title: An optimal randomized incremental gradient method

abstract: In this paper, we consider a class of finite-sum convex optimization problems whose objective function is given by the summation of $m$ ($\ge 1$) smooth components together with some other relatively simple terms. We first introduce a deterministic primal-dual gradient (PDG) method that can achieve the optimal black-box iteration complexity for solving these composite optimization problems using a primal-dual termination criterion. Our major contribution is to develop a randomized primal-dual gradient (RPDG) method, which needs to compute the gradient of only one randomly selected smooth component at each iteration, but can possibly achieve better complexity than PDG in terms of the total number of gradient evaluations. More specifically, we show that the total number of gradient evaluations performed by RPDG can be ${\cal O} (\sqrt{m})$ times smaller, both in expectation and with high probability, than those performed by deterministic optimal first-order methods under favorable situations. We also show that the complexity of the RPDG method is not improvable by developing a new lower complexity bound for a general class of randomized methods for solving large-scale finite-sum convex optimization problems. Moreover, through the development of PDG and RPDG, we introduce a novel game-theoretic interpretation for these optimal methods for convex optimization.


title: When Should Software Firms Commercialize New Products via Freemium Business Models?

abstract: In the software industry, a challenge firms often face is how to effectively commercialize innovations. An emerging business model increasingly embraced by entrepreneurs, called freemium, combines “free” and “premium” consumption in association with a product or service. In a nutshell, this model involves giving away for free a certain level or type of consumption while making money on premium consumption. We develop a unifying multi-period microeconomic framework with network externalities embedded into consumer learning in order to capture the essence of conventional for-fee models, several key freemium business models such as feature-limited or time-limited, and uniform market seeding models. Under moderate informativeness of word-of-mouth signals, we fully characterize conditions under which firms prefer freemium models, depending on consumer priors on the value of individual software modules, perceptions of crossmodule synergies, and overall value distribution across modules. Within our framework, we show that uniform seeding is always dominated by either freemium models or conventional for-fee models. We further discuss managerial and policy implications based on our analysis. Interestingly, we show that freemium, in one form or another, is always preferred from the social welfare perspective, and we provide guidance on when the firms need to be incentivized to align their interests with the society’s. Finally, we discuss how relaxing some of the assumptions of our model regarding costs or informativeness and heterogeneity of word of mouth may reduce the profit gap between seeding and the other models, and potentially lead to seeding becoming the preferred approach for the firm.


title: Is e-Learning the Solution for Individual Learning?

abstract: Despite the fact that e-Learning exists for a relatively long time, it is still in its infancy. Current eLearning systems on the market are limited to technical gadgets and organizational aspects of teaching, instead of supporting the learning. As a result the learner has become deindividualized and demoted to a noncritical homogenous user. One way out of this drawback is the creation of individual e-Learning materials. For this purpose a flexible multidimensional data model and the generation of individual content are the solution. It is necessary to enable the interaction between the learners and the content in e-Learning systems in the same manner.


title: A Robust Method for Transcript Quantification with RNA-seq Data

abstract: The advent of high throughput RNA-seq technology allows deep sampling of the transcriptome, making it possible to characterize both the diversity and the abundance of transcript isoforms. Accurate abundance estimation or transcript quantification of isoforms is critical for downstream differential analysis (e.g. healthy vs. diseased cells), but remains a challenging problem for several reasons. First, while various types of algorithms have been developed for abundance estimation, short reads often do not uniquely identify the transcript isoforms from which they were sampled. As a result, the quantification problem may not be identifiable, i.e. lacks a unique transcript solution even if the read maps uniquely to the reference genome. In this paper, we develop a general linear model for transcript quantification that leverages reads spanning multiple splice junctions to ameliorate identifiability. Second, RNA-seq reads sampled from the transcriptome exhibit unknown position-specific and sequence-specific biases. We extend our method to simultaneously learn bias parameters during transcript quantification to improve accuracy. Third, transcript quantification is often provided with a candidate set of isoforms, not all of which are likely to be significantly expressed in a given tissue type or condition. By resolving the linear system with LASSO our approach can infer an accurate set of dominantly expressed transcripts while existing methods tend to assign positive expression to every candidate isoform. Using simulated RNA-seq datasets, our method demonstrated better quantification accuracy than existing methods. The application of our method on real data experimentally demonstrated that transcript quantification is effective for differential analysis of transcriptomes.


title: Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers

abstract: Since the early 1960's, researchers have built a number of programming languages and environments with the intention of making programming accessible to a larger number of people. This article presents a taxonomy of languages and environments designed to make programming more accessible to novice programmers of all ages. The systems are organized by their primary goal, either to teach programming or to use programming to empower their users, and then, by each system's authors' approach, to making learning to program easier for novice programmers. The article explains all categories in the taxonomy, provides a brief description of the systems in each category, and suggests some avenues for future work in novice programming environments and languages.


title: An efficient automated parameter tuning framework for spiking neural networks

abstract: As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.


title: Identifying the security risks associated with governmental use of cloud computing

abstract: Abstract Cloud computing, which refers to an emerging computing model where machines in large data centers can be used to deliver services in a scalable manner, has become popular for corporations in need of inexpensive, large scale computing. Recently, the United States government has begun to utilize cloud computing architectures, platforms, and applications to deliver services and meet the needs of their constituents. Surrounding the use of cloud computing are many risks that can have major impacts on the information and services supported by this technology. This paper discusses the current use of cloud computing in government, and the risks–tangible and intangible–associated with its use. Examining specific cases of government cloud computing, this paper explores the level of understanding of the risks by the departments and agencies that implement this technology. This paper argues that a defined risk management program focused on cloud computing is an essential part of the government IT environment.


title: Current self-balance mechanism in multiphase back converter

abstract: Some of the recent applications in the field of the power supplies use multiphase converters to achieve fast dynamic response, smaller input/output filters, or better packaging. Typically, these converters have several paralleled power stages, with a current loop in each phase and a single voltage loop. The presence of the current loops avoids current imbalance among phases. The purpose of this paper is to demonstrate that, in CCM, with a proper design, there is an intrinsic mechanism of self-balance that reduces the current imbalance. Thus, in the buck converter, if natural zero-voltage switching (ZVS) is achieved in both transitions, the instantaneous inductor current compensates partially the different DC currents through the phases. The need for using n current loops will be finally determined by the application but not by the converter itself. Using the buck converter as a base, a multiphase converter has been developed. Several tests have been carried out in the laboratory and the results show clearly that, when the conditions are met, the phase currents are very well balanced even during transient conditions.


title: Does Foreign Direct Investment Accelerate Economic Growth

abstract: This paper uses new statistical techniques and two new databases to reassess the relationship between economic growth and FDI. After resolving biases plaguing past work, we find that the exogenous component of FDI does not exert a robust, independent influence on growth.


title: Bipolar Ripple Cancellation Method to Achieve Single-Stage Electrolytic-Capacitor-Less High-Power LED Driver

abstract: Conventional topologies for high-power LED drivers with high power factors (PFs) require large capacitances to limit the low frequency (100 or 120 Hz) LED current ripples. Electrolytic capacitors are commonly used because they are the only capacitors with sufficient energy density to accommodate high-power applications. However, the short life span of electrolytic capacitors significantly reduces the life span of the entire LED lighting fixture, which is undesirable. This paper proposes a bipolar (ac) ripple cancellation method with two different full-bridge power structures to cancel the low-frequency ac ripple in the LED current and minimize the output capacitance requirement, enabling the use of long-life film capacitors. Compared with the existing technologies, the proposed circuit achieves zero double-line-frequency current ripple through LED lamps and achieves a high PF and high efficiency. A 100-W (150 V/0.7 A) LED driver prototype was built which demonstrates that the proposed method can achieve the same double-line-frequency LED current ripple with only 44- $\mu \text{F}$ film capacitors, compared with the 4700- $\mu \text{F}$ electrolytic capacitors required in the conventional single-stage LED drivers. Meanwhile, the proposed prototype has achieved a peak power efficiency of 92.5%, benefiting from active clamp technology.


title: Online Keyword Spotting with a Character-Level Recurrent Neural Network

abstract: In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech. The RNN is end-to-end trained with connectionist temporal classification (CTC) to generate the probabilities of character and word-boundary labels. There is no need for the phonetic transcription, senone modeling, or system dictionary in training and testing. Also, keywords can easily be added and modified by editing the text based keyword list without retraining the RNN. Moreover, the unidirectional RNN processes an infinitely long input audio streams without pre-segmentation and keywords are detected with low-latency before the utterance is finished. Experimental results show that the proposed keyword spotter significantly outperforms the deep neural network (DNN) and hidden Markov model (HMM) based keyword-filler model even with less computations.


title: Collaborative Learning: Higher Education, Interdependence, and the Authority of Knowledge by Kenneth Bruffee: A Critical Study

abstract: In his very readable book on collaborative learning, Bruffee tells us, "[f]or a decade or more, reports on the state of American higher education have complained that many undergraduates tend to be authority-dependent, passive, irresponsible, overly competitive, and suspicious of their peers" (8). Bruffee's hope is to help overcome these ills by arguing that "knowledge is a socially constructed, sociolinguistic entity and that learning is inherently an interdependent, sociolinguistic process" (8). While I tend to agree with this characterization of the state of higher education, I shall argue that Bruffee's postmodern turn, according to which knowledge is a social construct, is a theoretical dead end. Despite some contrary advertisement, Bruffee is not merely calling for a change in the process of education by providing, for instance, more interdependent student-centered activities. In asking that we re-conceptualize our understanding of knowledge, Bruffee's focus is not merely on method, for his concept of process is heavily theory-laden. In what follows, therefore, I examine not his many excellent techniques for encouraging interdependent student activities, but his postmodern assumptions. Unearthing these assumptions and their logical implications brings out the flaws in Bruffee's view that his "pedagogy of cultural change" is an educational, social, or cultural improvement. My method in what follows is to embed Bruffee's theoretical project in a larger context and distinguish, as he does not, between humanistic and materialistic needs. This will aid us in recognizing that educational woes are not due to a foundational understanding of knowledge, but to a faulty foundational understanding. In classical culture there was a community of interdependent inquirers believing in and seeking a shared, objective common good, which we now seldom do. What dominates in our culture is an objective mode of knowing where the paradigm of successful explanation focuses on manipulation and control, a paradigm that has been described by E. M. Adams in The Metaphysics of Self and World and in an anthology entitled Naturalism: A Critical Appraisal edited by Steven Wagner and Richard Warner. This culturally dominant epistemological stance leads us to concentrate on materialistic needs, the kind of human needs that can be satisfied by manipulatory action. It also forces us to seek the kind of knowledge that will increase our power to satisfy these needs. As a result there has been a shift in emphasis from humanistic to materialistic interests and values. Humanistic needs, human needs beyond the realm of manipulatory power, are thus ignored. Included among humanistic needs are the need for an identity, the need for intersubjectivity, the need for self-respect, the need for meaningful relationships, the need for meaningful experiences and activities, the need for self-expression, and so on. What we should realize is that the satisfaction of humanistic needs does not result solely from the scientific mode of knowing that emphasizes the manipulation of factual structures aimed at the control of the material conditions under which we live. For the satisfaction of our material needs, we seek knowledge that enhances our power to manipulate and control our environment. To rebuild a decaying neighborhood, for instance, we rely on the methods and procedures of the natural sciences to acquire knowledge of the factual structures in need of modification. Thus we bring in the engineers, the city planners, the architects, the demographic experts, and so on. But the methods that enable us to rebuild do not enable us to know whether we ought to do what we are able to do. Recognizing that we ought to rebuild our physical slums requires a well-functioning society intent on satisfying both our material and humanistic needs. To be morally aware of all the reasons why we ought to rebuild a physical slum requires that we focus our attention on the humanistic as well as the materialistic needs of those living in the slums; it is this dual attention that drives the moral decision to rebuild. …


title: Recommending Investors for Crowdfunding Projects

abstract: To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.


title: LAST: locality-aware sector translation for NAND flash memory-based storage systems

abstract: As flash memory technologies quickly improve, NAND flash memory-based storage devices are becoming a viable alternative as a secondary storage solution for general-purpose computing systems such as personal computers and enterprise server systems. Most existing flash translation layer (FTL) schemes are, however, ill-suited for such systems because they were optimized for storage write patterns of embedded systems only. In this paper, we propose a new flash management technique called LAST which is optimized for access characteristics of general-purpose computing systems. By exploiting the locality of storage access patterns, LAST reduces the garbage collection overhead significantly, thus increasing the I/O performance of flash-based storage devices. Our experimental results show that the proposed technique reduces the garbage collection overhead by 54% over the existing flash memory management techniques.


GeoClustering: A web service for geospatial clustering


title: Affiliate Marketing in Travel and Tourism

abstract: This paper explores, through an analysis of the existing literature and in-depth interviews with key stakeholders, the use of affiliate marketing within the travel and tourism industry. The study findings show that affiliate marketing is becoming a key strategic channel for distribution of travel and tourism product and that the discipline is maturing with the advent of affiliate networks and the increasing sophistication of both merchants and affiliates. The findings also show that are still several problematic aspects linked to this form of online distribution mainly in relation to issues of brand management and affiliate fraud.


title: Chunking of Large Multidimensional Arrays

abstract: Data intensive scientific computations as well on-lineanalytical processing applications as are done on very large datasetsthat are modeled as k-dimensional arrays. The storage organization ofsuch arrays on disks is done by partitioning the large global array intofixed size hyper-rectangular sub-arrays called chunks or tiles that formthe units of data transfer between disk and memory. Typical queriesinvolve the retrieval of sub-arrays in a manner that accesses all chunksthat overlap the query results. An important metric of the storageefficiency is the expected number of chunks retrieved over all suchqueries. The question that immediately arises is "what shapes of arraychunks give the minimum expected number of chunks over a query workload?"In this paper we develop two probabilistic mathematical models of theproblem and provide exact solutions using steepest descent and geometricprogramming methods. Experimental results, using synthetic workloads onreal life data sets, show that our chunking is much more efficient thanthe existing approximate solutions.


title: Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

abstract: Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is data-driven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar times-series data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events~(e.g. high wind day) or time of the year~(e,g. solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques.


REVERSE OSMOSIS IN DESALINATION PLANTS


title: One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach

abstract: Deep learning, even if it is very successful nowadays, traditionally needs very large amounts of labeled data to perform excellent on the classification task. In an attempt to solve this problem, the one-shot learning paradigm, which makes use of just one labeled sample per class and prior knowledge, becomes increasingly important. In this paper, we propose a new one-shot learning method, dubbed MoVAE (Mixture of Variational AutoEncoders), to perform classification. Complementary to prior studies, MoVAE represents a shift of paradigm in comparison with the usual one-shot learning methods, as it does not use any prior knowledge. Instead, it starts from zero knowledge and one labeled sample per class. Afterward, by using unlabeled data and the generalization learning concept (in a way, more as humans do), it is capable to gradually improve by itself its performance. Even more, if there are no unlabeled data available MoVAE can still perform well in one-shot learning classification. We demonstrate empirically the efficiency of our proposed approach on three datasets, i.e. the handwritten digits (MNIST), fashion products (Fashion-MNIST), and handwritten characters (Omniglot), showing that MoVAE outperforms state-of-the-art one-shot learning algorithms.


title: Learning Distributed Representations of Sentences from Unlabelled Data

abstract: Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.


title: Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation

abstract: The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently, our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on Chinese-English and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.


title: Interactive ramp merging planning in autonomous driving: Multi-merging leading PGM (MML-PGM)

abstract: Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles.


title: Classification in the Presence of Label Noise: A Survey

abstract: Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.


title: Scalable MCMC for Mixed Membership Stochastic Blockmodels

abstract: We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current state-of-the-art algorithm based on stochastic variational inference. In addition we develop an approximation that can handle models that entertain a very large number of communities. The experimental results show that SG-MCMC strictly dominates competing algorithms in all cases.


Outlier Detection using Granular Box Regression Methods


title: Microbiological characteristic of anthills of Lasius niger

abstract: The characteristics of microbial communities of the anthills of Lasius niger compared to the soil beyond the area of the strong effect of ants is given.


title: Packet error rate analysis of decode-and-forward wireless networks with internode SR-ARQ protocols

abstract: This paper analyzes end-to-end packet error rate of decode-and-forward cooperative networks over Rayleigh fading. Packet error rate expressions are derived when node cooperation is applied with a selective repeat automatic retransmission request scheme and confirmed by Monte-Carlo simulations. The impact of average signal-to-noise ratio values, network dimension and number of retransmission attempts when maximal-ratio combining is performed at the destination are discussed.


title: Stationary and mobile battery energy storage systems for smart grids

abstract: Renewable energy is a key technology in reducing global carbon dioxide emissions. Currently, penetration of intermittent renewable energies in most power grids is low, such that the impact of renewable energy's intermittency on grid stability is controllable. Utility scale energy storage systems can enhance stability of power grids with increasing share of intermittent renewable energies. With the grid communication network in smart grids, mobile battery systems in battery electric vehicles and plug-in hybrid electric vehicles can also be used for energy storage and ancillary services in smart grids. This paper will review the stationary and mobile battery systems for grid voltage and frequency stability control in smart grids with increasing shares of intermittent renewable energies. An optimization algorithm on vehicle-to-grid operation will also be presented.


title: Deep Information Propagation

abstract: We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory. We show the existence of depth scales that naturally limit the maximum depth of signal propagation through these random networks. Our main practical result is to show that random networks may be trained precisely when information can travel through them. Thus, the depth scales that we identify provide bounds on how deep a network may be trained for a specific choice of hyperparameters. As a corollary to this, we argue that in networks at the edge of chaos, one of these depth scales diverges. Thus arbitrarily deep networks may be trained only sufficiently close to criticality. We show that the presence of dropout destroys the order-to-chaos critical point and therefore strongly limits the maximum trainable depth for random networks. Finally, we develop a mean field theory for backpropagation and we show that the ordered and chaotic phases correspond to regions of vanishing and exploding gradient respectively.


title: A System-Fault-Risk Framework for cyber attack classification

abstract: Computer and network systems fall victim to many cyber attacks of different forms. To reduce the risks of cyber attacks, an organization needs to understand and assess them, make decisions about what types of barriers or protection mechanisms are necessary to defend against them, and decide where to place such mechanisms. Understanding cyber attack characteristics (threats, attack activities, state and performance impact, etc.) helps in choosing effective barriers. Understanding the assets affected by cyber attacks helps decide where to place such barriers. To develop these understandings, we classify attacks in a comprehensive, sensible format. This paper presents the System-Fault-Risk (SFR) framework for cyber attack classification, which we base on a scientific foundation, combining theories from system engineering, fault modeling, and risk-assessment. Our work extends existing classifications with a focus on separating cause and effect, and further refining effects to include state and performance.


title: An automatic vision-based malaria diagnosis system.

abstract: Malaria is a worldwide health problem with 225 million infections each year. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy-to-use, quantitative cartridge-scanner system for vision-based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger-prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two-step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5,420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11,780 images containing healthy blood. From finger prick to result, the prototype cartridge-scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy-to-use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the scanner.


title: Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems

abstract: The detection of fastener defects is an important task in railway inspection systems, and it is frequently performed to ensure the safety of train traffic. Traditional inspection is usually operated by trained workers who walk along railway lines to search for potential risks. However, the manual inspection is very slow, costly, and dangerous. This paper proposes an automatic visual inspection system for detecting partially worn and completely missing fasteners using probabilistic topic model. Specifically, our method is able to simultaneously model diverse types of fasteners with different orientations and illumination conditions using unlabeled data. To assess the damages, the test fasteners are compared with the trained models and automatically ranked into three levels based on the likelihood probability. The experimental results demonstrate the effectiveness of this method.


Enterococcus Faecalis Infective Endocarditis and Colorectal Carcinoma: Case of New Association Gaining Ground


title: Visualizing Time-Oriented Data -- A Systematic View

abstract: The analysis of time-oriented data is an important task in many application scenarios. In recent years, a variety of techniques for visualizing such data have been published. This variety makes it difficult for prospective users to select methods or tools that are useful for their particular task at hand. In this article, we develop and discuss a systematic view on the diversity of methods for visualizing time-oriented data. With the proposed categorization we try to untangle the visualization of time-oriented data, which is such an important concern in Visual Analytics. The categorization is not only helpful for users, but also for researchers to identify future tasks in Visual Analytics.


title: Enzastaurin (LY317615), an Oral Protein Kinase C β Inhibitor, Induces Apoptosis in Multiple Myeloma Cell Lines.

abstract: The protein kinase C (PKC) family consists of 11 serine/threonine protein kinase isoforms that are involved with cell proliferation and differentiation, gene transcription, and tumor-induced angiogenesis. The PKC pathway has been shown to play a role in the regulation of cell growth in several hematologic malignancies. However, in multiple myeloma (MM), the role of the PKC pathway has not been extensively studied. Enzastaurin (LY317615), an acyclic bisindolylmaleimide, is an oral inhibitor of the PKC β isozyme. Enzastaurin has been reported to induce apoptosis and suppress proliferation in the HCT116 colon cancer cell line by inhibiting the AKT pathway. The objective of this study was to assess the efficacy of Enzastaurin in inducing apoptosis in MM cell lines and to investigate possible mechanisms of apoptosis. A spectrum of MM cell lines, with unique characteristics (dexamethasone-sensitive, dexamethasone-resistant, chemotherapy-sensitive, chemotherapy-resistant) were treated with Enzastaurin. There is evidence of cell death in all cell lines at clinically significant concentrations (1–3μM) after 72 hours of treatment. The dexamethasone-sensitive MM1.S cell line was used to further assess the effect of Enzastaurin in the presence of dexamethasone, insulin-like growth factor-1 (IGF-1), and IL-6. IGF-1 and IL-6 are potent growth factors for MM and have been observed to blunt the cytotoxic activity of dexamethasone. Dexamethasone and Enzastaurin appear to have an additive effect in the induction of apoptosis. In addition, while IGF-1 slightly decreases the effect of Enzastaurin, IL-6 has no effect. Enzastaurin treatment also induces a decrease in GSK3β and AKTSerine473 phosphorylation. GSK3β phosphorylation is thought to be a reliable pharmacodynamic marker for Enzastaurin activity. In conclusion, these data indicate that Enzastaurin induces apoptosis in MM cells and suppression of the AKT pathway may be one of the mechanisms by which Enzastaurin exerts its anti-myeloma activity.


IMPROVING SEQ2SEQ CONVERSATION MODELS


title: AEROSTACK: An architecture and open-source software framework for aerial robotics

abstract: To simplify the usage of the Unmanned Aerial Systems (UAS), extending their use to a great number of applications, fully autonomous operation is needed. There are many open-source architecture frameworks for UAS that claim the autonomous operation of UAS, but they still have two main open issues: (1) level of autonomy, being in most of the cases limited and (2) versatility, being most of them designed specifically for some applications or aerial platforms. As a response to these needs and issues, this paper presents Aerostack, a system architecture and open-source multi-purpose software framework for autonomous multi-UAS operation. To provide higher degrees of autonomy, Aerostack's system architecture integrates state of the art concepts of intelligent, cognitive and social robotics, based on five layers: reactive, executive, deliberative, reflective, and social. To be a highly versatile practical solution, Aerostack's open-source software framework includes the main components to execute the architecture for fully autonomous missions of swarms of UAS; a collection of ready-to-use and flight proven modular components that can be reused by the users and developers; and compatibility with five well known aerial platforms, as well as a high number of sensors. Aerostack has been validated during three years by its successful use on many research projects, international competitions and exhibitions. To corroborate this fact, this paper also presents Aerostack carrying out a fictional fully autonomous indoors search and rescue mission.


title: Monitoring road traffic with a UAV-based system

abstract: Unmanned Aerial Vehicles (UAVs) are becoming an attractive solution for road traffic monitoring because of their mobility, low cost, and broad view range. Up to now, existing traffic monitoring systems based on UAVs only use one UAV with fixed trajectory to extract information about vehicles. In this paper, we propose a road traffic monitoring system using multiple UAVs. We develop a method to generate adaptive UAVs trajectories, which is based on the tracking of moving points in the UAV field of view. Also we generate UAVs trajectories using mobility models that are usually used to model vehicles mobility. UAVs monitor the traffic on a city road, they are responsible for collecting and sending, in real time, vehicle information to a traffic processing center for traffic regulation purposes. We show that the performance of our system is better than the performance of the fixed UAV trajectory traffic monitoring system in terms of coverage rates and events detection rates.


title: Multi-robot collision avoidance with localization uncertainty

abstract: This paper describes a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i.e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level. While such methods as Optimal Reciprocal Collision Avoidance guarantee local collision-free motion for a large number of robots, given perfect knowledge of positions and speeds, a realistic implementation requires further extensions to deal with inaccurate localization and message passing delays. The presented algorithm bounds the error introduced by localization and combines the computation for collision-free motion with localization uncertainty. We provide an open source implementation using the Robot Operating System (ROS). The system is tested and evaluated with up to eight robots in simulation and on four differential drive robots in a real-world situation.


title: Eurospeech 2001- Scandinavia EVALUATION OF CROSS-LANGUAGE VOICE CONVERSION BASED ON GMM AND STRAIGHT

abstract: Voice conversion is a technique for producing utterances using any target speakers’ voice from a single source speaker’s utterance. In this paper, we apply cross-language voice conversion between Japanese and English to a system based on a Gaussian Mixture Model (GMM) method and STRAIGHT, a high quality vocoder. To investigate the effects of this conversion system across different languages, we recorded two sets of bilingual utterances and performed voice conversion experiments using a mapping function which converts parameters of acoustic features for a source speaker to those of a target speaker. The mapping functions were trained using bilingual databases of both Japanese and English speech. In an objective evaluation using Mel cepstrum distortion (Mel CD), it was confirmed that the system can perform cross-language voice conversion with the same performance as that within a single-language.


title: ULA-OP 256: A 256-Channel Open Scanner for Development and Real-Time Implementation of New Ultrasound Methods

abstract: Open scanners offer an increasing support to the ultrasound researchers who are involved in the experimental test of novel methods. Each system presents specific performance in terms of number of channels, flexibility, processing power, data storage capability, and overall dimensions. This paper reports the design criteria and hardware/software implementation details of a new 256-channel ultrasound advanced open platform. This system is organized in a modular architecture, including multiple front-end boards, interconnected by a high-speed (80 Gb/s) ring, capable of finely controlling all transmit (TX) and receive (RX) signals. High flexibility and processing power (equivalent to 2500 GFLOP) are guaranteed by the possibility of individually programming multiple digital signal processors and field programmable gate arrays. Eighty GB of on-board memory are available for the storage of prebeamforming, postbeamforming, and baseband data. The use of latest generation devices allowed to integrate all needed electronics in a small size ( $34~\textrm {cm} \times 30~\textrm {cm} \times 26$ cm). The system implements a multiline beamformer that allows obtaining images of 96 lines by 2048 depths at a frame rate of 720 Hz (expandable to 3000 Hz). The multiline beamforming capability is also exploited to implement a real-time vector Doppler scheme in which a single TX and two independent RX apertures are simultaneously used to maintain the analysis over a full pulse repetition frequency range.


title: Energy consumption and water production cost of conventional and renewable-energy-powered desalination processes

abstract: Abstract Desalination technologies improve water quality, greatly reduce water shortage problems, and improve quality of life and economic status. Two main technologies are currently used in water desalination: thermal (phase-change) processes and membrane processes. The primary thermal distillation processes include multistage flash distillation (MSF), multi-effect distillation (MED), and vapor compression (VC). The VC process encompasses two types: mechanical (MVC) and thermal (TVC). The common membrane desalination processes include reverse osmosis (RO) and electrodialysis (ED and EDR). Energy cost, operational and maintenance cost, and capital investment are the main contributors to the water production cost of any of these processes. The energy cost is responsible for about 50% of the produced water cost. For thermal distillation processes (MSF, MED, and TVC), two energy forms are required for the operation: (1) low-temperature heat, which represents the main portion of the energy input and is usually supplied to the system by a number of external sources (e.g., fossil fuel, waste energy, nuclear, solar) and (2) electricity, which is used to drive the system's pumps and other electrical components. For the MVC thermal distillation process, only electricity is needed. For membrane processes (RO and ED), only electricity is required as an energy input. Renewable energy systems such as solar thermal, solar photovoltaic, wind, and geothermal technologies are currently used as energy suppliers for desalination systems. These renewable resources are now a proven technology and remain economically promising for remote regions, where connection to the public electric grid is either not cost effective or feasible, and where water scarcity is severe. As the technologies continue to improve, and as fresh water becomes scarce and fossil fuel energy prices rise, renewable energy desalination becomes more viable economically. The technical features, energy consumption, environmental considerations, and potential of renewable energy use in driving the main desalination processes are reviewed and analyzed in this paper. The current and projected costs of water produced from conventional and renewable-energy-driven processes are discussed and compared.


title: New bottle but old wine: A research of cyberbullying in schools

abstract: This study investigates the nature and the extent of adolescences' experience of cyberbullying. A survey study of 177 grade seven students in an urban city is conducted. In this paper, ''cyberbullying'' refers to bullying via electronic communication tools. The results show that almost 54% of the students were victims of traditional bullying and over a quarter of them had been cyber-bullied. Almost one in three students had bullied others in the traditional form, and almost 15% had bullied others using electronic communication tools. Almost 60% of the cyber victims are females, while over 52% of cyber-bullies are males. Majority of the cyber-bully victims and bystanders did not report the incidents to adults.


title: Winding Configurations and Optimal Stator and Rotor Pole Combination of Flux-Switching PM Brushless AC Machines

abstract: A simple analytical method is developed to compare the combinations of stator and rotor pole numbers in flux-switching permanent magnet (PM) machines in terms of back electromotive force (EMF) and electromagnetic torque. The winding connections and winding factors of machines having all poles and alternate poles wound, and different numbers of phases, from two to six, are determined by the coil-EMF vectors. Their differences from analyzing the conventional fractional-slot PM machines with concentrated nonoverlapping windings are highlighted. The general conditions are established for balanced symmetrical back-EMF waveform. It shows that the optimized rotor pole number should be close to the number of stator poles, whereas larger torque can be obtained by the machine with relatively higher rotor pole number. The analysis is validated by finite-element analyses and experiment.


title: Learning active facial patches for expression analysis

abstract: In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g., around mouth, eye), we try to discover the common and specific patches which are important to discriminate all the expressions and only a particular expression, respectively. A two-stage multi-task sparse learning (MTSL) framework is proposed to efficiently locate those discriminative patches. In the first stage MTSL, expression recognition tasks, each of which aims to find dominant patches for each expression, are combined to located common patches. Second, two related tasks, facial expression recognition and face verification tasks, are coupled to learn specific facial patches for individual expression. Extensive experiments validate the existence and significance of common and specific patches. Utilizing these learned patches, we achieve superior performances on expression recognition compared to the state-of-the-arts.


title: The SmartLogic Tool: Analysing and Testing Smart Card Protocols

abstract: This paper introduces the Smart Logic, which is a flexible smart card research tool that gives complete control over the smart card communication channel for eavesdropping, man-in-the-middle attacks, relaying and card emulation. The hardware is available off-the-shelf at a price of about 100 euros. Furthermore, the necessary firm- and software is open source. The Smart Logic provides essential functionality for smart card protocol research and testing. This is demonstrated by reproducing two attack scenarios. The first attack is on an implementation of the EMV payment protocol where a payment terminal is forced to do a rollback to plaintext PIN instead of using encrypted PIN. The second attack is a relay of a smart card payment over a 20 km distance. We also show that this distance can be increased to at least 10.000 km.


title: Learning Analytics in Massive Open Online Courses

abstract: Educational technology has obtained great importance over the last fifteen years. At present, the umbrella of educational technology incorporates multitudes of engaging online environments and fields. Learning analytics and Massive Open Online Courses (MOOCs) are two of the most relevant emerging topics in this domain. Since they are open to everyone at no cost, MOOCs excel in attracting numerous participants that can reach hundreds and hundreds of thousands. Experts from different disciplines have shown significant interest in MOOCs as the phenomenon has rapidly grown. In fact, MOOCs have been proven to scale education in disparate areas. Their benefits are crystallized in the improvement of educational outcomes, reduction of costs and accessibility expansion. Due to their unusual massiveness, the large datasets of MOOC platforms require advanced tools and methodologies for further examination. The key importance of learning analytics is reflected here. MOOCs offer diverse challenges and practices for learning analytics to tackle. In view of that, this thesis combines both fields in order to investigate further steps in the learning analytics capabilities in MOOCs. The primary research of this dissertation focuses on the integration of learning analytics in MOOCs, and thereafter looks into examining students' behavior on one side and bridging MOOC issues on the other side. The research was done on the Austrian iMooX xMOOC platform. We followed the prototyping and case studies research methodology to carry out the research questions of this dissertation. The main contributions incorporate designing a general learning analytics framework, learning analytics prototype, records of students' behavior in nearly every MOOC's variables (discussion forums, interactions in videos, self-assessment quizzes, login frequency), a cluster of student engagement...


title: Optimizing Collision Detection based on OBB Trees Generated with a Genetic Algorithm

abstract: In this paper a method for generating Oriented Bounding Boxes (OBB) using genetic algorithms (GA) is proposed. OBBs are used in a hierarchy of bounding volumes to detect collisions between objects. Currently, the most used method for generating OBBs is based on the covariance matrix (CV) of the points of the model, but it has many flaws and in most cases does not find the optimum OBB. We performed several tests with both methods (CV vs. GA). Generally the GA method provides a better adjustment of the OBB to the geometry of the models than the CV method. The metric used for measuring the adjustment of the OBB was the volume of the OBB. The time required for performing the collision detection was also tested, obtaining better results with the GA method. It is suggested to use the GA method when the models are rigid and will not be deformed.


title: Accurate Single Stage Detector Using Recurrent Rolling Convolution

abstract: Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are deep in context. We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.


title: Measuring mobile users’ concerns for information privacy

abstract: The evolution of mobile network technologies and smartphones has provided mobile consumers with unprecedented access to Internet and value-added services while on the move. Privacy issues in such context become critically important because vendors may access a large volume of personal information. Although several pioneering studies have examined general privacy risks, few systematic attempts have been made to provide a theory-driven framework on the specific nature of privacy concerns among mobile consumers. To fill the gap in the literature, this article introduced a 9-item scale, which was shown to reasonably represent the dimensionality of mobile users' information privacy concerns (MUIPC), categorized as perceived surveillance, perceived intrusion, and secondary use of personal information. Through a survey study (n=310), the three-factor structure of MUIPC as revealed in exploratory factor analysis was further confirmed through confirmatory factor analysis. Further analysis revealed that the second-order model of MUIPC performed better than its first-order model.


title: Web-of-Objects (WoO)-Based Context Aware Emergency Fire Management Systems for the Internet of Things

abstract: Recent advancements in the Internet of Things (IoT) and the Web of Things (WoT) accompany a smart life where real world objects, including sensing devices, are interconnected with each other. The Web representation of smart objects empowers innovative applications and services for various domains. To accelerate this approach, Web of Objects (WoO) focuses on the implementation aspects of bringing the assorted real world objects to the Web applications. In this paper; we propose an emergency fire management system in the WoO infrastructure. Consequently, we integrate the formation and management of Virtual Objects (ViO) which are derived from real world physical objects and are virtually connected with each other into the semantic ontology model. The charm of using the semantic ontology is that it allows information reusability, extensibility and interoperability, which enable ViOs to uphold orchestration, federation, collaboration and harmonization. Our system is context aware, as it receives contextual environmental information from distributed sensors and detects emergency situations. To handle a fire emergency, we present a decision support tool for the emergency fire management team. The previous fire incident log is the basis of the decision support system. A log repository collects all the emergency fire incident logs from ViOs and stores them in a repository.


title: Free viewpoint action recognition using motion history volumes

abstract: Action recognition is an important and challenging topic in computer vision, with many important applications including video surveillance, automated cinematography and understanding of social interaction. Yet, most current work in gesture or action interpretation remains rooted in view-dependent representations. This paper introduces Motion History Volumes (MHV) as a free-viewpoint representation for human actions in the case of multiple calibrated, and background-subtracted, video cameras. We present algorithms for computing, aligning and comparing MHVs of different actions performed by different people in a variety of viewpoints. Alignment and comparisons are performed efficiently using Fourier transforms in cylindrical coordinates around the vertical axis. Results indicate that this representation can be used to learn and recognize basic human action classes, independently of gender, body size and viewpoint.


title: Short-Term Memory, Working Memory, and Inhibitory Control in Children with Difficulties in Arithmetic Problem Solving

abstract: The relations between short-term memory, working memory, inhibitory control, and arithmetic word problem solution were studied in children who were poor in arithmetic problem solving (n = 23). The children were compared with a group of good problem solvers (n = 26), matched for vocabulary, age, and gender. The results corroborate the hypothesis of poor problem solvers' general deficit in inhibitory processes. They had lower scores and made more intrusion errors in a series of working memory tasks requiring inhibition of irrelevant information. The results showed that problem solving performance is related to the ability of reducing the accessibility of nontarget and irrelevant information in memory. Span tasks that imply passive storage of information showed that poor problem solvers were impaired when they have to retain numerical information, but they did not differ from children who did not have difficulty with mathematics when the material included words.


title: Adaptive rendering with non-local means filtering

abstract: We propose a novel approach for image space adaptive sampling and filtering in Monte Carlo rendering. We use an iterative scheme composed of three steps. First, we adaptively distribute samples in the image plane. Second, we denoise the image using a non-linear filter. Third, we estimate the residual per-pixel error of the filtered rendering, and the error estimate guides the sample distribution in the next iteration. The effectiveness of our approach hinges on the use of a state of the art image denoising technique, which we extend to an adaptive rendering framework. A key idea is to split the Monte Carlo samples into two buffers. This improves denoising performance and facilitates variance and error estimation. Our method relies only on the Monte Carlo samples, allowing us to handle arbitrary light transport and lens effects. In addition, it is robust to high noise levels and complex image content. We compare our approach to a state of the art adaptive rendering technique based on adaptive bandwidth selection and demonstrate substantial improvements in terms of both numerical error and visual quality. Our framework is easy to implement on top of standard Monte Carlo renderers and it incurs little computational overhead.


title: Lattice Point Enumeration on Block Reduced Bases

abstract: When analyzing lattice-based cryptosystems, we often need to solve the Shortest Vector Problem (SVP) in some lattice associated to the system under scrutiny. The go-to algorithms in practice to solve SVP are enumeration algorithms, which usually consist of a preprocessing step, followed by an exhaustive search. Obviously, the two steps offer a trade-off and should be balanced in their running time in order to minimize the overall complexity. In practice, the most common approach to control this trade-off is to use block reduction algorithms during the preprocessing. Despite the popularity of this approach, it lacks any well founded analysis and all practical approaches seem to use ad hoc parameters. This weakens our confidence in the cryptanalysis of the systems. In this work, we aim to shed light on at least one side of this trade-off and analyze the effect of block reduction on the exhaustive search. For this, we give asymptotic worst case bounds and present results from both experiments and simulation that show its average case behavior in practice.


title: Evolving Designs By Generating Useful Complex Gene Structures

abstract: This chapter presents two examples of work for evolving designs by generating useful complex gene structures. where the first example uses a genetic engineering approach whereas the other uses a growth model of form. Both examples have as their motivation to overcome the combinatorial effect of large design spaces by focussing the search in useful areas. This focussing is achieved by starting with design spaces defined by low-level basic genes and creating design spaces defined by increasingly more complex gene structures. In both cases the low-level basic genes represent simple design actions which when executed produce parts of design solutions. Both works are exemplified in the domain of architectural floor plans.


title: BitScope: Automatically dissecting malicious binaries

abstract: Automatic analysis of malicious binaries is necessary in order to scale with the rapid development and recovery of malware found in the wild. The results of automatic analysis are useful for creating defense systems and understanding the current capabilities of attackers. We propose an approach for automatic dissection of malicious binaries which can answer fundamental questions such as what behavior they exhibit, what are the relationships between their inputs and outputs, and how an attacker may be using the binary. We implement our approach in a system called BitScope. At the core of BitScope is a system which allows us to execute binaries with symbolic inputs. Executing with symbolic inputs allows us to reason about code paths without constraining the analysis to a particula r input value.


title: Extended-enterprise supply-chain management at IBM personal systems group and other divisions

abstract: In 1994, IBM began to reengineer its global supply chain. It wanted to achieve quick responsiveness to customers with minimal inventory. To support this effort, we developed an extended-enterprise supply-chain analysis tool, the Asset Management Tool (AMT). AMT integrates graphical process modeling, analytical performance optimization, simulation, activity-based costing, and enterprise database connectivity into a system that allows quantitative analysis of extended supply chains. IBM has used AMT to study such issues as inventory budgets, turnover objectives, customer-service targets, and new-product introductions. We have implemented it at a number of IBM business units and their channel partners. AMT benefits include over $750 million in material costs and price-protection expenses saved in 1998.


title: Physical Authentication Using Random Number Generated (RNG) Keypad Based on One Time Pad (OTP) Concept

abstract: A physical security is best described as securing the perimeter of an area. Most organisation tends to focus on securing their data over the wires and overlooked threats coming from inside their premises. Servers are protected with maximum security configurations and firewall, however the server room are accessible by unauthorized personnel, access card often left behind, broken or missing main door entrance although secured with access card system, vendors, consultants and visitors were not accompanied by staff during visitation, piggy-backing, tailgating, shoulder surfing and keylogging cases are common scenario that can be seen inside an organization. To minimize or/and to overcome the mentioned problems, an enhanced type of physical security method is proposed which is authentication using Random Number Generated (RNG) Keypad based on One Time Pad Concept. It is a computational physical device designed to generate a sequence of numbers or symbols that will appear randomly using Pseudo-random numbers algorithm each time One Time Password (OTP) is keyed in. It is typically generated by a token possessed by the user and it is the input to the authentication system. The input OTP is compared to an OTP generated by the system. If it matches, the user is allowed to access the system. It is hoped that by having this prototype system, common physical security countermeasure weaknesses can be minimized while assuring confidentiality, integrity and availability of the data stays protected with minimum cost and minimal configurations.


title: A mass spectrometry method for mapping the interface topography of interacting proteins, illustrated by the melittin-calmodulin system

abstract: Abstract The shielding of lysine groups from acetylation by acetic anhydride has been used to identify the regions of calmodulin in contact with melittin in the 1:1 complex. The estimation of the degree of acetylation was done by examining cyanogen bromide and cyanogen bromide/trypsin digests by mass spectrometry. Evidence was obtained that lysines-21, −75, and −148 are protected to some extent, with the implication that both the N- and C-terminal lobes and the connecting strand are involved in the interaction.


title: A back-illuminated 3D-stacked single-photon avalanche diode in 45nm CMOS technology

abstract: We report on the world's first back-illuminated 3D-stacked single-photon avalanche diode (SPAD) in 45nm CMOS technology. This SPAD achieves a dark count rate of 55.4cps/μm2, a maximum photon detection probability of 31.8% at 600nm, over 5% in the 420–920nm wavelength range, and timing jitter of 107.7ps at 2.5V excess bias voltage and room temperature. To the best of our knowledge, these are the best results ever reported for any back-illuminated 3D-stacked SPAD technology.


title: Spectral collaborative filtering

abstract: Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.


title: Energy-Efficient Virtual Machine Replication for Data Centers

abstract: Virtual Machine (VM) replication is used primarily for achieving fault tolerance and load balancing of requests. We tackle the virtual machine replication problem (VMR) for data centers and take into account reducing the total power consumption. Each VM will be replicated R times on different physical machines (PMs). As such, in case of a server failure, there will be at least another PM that has the requested VM. Solving the VMR problem is equivalent to minimum cost flow problem, that can be solved optimally. After replication of VMs, we turn off the inactive PMs in order to reduce the power consumption even more. We simulate different data center scenarios and analyse how many inactive PMs we have achieved.


title: Singular Spectrum Analysis for Time Series

abstract: Introduction: Preliminaries.- SSA Methodology and the Structure of the Book.- SSA Topics Outside the Scope of this Book.- Common Symbols and Acronyms.- Basic SSA: The Main Algorithm.- Potential of Basic SSA.- Models of Time Series and SSA Objectives.- Choice of Parameters in Basic SSA.- Some Variations of Basic SSA.- SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms.- LRR and Associated Characteristic Polynomials.- Recurrent Forecasting as Approximate Continuation.- Confidence Bounds for the Forecast.- Summary and Recommendations on Forecasting Parameters.- Case Study: 'Fortified Wine'.- Missing Value Imputation.- Subspace-Based Methods and Estimation of Signal Parameters.- SSA and Filters.


title: Hierarchical Actor-Critic

abstract: We present a novel approach to hierarchical reinforcement learning called Hierarchical Actor-Critic (HAC). HAC aims to make learning tasks with sparse binary rewards more efficient by enabling agents to learn how to break down tasks from scratch. The technique uses of a set of actor-critic networks that learn to decompose tasks into a hierarchy of subgoals. We demonstrate that HAC significantly improves sample efficiency in a series of tasks that involve sparse binary rewards and require behavior over a long time horizon.


title: Instance Embedding Transfer to Unsupervised Video Object Segmentation

abstract: We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which allows us to link objects together over time. Thus, we adapt the instance networks trained on static images to video object segmentation and incorporate the embeddings with objectness and optical flow features, without model retraining or online fine-tuning. The proposed method outperforms state-of-the-art unsupervised segmentation methods in the DAVIS dataset and the FBMS dataset.


title: Determining single tuition fee of higher education in Indonesia: A comparative analysis of data mining classification algorithms

abstract: Student's Single Tuition Fee or Uang Kuliah Tunggal (UKT) is a subsidy policy in higher education by the Indonesian government. This policy regulates the tuition fees incurred by each student at each semester in every higher education institutions. Since the cost of UKT expenses is influenced by the financial ability of each student, therefore the cost of education among students must be grouped into several classes. Until recently, there has been no standard to make such classification whereas such determination is an important task to solve by every higher institution in Indonesia. This study aims to compare five data mining classification algorithms (Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Decision Tree and SVM) to find the best algorithm for the case of determining the UKT classes. The experiment is conducted using 230 training data and 10-fold cross-validation evaluation. Based on the result, Decision Tree managed to obtain average accuracy value of 0.814 or 81.4%. Finally, Decision Tree is used to classify the UKT classes of3258 data of students.


Supervised and Unsupervised Approaches to the Ontology-Based Disambiguation of JSON Documents


title: Space is the Machine

abstract: Since 'The social logic of space' was published in 1984, Bill Hillier and his colleagues at University College London have been conducting research on how space features in the form and functioning of buildings and cities. A key outcome is the concept of 'spatial configuration' - meaning relations which take account of other relations in a complex. New techniques have been developed and applied to a wide range of architectural and urban problems. The aim of this book is to assemble some of this work and show how it leads the way to a new type of theory of architecture: an 'analytic' theory in which understanding and design advance together. The success of configurational ideas in bringing to light the spatial logic of buildings and cities suggests that it might be possible to extend these ideas to other areas of the human sciences where problems of configuration and pattern are critical.


title: Gene therapy for metachromatic leukodystrophy

abstract: Leukodystrophies (LDs) are rare, often devastating genetic disorders with neurologic symptoms. There are currently no disease-specific therapeutic approaches for these diseases. In this review we use metachromatic leukodystrophy as an example to outline in the brief the therapeutic approaches to MLD that have been tested in animal models and in clinical trials, such as enzyme-replacement therapy, bone marrow/umbilical cord blood transplants, ex vivo transplantation of genetically modified hematopoietic stem cells, and gene therapy. These studies suggest that to be successful the ideal therapy for MLD must provide persistent and high level expression of the deficient gene, arylsulfatase A in the CNS. Gene therapy using adeno-associated viruses is therefore the ideal choice for clinical development as it provides the best balance of potential for efficacy with reduced safety risk. Here we have summarized the published preclinical data from our group and from others that support the use of a gene therapy with AAVrh.10 serotype for clinical development as a treatment for MLD, and as an example of the potential of gene therapy for LDs especially for Krabbe disease, which is the focus of this special issue. © 2016 Wiley Periodicals, Inc.


title: A Wideband Log Periodic Antenna with Fractal Koch Geometry and Rectangular Stub

abstract: This paper deals with the development of wideband log periodic antenna by applying series iteration fractal Koch geometry and distributed inductive load. Rectangular stub is inserted at each end of fractal Koch structure for size reduction purposes. The dimension of conventional series iteration log periodic fractal Koch antenna (LPFKA) can be reduced by 18.24% with the addition of rectangular stub. The proposed antenna has a dimension of 285 mm x 245 mm deployed on FR-4 Epoxy dielectric substrate with relative permittivity of 4.7 and thickness of 1.6 mm. This antenna was designed to operate from 0.47 GHz to 0.79 GHz which focuses on Ultra High Frequency (UHF) Television White Space (TVWS) band applications. According to the simulation result, the proposed antenna achieves stable radiation patterns throughout the operating frequency band with the gains varies from 5.64 dBi to 6.7 dBi. The proposed antenna is a possible candidate for UHF TVWS applications.


Applications of rare-earth permanent magnets in electrical machines : from motors for niche applications to hybrid electric vehicles


title: Machine learning based power quality event classification using wavelet — Entropy and basic statistical features

abstract: Today's industrial environment is smarter than ever before. Most production lines include electrical devices which are able to communicate each other and controlled from a single station with automation systems. Most of those elements have an internet connection link known as industrial internet. Development of smart technology with industrial internet comes with a need of monitoring. Monitoring technologies are emergent systems that focus on fault detection, grid self — healings and online tracking of power quality issues. Present study deals with one of the essential part of an electricity grid monitoring system called power quality event classification in a manner of machine learning topic. Power quality events to be processed are generated synthetically by means of a comprehensive software tool. Classification of real-like dataset is executed using extreme learning machine which is an extremely fast learning algorithm applied to single layer neural networks. Basic statistical criteria and wavelet — entropy methods are handled to achieve distinctive features of dataset. As a performance evaluation instrument, conventional artificial neural network structure is run too. Detailed results are discussed to prove the satisfactory performance of proposed pattern recognition model.


title: Body Extender: Whole body exoskeleton for human power augmentation

abstract: The PERCRO laboratory of Scuola Superiore Sant'Anna has recently completed the development and functional assessment of the Body Extender (BE) system, an advanced wearable robot expressly conceived for augmenting the human strength for handling of heavy materials in unstructured environment. The system is composed by four robotic limbs with anthropomorphic kinematics and has a total of 22 independently actuated degrees of freedom. The leg locomotion and the force servo-amplification allow operations in environments that are hardly accessible by the conventional handling systems preserving the force sensibility during the manipulative tasks. Possible applications are handling of military materials in narrow spaces, rescuing of victims in natural and human provoked disasters and handling of heavy parts in the manufacturing of large products. The paper reports the system specifications taken as a reference for the design, the criteria and verification methods, the architectural solutions used for the implementation of mechanics, electronic and control components and the results of the preliminary experimental assessment.


title: Totipotency: What it is and what it is not

abstract: There is surprising confusion surrounding the concept of biological totipotency, both within the scientific community and in society at large. Increasingly, ethical objections to scientific research have both practical and political implications. Ethical controversy surrounding an area of research can have a chilling effect on investors and industry, which in turn slows the development of novel medical therapies. In this context, clarifying precisely what is meant by “totipotency” and how it is experimentally determined will both avoid unnecessary controversy and potentially reduce inappropriate barriers to research. Here, the concept of totipotency is discussed, and the confusions surrounding this term in the scientific and nonscientific literature are considered. A new term, “plenipotent,” is proposed to resolve this confusion. The requirement for specific, oocyte-derived cytoplasm as a component of totipotency is outlined. Finally, the implications of twinning for our understanding of totipotency are d...


title: Graphic logo detection with deep region-based convolutional networks

abstract: Logo detection is a challenging task with many practical applications in our daily life and intellectual property protection. The two main obstacles here are lack of public logo datasets and effective design of logo detection structure. In this paper, we first manually collected and annotated 6,400 images and mix them with FlickrLogo-32 dataset, forming a larger dataset. Secondly, we constructed Faster R-CNN frameworks with several widely used classification models for logo detection. Furthermore, the transfer learning method was introduced in the training process. Finally, clustering was used to guarantee suitable hyper-parameters and more precise anchors of RPN. Experimental results show that the proposed framework outper-forms the state of-the-art methods with a noticeable margin.


title: Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

abstract: Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite. In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. To address the challenges caused by the discrete input space, we propose a projected gradient method combined with group lasso and gradient regularization. To handle the almost infinite output space, we design some novel loss functions to conduct non-overlapping attack and targeted keyword attack. We apply our algorithm to machine translation and text summarization tasks, and verify the effectiveness of the proposed algorithm: by changing less than 3 words, we can make seq2seq model to produce desired outputs with high success rates. On the other hand, we recognize that, compared with the well-evaluated CNN-based classifiers, seq2seq models are intrinsically more robust to adversarial attacks.


title: Community of inquiry as a theoretical framework to foster "epistemic engagement" and "cognitive presence" in online education

abstract: In this paper, several recent theoretical conceptions of technology-mediated education are examined and a study of 2159 online learners is presented. The study validates an instrument designed to measure teaching, social, and cognitive presence indicative of a community of learners within the community of inquiry (CoI) framework [Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2, 1-19; Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7-23]. Results indicate that the survey items cohere into interpretable factors that represent the intended constructs. Further it was determined through structural equation modeling that 70% of the variance in the online students' levels of cognitive presence, a multivariate measure of learning, can be modeled based on their reports of their instructors' skills in fostering teaching presence and their own abilities to establish a sense of social presence. Additional analysis identifies more details of the relationship between learner understandings of teaching and social presence and its impact on their cognitive presence. Implications for online teaching, policy, and faculty development are discussed.


title: Unicorn: A System for Searching the Social Graph

abstract: Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook's Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook's search offerings.


title: Morphology and Physiological Changes of Genital Skin and Mucosa

abstract: The morphology and physiology of both the vulva and vagina undergo characteristic age-related changes over a lifetime. At birth, these tissues exhibit the effects of residual maternal estrogens. During puberty, the vulva and vagina mature under the influence of adrenal and gonadal steroid hormones. During the reproductive years, the vagina responds to ovarian steroid hormone cycling, and both tissues adapt to the needs of pregnancy and delivery. Following menopause, the vulva and vagina atrophy. A rise in the prevalence of incontinence among older women increases the risk of vulvar and perineal dermatitis. This chapter covers the morphology and physiology of the genital area from infancy to old age.


title: Generation of WebML hypertext models from legacy Web Applications

abstract: In the last years one of the main concerns of the software industry has been to reengineer their legacy Web Applications (WAs) to take advantage of the benefits introduced by Rich Internet Applications (RIAs). In this context, Model Driven (MD) Web Engineering approaches have been extended with new modeling primitives to obtain the benefits provided by RIA features. However, during the last decade, widespread language-specific Web frameworks have supported actual Web system development. In this paper we present a MD reverse engineering process to obtain a conceptual representation of the navigational concern of a legacy Web application (LWA), as an initial step within a LWA-to-RIA modernization process. WebML is selected as the target MD Web Engineering approach.


title: Multi-Objective Deep Q-Learning with Subsumption Architecture

abstract: In this work we present a method for using Deep Q-Networks (DQNs) in multi-objective tasks. Deep Q-Networks provide remarkable performance in single objective tasks learning from high-level visual perception. However, in many scenarios (e.g in robotics), the agent needs to pursue multiple objectives simultaneously. We propose an architecture in which separate DQNs are used to control the agent's behaviour with respect to particular objectives. In this architecture we use signal suppression, known from the (Brooks) subsumption architecture, to combine outputs of several DQNs into a single action. Our architecture enables the decomposition of the agent's behaviour into controllable and replaceable sub-behaviours learned by distinct modules. To evaluate our solution we used a game-like simulator in which an agent - provided with high-level visual input - pursues multiple objectives in a 2D world. Our solution provides benefits of modularity, while its performance is comparable to the monolithic approach.


title: An Intelligent Anti-phishing Strategy Model for Phishing Website Detection

abstract: As a new form of malicious software, phishing websites appear frequently in recent years, which cause great harm to online financial services and data security. In this paper, we design and implement an intelligent model for detecting phishing websites. In this model, we extract 10 different types of features such as title, keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been employed for automatic phishing categorization. Case studies on large and real daily phishing websites collected from King soft Internet Security Lab demonstrate that our proposed model outperforms other commonly used anti-phishing methods and tools in phishing website detection.


title: Inductive Power Transfer

abstract: Inductive power transfer (IPT) was an engineering curiosity less than 30 years ago, but, at that time, it has grown to be an important technology in a variety of applications. The paper looks at the background to IPT and how its development was based on sound engineering principles leading on to factory automation and growing to a $1 billion industry in the process. Since then applications for the technology have diversified and at the same time become more technically challenging, especially for the static and dynamic charging of electric vehicles (EVs), where IPT offers possibilities that no other technology can match. Here, systems that are ten times more powerful, more tolerant of misalignment, safer, and more efficient may be achievable, and if they are, IPT can transform our society. The challenges are significant but the technology is promising.


title: A closer look: Small object detection in faster R-CNN

abstract: Faster R-CNN is a well-known approach for object detection which combines the generation of region proposals and their classification into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by the weak performance of Faster R-CNN on small object instances, we perform a detailed examination of both the proposal and the classification stage, examining their behavior for a wide range of object sizes. Additionally, we look at the influence of feature map resolution on the performance of those stages. We introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the Flicker data set improving the detection performance on small object instances.


title: Potentials of Mobile Technology for K-12 Education: An Investigation of iPod touch Use for English Language Learners in the United States

abstract: This case study investigated a m-learning initiative by a large school district in the United States to provide iPod touch devices 24/7 to teachers and students of English Language Learners. We described the initiative and presented the research findings of its implementation for two years at elementary and middle school levels. The results revealed the iPod touch was used to support language and content learning, provide differentiated instructional support, and extend learning time from classroom to home. However, several challenges were identified such as significant time demand on the teachers, technical issues, the need for professional training and dedicated support staff. Implications for teachers, instructional technologists, school administrators, and researchers were discussed.


AOSD Security: A Practical Assessment


Compositional Semantics using Feature-Based Models from WordNet


title: Dipole Modeling of Magnetic Flux Leakage

abstract: In this paper, we present an analytical model to represent the 3-D magnetic flux leakage (MFL) field due to the occurrence of a surface-breaking defect in a ferromagnetic specimen. This situation is frequently encountered in the nondestructive evaluation (NDE) of energy pipelines using the MFL technique. The model is derived from first principles, and utilizes the concept of dipolar magnetic charge induction to yield the 3-D MFL field in terms of surface integrals. The magnetic flux density in the specimen is assumed to be in the saturation region, and the permeability is assumed to be locally constant in the vicinity of the defect. The model uses just two geometric parameters and is capable of reproducing results that have been obtained experimentally in the literature. 3-D MFL field simulations obtained from the model facilitate a better understanding of the effect of a surface-breaking defect on the magnetic field in its vicinity. Furthermore, we simulate and analyze the 3-D MFL field in the 3-D space around the defect. This analysis yields numerous properties regarding the spatial characteristics of the three orthogonal components of the MFL field of the defect.


title: A method for automatic matching of multi-timepoint findings for enhanced clinical workflow

abstract: Non-interventional diagnostics (CT or MR) enables early identification of diseases like cancer. Often, lesion growth assessment done during follow-up is used to distinguish between benign and malignant ones. Thus correspondences need to be found for lesions localized at each time point. Manually matching the radiological ::: findings can be time consuming as well as tedious due to possible differences in orientation and position between ::: scans. Also, the complicated nature of the disease makes the physicians to rely on multiple modalities (PETCT, PET-MR) where it is even more challenging. Here, we propose an automatic feature-based matching that is robust to change in organ volume, subpar or no registration that can be done with very less computations. Traditional matching methods rely mostly on accurate image registration and applying the resulting deformation map on the findings coordinates. This has disadvantages when accurate registration is time-consuming or may not be possible due to vast organ volume differences between scans. Our novel matching proposes supervised learning by taking advantage of the underlying CAD features that are already present and considering the matching as a classification problem. In addition, the matching can be done extremely fast and at reasonable accuracy even when the image registration fails for some reason. Experimental results ∗ on real-world multi-time point thoracic CT data showed an accuracy of above 90% with negligible false positives on a variety of registration scenarios.


title: Patterns of Data-Infused Business Model Innovation

abstract: Business models have been a concept widely discussed over the last 20 years. The increasing availability of data and the growing capability to exploit them with analytics has sparked a new set of discussions, though: it is claimed that data and analytics bring to bear entirely new "data-based" or "data-driven" business models. However, there is neither a common understanding of these business models nor of the ways existing business models are transformed into those. This paper aims to create a coherent framework and common understanding of the infusion of business models by data and analytics. Contrasting popular views, our conceptual analysis reveals that there are no data-driven business models per se, instead, the utilization of data and analytics opens a "continuum" of transformation options for business models. We identify five distinct patterns in which the use of data may alter the business model, illustrate them with representative case studies and evaluate the patterns analyzing a sample of 115 industry business models. This paper will contribute to the fundamental understanding of how the use of data and the application of analytics may trigger business model innovation. The identified patterns will provide guidance to practitioners on how to utilize (big) data and analytics, in particular to draw attention to those that still seem underutilized for innovation. The developed pattern concept will also open up a broad agenda for future research.