A Critical Review of Marketing Research on Diffusion of New Products

We critically examine alternate models of the diffusion of new products and the turning points of the diffusion curve. On each of these topics, we focus on the drivers, specifications, and estimation methods researched in the literature. We discover important generalizations about the shape, parameters, and turning points of the diffusion curve and the characteristics of diffusion across early stages of the product life cycle. We point out directions for future research. Because new products affect every aspect of the life of individuals, communities, countries, and economies, the study of the diffusion of innovations is of vital importance. Researchers have studied this topic in various disciplines, including marketing, economics, medicine, agriculture, sociology, anthropology, geography, and technology management. We present a critical review of research on the diffusion of new products primarily in the marketing literature, but also in the economics and geography literature. We use the word product broadly to cover any good, service, idea, or person. We distinguish the term new product from the broader term innovation, which refers to both new product and new method, practice, institution, or social entity. Even though we restrict our review to the marketing literature, which focuses on the diffusion of new products, the implications of our review may hold as well as for the study of the diffusion of innovations in other disciplines. The marketing literature on this topic is vast, dating back at least as early as the publication by Fourt and Woodlock (1960). The term diffusion has been used differently in two groups of literatures. Within economics and most nonmarketing disciplines, diffusion is defined as the spread of an innovation across social groups over time (Brown, 1981; Stoneman, 2002). As such, the phenomenon is separate from the drivers, which can be consumer income, the product’s price, word-of-mouth communication, and so on. In marketing and communication, diffusion typically has come to mean the communication of an innovation through the population (Golder and Tellis, 1998; Mahajan, Muller, and Wind, 2000a; Mahajan, Muller, and Bass, 1990; Rogers 1995). In this sense, the phenomenon (spread of a product) is synonymous with its underlying driver (communication). The Webster (2004) definition of the noun “diffusion” is “the spread of a cultural or technological practice or innovation from one region or people to another, as by trade or conquest” and the verb “diffusing” is “pour, spread out or disperse in every direction; spread or scatter widely.” This latter interpretation is synonymous with the term’s use in economics and most other disciplines. In addition, some researchers in marketing have subscribed to the definition used in economics (Bemmaor, 1994; Dekimpe, Parker, and Sarvary, 2000a; Van den Bulte and 40 DEEPA CHANDRASEKARAN AND GERARD J. TELLIS Stremersch, 2004). Hence, in this review, we define diffusion as the spread of an innovation across markets over time. Researchers commonly measure diffusion using the sales and especially the market penetration of a new product during the early stages of its life cycle. To characterize this phenomenon carefully, we adopt the definitions of the stages and turning points of the product’s life cycle by Golder and Tellis (2004): 1. Commercialization is the date a new product is first sold. 2. Takeoff is the first dramatic and sustained increase in a new product’s sales. 3. Introduction is the period from a new product’s commercialization until its takeoff. 4. Slowdown is the beginning of a period of level, slowly increasing, or temporarily decreasing product sales after takeoff. 5. Growth is the period from a new product’s takeoff until its slowdown. 6. Maturity is the period from a product’s slowdown until sales begin a steady decline. Hence, there are two key turning points in the diffusion curve: takeoff and slowdown. Prior reviews address various aspects of the marketing literature on the diffusion of new products. For example, Mahajan, Muller, and Bass (1990) provide an excellent overview of the Bass model, its extensions, and some directions for further research. Parker (1994) provides an overview of the Bass model and evaluates the various estimation techniques, forecasting abilities, and specification improvements of the model. Mahajan, Muller, and Bass (1995) summarize the generalizations from applications of the Bass model. An edited volume by Mahajan, Muller, and Wind (2000b) covers in depth various topics in diffusion models, such as specification, estimation, and applications. Sultan, Farley, and Lehmann (1990) and Van den Bulte and Stremersch (2004) meta-analyze the diffusion parameters of the Bass model. The current review differs from prior reviews in two important aspects. First, the prior reviews focus on the S-curve of cumulative sales of a new product, mostly covering growth. This review focuses on phenomena besides the S-curve, such as takeoff and slowdown. Second, the above reviews focus mainly on the Bass model. This review considers the Bass model as well as other models of diffusion and drivers of new product diffusion other than communication. Our key findings and the most useful part of our study is the discovery of potential generalizations from past research. For the benefit of readers who are familiar with this topic, we present these generalizations before details of the measures, models, and methods used in past research. (Readers who are unfamiliar with the topic may want to read the Potential Generalizations section last). Therefore, we organize the rest of the chapter as follows. In the next section, we summarize potential generalizations from prior research. In the third section, we point out limitations of past research and directions for future research. In the fourth section, we evaluate key models and drivers of the diffusion curve. In the fifth section, we evaluate models of the key turning points in diffusion: takeoff and slowdown. Potential Generalizations We use the term potential generalizations or regularities to describe empirical findings with substantial support. By substantial, we mean that support comes from reviews or meta-analyses of the literature or individual studies with a large sample of over ten categories or ten countries. Table 2.1 lists the studies on which the potential generalizations are based. This section covers important findings about the shape of the diffusion curve, parameters of the Bass models, the turning points of diffusion, and findings across stages of the diffusion curve. MARKETING RESEARCH ON DIFFUSION OF NEW PRODUCTS 41 Shape of the Diffusion Curve The most important and most widely reported finding about new product diffusion relates to the shape of the diffusion curve (see Figure 2.1). Numerous studies in a variety of disciplines suggest that (with the exception of entertainment products) the plot of cumulative sales of new products against time is an S-shaped curve (e.g., Mahajan, Muller, and Bass 1990; Mahajan, Muller, and Wind, 2000a). Parameters of the Bass Model Most of the marketing studies use the Bass diffusion model to capture the S-shaped curve of new products sales (see later section for explanation). This model has three key parameters: the coefficient of innovation or external influence (p), the coefficient of imitation or internal influence (q), and the market potential (α or m). Table 2.1 Studies Included for Assessing Potential Generalizations Authors Categories Countries Gatignon, Eliashberg, and 6 consumer durables 14 European countries Robertson (1989) Mahajan, Muller, and Bass (1990) Numerous studies Sultan, Farley, and Lehmann (1990) 213 applications United States, European countries Helsen, Jedidi, and DeSarbo (1993) 3 consumer durables 11 European countries and United States Ganesh and Kumar (1996) 1 industrial product 10 European countries, United States, and Japan Ganesh, Kumar, Subramaniam (1997) 4 consumer durables 16 European countries Golder and Tellis (1997) 31 consumer durables United States Putsis et al., (1997) 4 consumer durables 10 European countries Dekimpe, Parker, and Sarvary (1998) 1 service 74 countries Kumar, Ganesh, and Echambadi (1998) 5 consumer durables 14 European countries Golder and Tellis (1998) 10 consumer durables United States Kohli, Lehmann, and Pae (1999) 32 appliances, house United States wares and electronics Dekimpe, Parker, and Sarvary (2000) 1 innovation More than 160 countries Mahajan, Muller, and Wind (2000) Numerous studies Van den Bulte (2000) 31 consumer durables United States Talukdar, Sudhir, and Ainslie (2002) 6 consumer durables 31 countries Agarwal and Bayus (2002) 30 innovations United States Goldenberg, Libai, and Muller (2002) 32 innovations United States Tellis, Stremersch, and Yin (2003) 10 consumer durables 16 European countries Golder and Tellis (2004) 30 consumer durables United States Stremersch and Tellis (2004) 10 consumer durables 16 European countries Van den Bulte and Stremersch (2004) 293 applications 28 countries <<PLEASE SPECIFIY 2000a OR 2000b>> <<PLEASE SPECIFIY 2000a OR 2000b>> 42 DEEPA CHANDRASEKARAN AND GERARD J. TELLIS Coefficient of Innovation • The mean value of the coefficient of innovation for a new product lies between 0.0007 and .03 (Sultan, Farley, and Lehmann, 1990; Talukdar, Sudhir, and Ainslie, 2002; Van den Bulte and Stremersch, 2004). • The mean value of the coefficient of innovation for a new product is 0.001 for developed countries and 0.0003 for developing countries (Talukdar, Sudhir, and Ainslie, 2002). • The coefficient of innovation is higher for European countries than for the United States (Sultan, Farley, and Lehmann, 1990). Coefficient of Imitation • The mean value of the coefficient of imitation for a new product lies between 0.38 and 0.53 (Sultan, Farley, and Lehmann, 1990; Talukdar, Sudhir, and Ainslie, 2002; Van den Bulte and Stremersch, 2004). • Indu


GPU-based cone beam computed tomography

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s.


Achievement Goals in the Classroom : Students ' Learning Strategies and Motivation Processes

We studied how specific motivational processes are related to the salience of mastery and performance goals in actual classroom settings. One hundred seventy-six students attending a junior high/high school for academically advanced students were randomly selected from one of their classes and responded to a questionnaire on their perceptions of the classroom goal orientation, use of effective learning strategies, task choices, attitudes, and causal attributions. Students who perceived an emphasis on mastery goals in the classroom reported using more effective strategies, preferred challenging tasks, had a more positive attitude toward the class, and had a stronger belief that success follows from one's effort. Students who perceived performance goals as salient tended to focus on their ability, evaluating their ability negatively and attributing failure to lack of ability. The pattern and strength of the findings suggest that the classroom goal orientation may facilitate the maintenance of adaptive motivation patterns when mastery goals are salient and are adopted by students.


Calibrating the COCOMO II Post-Architecture Model

The COCOMO II model was created to meet the need for a cost model that accounted for future software development practices. This resulted in the formulation of three submodels for cost estimation, one for composing applications, one for early lifecycle estimation and one for detailed estimation when the architecture of the product is understood. This paper describes the calibration procedures for the last model, Post-Architecture COCOMO II model, from eighty-three observations. The results of the multiple regression analysis and their implications are discussed. Future work includes further analysis of the PostArchitecture model, calibration of the other models, derivation of maintenance parameters, and refining the effort distribution for the model output.


Meditation states and traits: EEG, ERP, and neuroimaging studies.

Neuroelectric and imaging studies of meditation are reviewed. Electroencephalographic measures indicate an overall slowing subsequent to meditation, with theta and alpha activation related to proficiency of practice. Sensory evoked potential assessment of concentrative meditation yields amplitude and latency changes for some components and practices. Cognitive event-related potential evaluation of meditation implies that practice changes attentional allocation. Neuroimaging studies indicate increased regional cerebral blood flow measures during meditation. Taken together, meditation appears to reflect changes in anterior cingulate cortex and dorsolateral prefrontal areas. Neurophysiological meditative state and trait effects are variable but are beginning to demonstrate consistent outcomes for research and clinical applications. Psychological and clinical effects of meditation are summarized, integrated, and discussed with respect to neuroimaging data.


Weighted Association Rule Mining using weighted support and significance framework

We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatornal explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the "downward closure property" in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a "weighted downward closure property". A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.


A low-power Adaboost-based object detection processor using Haar-like features

This paper presents an architecture of a low-power real-time object detection processor using Adaboost with Haar-Like features. We employ a register array based architecture, and introduce two architectural-level power optimization techniques; signal gating domain for integral image extraction, and low-power integral image update. The power efficiency of our proposed architecture including nine classifiers is estimated to be 0.64mW/fps when handling VGA(640 × 480) 70fps video.


A study on evolution of email spam over fifteen years

Email spam is a persistent problem, especially today, with the increasing dedication and sophistication of spammers. Even popular social media sites such as Facebook, Twitter, and Google Plus are not exempt from email spam as they all interface with email systems. With an “arms-race” between spammers and spam filter developers, spam has been continually changing over the years. In this paper, we analyze email spam trends on a dataset collected by the Spam Archive, which contains 5.1 million spam emails spread over 15 years (1998-2013). We use statistical analysis techniques on different headers in email messages (e.g. content type and length) and embedded items in message body (e.g. URL links and HTML attachments). Also, we investigate topic drift by applying topic modeling on the content of email spam. Moreover, we extract sender-to-receiver IP routing networks from email spam and perform network analysis on it. Our results show the dynamic nature of email spam over one and a half decades and demonstrate that the email spam business is not dying but changing to be more capricious.


Applying persuasive design in a diabetes mellitus application

This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.


Analysis of modulation schemes for Bluetooth-LE module for Internet-of-Things (IoT) applications

Bluetooth transceivers have been in the active area of recent research being the key component of physical layer in the Bluetooth technology. The low power consumption of Bluetooth low energy (LE) devices compared to the conventional Bluetooth devices has enhanced its importance in Internet-of-Things (IoT) applications. Therefore, Bluetooth low energy device based solution needs expansion in the IoT network infrastructure. The transceivers in the literature and modulation schemes are compared and summarized. Energy consumption of modulation schemes in Bluetooth communication are analyzed and compared using the model presented in this work. In this approach considering both circuit and signal power consumption, optimum modulation order for minimum energy consumption has been found using numerical calculation and relation between signal to noise ratio (SNR) and channel capacity. Battery life for IoT sensors using Bluetooth LE technology as a wireless link has been analyzed considering multiple transaction times for transmitters having different power consumption. MFSK and more bandwidth-efficient GFSK are identified as low energy solution for all smart devices.


Wheat glutenin subunits and dough elasticity: findings of the EUROWHEAT project

*IACR-Long Ashton Research Station, Department of Agricultural Sciences, University of Bristol, Long Ashton BS41 9AF, UK (tel: +1275-392181; fax: +1275-394299; e-mail: peter.shewry@bbsrc.ac.uk) {Institut National de la Recherche Agronomique, Centre de Recherches de Nantes, Laboratoire de BiochimieetdeTechnologiedesProtéines,B.P.71627, Ruede laGéraudière,Nantes 44316,Cedex03, France xDipartimento di Agrobiologia ed Agrochimica, Via San Camillo de Lellis, Viterbo 01100, Lazio, Italy {Institute of Food Research, Norwich Laboratory, Norwich Research Park, Colney Lane, Norwich NR4 7UA, UK


Insights into Human Behavior from Lesions to the Prefrontal Cortex

The prefrontal cortex (PFC), a cortical region that was once thought to be functionally insignificant, is now known to play an essential role in the organization and control of goal-directed thought and behavior. Neuroimaging, neurophysiological, and modeling techniques have led to tremendous advances in our understanding of PFC functions over the last few decades. It should be noted, however, that neurological, neuropathological, and neuropsychological studies have contributed some of the most essential, historical, and often prescient conclusions regarding the functions of this region. Importantly, examination of patients with brain damage allows one to draw conclusions about whether a brain area is necessary for a particular function. Here, we provide a broad overview of PFC functions based on behavioral and neural changes resulting from damage to PFC in both human patients and nonhuman primates.


A lane detection algorithm based on reliable lane markings

This paper proposes a robust and effective vision-based lane detection approach. First, two binary images are obtained from the region of interest of gray-scale images. The obtained binary images are merged by a novel neighborhood AND operator and then transformed to a bird's eye view (BEV) via inverse perspective mapping. Then, gaussian probability density functions are fit to the left and right regions of a histogram image acquired from the BEV. Finally, a polynomial lane model is estimated from the identified regions. Experimental results show that the proposed method accurately detects lanes in complex situations including worn-out and curved lanes.


Socio-cognitive gamification: general framework for educational games

Gamification of learning material has received much interest from researchers in the past years. This paper aims to further improve such learning experience by applying socio-cognitive gamification to educational games. Dynamic difficulty adjustment (DDA) is a well-known tool in optimizing gaming experience. It is a process to control the parameters in a video game automatically based on user experience in real-time. This method can be extended by using a biofeedback-approach, where certain aspects of the player’s ability is estimated based on physiological measurement (e.g. eye tracking, ECG, EEG). Here, we outline the design of a biofeedback-based framework that supports dynamic difficulty adjustment in educational games. It has a universal architecture, so the concept can be employed to engage users in non-game contexts as well. The framework accepts input from the games, from the physiological sensors and from the so-called supervisor unit. This special unit empowers a new social aspect by enabling another user to observe or intervene during the interaction. To explain the game-user interaction itself in educational games we propose a hybrid model.


EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks – especially when the policies are represented using rich function approximators like deep neural networks. Modelbased methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including to unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.


DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning

Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers are learned that operate at a fine timescale and which achieve robust walking gaits that satisfy stepping-target and style objectives. Second, high-level controllers are then learned which plan at the timescale of steps by invoking desired step targets for the low-level controller. The high-level controller makes decisions directly based on high-dimensional inputs, including terrain maps or other suitable representations of the surroundings. Both levels of the control policy are trained using deep reinforcement learning. Results are demonstrated on a simulated 3D biped. Low-level controllers are learned for a variety of motion styles and demonstrate robustness with respect to force-based disturbances, terrain variations, and style interpolation. High-level controllers are demonstrated that are capable of following trails through terrains, dribbling a soccer ball towards a target location, and navigating through static or dynamic obstacles.


Terrain-adaptive locomotion skills using deep reinforcement learning

Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.


The Arcade Learning Environment: An Evaluation Platform for General Agents

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.


Software development governance: A meta-management perspective

Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.


Automated extraction of product comparison matrices from informal product descriptions

Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users. Preprint submitted to JSS January 5, 2017


A General Lower Bound on the Number of Examples Needed for Learning

We prove a lower bound of ( ln + VCdim(C) ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and and are the accuracy and con dence parameters. This improves the previous best lower bound of ( ln + VCdim(C)), and comes close to the known general upper bound of O( ln + VCdim(C) ln ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor. A. Ehrenfeucht was supported by NSF grant MCS-8305245, D. Haussler by ONR grant N00014-86-K-0454, M. Kearns by ONR grant N00014-86-K-0454 and an A.T. & T. Bell Laboratories Scholarship, and L. Valiant by grants ONR-N00014-85-K-0445, NSF-DCR-8600379, DAAL03-86-K-0171 and by the SERC of the U.K. Part of this research was done while M. Kearns was visiting U.C. Santa Cruz.


On-demand virtualization for live migration in bare metal cloud

The level of demand for bare-metal cloud services has increased rapidly because such services are cost-effective for several types of workloads, and some cloud clients prefer a single-tenant environment due to the lower security vulnerability of such enviornments. However, as the bare-metal cloud does not utilize a virtualization layer, it cannot use live migration. Thus, there is a lack of manageability with the bare-metal cloud. Live migration support can improve the manageability of bare-metal cloud services significantly.
 This paper suggests an on-demand virtualization technique to improve the manageability of bare-metal cloud services. A thin virtualization layer is inserted into the bare-metal cloud when live migration is requested. After the completion of the live migration process, the thin virtualization layer is removed from the host. We modified BitVisor [19] to implement on-demand virtualization and live migration on the x86 architecture.
 The elapsed time of on-demand virtualization was negligible. It takes about 20 ms to insert the virtualization layer and 30 ms to remove the one. After removing the virtualization layer, the host machine works with bare-metal performance.


Gender identity and sexual orientation in women with borderline personality disorder.

INTRODUCTION
In the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, text revision (DSM-IV-TR) (and earlier editions), a disturbance in "identity" is one of the defining features of borderline personality disorder (BPD). Gender identity, a person's sense of self as a male or a female, constitutes an important aspect of identity formation, but this construct has rarely been examined in patients with BPD.

AIMS
In the present study, the presence of gender identity disorder or confusion was examined in women diagnosed with BPD.

MAIN OUTCOME MEASURES
We used a validated dimensional measure of gender dysphoria. Recalled gender identity and gender role behavior from childhood was also assessed with a validated dimensional measure, and current sexual orientation was assessed by two self-report measures.

METHODS
A consecutive series of 100 clinic-referred women (mean age, 34 years) with BPD participated in the study. The women were diagnosed with BPD using the International Personality Disorder Exam-BPD Section.

RESULTS
None of the women with BPD met the criterion for caseness on the dimensional measure of gender dysphoria. Women who self-reported either a bisexual or a homosexual sexual orientation had a significantly higher score on the dimensional measure of gender dysphoria than the women who self-reported a heterosexual sexual orientation, and they also recalled significantly more cross-gender behavior during childhood. Results were compared with a previous study on a diagnostically heterogeneous group of women with other clinical problems.

CONCLUSION
The importance of psychosexual assessment in the clinical evaluation of patients with BPD is discussed.


Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier Hayder

Cervical cancer threatens the lives of many women in our world today. In 2014, the number of women infected with this disease in the United States was 12,578, of which 4,115 died, with a death rate of nearly 32%. Cancer data, including cervical cancer datasets, represent a significant challenge data mining techniques because absence of different costs for error cases. The proposed model present a cost sensitive classifiers that has three main stages; the first stage is prepressing the original data to prepare it for classification model which is build based on decision tree classifier with cost selectivity and finallyevaluation the proposed model based on many metrics in addition to apply a cross validation.The proposed model provides more accurate result in both binary class and multi class classification. It has a TP rate (0.429) comparing with (0.160) for typical decision tree in binary class task.


Sequencing technologies — the next generation

Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.


Multi-Bias Non-linear Activation in Deep Neural Networks

As a widely used non-linear activation, Rectified Linear Unit (ReLU) separates noise and signal in a feature map by learning a threshold or bias. However, we argue that the classification of noise and signal not only depends on the magnitude of responses, but also the context of how the feature responses would be used to detect more abstract patterns in higher layers. In order to output multiple response maps with magnitude in different ranges for a particular visual pattern, existing networks employing ReLU and its variants have to learn a large number of redundant filters. In this paper, we propose a multi-bias non-linear activation (MBA) layer to explore the information hidden in the magnitudes of responses. It is placed after the convolution layer to decouple the responses to a convolution kernel into multiple maps by multi-thresholding magnitudes, thus generating more patterns in the feature space at a low computational cost. It provides great flexibility of selecting responses to different visual patterns in different magnitude ranges to form rich representations in higher layers. Such a simple and yet effective scheme achieves the stateof-the-art performance on several benchmarks.


Constraints in the IoT: The World in 2020 and Beyond

The Internet of Things (IoT), often referred as the future Internet; is a collection of interconnected devices integrated into the world-wide network that covers almost everything and could be available anywhere. IoT is an emerging technology and aims to play an important role in saving money, conserving energy, eliminating gap and better monitoring for intensive management on a routine basis. On the other hand, it is also facing certain design constraints such as technical challenges, social challenges, compromising privacy and performance tradeoffs. This paper surveys major technical limitations that are hindering the successful deployment of the IoT such as standardization, interoperability, networking issues, addressing and sensing issues, power and storage restrictions, privacy and security, etc. This paper categorizes the existing research on the technical constraints that have been published in the recent years. With this categorization, we aim to provide an easy and concise view of the technical aspects of the IoT. Furthermore, we forecast the changes influenced by the IoT. This paper predicts the future and provides an estimation of the world in year 2020 and beyond. Keywords—Internet of Things; Future Internet; Next generation network issues; World-wide network; 2020


Don't Get Caught in the Cold, Warm-up Your JVM: Understand and Eliminate JVM Warm-up Overhead in Data-Parallel Systems

Many widely used, latency sensitive, data-parallel distributed systems, such as HDFS, Hive, and Spark choose to use the Java Virtual Machine (JVM), despite debate on the overhead of doing so. This paper analyzes the extent and causes of the JVM performance overhead in the above mentioned systems. Surprisingly, we find that the warm-up overhead, i.e., class loading and interpretation of bytecode, is frequently the bottleneck. For example, even an I/O intensive, 1GB read on HDFS spends 33% of its execution time in JVM warm-up, and Spark queries spend an average of 21 seconds in warm-up. The findings on JVM warm-up overhead reveal a contradiction between the principle of parallelization, i.e., speeding up long running jobs by parallelizing them into short tasks, and amortizing JVM warm-up overhead through long tasks. We solve this problem by designing HotTub, a new JVM that amortizes the warm-up overhead over the lifetime of a cluster node instead of over a single job by reusing a pool of already warm JVMs across multiple applications. The speed-up is significant. For example, using HotTub results in up to 1.8X speedups for Spark queries, despite not adhering to the JVM specification in edge cases.


HellRank: a Hellinger-based centrality measure for bipartite social networks

Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.


The biology of VEGF and its receptors

Vascular endothelial growth factor (VEGF) is a key regulator of physiological angiogenesis during embryogenesis, skeletal growth and reproductive functions. VEGF has also been implicated in pathological angiogenesis associated with tumors, intraocular neovascular disorders and other conditions. The biological effects of VEGF are mediated by two receptor tyrosine kinases (RTKs), VEGFR-1 and VEGFR-2, which differ considerably in signaling properties. Non-signaling co-receptors also modulate VEGF RTK signaling. Currently, several VEGF inhibitors are undergoing clinical testing in several malignancies. VEGF inhibition is also being tested as a strategy for the prevention of angiogenesis, vascular leakage and visual loss in age-related macular degeneration.


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

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.


Online 3D acquisition and model integration

This paper presents a system which yields complete 3D models in a fast and inexpensive way. The major building blocks are a high-speed structured light range scanner and a registration module. The former generates ’raw’ range data whereas the latter performs a fast registration (ICP) and renders the partially integrated model as a preview of the final result. As the scanner uses only a single image to make a reconstruction, it is possible to scan while the object is moved. The use of an adaptive projection pattern gives a more robust behaviour. This allows the system to deal more easily with complicated geometry and texture than most other systems. During the scanning process the model is built up incrementally and rendered on the screen. This real-time visual feedback allows the user to check the current state of the 3D model. Holes or other flaws can be detected during the acquisition process itself. This speeds up the model building process, and solves indirectly the problem of view planning. The whole real-time pipeline comprising acquisition, merging and visualization only uses off-the-shel f hardware. A regular desktop PC connected to a camera and LCD projector is turned into a high-speed scanner and modeler. The current implementation has a throughput of approx. 5 fps.


Opinion Mining in Online Reviews About Distance Education Programs

The popularity of distance education programs is increasing at a fast pace. En par with this development, online communication in fora, social media and reviewing platforms between students is increasing as well. Exploiting this information to support fellow students or institutions requires to extract the relevant opinions in order to automatically generate reports providing an overview of pros and cons of different distance education programs. We report on an experiment involving distance education experts with the goal to develop a dataset of reviews annotated with relevant categories and aspects in each category discussed in the specific review together with an indication of the sentiment. Based on this experiment, we present an approach to extract general categories and specific aspects under discussion in a review together with their sentiment. We frame this task as a multi-label hierarchical text classification problem and empirically investigate the performance of different classification architectures to couple the prediction of a category with the prediction of particular aspects in this category. We evaluate different architectures and show that a hierarchical approach leads to superior results in comparison to a flat model which makes decisions independently. This work has been performed while the first and last authors were at Bielefeld University.


Real-time large-scale dense RGB-D SLAM with volumetric fusion

We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.


The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective.

We examine two prevailing, yet surprisingly contradictory, theories of human walking. The six determinants of gait are kinematic features of gait proposed to minimize the energetic cost of locomotion by reducing the vertical displacement of the body center of mass (COM). The inverted pendulum analogy proposes that it is beneficial for the stance leg to behave like a pendulum, prescribing a more circular arc, rather than a horizontal path, for the COM. Recent literature presents evidence against the six determinants theory, and a simple mathematical analysis shows that a flattened COM trajectory in fact increases muscle work and force requirements. A similar analysis shows that the inverted pendulum fares better, but paradoxically predicts no work or force requirements. The paradox may be resolved through the dynamic walking approach, which refers to periodic gaits produced almost entirely by the dynamics of the limbs alone. Demonstrations include passive dynamic walking machines that descend a gentle slope, and active dynamic walking robots that walk on level ground. Dynamic walking takes advantage of the inverted pendulum mechanism, but requires mechanical work to transition from one pendular stance leg to the next. We show how the step-to-step transition is an unavoidable energetic consequence of the inverted pendulum gait, and gives rise to predictions that are experimentally testable on humans and machines. The dynamic walking approach provides a new perspective, focusing on mechanical work rather than the kinematics or forces of gait. It is helpful for explaining human gait features in a constructive rather than interpretive manner.


Comparing CORBA and Web-Services in view of a Service Oriented Architecture

The concept of Service Oriented Architecture revolves around registering services as tasks. These tasks are accomplished collectively by various disparate components seamlessly connected to one another. The task of interlinking these components may be considered amongst the most convoluted and difficult tasks currently faced by software practitioners. This paper attempts to show that although middleware technologies can be solely utilized to develop service oriented architecture, however such architecture would severely lack quality, interoperability and ease of implementation. In order to resolve these complexities and complications this paper proposes Web Services as an alternative to Middleware, for the realization of a fully functional interoperable and an automated SOA which conforms to the characteristics of a SOA. This paper provides an abstract implementation model of a SOA using both middleware and web services. It then attempts to point out the implementation and accepted benefits of the latter, especially when legacy applications are involved. Emphasize is laid out on the significance of interoperability since it assists in mobility and other corporate benefits. The paper concludes that when interoperability along with its benefits of mobility, expansion, costs, simplicity and enterprise integration are required in the construction of a SOA then web services should be the definite integration choice. The paper also highlights the importance of object oriented middleware, along with situations in which it might be preferred over web services.


Gentle Adaboost algorithm for weld defect classification

In this paper, we present a new strategy for automatic classification of weld defects in radiographs based on Gentle Adaboost algorithm. Radiographic images were segmented and moment-based features were extracted and given as input to Gentle Adaboost classifier. The performance of our classification system is evaluated using hundreds of radiographic images. The classifier is trained to classify each defect pattern into one of four classes: Crack, Lack of penetration, Porosity, and Solid inclusion. The experimental results show that the Gentle Adaboost classifier is an efficient automatic weld defect classification algorithm and can achieve high accuracy and is faster than support vector machine (SVM) algorithm, for the tested data.


Artificial Neural Networks for Misuse Detection

Misuse detection is the process of attempting to identify instances of network attacks by comparing current activity against the expected actions of an intruder. Most current approaches to misuse detection involve the use of rule-based expert systems to identify indications of known attacks. However, these techniques are less successful in identifying attacks which vary from expected patterns. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. We present an approach to the process of misuse detection that utilizes the analytical strengths of neural networks, and we provide the results from our preliminary analysis of this approach.


A General Greedy Approximation Algorithm with Applications

Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. In this paper, we present a general greedy algorithm for solving a class of convex optimization problems. We derive a bound on the rate of approximation for this algorithm, and show that our algorithm includes a number of earlier studies as special cases.


Traffic sign detection and recognition based on random forests

In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI raffic sign detection raffic sign recognition olor segmentation andom forests upport vector machines (SVMs) istogram of oriented gradients (HOG) color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods. © 2016 Elsevier B.V. All rights reserved. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 ocal self-similarity (LSS)


Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis

Deep learning has recently attracted a lot of attention, mainly thanks to substantial gains in terms of effectiveness. However, there is still room for significant improvement, especially when dealing with use cases that come with a limited availability of data, as is often the case in the area of medical image analysis. In this paper, we introduce a novel approach for early diagnosis of glaucoma in high-resolution FUNDUS images, only requiring a small number of training samples. In particular, we developed a predictive model based on a matching neural network architecture, integrating a high-resolution deep convolutional network that allows preserving the high-fidelity nature of the medical images. Our experimental results show that our predictive model is able to obtain higher levels of effectiveness than vanilla deep convolutional neural networks.


NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction

This paper describes our system used in the Aspect Based Sentiment Analysis Task 12 of SemEval-2015. Our system is based on two supervised machine learning algorithms: sigmoidal feedforward network to train binary classifiers for aspect category classification (Slot 1), and Conditional Random Fields to train classifiers for opinion target extraction (Slot 2). We extract a variety of lexicon and syntactic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances, ranking 1st for three of the evaluations (Slot 1 for both restaurant and laptop domains, and Slot 1 & 2) and 2nd for Slot 2 evaluation.


SemEval-2016 Task 5: Aspect Based Sentiment Analysis

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.


A Logic Programming Approach to Aspect Extraction in Opinion Mining

Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic Programming, particularly Answer Set Programming (ASP), can be used to elegantly and efficiently implement the key components of syntax based aspect extraction. Specifically, the well known double propagation (DP) method is implemented using 8 ASP rules that naturally model all key ideas in the DP method. Our experiment on a widely used data set also shows that the ASP implementation is much faster than a Java-based implementation. Syntactical approach has its limitation too. To further improve the performance of syntactical approach, we identify a set of general words from Word Net that have little chance to be an aspect and prune them when extracting aspects. The concept of general words and their pruning are concisely captured by 10 new ASP rules, and a natural extension of the 8 rules for the original DP method. Experimental results show a major improvement in precision with almost no drop in recall compared with those reported in the existing work on a typical benchmark data set. Logic Programming provides a convenient and effective tool to encode and thus test knowledge needed to improve the aspect extraction methods so that the researchers can focus on the identification and discovery of new knowledge to improve aspect extraction.


Cortical demyelination and diffuse white matter injury in multiple sclerosis.

Focal demyelinated plaques in white matter, which are the hallmark of multiple sclerosis pathology, only partially explain the patient's clinical deficits. We thus analysed global brain pathology in multiple sclerosis, focusing on the normal-appearing white matter (NAWM) and the cortex. Autopsy tissue from 52 multiple sclerosis patients (acute, relapsing-remitting, primary and secondary progressive multiple sclerosis) and from 30 controls was analysed using quantitative morphological techniques. New and active focal inflammatory demyelinating lesions in the white matter were mainly present in patients with acute and relapsing multiple sclerosis, while diffuse injury of the NAWM and cortical demyelination were characteristic hallmarks of primary and secondary progressive multiple sclerosis. Cortical demyelination and injury of the NAWM, reflected by diffuse axonal injury with profound microglia activation, occurred on the background of a global inflammatory response in the whole brain and meninges. There was only a marginal correlation between focal lesion load in the white matter and diffuse white matter injury, or cortical pathology, respectively. Our data suggest that multiple sclerosis starts as a focal inflammatory disease of the CNS, which gives rise to circumscribed demyelinated plaques in the white matter. With chronicity, diffuse inflammation accumulates throughout the whole brain, and is associated with slowly progressive axonal injury in the NAWM and cortical demyelination.


Essential communication practices for Extreme Programming in a global software development team

We conducted an industrial case study of a distributed team in the USA and the Czech Republic that used Extreme Programming. Our goal was to understand how this globally-distributed team created a successful project in a new problem domain using a methodology that is dependent on informal, face-to-face communication. We collected quantitative and qualitative data and used grounded theory to identify four key factors for communication in globally-distributed XP teams working within a new problem domain. Our study suggests that, if these critical enabling factors are addressed, methodologies dependent on informal communication can be used on global software development projects.


The application of virtual reality to (chemical engineering) education

Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This poster presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual chemical plants, virtual laboratory accidents, and a virtual UIC campus. The first area provides guided exploration of domains otherwise inaccessible, such as the interior of operating reactors and microscopic reaction mechanisms. The second promotes safety by demonstrating the consequences of not following proper lab safety procedures. And the third provides valuable guidance for (foreign) visitors. All programs developed are available on the Web, for free download to any interested parties.


Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.


Pondering the Concept of Abstraction in (Illustrative) Visualization

We explore the concept of abstraction as it is used in visualization, with the ultimate goal of understanding and formally defining it. Researchers so far have used the concept of abstraction largely by intuition without a precise meaning. This lack of specificity left questions on the characteristics of abstraction, its variants, its control, or its ultimate potential for visualization and, in particular, illustrative visualization mostly unanswered. In this paper we thus provide a first formalization of the abstraction concept and discuss how this formalization affects the application of abstraction in a variety of visualization scenarios. Based on this discussion, we derive a number of open questions still waiting to be answered, thus formulating a research agenda for the use of abstraction for the visual representation and exploration of data. This paper, therefore, is intended to provide a contribution to the discussion of the theoretical foundations of our field, rather than attempting to provide a completed and final theory.


Haraka - Efficient Short-Input Hashing for Post-Quantum Applications

Recently, many efficient cryptographic hash function design strategies have been explored, not least because of the SHA-3 competition. These designs are, almost exclusively, geared towards high performance on long inputs. However, various applications exist where the performance on short (fixed length) inputs matters more. Such hash functions are the bottleneck in hash-based signature schemes like SPHINCS or XMSS, which is currently under standardization. Secure functions specifically designed for such applications are scarce. We attend to this gap by proposing two short-input hash functions (or rather simply compression functions). By utilizing AES instructions on modern CPUs, our proposals are the fastest on such platforms, reaching throughputs below one cycle per hashed byte even for short inputs, while still having a very low latency of less than 60 cycles. Under the hood, this results comes with several innovations. First, we study whether the number of rounds for our hash functions can be reduced, if only second-preimage resistance (and not collision resistance) is required. The conclusion is: only a little. Second, since their inception, AES-like designs allow for supportive security arguments by means of counting and bounding the number of active S-boxes. However, this ignores powerful attack vectors using truncated differentials, including the powerful rebound attacks. We develop a general tool-based method to include arguments against attack vectors using truncated differentials.


Texas 3D Face Recognition Database

We make the Texas 3D Face Recognition Database available to researchers in three dimensional (3D) face recognition and other related areas. This database contains 1149 pairs of high resolution, pose normalized, preprocessed, and perfectly aligned color and range images of 118 adult human subjects acquired using a stereo camera. The images are accompanied with information about the subjects' gender, ethnicity, facial expression, and the locations of 25 manually located anthropometric facial fiducial points. Specific partitions of the data for developing and evaluating 3D face recognition algorithms are also included.


Maximum Entropy Deep Inverse Reinforcement Learning

This paper presents a general framework for employing deep architectures in particular neural networks to solve the inverse reinforcement learning (IRL) problem. Specifically, we propose to exploit the representational capacity and favourable computational complexity of deep networks to approximate complex, nonlinear reward functions. We show that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations. This makes it especially well-suited for applications in life-long learning scenarios commonly encountered in robotics. We demonstrate that our approach achieves performance commensurate to the state-of-the-art on existing benchmarks already with simple, comparatively shallow network architectures while significantly outperforming the stateof-the-art on an alternative benchmark based on more complex, highly varying reward structures representing strong interactions between features. Furthermore, we extend the approach to include convolutional layers in order to eliminate the dependency on precomputed features of current algorithms and to underline the substantial gain in flexibility in framing IRL in the context of deep learning.


Distributed Secret Sharing Approach With Cheater Prevention Based on QR Code

QR barcodes are used extensively due to their beneficial properties, including small tag, large data capacity, reliability, and high-speed scanning. However, the private data of the QR barcode lacks adequate security protection. In this article, we design a secret QR sharing approach to protect the private QR data with a secure and reliable distributed system. The proposed approach differs from related QR code schemes in which it uses the QR characteristics to achieve secret sharing and can resist the print-and-scan operation. The secret can be split and conveyed with QR tags in the distribution application, and the system can retrieve the lossless secret when authorized participants cooperate. General browsers can read the original data from the marked QR tag via a barcode reader, and this helps reduce the security risk of the secret. Based on our experiments, the new approach is feasible and provides content readability, cheater detectability, and an adjustable secret payload of the QR barcode.


Big Data analytics. Three use cases with R, Python and Spark

Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments : R, Python Scikit-learn, ∗Université de Toulouse – INSA, Institut de Mathématiques, UMR CNRS 5219 †Institut de Mathématiques, UMR CNRS 5219 ‡Université de Toulouse – UT3, Institut de Mathématiques, UMR CNRS 5219 1 ar X iv :1 60 9. 09 61 9v 1 [ st at .A P] 3 0 Se p 20 16 Spark MLlib on three public use cases : character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient for data munging and recommendation by collaborative filtering (non-negative factorization), current implementations of conventional learning methods (logistic regression, random forests) in MLlib or SparkML do not ou poorly compete habitual use of these methods (R, Python Scikit-learn) in an integrated or undistributed architecture.


Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set

ISSN 2250 – 1061 | © 2011 Bonfring Abstract--In machine learning, classification refers to a step by step procedure for designating a given piece of input data into any one of the given categories. There are many classification problem occurs and need to be solved. Different types are classification algorithms like tree-based, rule-based, etc are widely used. This work studies the effectiveness of Rule-Based classifiers for classification by taking a sample data set from UCI machine learning repository using the open source machine learning tool. A comparison of different rulebased classifiers used in Data Mining and a practical guideline for selecting the most suited algorithm for a classification is presented and some empirical criteria for describing and evaluating the classifiers are given.


Comparison of high-speed switched reluctance machines with conventional and toroidal windings

This paper presents designs of 50,000 rpm 6/4 switched reluctance motors (SRM's) with a focus of the comparative study on conventional and toroidal windings. There are four different machines compared in this paper, while the first is a conventional SRM, and the other three are toroidal winding machines. The first toroidal SRM (TSRM1) employs the conventional asymmetric convert and the same switching sequence as conventional SRM (CSRM). Therefore, an equivalent magnetic performance is observed. The second toroidal SRM (TSRM2) introduces a 12-switch converter topology. With a proper coil connection and switching sequence, all the coils are active and contribute to the flux and torque generation at the same time. The analysis shows that for the same amount of copper losses, TSRM2 yields a 50% higher output torque and power at rated speed than CSRM, while TSRM1 only generates half the torque of CSRM. The third toroidal SRM is a resized TSRM2, which is presented with the same envelope dimension of CSRM (same volumetric comparison). The comparison shows it's competitive to CSRM, especially as toroidal-winding can achieve higher filling factor during the manufacture process of winding.


Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition

Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.


Efficient Architecture Search by Network Transformation

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e.g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4.23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.


Iron Loss Reduction in an Interior PM Automotive Alternator

This paper examines the iron loss characteristics of a high-flux interior permanent-magnet machine. The machine was designed as a concept demonstrator for a 6-kW automotive alternator and has a wide field-weakening range. Initial experimental results revealed a high iron loss during field-weakening operation. Finite-element analysis was used to investigate the cause of the high iron losses and to predict their magnitude as a function of speed. The effects of changes in the machine design were examined in order to reduce iron losses and hence improve the machine performance


Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions

Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require many thousands of gradient-based weight updates and unique image examples for training. Ideally, the models would rapidly learn visual concepts from only a handful of examples, similar to the manner in which humans learns across many vision tasks. In this paper, we show how 1) neural attention and 2) meta learning techniques can be used in combination with autoregressive models to enable effective few-shot density estimation. Our proposed modifications to PixelCNN result in state-of-the art few-shot density estimation on the Omniglot dataset. Furthermore, we visualize the learned attention policy and find that it learns intuitive algorithms for simple tasks such as image mirroring on ImageNet and handwriting on Omniglot without supervision. Finally, we extend the model to natural images and demonstrate few-shot image generation on the Stanford Online Products dataset.


A Strategic Analysis of Electronic Marketplaces


On the Convergence of Adam and Beyond


New Fast and Accurate Jacobi SVD Algorithm. I

This paper is the result of contrived efforts to break the barrier between numerical accuracy and run time efficiency in computing the fundamental decomposition of numerical linear algebra – the singular value decomposition (SVD) of a general dense matrix. It is an unfortunate fact that the numerically most accurate one–sided Jacobi SVD algorithm is several times slower than generally less accurate bidiagonalization based methods such as the QR or the divide and conquer algorithm. Despite its sound numerical qualities, the Jacobi SVD is not included in the state of the art matrix computation libraries and it is even considered obsolete by some leading researches. Our quest for a highly accurate and efficient SVD algorithm has led us to a new, superior variant of the Jacobi algorithm. The new algorithm has inherited all good high accuracy properties, and it outperforms not only the best implementations of the one–sided Jacobi algorithm but also the QR algorithm. Moreover, it seems that the potential of the new approach is yet to be fully exploited.


Predictive Control in Power Electronics and Drives

Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.


Anomaly Detection using Autoencoders in High Performance Computing Systems

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).


Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks

Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69% and 80%, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71% for cardiac views and 81% for non-cardiac views.


Data Security and Privacy Protection Issues in Cloud Computing

It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.


INTEGRATING MOOCs IN BLENDED COURSES

Recent studies appreciate that "MOOCs bring an impetus of reform, research and innovation to the Academy" [9]. Even though MOOCs are usually developed and delivered as independent online courses, experiments to wrap formal university courses around existing MOOCs are reported by teachers and researchers in different articles [3], [4], [8], [17]. This paper describes a new approach, in which the participation of students in different MOOCs was integrated in a blended course run on a social mobile LMS. The topics of MOOCs delivered on specific platforms and having particular characteristics were connected with the Fall 2013 undergraduate course of Web Programming, at University Politehnica Timisoara, Romania, facilitated by the first author, the co-authors providing a shadow/peer facilitation, for tailoring the course scenario. The main parts of this study deal with: a) The reasons to integrate MOOCs in the university course. b) How the course was designed, how the students’ activities on different MOOC platforms were assessed and integrated in the course scenario. c) The results of a survey that evaluates students’ experiences related to MOOCs: a number of MOOC features were assessed [14]; answers to a few problems are also analysed: Did the participation in MOOCs support students to clarify and expand the course issues? What are students' suggestions for a more active participation in MOOCs? By comparing the learning scenarios of MOOCs with the Web Programming blended course, how can the course and its virtual space be improved? Do students consider that the MOOCs phenomenon is important for professional and personal development? The conclusions of the paper can be used by other teachers/instructors for integrating MOOCs in the courses they deliver/facilitate.


SQLrand: Preventing SQL Injection Attacks

We present a practical protection mechanism against SQL injection attacks. Such attacks target databases that are accessible through a web frontend, and take advantage of flaws in the input validation logic of Web components such as CGI scripts. We apply the concept of instruction-set randomization to SQL, creating instances of the language that are unpredictable to the attacker. Queries injected by the attacker will be caught and terminated by the database parser. We show how to use this technique with the MySQL database using an intermediary proxy that translates the random SQL to its standard language. Our mechanism imposes negligible performance overhead to query processing and can be easily retrofitted to existing systems.


Finding application errors and security flaws using PQL: a program query language

A number of effective error detection tools have been built in recent years to check if a program conforms to certain design rules. An important class of design rules deals with sequences of events asso-ciated with a set of related objects. This paper presents a language called PQL (Program Query Language) that allows programmers to express such questions easily in an application-specific context. A query looks like a code excerpt corresponding to the shortest amount of code that would violate a design rule. Details of the tar-get application's precise implementation are abstracted away. The programmer may also specify actions to perform when a match is found, such as recording relevant information or even correcting an erroneous execution on the fly.We have developed both static and dynamic techniques to find solutions to PQL queries. Our static analyzer finds all potential matches conservatively using a context-sensitive, flow-insensitive, inclusion-based pointer alias analysis. Static results are also use-ful in reducing the number of instrumentation points for dynamic analysis. Our dynamic analyzer instruments the source program to catch all violations precisely as the program runs and to optionally perform user-specified actions.We have implemented the techniques described in this paper and found 206 errors in 6 large real-world open-source Java applica-tions containing a total of nearly 60,000 classes. These errors are important security flaws, resource leaks, and violations of consis-tency invariants. The combination of static and dynamic analysis proves effective at addressing a wide range of debugging and pro-gram comprehension queries. We have found that dynamic analysis is especially suitable for preventing errors such as security vulner-abilities at runtime.


A First Step Towards Automated Detection of Buffer Overrun Vulnerabilities

We describe a new technique for finding potential buffer overrun vulnerabilities in security-critical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can be eliminated before code is deployed. We have implemented our design and used our prototype to find new remotely-exploitable vulnerabilities in a large, widely deployed software package. An earlier hand audit missed


StackGuard: Automatic Adaptive Detection and Prevention of Buffer-Overflow Attacks

This paper presents a systematic solution to the persistent problem of buffer overflow attacks. Buffer overflow attacks gained notoriety in 1988 as part of the Morris Worm incident on the Internet. While it is fairly simple to fix individual buffer overflow vulnerabilities, buffer overflow attacks continue to this day. Hundreds of attacks have been discovered, and while most of the obvious vulnerabilities have now been patched, more sophisticated buffer overflow attacks continue to emerge. We describe StackGuard: a simple compiler technique that virtually eliminates buffer overflow vulnerabilities with only modest performance penalties. Privileged programs that are recompiled with the StackGuard compiler extension no longer yield control to the attacker, but rather enter a fail-safe state. These programs require no source code changes at all, and are binary-compatible with existing operating systems and libraries. We describe the compiler technique (a simple patch to gcc), as well as a set of variations on the technique that tradeoff between penetration resistance and performance. We present experimental results of both the penetration resistance and the performance impact of this technique. This research is partially supported by DARPA contracts F3060296-1-0331 and F30602-96-1-0302. Ryerson Polytechnic University


Securing web application code by static analysis and runtime protection

Security remains a major roadblock to universal acceptance of the Web for many kinds of transactions, especially since the recent sharp increase in remotely exploitable vulnerabilities have been attributed to Web application bugs. Many verification tools are discovering previously unknown vulnerabilities in legacy C programs, raising hopes that the same success can be achieved with Web applications. In this paper, we describe a sound and holistic approach to ensuring Web application security. Viewing Web application vulnerabilities as a secure information flow problem, we created a lattice-based static analysis algorithm derived from type systems and typestate, and addressed its soundness. During the analysis, sections of code considered vulnerable are instrumented with runtime guards, thus securing Web applications in the absence of user intervention. With sufficient annotations, runtime overhead can be reduced to zero. We also created a tool named.WebSSARI (Web application Security by Static Analysis and Runtime Inspection) to test our algorithm, and used it to verify 230 open-source Web application projects on SourceForge.net, which were selected to represent projects of different maturity, popularity, and scale. 69 contained vulnerabilities. After notifying the developers, 38 acknowledged our findings and stated their plans to provide patches. Our statistics also show that static analysis reduced potential runtime overhead by 98.4%.


An investigation into language model data augmentation for low-resourced STT and KWS

This paper reports on investigations using two techniques for language model text data augmentation for low-resourced automatic speech recognition and keyword search. Lowresourced languages are characterized by limited training materials, which typically results in high out-of-vocabulary (OOV) rates and poor language model estimates. One technique makes use of recurrent neural networks (RNNs) using word or subword units. Word-based RNNs keep the same system vocabulary, so they cannot reduce the OOV, whereas subword units can reduce the OOV but generate many false combinations. A complementary technique is based on automatic machine translation, which requires parallel texts and is able to add words to the vocabulary. These methods were assessed on 10 languages in the context of the Babel program and NIST OpenKWS evaluation. Although improvements vary across languages with both methods, small gains were generally observed in terms of word error rate reduction and improved keyword search performance.


Supervised Dynamic and Adaptive Discretization for Rule Mining

Association rule mining is a well-researched topic in data mining. However, a common limitation with existing algorithms is that they mainly deal with categorical data. In this work we propose a methodology that allows adaptive discretization and quantitative rule discovery in large mixed databases. More specifically, we propose a top-down, recursive approach to find ranges of values for continuous attributes that result in high confidence rules. Our approach allows any given continuous attribute to be discretized in multiple ways. Compared to a global discretization scheme, our approach makes it possible to capture different intervariable interactions. We applied our algorithm to various synthetic and real datasets, including Intel manufacturing data that motivated our research. The experimental results and analysis indicate that our algorithm is capable of finding more meaningful rules for multivariate data, in addition to being more efficient than the state-of-the-art techniques.


Modeling social influence through network autocorrelation: constructing the weight matrix

Many physical and social phenomena are embedded within networks of interdependencies, the so-called ‘context’ of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models, hinge upon the chosen specification of weight matrix W, the elements of which represent the influence pattern present in the network. In this paper I discuss how social influence processes can be incorporated in the specification of W. Theories of social influence center around ‘communication’ and ‘comparison’; it is discussed how these can be operationalized in a network analysis context. Starting from that, a series of operationalizations of W is discussed. Finally, statistical tests are presented that allow an analyst to test various specifications against one another or pick the best fitting model from a set of models. © 2002 Elsevier Science B.V. All rights reserved.


Application of generalized Bagley-polygon four-port power dividers to designing microwave dual-band bandpass planar filters

A new type of microwave dual-band bandpass planar filter based on signal-interference techniques is reported. The described filter approach consists of transversal filtering sections made up of generalized Bagley-polygon four-port power dividers. This transversal section, by exploiting feedforward signal-interaction concepts, enables dual-band bandpass filtering transfer functions with several transmission zeros to be obtained. A set of closed formulas and guidelines for the analytical synthesis of the dual-passband transversal filtering section are derived. Moreover, its practical usefulness is proven with the development and testing of a 2.75/3.25-GHz dual-band microstrip prototype.


Measuring user acceptance of emerging information technologies: an assessment of possible method biases

The measurement scales for the perceived usefulness and perceived ease of we constructs introduced by Davis [12/ have become widely used for forecasting user acceptance of emerging information technologies. An experiment was conducted to examine whether grouping of items caused artifactual inflation of reliability and validity measures. We found support for our hypothesis that the reliability and validity stemmed notfrom item grouping butfrom the constructs of perceived usefulness and perceived ease of use being clearly defined, and the items used co measure each of these consn-ucts clearly capturing the essence of the conscrucI.


Effect of gas type and flow rate on Cu free air ball formation in thermosonic wire bonding

0026-2714/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.microrel.2010.02.023 * Corresponding author. E-mail address: apequegn@uwaterloo.ca (A. Peque The development of novel Cu wires for thermosonic wire bonding is time consuming and the effects of shielding gas on the electrical flame off (EFO) process is not fully understood. An online method is used in this study for characterizing Cu free air balls (FABs) formed with different shielding gas types and flow rates. The ball heights before (HFAB) and after deformation (Hdef) are responses of the online method and measured as functions of gas flow rate. Sudden changes in the slopes of these functions, a non-parallelity of the two functions, and a large standard deviation of the HFAB measurements all identify FAB defects. Using scanning electron microscope (SEM) images in parallel with the online measurements golf-club shaped and pointed shaped FABs are found and the conditions at which they occur are identified. In general FAB defects are thought to be caused by changes in surface tension of the molten metal during EFO due to inhomogeneous cooling or oxidation. It is found that the convective cooling effect of the shielding gas increases with flow rate up to 0.65 l/min where the bulk temperature of a thermocouple at the EFO site decreases by 19 C. Flow rates above 0.7 l/min yield an undesirable EFO process due to an increase in oxidation which can be explained by a change in flow from laminar to turbulent. The addition of H2 to the shielding gas reduces the oxidation of the FAB as well as providing additional thermal energy during EFO. Different Cu wire materials yield different results where some perform better than others. 2010 Elsevier Ltd. All rights reserved.


Enclosed: a component-centric interface for designing prototype enclosures

This paper explores the problem of designing enclosures (or physical cases) that are needed for prototyping electronic devices. We present a novel interface that uses electronic components as handles for designing the 3D shape of the enclosure. We use the .NET Gadgeteer platform as a case study of this problem, and implemented a proof-of-concept system for designing enclosures for Gadgeteer components. We show examples of enclosures designed and fabricated with our system.


Evaluation Techniques for Storage Hierarchies

This paper introduces an efficient technique called “stack processing” that can be used in the cost-performance evaluation of a large class of storage hierarchies. The technique depends on a classification of page replacement algorithms as “stack algorithms” for which various properties are derived. These properties may be of use in the general areas of program modeling and system analysis, as well as in the evaluation of storage hierarchies. For a better understanding of storage hierarchies, we briefly review some basic concepts of their design.


Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.


Traffic Analysis of Encrypted Messaging Services: Apple iMessage and Beyond

Instant messaging services are quickly becoming the most dominant form of communication among consumers around the world. Apple iMessage, for example, handles over 2 billion messages each day, while WhatsApp claims 16 billion messages from 400 million international users. To protect user privacy, many of these services typically implement end-to-end and transport layer encryption, which are meant to make eavesdropping infeasible even for the service providers themselves. In this paper, however, we show that it is possible for an eavesdropper to learn information about user actions, the language of messages, and even the length of those messages with greater than 96% accuracy despite the use of state-of-the-art encryption technologies simply by observing the sizes of encrypted packets. While our evaluation focuses on Apple iMessage, the attacks are completely generic and we show how they can be applied to many popular messaging services, including WhatsApp, Viber, and Telegram.