoptera littoralis) with a highly virulent entomopathogen (nucleopolyhedrovirus), under dietary regimes varying in the content of protein and digestible carbohydrate. We found that dietary protein influenced both resistance to pathogen attack and constitutive immune function to a greater extent than did dietary carbohydrate, indicating higher protein costs of resistance than energy costs. Moreover, when allowed to self-compose their diet, insects surviving viral challenge increased their relative intake of protein compared with controls and those larvae dying of infection, thus demonstrating compensation for protein costs associated with resistance. These results suggest that the change in the host's nutritional demands to fight infection induces a compensatory shift in feeding behaviour.


title: Somatomotor mu rhythm amplitude correlates with rigidity during deep brain stimulation in Parkinsonian patients

abstract: Abstract Objective Parkinsonian patients have abnormal oscillatory activity within the basal ganglia-thalamocortical circuitry. Particularly, excessive beta band oscillations are thought to be associated with akinesia. We studied whether cortical spontaneous activity is modified by deep brain stimulation (DBS) in advanced Parkinson’s disease and if the modifications are related to the clinical symptoms. Methods We studied the effects of bilateral electrical stimulation of subthalamic nucleus (STN) on cortical spontaneous activity by magnetoencephalography (MEG) in 11 Parkinsonian patients. The artifacts produced by DBS were suppressed by tSSS algorithm. Results During DBS, UPDRS (Unified Parkinson’s Disease Rating Scale) rigidity scores correlated with 6–10 Hz and 12–20 Hz somatomotor source strengths when eyes were open. When DBS was off UPDRS action tremor scores correlated with pericentral 6–10 Hz and 21–30 Hz and occipital alpha source strengths when eyes open. Occipital alpha strength decreased during DBS when eyes closed. The peak frequency of occipital alpha rhythm correlated negatively with total UPDRS motor scores and with rigidity subscores, when eyes closed. Conclusion STN DBS modulates brain oscillations both in alpha and beta bands and these oscillations reflect the clinical condition during DBS. Significance MEG combined with an appropriate artifact rejection method enables studies of DBS effects in Parkinson’s disease and presumably also in the other emerging DBS indications.


title: Nonnegative Matrix Factorization for Spectral Data Analysis

abstract: Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data, which is inherently nonnegative. The identification and classification of space objects that cannot be imaged in the normal way with telescopes is an important but difficult problem for tracking thousands of objects, including satellites, rocket bodies, debris, and asteroids, in orbit around the earth. In this paper, we develop an effective nonnegative matrix factorization algorithm with novel smoothness constraints for unmixing spectral reflectance data for space object identification and classification purposes. Promising numerical results are presented using laboratory and simulated datasets.


title: Connectivity Neurofeedback Training Can Differentially Change Functional Connectivity and Cognitive Performance

abstract: Advances in functional magnetic resonance imaging have made it possible to provide real-time feedback on brain activity. Neurofeedback has been applied to therapeutic interventions for psychiatric disorders. Since many studies have shown that most psychiatric disorders exhibit abnormal brain networks, a novel experimental paradigm named connectivity neurofeedback, which can directly modulate a brain network, has emerged as a promising approach to treat psychiatric disorders. Here, we investigated the hypothesis that connectivity neurofeedback can induce the aimed direction of change in functional connectivity, and the differential change in cognitive performance according to the direction of change in connectivity. We selected the connectivity between the left primary motor cortex and the left lateral parietal cortex as the target. Subjects were divided into 2 groups, in which only the direction of change (an increase or a decrease in correlation) in the experimentally manipulated connectivity differed between the groups. As a result, subjects successfully induced the expected connectivity changes in either of the 2 directions. Furthermore, cognitive performance significantly and differentially changed from preneurofeedback to postneurofeedback training between the 2 groups. These findings indicate that connectivity neurofeedback can induce the aimed direction of change in connectivity and also a differential change in cognitive performance.


title: RFID Support for Accurate 3D Localization

abstract: This paper pursues RFID support for localization, aiming to pinpoint an object in 3D space. Given a set of RFID tags and/or readers deployed as reference points at known locations in a hexahedron (like shipping container or storage room), a passive and an active localization schemes are considered in this paper. Being the very first range-free 3D localization, our schemes depend solely on RFID tags and readers without other devices or sensors, and it avoids the need of distance estimation according to received wireless signal strength or phase difference. Our passive scheme locates an RFID tag attached to the target object, with both tags and readers as reference points. The active scheme locates an RFID reader, by iteratively determining a 3D sphere best covering the activated reference tags, referred to as the decision boundary optimization scheme (DeB). Results by simulations and testbed experiments using Alien RFID kits have been obtained, and they reveal that DeB outperforms its passive counterpart and achieves the localization error of 0.07 ft. Additionally, DeB yields better location accuracy and yet is much faster than a previous counterpart. With enhanced DeB (EDeB), accuracy of an object located near a hexahedron side or corner is improved considerably.


Collision Avoidance in Robot Navigation with deep reinforcement learning Project Plan


title: Challenging the Public Stigma of Mental Illness: A Meta-Analysis of Outcome Studies

abstract: Stigma associated with mental illness has malignant effects on the lives of people with serious mental illnesses. Many strategies have been used to combat public stigma—the prejudice and discrimination endorsed by the general population. To identify the most effective approaches, researchers conducted a meta-analysis of data from 72 outcome studies in 14 countries. Overall, strategies that include education about mental illness and contact with people who have mental illness are effective. For adults, contact appears to be more effective, whereas education seems to work best among adolescents.


title: Integrating Mathematical Thinking, Abstract Thinking, and Computational Thinking

abstract: In recent years, several groups of Computer Science educators have made a sustained effort to capture the essence of CS apart from programming. Three of these approaches are Mathematical Thinking, Abstract Thinking, and Computational Thinking. Each approach tries to clarify areas of CS that are not directly tied to writing computer programs. In a separate line of research, the current authors have been examining different ways to teach mathematics to CS students. We developed a Computational Math scale that measures the level of problem-solving gestalt exhibited by textbook authors. In this study, we relate our Computational Math framework to current research on Mathematical, Abstract, and Computational (MAC) Thinking. We counted words used frequently in research articles and compared them to words that form the Computational Math scale. Our results suggest an overall MAC Thinking framework that integrates a wide range of topics relevant to computing and programming.


title: Hypersensitivity Reactions to Nonsteroidal Anti-inflammatory Drugs in Children and Adolescents: Selective Reactions.

abstract: Nonsteroidal anti-inflammatory drugs (NSAIDs) are used throughout the world to treat pain and inflammation; however, they can trigger several types of drug hypersensitivity reactions (DHRs) in all age groups. Although most such reactions occur through activation of the leukotriene pathway without specific immunological recognition (cross-intolerance), a significant number of DHRs to NSAIDs are due to immunological mechanisms (selective reactions [SRs]). SRs are thought to be induced by specific IgE antibodies or by T cells. In this manuscript, we focus on SRs, which are of great concern in children and adolescents and comprise a heterogeneous set of clinical pictures ranging from mild entities such as urticaria/angioedema to potentially life-threatening conditions such as Stevens-Johnson syndrome/toxic epidermal necrolysis. Paracetamol and ibuprofen are the most frequent elicitors of IgE-mediated SRs, although pyrazolones have also been implicated. T cell-mediated reactions are infrequent in children but have been associated with ibuprofen, naproxen, and dipyrone. In this review, we analyze the available literature on SRs in children and adolescents, with emphasis on epidemiological data, mechanisms, and drugs involved, as well as on diagnostic procedures.


title: A Taxonomy of Job Scheduling on Distributed Computing Systems

abstract: Hundreds of papers on job scheduling for distributed systems are published every year and it becomes increasingly difficult to classify them. Our analysis revealed that half of these papers are barely cited. This paper presents a general taxonomy for scheduling problems and solutions in distributed systems. This taxonomy was used to classify and make publicly available the classification of $109$ scheduling problems and their solutions. These $109$ problems were further clustered into ten groups based on the features of the taxonomy. The proposed taxonomy will facilitate researchers to build on prior art, increase new research visibility, and minimize redundant effort.


title: Self-managed and blockchain-based vehicular ad-hoc networks

abstract: Combining Vehicle Ad-hoc Networks (VANETs) and Ethereum's blockchain-based application concepts enables transparent, self-managed and decentralized system which are self-regulating and in no need of a central managing authority.


title: Open Secrets and Wrong Rights: Automatic Satire Detection in English Text

abstract: Satire is an element of figurative language which often conveys feelings contrary to what is literally stated. It refers to a trenchant wit, irony, or sarcasm used to expose discredit vice or folly. The presence of a satirical utterance in text can entirely change the sentiment of the statement, hence it is necessary to distinguish between true positive statements and satirical ones.In this paper, we identify key value components and features for automatic satire detection. Our experiments have been carried out on three data sets, namely, tweets, product reviews and newswire articles. We examine the impact of a number of state of the art features as well as new generalised textual features.


title: Ten years of research in spectrum sensing and sharing in cognitive radio

abstract: Cognitive radio (CR) can successfully deal with the growing demand and scarcity of the wireless spectrum. To exploit limited spectrum efficiently, CR technology allows unlicensed users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the unlicensed users need to continuously monitor the licensed users' activities to avoid interference and collisions. How to obtain reliable results of the licensed users' activities is the main task for spectrum sensing. Based on the sensing results, the unlicensed users should adapt their transmit powers and access strategies to protect the licensed communications. The requirement naturally presents challenges to the implementation of CR. In this article, we provide an overview of recent research achievements of including spectrum sensing, sharing techniques and the applications of CR systems.


title: Algorithms for 3D Shape Scanning with a Depth Camera

abstract: We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable quality can also be obtained with a sensor of such low data quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.


Enabling Client-Side Crash-Resistance to Overcome Diversification and Information Hiding


title: Omni-Directional Mobility Using Active Split Offset Castors

abstract: An omni-directional mobility platform design concept using two active split offset castors (ASOC) and one or more conventional castors is presented. An ASOC module consists of two coaxial conventional wheels driven independently and connected to the platform via an offset link. The kinematics and implementation of the omni-directional platform is described and analyzed. Particular attention is paid to the system performance on uneven floors. The fundamental mechanics of the ASOC wheel scrubbing, which is critical to system wear and energy use, is analyzed and compared to conventional active castor designs. The effectiveness of the design is shown experimentally using an intelligent mobility aid for the elderly. @DOI: 10.1115/1.1767181#


title: Six Human Factors to Acceptability of Wearable Computers

abstract: Social acceptance of wearable computers is an important issue that has attracted interest since the emergence of the technology, as acceptance is the first step to successful diffusion. The user’s acceptance of a wearable technology is significantly affected by technological and human factors. Since the influence of technological factors is as crucial to acceptance as human factors, we opted to focus on the target of all technologies, the end user. The present paper reviewed research focusing on the user and variables that influenced the acceptance or rejection of wearable computers. We identified and evaluated six human factors that play a key role in the acceptance of technology: fundamental needs, cognitive activity, physical aspect, social aspect, demographic characteristic and technical experience. Our aim was to help researchers, device designers, and developers evaluate the acceptability and usability of their devices.


title: Grasping for a Purpose: Using Task Goals for Efficient Manipulation Planning

abstract: In this paper we propose an approach for efficient grasp selection for manipulation tasks of unknown objects. Even for simple tasks such as pick-and-place, a unique solution is rare to occur. Rather, multiple candidate grasps must be considered and (potentially) tested till a successful, kinematically feasible path is found. To make this process efficient, the grasps should be ordered such that those more likely to succeed are tested first. We propose to use grasp manipulability as a metric to prioritize grasps. We present results of simulation experiments which demonstrate the usefulness of our metric. Additionally, we present experiments with our physical robot performing simple manipulation tasks with a small set of different household objects.


title: Maternal Depressive Symptoms and Achievement-Related Outcomes among Adolescent Daughters Variations by Family Structure

abstract: The present study examined whether early adolescent females' school grades, educational aspirations, educational expectations, and the discrepancy between their aspirations and expectations could be predicted by maternal depressive symptoms. Of special interest was the potential moderating role of family structure, that is, whether the girls living in single-mother households were more vulnerable to the effects of maternal depressive mood state than were the girls living in intact, two-parent families. Questionnaire data were gathered from 44 mother-daughter dyads. Analyses indicated that for the single-mother/daughter subsample only, maternal depression scores predicted daughters' educational expectations over and above the effects of key parenting practices and level of maternal education. Possible reasons for the moderating effect of family structure, and the significance of examining academic-related perceptions among early adolescent girls are discussed Themes including daughters serving as emotional...


A theory of reasoned action: some applications and implications.


title: Purely URL-based topic classification

abstract: Given only the URL of a web page, can we identify its topic? This is the question that we examine in this paper. Usually, web pages are classified using their content, but a URL-only classifier is preferable, (i) when speed is crucial, (ii) to enable content filtering before an (objection-able) web page is downloaded, (iii) when a page's content is hidden in images, (iv) to annotate hyperlinks in a personalized web browser, without fetching the target page, and (v) when a focused crawler wants to infer the topic of a target page before devoting bandwidth to download it. We apply a machine learning approach to the topic identification task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classifiers for each topic, we achieve typical F-measure values between 80 and 85, and a typical precision of around 85. We also ran experiments on a small data set of university web pages. For the task of classifying these pages into faculty, student, course and project pages, our methods improve over previous approaches by 13.8 points of F-measure.


title: Work in Progress: K-Nearest Neighbors Techniques for ABAC Policies Clustering

abstract: In this paper, we present an approach based on the K-Nearest Neighbors algorithms for policies clustering that aims to reduce the ABAC policies dimensionality for high scale systems. Since ABAC considers a very large set of attributes for access decisions, it turns out that using such model for large scale systems might be very complicated. To date, researchers have proposed to use data mining techniques to discover roles for RBAC system construction. In this work in progress, we consider the usage of KNN-based techniques for the classification of ABAC policies based on similarity computations of rules in order to enhance the ABAC flexibility and to reduce the number of policy rules.


An Attack/Decay Model for Piano Transcription


Chapter 3 An Introduction to Stochastic Epidemic Models


title: ClassView: hierarchical video shot classification, indexing, and accessing

abstract: Recent advances in digital video compression and networks have made video more accessible than ever. However, the existing content-based video retrieval systems still suffer from the following problems. 1) Semantics-sensitive video classification problem because of the semantic gap between low-level visual features and high-level semantic visual concepts; 2) Integrated video access problem because of the lack of efficient video database indexing, automatic video annotation, and concept-oriented summary organization techniques. In this paper, we have proposed a novel framework, called ClassView, to make some advances toward more efficient video database indexing and access. 1) A hierarchical semantics-sensitive video classifier is proposed to shorten the semantic gap. The hierarchical tree structure of the semantics-sensitive video classifier is derived from the domain-dependent concept hierarchy of video contents in a database. Relevance analysis is used for selecting the discriminating visual features with suitable importances. The Expectation-Maximization (EM) algorithm is also used to determine the classification rule for each visual concept node in the classifier. 2) A hierarchical video database indexing and summary presentation technique is proposed to support more effective video access over a large-scale database. The hierarchical tree structure of our video database indexing scheme is determined by the domain-dependent concept hierarchy which is also used for video classification. The presentation of visual summary is also integrated with the inherent hierarchical video database indexing tree structure. Integrating video access with efficient database indexing tree structure has provided great opportunity for supporting more powerful video search engines.


title: Leveraging business-IT alignment through enterprise architecture--an empirical study to estimate the extents

abstract: Achieving business-IT alignment (BITA) as a long-term and appraising management issue can be accomplished in a few ways, enterprise architecture (EA) being one of them. This paper attempts to give a critical understanding of the effects of performing EA on different aspects of BITA maturity through a global survey. A total of 236 respondents from 60 countries, a relatively large response for a survey, were selected. The main purpose of the research is to examine these impacts and to identify directions for innovative practices in the future, the unique contributions of this work. A questionnaire designed on the Luftman's maturity model as well as various other statistical methods, including PLS path modeling, Wilcoxon matched-pairs signed-ranks test and Mann---Whitney U test, are applied to understand how the EA can deliver benefits. The implications of our findings in this study as well as its limitations are discussed from different viewpoints to enable both academics and practitioners to detect the flaws in the existing EA frameworks and propose improvements.


title: 64-APSK Constellation and Mapping Optimization for Satellite Broadcasting Using Genetic Algorithms

abstract: DVB-S2 and DVB-SH satellite broadcasting standards currently deploy 16- and 32-amplitude phase shift keying (APSK) modulation using the consultative committee for space data systems (CCSDS) mapping. Such standards also include hierarchical modulation as a mean to provide unequal error protection in highly variable channels over satellite. Foreseeing the increasing need for higher data rates, this paper tackles the optimization of 64-APSK constellations to minimize the mean square error between the original and received symbol. Optimization is performed according to the sensitivity of the data to the channel errors, by means of genetic algorithms, a well-known technique currently used in a variety of application domains, when close form solutions are impractical. Test results show that through non-uniform constellation and asymmetric symbol mapping, it is possible to significantly reduce the distortion while preserving bandwidth efficiency. Tests performed on real signals based on perceptual quality measurements allow validating the proposed scheme against conventional 64-APSK constellations and CCSDS mapping.


title: Solid-state circuit breakers for Medium Voltage DC power

abstract: How can the large power needs of future electric ships for propulsion, radar, and weapons systems be met given the prohibitive size and weight of the required number of standard 60 Hz AC generators and transformers? A solution may lie in a rugged, efficient, and compact solid-state, 10–20 kV Medium Voltage DC (MVDC) circuit breaker (Figure 1). In this paper, Diversified Technologies, Inc. will describe the architecture of a Medium Voltage DC (MVDC) circuit breaker. The solid-state, 10–20 kV class breaker is a breakthrough that establishes the viability of MVDC power distribution. It delivers extremely fast fault interruption, low peak currents, flexible and programmable coordination, and mechanical isolation; the keys to reliable and safe operation.


title: Usability testing: A software engineering perspective

abstract: Background: Software development methodologies adapt customer collaboration in the software development. Because of this, end users of the software participate in the usability testing to give their feedback to the analysts. Objective: The goal of this paper is to explore the design issues of the usability testing from the user's perspective. Method: We followed a systematic approach from the software engineering perspective in preparing the artifacts and conducting the usability testing by defining suitable metrics in a case study. Results: We identified several design issues while performing the usability testing. Metric values and hit-rates of design issues are presented. We also highlighted our lessons learned and recommendations based on our study. Limitations: Our case study is medium sized and results may not be applicable to the industry applications. However, the lessons learned and recommendations from this study are applicable. Conclusion: Users cannot finish complex tasks with no unanswered questions and the identified design issues help in improving user interfaces.


title: Learning the Pseudoinverse Solution to Network Weights

abstract: The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional space; perform a nonlinear activation; and then solve for a regression or classification output by means of a mathematical pseudoinverse operation. In the field of neuromorphic engineering, these methods are increasingly popular for synthesizing biologically plausible neural networks, but the ''learning method''-computation of the pseudoinverse by singular value decomposition-is problematic both for biological plausibility and because it is not an online or an adaptive method. We present an online or incremental method of computing the pseudoinverse precisely, which we argue is biologically plausible as a learning method, and which can be made adaptable for non-stationary data streams. The method is significantly more memory-efficient than the conventional computation of pseudoinverses by singular value decomposition.


title: Generation meets recommendation: proposing novel items for groups of users

abstract: Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences. In this paper, we present a joint problem formalization of these interrelated issues, and propose generative methods that address these questions simultaneously. Specifically, we leverage the latent space obtained by training a deep generative model---the Variational Autoencoder (VAE)---via a loss function that incorporates both rating performance and item reconstruction terms. We then apply a greedy search algorithm that utilizes this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing. An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items. As case studies on real-world data, we applied our method on the MART abstract art and Movielens Tag Genome dataset, which resulted in promising results: small and diverse sets of novel items.


title: Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association

abstract: Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities. Diverse auxiliary information has been utilized to improve the visual feature learning. In this paper, we propose to exploit natural language description as additional training supervisions for effective visual features. Compared with other auxiliary information, language can describe a specific person from more compact and semantic visual aspects, thus is complementary to the pixel-level image data. Our method not only learns better global visual feature with the supervision of the overall description but also enforces semantic consistencies between local visual and linguistic features, which is achieved by building global and local image-language associations. The global image-language association is established according to the identity labels, while the local association is based upon the implicit correspondences between image regions and noun phrases. Extensive experiments demonstrate the effectiveness of employing language as training supervisions with the two association schemes. Our method achieves state-of-the-art performance without utilizing any auxiliary information during testing and shows better performance than other joint embedding methods for the image-language association.


title: John von Neumann's self-replicating machine — Critical components required

abstract: This paper introduces the concept of a physical self-replicating machine for deployment on the Moon utilizing raw material available on the Moon. A detailed but selective review is given in order to highlight clearly the novel aspects of the concept presented. In particular, it is hypothesized that if critical components - electric motors and vacuum tubes - can be 3D printed from the limited repertoire of lunar materials, 3D printing constitutes an effective universal construction mechanism. This follows from the observation that mechatronic components are the core constituent parts of all robotic mechanisms and that the robot is the key to von Neumann's universal constructor. Steps toward 3D printing of electric motors in particular and strategies toward the same regarding electronics is presented.


Learning Foreign Languages Using Mobile Applications


title: An enhanced noise cancelling system for a comprehensive monitoring and control of baby environments

abstract: Due to the radical environmental changes, in terms of sound pollution and global warming that the world is subject to, today's climate (noise, temperature, and humidity) impacts newborns and babies environments. In order to achieve better living conditions in a baby room, monitoring and control systems became very important. Existing systems only monitor and control the temperature, humidity, and light in baby rooms. This paper intends to build on the existing related work and suggests an enhanced noise cancelling system for a comprehensive monitoring and control to overcome the sound pollution and make the baby rooms more comfortable. The proposed system design and implementation are discussed and the corresponding components are detailed with their interactions. Additionally, a draft cost estimation is presented.


title: Topological Value Iteration Algorithm for Markov Decision Processes

abstract: Value Iteration is an inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Among them, LAO, LRTDP and HDP are state-of-theart ones. All of these use reachability analysis and heuristics to avoid some unnecessary backups. However, none of these approaches fully exploit the graphical features of the MDPs or use these features to yield the best backup sequence of the state space. We introduce an algorithm named Topological Value Iteration (TVI) that can circumvent the problem of unnecessary backups by detecting the structure of MDPs and backing up states based on topological sequences. We prove that the backup sequence TVI applies is optimal. Our experimental results show that TVI outperforms VI, LAO, LRTDP and HDP on our benchmark MDPs.


title: Review of passive imaging polarimetry for remote sensing applications

abstract: Imaging polarimetry has emerged over the past three decades as a powerful tool to enhance the information available in a variety of remote sensing applications. We discuss the foundations of passive imaging polarimetry, the phenomenological reasons for designing a polarimetric sensor, and the primary architectures that have been exploited for developing imaging polarimeters. Considerations on imaging polarimeters such as calibration, optimization, and error performance are also discussed. We review many important sources and examples from the scientific literature.


title: SecuBat: a web vulnerability scanner

abstract: As the popularity of the web increases and web applications become tools of everyday use, the role of web security has been gaining importance as well. The last years have shown a significant increase in the number of web-based attacks. For example, there has been extensive press coverage of recent security incidences involving the loss of sensitive credit card information belonging to millions of customers.Many web application security vulnerabilities result from generic input validation problems. Examples of such vulnerabilities are SQL injection and Cross-Site Scripting (XSS). Although the majority of web vulnerabilities are easy to understand and to avoid, many web developers are, unfortunately, not security-aware. As a result, there exist many web sites on the Internet that are vulnerable.This paper demonstrates how easy it is for attackers to automatically discover and exploit application-level vulnerabilities in a large number of web applications. To this end, we developed SecuBat, a generic and modular web vulnerability scanner that, similar to a port scanner, automatically analyzes web sites with the aim of finding exploitable SQL injection and XSS vulnerabilities. Using SecuBat, we were able to find many potentially vulnerable web sites. To verify the accuracy of SecuBat, we picked one hundred interesting web sites from the potential victim list for further analysis and confirmed exploitable flaws in the identified web pages. Among our victims were well-known global companies and a finance ministry. Of course, we notified the administrators of vulnerable sites about potential security problems. More than fifty responded to request additional information or to report that the security hole was closed.


title: Subliminal Advertising: What you see is what you Get:

abstract: This paper provides an evaluation of the evidence and arguments advanced in support of the effectiveness of various subliminal advertising techniques. Such practices are purported to influence cons...


title: An Efficient Signature Scheme from Bilinear Pairings and Its Applications

abstract: In Asiacrypt2001, Boneh, Lynn, and Shacham [8] proposed a short signature scheme (BLS scheme) using bilinear pairing on certain elliptic and hyperelliptic curves. Subsequently numerous cryptographic schemes based on BLS signature scheme were proposed. BLS short signature needs a special hash function [6,1,8]. This hash function is probabilistic and generally inefficient. In this paper, we propose a new short signature scheme from the bilinear pairings that unlike BLS, uses general cryptographic hash functions such as SHA-1 or MD5, and does not require special hash functions. Furthermore, the scheme requires less pairing operations than BLS scheme and so is more efficient than BLS scheme. We use this signature scheme to construct a ring signature scheme and a new method for delegation. We give the security proofs for the new signature scheme and the ring signature scheme in the random oracle model.


title: A simplified thermal model for variable speed self cooled industrial induction motor

abstract: In this paper, a simplified thermal model for variable speed self cooled induction motors is proposed and experimentally verified. The thermal model is based on simple equations that are compared with more complex equations well known in literature. The proposed thermal model allows to predict the over temperature in the main parts of the motor, starting from the measured or the estimated losses in the machine. In the paper the description of the thermal model set up is reported in detail. Finally, the model is used to define the correct power derating for a variable speed PWM induction motor drive.


title: The Copenhagen Neuroaesthetics conference: Prospects and pitfalls for an emerging field

abstract: Neuroaesthetics is a young field of research concerned primarily with the neural basis of cognitive and affective processes engaged when an individual takes an aesthetic or artistic approach towards a work of art, a non-artistic object or a natural phenomenon. In September 2009, the Copenhagen Neuroaesthetics Conference brought together leading researchers in the field to present and discuss current advances. We summarize some of the principal themes of the conference, placing neuroaesthetics in a historical context and discussing its scope and relation to other disciplines. We also identify what we believe to be the key outstanding questions, the main pitfalls and challenges faced by the field, and some promising avenues for future research.


title: Working memory task performance and chunking in early Alzheimer's disease

abstract: Background ::: ::: Chunking is a powerful encoding strategy that significantly improves working memory performance in normal young people. ::: ::: Aims ::: ::: To investigate chunking in patients with mild Alzheimer’s disease and in a control group of elderly people without cognitive impairment. ::: ::: Method ::: ::: People with mild Alzheimer’s disease (n = 28) were recruited and divided according to Mini-Mental State Examination score into mild and very mild disease groups. A control group of 15 elderly individuals was also recruited. All participants performed digit and spatial working memory tasks requiring either unstructured sequences or structured sequences (which encourage chunking of information) to be recalled. ::: ::: Results ::: ::: The control group and both disease groups performed significantly better on structured trials of the digit working memory tasks, indicating successful use of chunking strategies to improve verbal working memory performance. The control and very mild disease groups also performed significantly better on structured trials of the spatial task, whereas those with mild disease demonstrated no significant difference between the structured and unstructured spatial conditions. ::: ::: Conclusions ::: ::: The ability to use chunking as an encoding strategy to improve verbal working memory performance is preserved at the mild stage of Alzheimer’s disease, whereas use of chunking to improve spatial working memory is impaired by this stage. Simple training in the use of chunking might be a beneficial therapeutic strategy to prolong working memory functioning in patients at the earliest stage of Alzheimer’s disease.


title: Exploring Principles-of-Art Features For Image Emotion Recognition

abstract: Emotions can be evoked in humans by images. Most previous works on image emotion analysis mainly used the elements-of-art-based low-level visual features. However, these features are vulnerable and not invariant to the different arrangements of elements. In this paper, we investigate the concept of principles-of-art and its influence on image emotions. Principles-of-art-based emotion features (PAEF) are extracted to classify and score image emotions for understanding the relationship between artistic principles and emotions. PAEF are the unified combination of representation features derived from different principles, including balance, emphasis, harmony, variety, gradation, and movement. Experiments on the International Affective Picture System (IAPS), a set of artistic photography and a set of peer rated abstract paintings, demonstrate the superiority of PAEF for affective image classification and regression (with about 5% improvement on classification accuracy and 0.2 decrease in mean squared error), as compared to the state-of-the-art approaches. We then utilize PAEF to analyze the emotions of master paintings, with promising results.


title: Enhancing security of Android & IOS by implementing need-based security (NBS)

abstract: The most popular Smartphone platforms i.e. Android and iOS are equipped with the built-in security features to safeguard their end users. Android, being an Open Source Mobile Operating System, has some security vulnerabilities. Such limitations are also present in iOS which is a proprietary platform with some open source components. In this paper we will compare in detail the security features of Android and iOS, with the intent to integrate the need based security (NBS) model in Android which selectively grants permission to access resources on a Smartphone at run time. This paper proposes the implementation of a reverse engineering process which restricts an app's permissions and provides a need based mechanism to access resources. The repackaged app with need based security will run on all devices that were supported by the original application.


title: Improving Intrusion Detection on Snort Rules for Botnets Detection

abstract: The Botnets has become a serious problem in network security. An organization should find the solutions to protect the data and network system to reduce the risk of the Botnets. The Snort Intrusion Detection System (Snort-IDS) is the popular usage software protection of the network security in the world and utilizes the rules to match the data packets traffic. There are some existing rules which can detect Botnets. This paper, improves the Snort-IDS rules for Botnets detection and we analyze Botnets behaviors in three rules packet such as Botnets_attack_1.rules, Botnets_attack_2.rules, and Botnets_ attack_3- .rules. Moreover, we utilize the MCFP dataset, which includes five files such as CTU-Malware-Capture-Botnet-42, CTU-Malware-Capture-Botnet-43, CTU-Malware-Capture-Botnet-47, CTU-Malware-Capture-Botnet-49, and CTU-Malware-Capture-Botnet-50 with three rule files of the Snort-IDS rules. The paper has particularly focused on three rule files for performance evaluation for efficiency of detection and the performance evaluation of fallibility for Botnets Detection. The performance of each rule is evaluated by detecting each packet. The experimental results shown that, the case of Botnets_attack_1.rules file can maximally detect Botnets detection for 809075 alerts, the efficiency of detection and fallibility for Botnets detection are 94.81% and 5.17%, respectively. Moreover, in the case of Botnets_attack_2.rules file, it can detect Botnets up to 836191 alerts, having efficiency of detection and fallibility for Botnets detection are 97.81% and 2.90%, respectively. The last case Botnets_attack_3.rules file can detect Botnets 822711 alerts, it can 93.72% of efficiency of detection and the value of fallibility is 6.27%. The Botnets_attack_2.rules file is most proficient rule for Botnets detection, because it has a high efficiency of detection for detection and a less of fallibility.


title: Cyberbullying a modern form of bullying: let’s talk about this health and social problem

abstract: Cyberbullying or electronic aggression has already been designated as a serious public health threat. Cyberbullying should also be considered as a cause for new onset psychological symptoms, somatic symptoms of unclear etiology or a drop in academic performance. Pediatricians should be trained to play a major role in caring for and supporting the social and developmental well-being of children.


title: Direct impingement cooling of LED by Piezo fan

abstract: In this paper, an innovative heat management solution for the Light Emitting Diodes (LEDs) based on direct impingement cooling by Piezo fan has been proposed and evaluated. Round Piezo fans with 26 to 30 mm blade diameter, 1 to 5 mm fan thickness and narrow flow orifice (∼ 3 to 7.5 mm) were developed which could provide very high velocity air jet (> 10 m/s) for impinging heat source footprint directly. These fans were driven at high operating frequency (300 to 550 Hz) and voltage (40 to 50 V rms ) to get high impact air jet at fan outlet. Different test cases have been studied to demonstrate and characterize the cooling capability of the Piezo fan based thermal solutions. Direct impingement cooling of the 5 × 5 mm2 heat source with Piezo fan (30 mm diameter, 1.1 mm thickness and 7.5 mm orifice width) inclined at 35° to horizontal was tested which was able to reduce source temperature by 3 to 4 times as compared to natural convection cooling. Similarly, incorporation of Piezo fan for cooling multiple chips (x5) LED package with 9 W output heat load reduced the source temperature by more than 42 °C. Automotive headlamp with two LED packages and single heat sink was impinge cooled by two dedicated Piezo fans which reduce the source temperature by 7.6 °C and enhance the heat sink heat dissipation capacity by 48% due to improvement in the enclosure heat transfer coefficient. Piezo based impingement cooling can therefore increase the heat removal from heat source as well as can reduce the size of the heat sink designed for natural convection cooling. Based on these results, impingement type Piezo fan can be classified as standalone as well as heat sink integrated cooling solution for LED packages.


title: Using K Nearest Neighbors for text segmentation with feature similarity

abstract: In this research, we propose the version of K Nearest Neighbor which considers similarity among attributes for computing the similarity between feature vectors. The text segmentation task is viewed into the binary classification where each pair of sentences or paragraphs is classified into whether we put the boundary or not, and the proposed version resulted in the successful results in previous works concerned with the text categorization and clustering. In this research, we define the similarity measure based on both attributes and values, modify the KNN using it, and apply the modified version into the text segmentation task. We may expect more compact representation of data items and improved performance in the text segmentation task as well as other tasks of text mining. Therefore, the goal of this research is to implement the text segmentation system which provides the benefits.


Deep Constrained Local Models for Facial Landmark Detection.


title: Non-negative matrix factorization with sparseness constraints

abstract: Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of `sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems.


title: MMALFM: Explainable Recommendation by Leveraging Reviews and Images

abstract: Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this article, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users’ preferences and items’ features from different aspects, and also estimate the aspect importance of a user toward an item. Then, the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user’s preferences and the targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item visual features can improve the prediction performance—the effects of item image features on improving the prediction results depend on the importance of the visual features for the items; and (3) our model can explicitly interpret the predicted results in great detail.


title: Statistical continuum theory for inelastic behavior of a two-phase medium

abstract: Abstract A statistical continuum mechanics formulation is presented to predict the inelastic behavior of a medium consisting of two isotropic phases. The phase distribution and morphology are represented by a two-point probability function. The isotropic behavior of the single phase medium is represented by a power law relationship between the strain rate and the resolved local shear stress. It is assumed that the elastic contribution to deformation is negligible. A Green’s function solution to the equations of stress equilibrium is used to obtain the constitutive law for the heterogeneous medium. This relationship links the local velocity gradient to the macroscopic velocity gradient and local viscoplastic modulus. The statistical continuum theory is introduced into the localization relation to obtain a closed form solution. Using a Taylor series expansion an approximate solution is obtained and compared to the Taylor’s upper-bound for the inelastic effective modulus. The model is applied for the two classical cases of spherical and unidirectional discontinuous fiber-reinforced two-phase media with varying size and orientation.


title: A Survey of Advances in Botnet Technologies

abstract: Botnets have come a long way since their inception a few decades ago. Originally toy programs written by network hobbyists, modern-day botnets can be used by cyber criminals to steal billions of dollars from users, corporations, and governments. This paper will look at cutting-edge botnet features and detection strategies from over a dozen research papers, supplemented by a few additional sources. With this data, I will then hypothesize what the future of botnets might hold.


title: Importance of integrating nanotechnology with pharmacology and physiology for innovative drug delivery and therapy – an illustration with firsthand examples

abstract: Nanotechnology has been applied extensively in drug delivery to improve the therapeutic outcomes of various diseases. Tremendous efforts have been focused on the development of novel nanoparticles and delineation of the physicochemical properties of nanoparticles in relation to their biological fate and functions. However, in the design and evaluation of these nanotechnology-based drug delivery systems, the pharmacology of delivered drugs and the (patho-)physiology of the host have received less attention. In this review, we discuss important pharmacological mechanisms, physiological characteristics, and pathological factors that have been integrated into the design of nanotechnology-enabled drug delivery systems and therapies. Firsthand examples are presented to illustrate the principles and advantages of such integrative design strategies for cancer treatment by exploiting 1) intracellular synergistic interactions of drug-drug and drug-nanomaterial combinations to overcome multidrug-resistant cancer, 2) the blood flow direction of the circulatory system to maximize drug delivery to the tumor neovasculature and cells overexpressing integrin receptors for lung metastases, 3) endogenous lipoproteins to decorate nanocarriers and transport them across the blood-brain barrier for brain metastases, and 4) distinct pathological factors in the tumor microenvironment to develop pH- and oxidative stress-responsive hybrid manganese dioxide nanoparticles for enhanced radiotherapy. Regarding the application in diabetes management, a nanotechnology-enabled closed-loop insulin delivery system was devised to provide dynamic insulin release at a physiologically relevant time scale and glucose levels. These examples, together with other research results, suggest that utilization of the interplay of pharmacology, (patho-)physiology and nanotechnology is a facile approach to develop innovative drug delivery systems and therapies with high efficiency and translational potential.


title: Spoofing Mitigation, Robust Collision Avoidance, and Opportunistic Receiver Localisation Using a New Signal Processing Scheme for ADS-B or AIS

abstract: A signal processing scheme based on phase interferometry is developed here that provides anti-spoof capabilities for ADS-B or AIS using a compact, dual-antenna softwaredenied radio. The expected performance using existing radio technologies and two short baselines is demonstrated for ADS-B. The abilities of the proposed method include spoof detection (ADS-B, AIS and GNSS), collision avoidance in the presence of spoofers, and self-localisation in the absence of GPS. The system can provide bearing estimates of other aircraft in the vicinity broadcasting legitimate or spoofed ADS-B with accuracies of a degree with an antenna baseline of one metre, and self-positioning accuracies of approximately 100 metres for the same baseline. The applicability of the technique to AIS in the maritime domain is also discussed.


title: Modeling and Control of a Grid-connected Wind/PV Hybrid Generation System

abstract: This paper deals with power control of a wind and solar hybrid generation system for interconnection operation with electric distribution system. The proposed system consists of a variable-speed direct-drive wind generator, wind-side converter, solar array, dc-dc converter and grid interface inverter. Power control strategy is to extract the maximum energy available from varying condition of wind speed and solar irradiance while maintaining power quality at a satisfactory level. In order to capture the maximum power, variable speed control is employed for wind turbine and maximum power point tracking is applied for photovoltaic system. The grid interface inverter transfers the energy drawn from the wind turbine and PV array into the grid by keeping common dc voltage constant. Modeling and simulation study on the entire control scheme is carried out using a power system transient analysis tool, PSCAD/EMTDC. The simulation results show the control performance and dynamic behavior of the wind/PV system


title: Algorithmic Bias in Autonomous Systems

abstract: Algorithms play a key role in the functioning of autonomous systems, and so concerns have periodically been raised about the possibility of algorithmic bias. However, debates in this area have been hampered by different meanings and uses of the term, "bias." It is sometimes used as a purely descriptive term, sometimes as a pejorative term, and such variations can promote confusion and hamper discussions about when and how to respond to algorithmic bias. In this paper, we first provide a taxonomy of different types and sources of algorithmic bias, with a focus on their different impacts on the proper functioning of autonomous systems. We then use this taxonomy to distinguish between algorithmic biases that are neutral or unobjectionable, and those that are problematic in some way and require a response. In some cases, there are technological or algorithmic adjustments that developers can use to compensate for problematic bias. In other cases, however, responses require adjustments by the agent, whether human or autonomous system, who uses the results of the algorithm. There is no "one size fits all" solution to algorithmic bias.


title: Fault detection and diagnosis for smart buildings: State of the art, trends and challenges

abstract: Worldwide, buildings account for ca. 40% of the total energy consumption and ca. 20% of the total CO2 emissions. While most of the energy goes into primary building use, a significant amount of energy is wasted due to malfunctioning building system equipment and wrongly configured Building Management Systems (BMS). For example, wrongly configured setpoints or building equipment, or misplaced sensors and actuators, can contribute to deviations of the real energy consumption from the predicted one. Our paper is motivated by these posed challenges and aims at pinpointing the types of problems in the BMS components that can affect the energy efficiency of a building, as well as review the methods that can be utilized for their discovery and diagnosis. The goal of the paper is to highlight the challenges that lie in this problem domain, as well as provide a strategy how to counterfeit them.


title: Deep Learning for Explicitly Modeling Optimization Landscapes

abstract: In all but the most trivial optimization problems, the structure of the solutions exhibit complex interdependencies between the input parameters. Decades of research with stochastic search techniques has shown the benefit of explicitly modeling the interactions between sets of parameters and the overall quality of the solutions discovered. We demonstrate a novel method, based on learning deep networks, to model the global landscapes of optimization problems. To represent the search space concisely and accurately, the deep networks must encode information about the underlying parameter interactions and their contributions to the quality of the solution. Once the networks are trained, the networks are probed to reveal parameter combinations with high expected performance with respect to the optimization task. These estimates are used to initialize fast, randomized, local search algorithms, which in turn expose more information about the search space that is subsequently used to refine the models. We demonstrate the technique on multiple optimization problems that have arisen in a variety of real-world domains, including: packing, graphics, job scheduling, layout and compression. The problems include combinatoric search spaces, discontinuous and highly non-linear spaces, and span binary, higher-cardinality discrete, as well as continuous parameters. Strengths, limitations, and extensions of the approach are extensively discussed and demonstrated.


title: Intraclass correlations: Uses in assessing rater reliability.

abstract: Abstract Reliability coefficients often take the form of intraclass correlation coefficients. In this article, guidelines are given for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges. Relevant to the choice of the coefficient are the appropriate statistical model for the reliability and the application to be made of the reliability results. Confidence intervals for each of the forms are reviewed.


title: Human skin color detection in RGB space with Bayesian estimation of beta mixture models

abstract: Human skin color detection plays an important role in the applications of skin segmentation, face recognition, and tracking. To build a robust human skin color classifier is an essential step. This paper presents a classifier based on beta mixture models (BMM), which uses the pixel values in RGB space as the features. We propose a Bayesian estimation method based on the variational inference framework to approximate the posterior distribution of the parameters in the BMM and take the posterior mean as a point estimate of the parameters. The well-known Compaq image database is used to evaluate the performance of our BMM based classifier. Compared to some other skin color detection methods, our BMM based classifier shows a better recognition performance.


title: Geography and social distribution of malaria in Indonesian Papua: a cross-sectional study

abstract: BACKGROUND ::: Despite being one of the world's most affected regions, only little is known about the social and spatial distributions of malaria in Indonesian Papua. Existing studies tend to be descriptive in nature; their inferences are prone to confounding and selection biases. At the same time, there remains limited malaria-cartographic activity in the region. Analysing a subset (N = 22,643) of the National Basic Health Research 2007 dataset (N = 987,205), this paper aims to quantify the district-specific risk of malaria in Papua and to understand how socio-demographic/economic factors measured at individual and district levels are associated with individual's probability of contracting the disease. ::: ::: ::: METHODS ::: We adopt a Bayesian hierarchical logistic regression model that accommodates not only the nesting of individuals within the island's 27 administrative units but also the spatial autocorrelation among these locations. Both individual and contextual characteristics are included as predictors in the model; a normal conditional autoregressive prior and an exchangeable one are assigned to the random effects. Robustness is then assessed through sensitivity analyses using alternative hyperpriors. ::: ::: ::: RESULTS ::: We find that rural Papuans as well as those who live in poor, densely forested, lowland districts are at a higher risk of infection than their counterparts. We also find age and gender differentials in malaria prevalence, if only to a small degree. Nine districts are estimated to have higher-than-expected malaria risks; the extent of spatial variation on the island remains notable even after accounting for socio-demographic/economic risk factors. ::: ::: ::: CONCLUSIONS ::: Although we show that malaria is geography-dependent in Indonesian Papua, it is also a disease of poverty. This means that malaria eradication requires not only biological (proximal) interventions but also social (distal) ones.


title: Recurrent Capsule Network for Relations Extraction: A Practical Application to the Severity Classification of Coronary Artery Disease.

abstract: Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we propose a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed RCN model achieves a F$_1$-score of 0.9641 in relation extraction, which outperforms the baseline methods.


title: Motivating the Rules of the Game for Adversarial Example Research

abstract: Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation.


title: Reconfigurable Square-Ring Patch Antenna With Pattern Diversity

abstract: A single-feed reconfigurable square-ring patch antenna with pattern diversity is presented. The antenna structure has four shorting walls placed respectively at each edge of the square-ring patch, in which two shorting walls are directly connected to the patch and the others are connected to the patch via pin diodes. By controlling the states of the pin diodes, the antenna can be operated at two different modes: monopolar plat-patch and normal patch modes; moreover, the 10 dB impedance bandwidths of the two modes are overlapped. Consequently, the proposed antenna allows its radiation pattern to be switched electrically between conical and broadside radiations at a fixed frequency. Detailed design considerations of the proposed antenna are described. Experimental and simulated results are also shown and discussed


title: Safety and efficacy of the PrePex device for rapid scale-up of male circumcision for HIV prevention in resource-limited settings.

abstract: OBJECTIVE ::: To assess the safety and efficacy of the PrePex device for nonsurgical circumcision in adult males as part of a comprehensive HIV prevention program in Rwanda. ::: ::: ::: METHODS ::: Single-center 6-week noncontrolled study in which healthy men underwent circumcision using the PrePex device, which employs fitted rings to clamp the foreskin, leading to distal necrosis. In the first phase of the study, the feasibility of the procedure was tested on 5 subjects in a sterile environment; in the main phase, an additional 50 subjects were circumcised in a nonsterile setting by physicians or a nurse. Outcome measures included the rate of successful circumcision, time to complete healing, pain, and adverse events. ::: ::: ::: RESULTS ::: In the feasibility phase, all 5 subjects achieved complete circumcision without adverse events. In the main phase, all 50 subjects achieved circumcision with 1 case of diffuse edema after device removal, which resolved with minimal intervention. Pain was minimal except briefly during device removal (day 7 after placement in most cases). The entire procedure was bloodless, requiring no anesthesia, no suturing, and no sterile settings. Subjects had no sick/absent days associated with the procedure. Median time for complete healing was 21 days after device removal. There were no instances of erroneous placement and no mechanical problems with the device. ::: ::: ::: CONCLUSION ::: The PrePex device was safe and effective for nonsurgical adult male circumcision without anesthesia or sterile settings and may be useful in mass circumcision programs to reduce the risk of HIV infection, particularly in resource-limited settings.


DESIGNING PERSIAN FLORAL PATTERNS USING CIRCLE PACKING


Primary, Secondary, and Meta-Analysis of Research


title: Glioma growth inhibition by neurostatin and O-But GD1b

abstract: In spite of their low incidence, central nervous system tumors have elevated morbidity and mortality, being responsible for 2.3% of total cancer deaths. The ganglioside O-acetylated GD1b (O-Ac GD1b; neurostatin), present in the mammalian brain, and the semi-synthetic O-butyrylated GD1b (O-But GD1b) are potent glioma proliferation inhibitors, appearing as possible candidates for the treatment of nervous system tumors. Tumoral cell division inhibitory activity in culture correlated with growth inhibition of glioma xenotransplants in Foxn1(nu) nude mice and intracranial glioma allotransplants. Both O-Ac GD1b and O-But GD1b inhibited in vivo cell proliferation, induced cell cycle arrest, and potentiated immune cell response to the tumor. Furthermore, the increased stability of the butyrylated compound (O-But GD1b) enhanced its activity with respect to the acetylated ganglioside (neurostatin). These results are the first report of the antitumoral activity of neurostatin and a neurostatin-like compound in vivo and indicate that semi-synthetic O-acetylated and O-butyrylated gangliosides are potent antitumoral compounds that should be considered in strategies for brain tumor treatment.


title: An orchestrated survey of methodologies for automated software test case generation

abstract: Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring a comprehensive and authoritative treatment.


title: An empirical study of the effect of perceived risk upon intention to use online applications

abstract: Purpose – The Technology Acceptance Model (TAM ) has been used to interpret people's adoptions of several kinds of technology. However, while people accept online application service technology with security threats, the perceived risk is generally ignored by past TAM studies. This study proposes an extension of TAM to an online application context.Design/methodology/approach – The research was done through a survey of 1,259 registered users who had used a free trial version of online antivirus (OLA) applications.Findings – The results reveal that perceived risk indirectly impacts intentions to use an online application under security threats. Notably, while perceived usefulness is important to the trial‐and‐leave group, perceived risk more strongly influences the continuous use group.Originality/value – The findings offer a useful insight for software companies that typically offer free trial versions for continuous use as promotions.


Optimization of Complex SPARQL Analytical Queries.


The role of family practice in different health care systems: A comparison of reasons for encounter, diagnoses, and interventions in primary care populations in the Netherlands, Japan, Poland, and the United States


title: Defending against Internet worms: a signature-based approach

abstract: With the capability of infecting hundreds of thousands of hosts, worms represent a major threat to the Internet. The defense against Internet worms is largely an open problem. This paper investigates two important problems. Can a localized defense system detect new worms that were not seen before and moreover, capture the attack packets? How to identify polymorphic worms from the normal background traffic? We have two major contributions here. The first contribution is the design of a novel double-honeypot system, which is able to automatically detect new worms and isolate the attack traffic. The second contribution is the proposal of a new type of position-aware distribution signatures (PADS), which fit in the gap between the traditional signatures and the anomaly-based systems. We propose two algorithms based on expectation-maximization (EM) and Gibbs sampling for efficient computation of PADS from polymorphic worm samples. The new signature is capable of handling certain polymorphic worms. Our experiments show that the algorithms accurately separate new variants of the MSBlaster worm from the normal-traffic background.


title: Exploring Stereotypes and Biased Data with the Crowd

abstract: The goal of our research is to contribute information about how useful the crowd is at anticipating stereotypes that may be biasing a data set without a researcher's knowledge. The results of the crowd's prediction can potentially be used during data collection to help prevent the suspected stereotypes from introducing bias to the dataset. We conduct our research by asking the crowd on Amazon's Mechanical Turk (AMT) to complete two similar Human Intelligence Tasks (HITs) by suggesting stereotypes relating to their personal experience. Our analysis of these responses focuses on determining the level of diversity in the workers' suggestions and their demographics. Through this process we begin a discussion on how useful the crowd can be in tackling this difficult problem within machine learning data collection.


title: A Systematic Literature Review on Microservices

abstract: The cloud is an emerging paradigm which leads the way for different approaches and standards. The architectural styles are evolving based on the requirements of the cloud as well. In recent years microservices is seen as the architecture style for scalable, fast evolving cloud applications. As part of this paper, a systematic mapping study was carried out around microservices. It is aiming to find out the current trends around microservices, the motivation behind microservices research, emerging standards and the possible research gaps. The obtained results can help researchers and practitioner in software engineering domain who want to be aware of new trends about SOA and cloud computing.


title: Connectionist learning of belief networks

abstract: Abstract Connectionist learning procedures are presented for “sigmoid” and “noisy-OR” varieties of probabilistic belief networks. These networks have previously been seen primarily as a means of representing knowledge derived from experts. Here it is shown that the “Gibbs sampling” simulation procedure for such networks can support maximum-likelihood learning from empirical data through local gradient ascent. This learning procedure resembles that used for “Boltzmann machines”, and like it, allows the use of “hidden” variables to model correlations between visible variables. Due to the directed nature of the connections in a belief network, however, the “negative phase” of Boltzmann machine learning is unnecessary. Experimental results show that, as a result, learning in a sigmoid belief network can be faster than in a Boltzmann machine. These networks have other advantages over Boltzmann machines in pattern classification and decision making applications, are naturally applicable to unsupervised learning problems, and provide a link between work on connectionist learning and work on the representation of expert knowledge.


title: A novel approach to developer-soluble anti-reflective coatings for 248-nm lithography

abstract: A novel approach to developer-soluble bottom anti-reflective coatings (BARCs) for 248-nm lithography ::: was demonstrated. The BARC formulations are photosensitive, dye-filled systems incorporated with a ::: polymer binder. The films are generated by thermally crosslinking the polymer matrix, and are then ::: photochemically decrosslinked in order to render them soluble in developer solutions. The BARCs are ::: compatible with solvents commonly used in the industry. Easy modification of the films with regard to ::: optical properties for potential use with various substrates was also demonstrated. The BARCs exhibit ::: anisotropic development in aqueous tetramethylammonium hydroxide (TMAH) solutions subsequent to ::: simulated photoresist application, exposure, and post-exposure bake.


title: Large scale manifold transduction

abstract: We show how the regularizer of Transductive Support Vector Machines (TSVM) can be trained by stochastic gradient descent for linear models and multi-layer architectures. The resulting methods can be trained online, have vastly superior training and testing speed to existing TSVM algorithms, can encode prior knowledge in the network architecture, and obtain competitive error rates. We then go on to propose a natural generalization of the TSVM loss function that takes into account neighborhood and manifold information directly, unifying the two-stage Low Density Separation method into a single criterion, and leading to state-of-the-art results.


title: Evaluating deep learning in chum prediction for everything-as-a-service in the cloud

abstract: Cloud computing has seen rapid growth due to its massive scalability in storage and computing power. Leading the trend, IBM released a hybrid cloud development platform, based on infrastructure as a service. Although tens of thousands of customers visit the platform everyday, a large percentage of trial customers left as their free-trial access expired, and a high proportion of paying customers dropped their usage sharply or worse stopped their payment. Customer retention in marketing is critical for reduced cost in retaining temporary customers and higher profits from long-term customers. In this paper, we present a data-driven iterative churn prediction framework with a deep learning approach for everything as a service (XaaS) in the cloud, including a cloud platform or software. Moreover, to improve churn prediction analysis we propose a new temporal customer engagement analysis model, called Nascency, Intermediate, and Latest (NIL) analysis. The NIL analysis helps to capture the temporal changes from time-related usage features, obtaining the three standardized inputs from instances which have different lifetimes. To the best of our knowledge this is the first study to use deep learning for churn prediction with time-correlation features in cloud computing. Our experiments have revealed strengths and weaknesses of deep learning in churn prediction. We expect these insights to help us improve deep learning's performance in churn prediction, by exploiting its hierarchical abstraction capability.