title: No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics

abstract: Infrastructure-as-a-Service (IaaS) cloud platforms have brought two unprecedented changes to cluster provisioning practices. First, any (nonexpert) user can provision a cluster of any size on the cloud within minutes to run her data-processing jobs. The user can terminate the cluster once her jobs complete, and she needs to pay only for the resources used and duration of use. Second, cloud platforms enable users to bypass the traditional middleman---the system administrator---in the cluster-provisioning process. These changes give tremendous power to the user, but place a major burden on her shoulders. The user is now faced regularly with complex cluster sizing problems that involve finding the cluster size, the type of resources to use in the cluster from the large number of choices offered by current IaaS cloud platforms, and the job configurations that best meet the performance needs of her workload. In this paper, we introduce the Elastisizer, a system to which users can express cluster sizing problems as queries in a declarative fashion. The Elastisizer provides reliable answers to these queries using an automated technique that uses a mix of job profiling, estimation using black-box and white-box models, and simulation. We have prototyped the Elastisizer for the Hadoop MapReduce framework, and present a comprehensive evaluation that shows the benefits of the Elastisizer in common scenarios where cluster sizing problems arise.


title: Goal-Based Semantic Queries for Dynamic Processes in the Internet of Things

abstract: Internet of Things-aware process execution imposes new requirements on process modeling that are outside the scope of current modeling languages. Internet of Things (IoT) devices may vanish, appear or stay unknown during process execution, which renders process resource allocation at design time infeasible. Devices’ capabilities are often only available in a particular real-world context at runtime. This is not considered by current approaches that use services for encapsulating device functionality. We propose a novel approach to enable both service discovery and invocation for IoT-aware processes based on users’ goals that are defined as part of a process. We apply the Tropos goal modeling methodology to represent the dependencies between these goals and IoT device capabilities. Furthermore, we present a Semantic Access Layer (SAL) to transform these goals into service invocations using generated SPARQL queries. The SAL executes the queries on a knowledge base representing runtime domain knowledge about IoT services, their capabilities, and context. As a result, it invokes the identified IoT services and transfers the responses back to the process engine. The evaluation of our approach within several Smart Home scenarios shows an increase of flexibility and separation of concerns for scalable, IoT-aware process execution.


title: Stock market's price movement prediction with LSTM neural networks

abstract: Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. For that goal, a prediction model was built, and a series of experiments were executed and theirs results analyzed against a number of metrics to assess if this type of algorithm presents and improvements when compared to other Machine Learning methods and investment strategies. The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future.


title: Learning deep filter banks in parallel for texture recognition

abstract: Texture is an important visual clue for various classification and segmentation tasks in the scene understanding challenge. Today, successful deployment of deep learning algorithms for texture recognition leads to tremendous precisions on standard datasets. In this paper, we propose a new learning framework to train deep neural networks in parallel and with variable depth for texture recognition. Our framework learns scales, orientations and resolutions of texture filter banks. Due to the learning of parameters not the filters themselves, computational costs are highly reduced. It is also capable of extracting very deep features through distributed computing architectures. Our experiments on publicly available texture datasets show significant improvements in the recognition performance over other deep local descriptors in recently published benchmarks.


title: A Two-Pole Two-Zero Tunable Filter With Improved Linearity

abstract: Two miniaturized tunable filters with two zeros to result in sharp skirts were developed at 1.4-2.0 GHz on epsivr=6.15 substrate. The filters were built using single and back-to-back varactor diodes and compared for linearity characteristics. The single-diode filter has a 1-dB bandwidth of 5plusmn0.5% and an insertion loss of 2.5-1.8 dB. The back-to-back diode filter has a 1-dB bandwidth of 4.9 plusmn0.5% and an insertion loss of 2.9-1.25 dB (resonator Q of 56-125). The back-to-back diode filter was designed for improved linearity characteristics using two uniformly doped varactor diode and biased with a high impedance circuit. A detailed Volterra series analysis is done on the back-to-back diode including the effect of the bias network and diode mismatches. The measured IIP3 for the back-to-back diode tunable filter is 22-41 dBm depending on the bias voltage and is 13-15 dB better than the single-diode design. The power handling capabilities of both designs is explored using large-signal S 21 measurements. To our knowledge, these planar tunable filters represent state-of-the-art insertion loss and linearity characteristics performance with varactor diodes as the tuning elements.


title: Courteous Autonomous Cars

abstract: Typically, autonomous cars optimize for a combination of safety, efficiency, and driving quality. But as we get better at this optimization, we start seeing behavior go from too conservative to too aggressive. The car's behavior exposes the incentives we provide in its cost function. In this work, we argue for cars that are not optimizing a purely selfish cost, but also try to be courteous to other interactive drivers. We formalize courtesy as a term in the objective that measures the increase in another driver's cost induced by the autonomous car's behavior. Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly. We analyze the effect of courtesy in a variety of scenarios. We find, for example, that courteous robot cars leave more space when merging in front of a human driver. Moreover, we find that such a courtesy term can help explain real human driver behavior on the NGSIM dataset.


title: Selective Call Out and Real Time Bidding

abstract: Ads on the Internet are increasingly sold via ad exchanges such as RightMedia, AdECN and Doubleclick Ad Exchange. These exchanges allow real-time bidding, that is, each time the publisher contacts the exchange, the exchange "calls out" to solicit bids from ad networks. This solicitation introduces a novel aspect, in contrast to existing literature. This suggests developing a joint optimization framework which optimizes over the allocation and well as solicitation. ::: ::: We model this selective call out as an online recurrent Bayesian decision framework with bandwidth type constraints. We obtain natural algorithms with bounded performance guarantees for several natural optimization criteria. We show that these results hold under different call out constraint models, and different arrival processes. Interestingly, the paper shows that under MHR assumptions, the expected revenue of generalized second price auction with reserve is constant factor of the expected welfare. Also the analysis herein allow us prove adaptivity gap type results for the adwords problem.


title: Prevalence and risk factors of diastasis recti abdominis from late pregnancy to 6 months postpartum, and relationship with lumbo-pelvic pain.

abstract: Diastasis recti abdominis (DRA) is an impairment characterized by a midline separation of the rectus abdominis muscles along the linea alba. It has its onset during pregnancy and the first weeks following childbirth. There is scant knowledge on both prevalence and risk factors for development of the condition. The aim of this study was to investigate the prevalence of DRA at gestational week 35 and three timepoints postpartum, possible risk factors, and the relationship between DRA and lumbo-pelvic pain. Ultrasound images of inter rectus distance (IRD) were recorded in 84 healthy primiparous women, at three locations on the linea alba. The IRD was measured at: gestational week 35 and 6-8, 12-14, and 24-26 weeks postpartum. Diagnosis of DRA was defined as 16 mm at 2 cm below the umbilicus. Independent sample t-test and binary logistic regression was used to assess differences and risk factors in women with and without DRA and women with and without lumbo-pelvic pain. P < 0.05 was considered statistically significant. The prevalence of DRA decreased from 100% at gestational week 35-39% at 6 months postpartum. No statistically significant differences were found in prepregnancy body mass index (BMI), weight gain, baby's birth weight or abdominal circumference between women with and without DRA at 6 months postpartum. Women with DRA at 6 months postpartum were not more likely to report lumbo-pelvic pain than women without DRA. DRA is prevalent at 6 months postpartum, but is not linked with lumbo-pelvic pain.


title: Electromyographic Activity of Rectus Abdominis during a Suspension Push-Up Compared to Traditional Exercises

abstract: Snarr RL, Esco MR, Witte EV, Jenkins CT, Brannan RM. Electromyographic Activity of Rectus Abdominis During a Suspension Push-up Compared to Traditional Exercises. JEPonline 2013;16(3):1-8. The purpose of this study was to compare the electromyographic (EMG) activity of the rectus abdominis (RA) across three different exercises [i.e., suspension pushup (SPU), standard pushup (PU) and abdominal supine crunch (C)]. Fifteen apparently healthy men (n = 12, age = 25.75 ± 3.91 yrs) and women (n = 3, age = 22.33 ± 1.15) volunteered to participate in this study. The subjects performed four repetitions of SPU, PU, and C. The order of the exercises was randomized. Mean peak EMG activity of the RA was recorded across the 4 repetitions of each exercise. Raw (mV) and normalized (%MVC) values were analyzed. The results of this study showed that SPU and C elicited a significantly greater (P 0.05). This investigation indicated that SPU and C provided similar activation levels of the RA that were significantly greater than PU.


title: A novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks, based on the Internet of Things notion

abstract: Abstract The idea of the Internet of Things (IOT) notion is that everything within the global network is accessible and interconnected. As such Wireless Sensor Networks (WSN) play a vital role in such an environment, since they cover a wide application field. Such interconnection can be seen from the aspect of a remote user who can access a single desired sensor node from the WSN without the necessity of firstly connecting with a gateway node (GWN). This paper focuses on such an environment and proposes a novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks. The proposed scheme enables a remote user to securely negotiate a session key with a general sensor node, using a lightweight key agreement protocol. The proposed scheme ensures mutual authentication between the user, sensor node, and the gateway node (GWN), although the GWN is never contacted by the user. The proposed scheme has been adapted to the resource-constrained architecture of the WSN, thus it uses only simple hash and XOR computations. Our proposed scheme tackles these risks and the challenges posed by the IOT, by ensuring high security and performance features.


title: An Approach to Supporting Incremental Visual Data Classification

abstract: Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users’ control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.


title: Compressive sampling of ECG bio-signals: Quantization noise and sparsity considerations

abstract: Compressed sensing (CS) is an emerging signal processing paradigm that enables the sub-Nyquist processing of sparse signals; i.e., signals with significant redundancy. Electrocardiogram (ECG) signals show significant time-domain sparsity that can be exploited using CS techniques to reduce energy consumption in an adaptive data acquisition scheme. A measurement matrix of random values is central to CS computation. Signal-to-quantization noise ratio (SQNR) results with ECG signals show that 5- and 6-bit Gaussian random coefficients are sufficient for compression factors up to 6X and from 8X-16X, respectively, whereas 6-bit uniform random coefficients are needed for 2X-16X compression ratios.


title: Practical trigger-action programming in the smart home

abstract: We investigate the practicality of letting average users customize smart-home devices using trigger-action ("if, then") programming. We find trigger-action programming can express most desired behaviors submitted by participants in an online study. We identify a class of triggers requiring machine learning that has received little attention. We evaluate the uniqueness of the 67,169 trigger-action programs shared on IFTTT.com, finding that real users have written a large number of unique trigger-action interactions. Finally, we conduct a 226-participant usability test of trigger-action programming, finding that inexperienced users can quickly learn to create programs containing multiple triggers or actions.


title: Analysis of synonymous codon usage in Newcastle disease virus hemagglutinin–neuraminidase (HN) gene and fusion protein (F) gene

abstract: Newcastle disease virus (NDV) hemagglutinin-neuraminidase (HN) is a multifunctional protein, which possesses both the receptor recognition and neuraminidase activities. The fusion (F) protein is a type I membrane glycoprotein that mediates the merger of the viral envelope to the host cell membrane. Although the functions of the HN and F proteins have been well studied, however, the factors shaping synonymous codon usage bias and nucleotide composition in HN and F genes have been few reported. In our study, we analyzed synonymous codon usage using the 69 NDV HN and F genes, respectively. The general correlation between base composition and codon usage bias suggests that mutational pressure rather than natural selection is the main factor that determines the codon usage bias in HN and F genes. In addition, other factors, such as the aromaticity and hydrophobicity, also influence the codon usage variation among HN and F genes. This study represents the most comprehensive analysis to date of NDV HN and F genes codon usage patterns and provides a basic understanding of the mechanisms for codon usage bias.


title: Sequential Projection-Based Metacognitive Learning in a Radial Basis Function Network for Classification Problems

abstract: In this paper, we present a sequential projection-based metacognitive learning algorithm in a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is inspired by human metacognitive learning principles and has two components: a cognitive component and a metacognitive component. The cognitive component is a single-hidden-layer radial basis function network with evolving architecture. The metacognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudosamples are used for proper initialization of new hidden neurons to minimize the misclassification. The parameter update strategy uses projection-based direct minimization of hinge loss error. The interaction of the cognitive component and the metacognitive component addresses the what-to-learn, when-to-learn, and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from the University of California Irvine machine learning repository. The statistical performance evaluation on these problems proves the superior performance of the PBL-McRBFN classifier over results reported in the literature. Also, we evaluate the performance of the proposed algorithm on a practical Alzheimer's disease detection problem. The performance results on open access series of imaging studies and Alzheimer's disease neuroimaging initiative datasets, which are obtained from different demographic regions, clearly show that PBL-McRBFN can handle a problem with change in distribution.


title: The acceptance and use of computer based assessment

abstract: The effective development of a computer based assessment (CBA) depends on students' acceptance. The purpose of this study is to build a model that demonstrates the constructs that affect students' behavioral intention to use a CBA. The proposed model, Computer Based Assessment Acceptance Model (CBAAM) is based on previous models of technology acceptance such as Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Usage of Technology (UTAUT). Constructs from previous models were used such as Perceived Usefulness, Perceived Ease of Use, Computer Self Efficacy, Social Influence, Facilitating Conditions and Perceived Playfulness. Additionally, two new variables, Content and Goal Expectancy, were added to the proposed research model. Data were collected from 173 participants in an introductory informatics course using a survey questionnaire. Partial Least Squares (PLS) was used to test the measurement and the structural model. Results indicate that Perceived Ease of Use and Perceived Playfulness have a direct effect on CBA use. Perceived Usefulness, Computer Self Efficacy, Social Influence, Facilitating Conditions, Content and Goal Expectancy have only indirect effects. These eight variables explain approximately 50% of the variance of Behavioural Intention.


title: A Data Exfiltration and Remote Exploitation Attack on Consumer 3D Printers

abstract: With the increased popularity of 3D printers in homes, and industry sectors, such as biomedical and manufacturing, the potential for cybersecurity risks must be carefully considered. Risks may arise from factors such as printer manufacturers not having the requisite levels of security awareness, and not fully understanding the need for security measures to protect intellectual property, and other sensitive data that are stored, accessed, and transmitted from such devices. This paper examines the security features of two different models of MakerBot Industries’ consumer-oriented 3D printers and proposes an attack technique that is able to, not only, exfiltrate sensitive data, but also allow for remote manipulation of these devices. The attack steps are discretely modeled using a threat model to enable formal representation of the attack. Specifically, we found that the printers stored the previously printed and currently printing objects on an unauthenticated web server. We also ascertain that the transport layer security implementation on these devices was flawed, which severely affected the security of these devices and allowed for remote exploitation. Countermeasures to the attack that are implementable by both the manufacturer and the user of the printer are presented.


title: A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

abstract: We propose a novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the block-matching 3-D algorithm, recently proposed for additive white Gaussian noise denoising, but modifies its major processing steps in order to take into account the peculiarities of SAR images. A probabilistic similarity measure is used for the block-matching step, while the wavelet shrinkage is developed using an additive signal-dependent noise model and looking for the optimum local linear minimum-mean-square-error estimator in the wavelet domain. The proposed technique compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio (on simulated speckled images) and of perceived image quality.


Does Continuous Requirements Engineering need Continuous Software Engineering?


title: Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading

abstract: This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them. To that end, we introduce the Weaver model that uses a new way to relate a question to a textual context by weaving layers of recurrent networks, with the goal of making as few assumptions as possible as to how the information from both question and context should be combined to form the answer. We show empirically on six datasets that Weaver performs well in multiple conditions. For instance, it produces solid results on the very popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks (Weston et al., 2015) and greatly outperforms state-of-the-art methods for open domain question answering from text (Chen et al., 2017).


title: The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two‐photon microscopy of CLARITY intact brains

abstract: Abstract Diffusion tensor imaging (DTI) is used extensively in neuroscience to noninvasively estimate white matter (WM) microarchitecture. However, the diffusion signal is inherently ambiguous because it infers WM structure from the orientation of water diffusion and cannot identify the biological sources of diffusion changes. To compare inferred WM estimates to directly labeled axonal elements, we performed a novel within-subjects combination of high-resolution ex vivo DTI with two-photon laser microscopy of intact mouse brains rendered optically transparent by Clear Lipid-exchanged, Anatomically Rigid, Imaging/immunostaining compatible, Tissue hYdrogel (CLARITY). We found that myelin basic protein (MBP) immunofluorescence significantly correlated with fractional anisotropy (FA), especially in WM regions with coherent fiber orientations and low fiber dispersion. Our results provide evidence that FA is particularly sensitive to myelination in WM regions with these characteristics. Furthermore, we found that radial diffusivity (RD) was only sensitive to myelination in a subset of WM tracts, suggesting that the association of RD with myelin should be used cautiously. This combined DTI-CLARITY approach illustrates, for the first time, a framework for using brain-wide immunolabeling of WM targets to elucidate the relationship between the diffusion signal and its biological underpinnings. This study also demonstrates the feasibility of a within-subject combination of noninvasive neuroimaging and tissue clearing techniques that has broader implications for neuroscience research.


title: Memristor Emulator for Memristor Circuit Applications

abstract: A memristor emulator which imitates the behavior of a TiO2 memristor is presented. Our emulator is built from off-the-shelf solid state components. To develop real world memristor circuit applications, the emulator can be used for breadboard experiments in real time. Two or more memristor emulators can be connected in serial, in parallel, or in hybrid (serial and parallel combined) with identical or opposite polarities. With a simple change of connection, each memristor emulator can be switched between a decremental configuration or an incremental configuration. The hardware and spice simulation of the proposed emulator showed promising results that provides an alternative solution of hp TiO2 memristor model in real circuit.


title: Binary spectral minutiae representation with multi-sample fusion for fingerprint recognition

abstract: Biometric fusion is the approach to improve the biometric system performance by combining multiple sources of biometric information. The binary spectral minutiae representation is a method to represent a fingerprint minutiae set as a fixed-length binary string. This binary representation has the advantages of a fast operation and a small template storage. It also enables the combination of a biometric system with template protection schemes that require a fixed-length feature vector as input. In this paper, based on the spectral minutiae representation algorithm, we investigate the multi-sample fusion algorithms at the feature-, score-, and decision-level respectively. Furthermore, we propose different schemes to mask out unreliable bits. The algorithms are evaluated on the FVC2000-DB2 database and showed promising results.


title: GRIFFIN: Guarding Control Flows Using Intel Processor Trace

abstract: Researchers are actively exploring techniques to enforce control-flow integrity (CFI), which restricts program execution to a predefined set of targets for each indirect control transfer to prevent code-reuse attacks. While hardware-assisted CFI enforcement may have the potential for advantages in performance and flexibility over software instrumentation, current hardware-assisted defenses are either incomplete (i.e., do not enforce all control transfers) or less efficient in comparison. We find that the recent introduction of hardware features to log complete control-flow traces, such as Intel Processor Trace (PT), provides an opportunity to explore how efficient and flexible a hardware-assisted CFI enforcement system may become. While Intel PT was designed to aid in offline debugging and failure diagnosis, we explore its effectiveness for online CFI enforcement over unmodified binaries by designing a parallelized method for enforcing various types of CFI policies. We have implemented a prototype called GRIFFIN in the Linux 4.2 kernel that enables complete CFI enforcement over a variety of software, including the Firefox browser and its jitted code. Our experiments show that GRIFFIN can enforce fine-grained CFI policies with shadow stack as recommended by researchers at a performance that is comparable to software-only instrumentation techniques. In addition, we find that alternative logging approaches yield significant performance improvements for trace processing, identifying opportunities for further hardware assistance.


title: Fully Convolutional Grasp Detection Network with Oriented Anchor Box

abstract: In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism is proposed and a new matching strategy is used during the training process. An end-to-end fully convolutional neural network is employed in our work. The network consists of two parts: the feature extractor and multi-grasp predictor. The feature extractor is a deep convolutional neural network. The multi-grasp predictor regresses grasp rectangles from predefined oriented rectangles, called oriented anchor boxes, and classifies the rectangles into graspable and ungraspable. On the standard Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on image-wise split and object-wise split respectively, and outperforms the latest state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise split.


title: Increasing undergraduate nursing students’ cultural competence: an evaluation study

abstract: Background ::: Cultural competence has become increasingly important for Chinese health professionals because of internationalization and the opening up of China to overseas visitors and business as well as a growing awareness of the needs of minority groups within China. This study aimed to evaluate a workshop designed to improve cultural competence among Chinese undergraduate nursing students.


title: Efficient region search for object detection

abstract: We propose a branch-and-cut strategy for efficient region-based object detection. Given an oversegmented image, our method determines the subset of spatially contiguous regions whose collective features will maximize a classifier's score. We formulate the objective as an instance of the prize-collecting Steiner tree problem, and show that for a family of additive classifiers this enables fast search for the optimal object region via a branch-and-cut algorithm. Unlike existing branch-and-bounddetection methods designed for bounding boxes, our approach allows scoring of irregular shapes — which is especially critical for objects that do not conform to a rectangular window. We provide results on three challenging object detection datasets, and demonstrate the advantage of rapidly seeking best-scoring regions rather than subwindow rectangles.


title: Neural Network NARMA Control of a Gyroscopic Inverted Pendulum

abstract: The objective herein is to demonstrate the feasibility of a real-time digital control of an in verted pendulum for modeling and control, with emphasis on nonlinear auto regressive moving average based neural network (NARMA). The plant of interest is a novel Gyroscopic Inverted Pendulum (GIP) device that is nonlinear and open-loop unstable. The GIP balances a pendulum on its free knife-edge base using a flywhe el driven by DC motor fixated on the top. In this application, an indirect data-based technique is ta ken, where a model of the plant is identified on the bas is of input-output data and then used in the model-based design of a NARMA controller. The plant under digital PID control with I-adaptation provides initial stab ility at the beginning of a single layer NARMA neural network training. NARMA models of increasing complexity are used successively to generate input-output data for the training of multilayered NARMA models. In using a NARMA neural network the control laws are nonlinear and online adaptation of the model is possible to c apture un-modeled or time-varying dynamics. Such an environment provides for experimentation, data collection, system identification and real time con trol strategy implementation.


title: Statistical topic models for multi-label document classification

abstract: Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that probabilistic generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.


Forward-Backward Convolutional LSTM for Acoustic Modeling.


title: Resource Allocation for Interweave and Underlay CRs Under Probability-of-Interference Constraints

abstract: Efficient design of cognitive radios (CRs) calls for secondary users implementing adaptive resource allocation schemes that exploit knowledge of the channel state information (CSI), while at the same time limiting interference to the primary system. This paper introduces stochastic resource allocation algorithms for both interweave (also known as overlay) and underlay cognitive radio paradigms. The algorithms are designed to maximize the weighted sum-rate of orthogonally transmitting secondary users under average-power and probabilistic interference constraints. The latter are formulated either as short- or as long-term constraints, and guarantee that the probability of secondary transmissions interfering with primary receivers stays below a certain pre-specified level. When the resultant optimization problem is non-convex, it exhibits zero-duality gap and thus, due to a favorable structure in the dual domain, it can be solved efficiently. The optimal schemes leverage CSI of the primary and secondary networks, as well as the Lagrange multipliers associated with the constraints. Analysis and simulated tests confirm the merits of the novel algorithms in: i) accommodating time-varying settings through stochastic approximation iterations; and ii) coping with imperfect CSI.


title: Towards a Semantic Search Engine for Scientific Articles

abstract: Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a great challenge. As a starting point, semantic relations between keywords from scientific articles could be extracted in order to classify articles. This might help later in the process of browsing and searching for content in a meaningful scientific way. Indeed, by connecting keywords, the context of the article can be extracted. This paper aims to provide ideas to build such a smart search engine and describes the initial contributions towards achieving such an ambitious goal.


title: Conformance Checking of Processes Based on Monitoring Real Behavior

abstract: Many companies have adopted Process-aware Information Systems (PAIS) to support their business processes in some form. On the one hand these systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. On the other hand explicit process models describing how the business process should (or is expected to) be executed are frequently available. Together with the data recorded in the log, this situation raises the interesting question ''Do the model and the log conform to each other?''. Conformance checking, also referred to as conformance analysis, aims at the detection of inconsistencies between a process model and its corresponding execution log, and their quantification by the formation of metrics. This paper proposes an incremental approach to check the conformance of a process model and an event log. First of all, the fitness between the log and the model is measured (i.e., ''Does the observed process comply with the control flow specified by the process model?''). Second, the appropriateness of the model can be analyzed with respect to the log (i.e., ''Does the model describe the observed process in a suitable way?''). Appropriateness can be evaluated from both a structural and a behavioral perspective. To operationalize the ideas presented in this paper a Conformance Checker has been implemented within the ProM framework, and it has been evaluated using artificial and real-life event logs.


title: Integrating vision, touch and natural language in the control of a situation-oriented behavior-based humanoid robot

abstract: In the personal or service robotics domain very close interaction between humans and robots is crucial. To facilitate such a close interaction we propose to design the robot according to an anthropomorphic model, to combine visual and tactile sensing and to base human-robot communication on natural language. Furthermore, we argue that integrating these key technologies into a robot requires a certain kind of behavior-based system architecture that relies on an understanding of situations for the selection of the behavior to be executed. A robot implementing this architecture allows a human interface to be realized that makes the robot appear intelligent and easy to communicate and interact with. Results of first experiments are reported.


title: Autonomous MAV flight in indoor environments using single image perspective cues

abstract: We consider the problem of autonomously flying Miniature Aerial Vehicles (MAVs) in indoor environments such as home and office buildings. The primary long range sensor in these MAVs is a miniature camera. While previous approaches first try to build a 3D model in order to do planning and control, our method neither attempts to build nor requires a 3D model. Instead, our method first classifies the type of indoor environment the MAV is in, and then uses vision algorithms based on perspective cues to estimate the desired direction to fly. We test our method on two MAV platforms: a co-axial miniature helicopter and a toy quadrotor. Our experiments show that our vision algorithms are quite reliable, and they enable our MAVs to fly in a variety of corridors and staircases.


title: Quantitative evaluation of LDDMM, FreeSurfer, and CARET for cortical surface mapping

abstract: Cortical surface mapping has been widely used to compensate for individual variability of cortical shape and topology in anatomical and functional studies. While many surface mapping methods were proposed based on landmarks, curves, spherical or native cortical coordinates, few studies have extensively and quantitatively evaluated surface mapping methods across different methodologies. In this study we compared five cortical surface mapping algorithms, including large deformation diffeomorphic metric mapping (LDDMM) for curves (LDDMM-curve), for surfaces (LDDMM-surface), multi-manifold LDDMM (MM-LDDMM), FreeSurfer, and CARET, using 40 MRI scans and 10 simulated datasets. We computed curve variation errors and surface alignment consistency for assessing the mapping accuracy of local cortical features (e.g., gyral/sulcal curves and sulcal regions) and the curvature correlation for measuring the mapping accuracy in terms of overall cortical shape. In addition, the simulated datasets facilitated the investigation of mapping error distribution over the cortical surface when the MM-LDDMM, FreeSurfer, and CARET mapping algorithms were applied. Our results revealed that the LDDMM-curve, MM-LDDMM, and CARET approaches best aligned the local curve features with their own curves. The MM-LDDMM approach was also found to be the best in aligning the local regions and cortical folding patterns (e.g., curvature) as compared to the other mapping approaches. The simulation experiment showed that the MM-LDDMM mapping yielded less local and global deformation errors than the CARET and FreeSurfer mappings.


title: ULTRALOW INPUT VOLTAGE DCDC CONVERTER FOR MICRO ENERGY HARVESTING

abstract: Sensor nodes and other ultralow power devices can be powered with micro energy harvesting. In case of low environmental power thermo generators, solar cells, inductive generators or radio frequency (RF) energy harvesting circuits provide an extremely low output voltage that needs to be increased to generate a suitable power supply. This paper presents a DCDC converter that starts operation at 6 mV and 490 nW input power without needing a higher startup voltage to achiev e over 1 V unloaded DC output voltage. The circuit reaches an efficiency of 18 % and the results are experimentally verified by building a prototype.


title: Scalable Bayesian Rule Lists

abstract: We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we fully optimize over rule lists, striking a practical balance between accuracy, interpretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees; further, in many cases, the computational time is practical and often less than that of decision trees. The result is a probabilistic classifier (which estimates P(y = 1|x) for each x) that optimizes the posterior of a Bayesian hierarchical model over rule lists.


abstract: Abstract Background and aims Celiac disease remains underdiagnosed at endoscopy. We aimed to assess the utility of I-Scan (virtual chromo-endoscopy) to improve sensitivity of endoscopy to detect markers of villous atrophy in this condition. Methods Patients from 2 UK hospitals were studied in 3 groups. Group 1: standard high definition, white light endoscopy (WLE); Group 2: WLE plus I-Scan; Group 3: non-high definition control group. The presence of endoscopic markers was recorded. At least 4 duodenal biopsies were taken from all patients. Serology was performed concurrently and observations were compared with histology. Results 758 patients (62% female, mean age 52) were recruited (Group 1: 230; Group 2: 228; Group 3: 300). 135 (17.8%) new diagnoses of coeliac disease were made (21 Group 1; 24 Group 2; 89 Group 3). The sensitivity for detection of endoscopic markers of villous atrophy was significantly higher in both Group 1 (85.7%, p = 0.0004) and Group 2 (75%, p = 0.005) compared to non-high definition controls (41.6%). There was no significant difference between high definition only and I-Scan groups (p = 0.47). In non-high definition endoscopy a missed diagnosis was associated with lesser degrees of villous atrophy (p = 0.019) and low tTG titre (p = 0.007). Conclusions High definition endoscopy with or without I-Scan increases the detection of celiac disease during routine endoscopy.


title: A practical guide for probiotics applied to the case of antibiotic-associated diarrhea in The Netherlands

abstract: BACKGROUND Antibiotic-associated diarrhea (AAD) is a side-effect frequently associated with the use of broad spectrum antibiotics. Although a number of clinical studies show that co-administration of specific probiotics reduces the risk for AAD, there is still unclarity among healthcare professionals on the recommendation of probiotic products. OBJECTIVE This paper aims to provide a practical guide to inform healthcare professionals, patients and consumers about the exact product characteristics of available probiotics with a proven efficacy to prevent AAD. DESIGN and METHODS The workflow in this paper includes three consecutive steps: a systematic review of relevant clinical studies for effective probiotics by a meta-analysis, compilation of a list of available probiotic products, and recommendation of probiotic products that match effective formulations. Our systematic review on the efficacy of probiotics for the prevention of AAD included only studies with randomized, double blind placebo-controlled trials, a clear definition of antibiotic associated diarrhea, and a probiotic administration regime for at least the duration of the antibiotic therapy. RESULTS Using our inclusion criteria, we selected 32 out of 128 identified trials and pooled the results of these studies for each specific dairy product and food supplement. The results indicate a total of seven single or multiple-strain formulations favoring the probiotic treatment group, with the strain Lactobacillus rhamnosus GG being the most effective (relative risk ratio of probiotic versus placebo 0.30 with 95% CI 0.16 - 0.5]. We selected products for recommendation from a compiled list of all probiotic dairy products and food supplements available in The Netherlands and categorized them into groups of products showing effects against the incidence of AAD in at least one, two, or three independent clinical studies. We excluded all products which did not unambiguously declare on the label the specific probiotic strain(s) and the number of colony forming units. CONCLUSION Here, we present a practical guide that informs healthcare professionals and patients on the availability of probiotic products with a proven efficacy for the prevention of AAD. A© Kon. Ned. Mij. ter Bevordering der Pharmacie (KNMP). All rights reserved.


title: Active learning for class imbalance problem

abstract: The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.


title: Spectral–Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning

abstract: In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.


title: Mindfulness training as a clinical intervention: A conceptual and empirical review

abstract: Interventions based on training in mindfulness skills are becoming increasingly popular. Mindfulness involves intentionally bringing one's attention to the internal and external experiences occurring in the present moment, and is often taught through a variety of meditation exercises. This review summarizes conceptual approaches to mind-fulness and empirical research on the utility of mindfulness-based interventions. Meta-analytic techniques were incorporated to facilitate quantification of findings and comparison across studies. Although the current empirical literature includes many methodological flaws, findings suggest that mindfulness-based interventions may be helpful in the treatment of several disorders. Methodologically sound investigations are recommended in order to clarify the utility of these interventions.


title: Mechanical component design for multiple objectives using elitist non-dominated sorting GA

abstract: In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single simulation run. Recently, we proposed a much improved version of the originally proposed non-dominated sorting GA (we call NSGA-II) in that it is computationally faster, uses an elitist strategy, and it does not require fixing any niching parameter. In this paper, we use NSGA-II to handle constraints by using two implementations. On four mechanical component design problems borrowed from the literature, we show that the NSGA-II can find a much wider spread of solutions than classical methods and the NSGA. The results are encouraging and suggests immediate application of the proposed method to other more complex engineering design problems.


title: A Question Answer System Based on Confirmed Knowledge Developed by Using Mails Posted to a Mailing List

abstract: In this paper, we report a QA system which can answer how type questions based on the confirmed knowledge base which was developed by using mails posted to a mailing list. We first discuss a problem of developing a knowledge base by using natural language documents: wrong information in natural language documents. Then, we describe a method of detecting wrong information in mails posted to a mailing list and developing a knowledge base by using these mails. Finally, we show that question and answer mails posted to a mailing list can be used as a knowledge base for a QA system.


title: An inventory for measuring clinical anxiety: Psychometric properties.

abstract: Un nouvel inventaire auto-administre destine a mesurer l'anxiete pathologique, le «Beck Anxiety Cheklist» (BAI) est decrit, evalue et compare au «Hamilton Anxiety Rating Scale» (test avec lequel des correlations moderees sont trouvees)


title: Changedetection.net: A new change detection benchmark dataset

abstract: Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video dataset exists for benchmarking different methods. Presented here is a unique change detection benchmark dataset consisting of nearly 90,000 frames in 31 video sequences representing 6 categories selected to cover a wide range of challenges in 2 modalities (color and thermal IR). A distinguishing characteristic of this dataset is that each frame is meticulously annotated for ground-truth foreground, background, and shadow area boundaries — an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of change detection algorithms. This paper presents and discusses various aspects of the new dataset, quantitative performance metrics used, and comparative results for over a dozen previous and new change detection algorithms. The dataset, evaluation tools, and algorithm rankings are available to the public on a website1 and will be updated with feedback from academia and industry in the future.


title: Omega-3 fatty acids and neuropsychiatric disorders

abstract: Epidemiological evidence suggests that dietary consumption of the long chain omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), commonly found in fish or fish oil, may modify the risk for certain neuropsychiatric disorders. As evidence, decreased blood levels of omega-3 fatty acids have been associated with several neuropsychiatric conditions, including Attention Deficit (Hyperactivity) Disorder, Alzheimer's Disease, Schizophrenia and Depression. Supplementation studies, using individual or combination omega-3 fatty acids, suggest the possibility for decreased symptoms associated with some of these conditions. Thus far, however, the benefits of supplementation, in terms of decreasing disease risk and/or aiding in symptom management, are not clear and more research is needed. The reasons for blood fatty acid alterations in these disorders are not known, nor are the potential mechanisms by which omega-3 fatty acids may function in normal neuronal activity and neuropsychiatric disease prevention and/or treatment. It is clear, however, that DHA is the predominant n-3 fatty acid found in the brain and that EPA plays an important role as an anti-inflammatory precursor. Both DHA and EPA can be linked with many aspects of neural function, including neurotransmission, membrane fluidity, ion channel and enzyme regulation and gene expression. This review summarizes the knowledge in terms of dietary omega-3 fatty acid intake and metabolism, as well as evidence pointing to potential mechanisms of omega-3 fatty acids in normal brain functioning, development of neuropsychiatric disorders and efficacy of omega-3 fatty acid supplementation in terms of symptom management.


title: The design and implementation of new Vehicle Black Box using the OBD information

abstract: In this paper, the new Vehicle Black Box, which is named E-Black Box (Enhanced Black Box), is designed and developed. The E-Black Box is the combination of all the advantages of previous Black Boxes and Event Data Recorders (EDRs). The E-Black Box communicates with Electronic Control Units (ECUs) to read the status of vehicle via OBD port. It is equipped with camera that records the video in front of the vehicle. As the common vehicle Black Box, the E-Black Box also has Global Positioning System (GPS) for reading the current latitude and longitude of the vehicle point. The 9 Degrees of Freedom (DOF) of inertial sensor, which is incorporated triple-axis gyro senor, triple-axis accelerometer sensor and triple-axis magnetometer sensor, is integrated with E-Black Box to read the velocity, acceleration and orientation of vehicle after which we analyze the stability of vehicle during the travel. After collecting and synchronizing all data, the E-Black Box saves them in Secure Digital (SD) Card.


title: A Passive Gait-Based Weight-Support Lower Extremity Exoskeleton With Compliant Joints

abstract: This paper presents the design and analysis of a passive body weight (BW)-support lower extremity exoskeleton (LEE) with compliant joints to relieve compressive load in the knee. The biojoint-like mechanical knee decouples human gait into two phases, stance and swing, by a dual snap fit. The knee joint transfers the BW to the ground in the stance phases and is compliant to free the leg in the swing phases. Along with a leg dynamic model and a knee biomechanical model, the unmeasurable knee internal forces are simulated. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. The reduced knee forces confirm the effectiveness of the LEE in supporting human BW during walking and also provide a basis for computing the internal knee forces as a percentage of BW. Energy harvested from the hip spring reveals that the LEE can save human walking energy.


title: Energy-efficient hydraulic actuator position tracking using hydraulic system operation modes

abstract: Efficient actuation is an important requirement in electromechanical designs. Although hydraulic actuators are used extensively when high-magnitude forces are present in heavy machinery, they are n...


title: Just a game after all: Violent video game exposure and time spent playing effects on hostile feelings, depression, and visuospatial cognition

abstract: Research in the domain of video game violence continues to be contentious and debated. Scholars have examined both positive and negative effects of violent games, although results thus far have been inconclusive and systematic internal validity problems have been identified with past research. The current study adds to this growing literature by examining the effects of video game violence exposure and time spent playing on depression, hostility, and visuospatial cognition. This study improves upon previous research by matching game conditions carefully on confounding variables identified as problems by other scholars. In a laboratory setting, 100 participants were randomly assigned into one of six conditions based on two independent variables (time spent playing and type of video game). Results indicated that neither randomized video game play nor time spent playing a video game had any effect on depression, hostility, or, visuospatial cognition. Effect size estimates were below levels for practical significance. These results suggest that both positive and negative influences of violence in video games may be limited in scope.


title: Sequence analysis Clustal W and Clustal X version 2 . 0

abstract: Summary: The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems. ::: ::: Availability: The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/ ::: ::: Contact: clustalw@ucd.ie


title: Unraveling Blockchain based Crypto-currency System Supporting Oblivious Transactions: a Formalized Approach

abstract: User privacy is an important issue in a blockchain based transaction system. Bitcoin, being one of the most widely used blockchain based transaction system, fails to provide enough protection on users' privacy. Many subsequent studies focus on establishing a system that hides the linkage between the identities (pseudonyms) of users and the transactions they carry out in order to provide a high level of anonymity. Examples include Zerocoin, Zerocash and so on. It thus becomes an interesting question whether such new transaction systems do provide enough protection on users' privacy. In this paper, we propose a novel and effective approach for de-anonymizing these transaction systems by leveraging information in the system that is not directly related, including the number of transactions made by each identity and time stamp of sending and receiving. Combining probability studies with optimization tools, we establish a model which allows us to determine, among all possible ways of linking between transactions and identities, the one that is most likely to be true. Subsequent transaction graph analysis could then be carried out, leading to the de-anonymization of the system. To solve the model, we provide exact algorithms based on mixed integer linear programming. Our research also establishes interesting relationships between the de-anonymization problem and other problems studied in the literature of theoretical computer science, e.g., the graph matching problem and scheduling problem.


title: Classification With Ant Colony Optimization

abstract: Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques.


title: Comparing bare-hand-in-air Gesture and Object-in-hand Tangible User Interaction for Navigation of 3D Objects in Modeling

abstract: 3D modeling is used in Computer Graphics in various fields. Since the growth of gestures, virtual reality and embodied cognition, there have been various new technologies developed to either improve the modeling efficiency, or to provide more nature intuitive experience to the users. In this paper, from the user experience perspective, we try to compare these methods for navigation of 3D objects in the virtual modeling environment including: simple bare hand gestures, tangible user interfaces (TUI) with object in hand, as well as mouse/keyboard as the primary input. Based on embodied cognition theory, we hypothesis that the object-in-hand method might bring better user experience since the interaction between the object and hand can enhance the user's cognition while navigating a model. We present a conceptual design, with two approaches and three design models which demonstrate differences in user interaction with 3D modeling software.


title: Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning

abstract: Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time. The detection and tracking of malignant skin cancers and benign moles poses a particularly challenging problem due to the general uniformity of large skin patches, the fact that skin lesions vary little in their appearance, and the relatively small amount of data available. Here we introduce a novel data synthesis technique that merges images of individual skin lesions with full-body images and heavily augments them to generate significant amounts of data. We build a convolutional neural network (CNN) based system, trained on this synthetic data, and demonstrate superior performance to traditional detection and tracking techniques. Additionally, we compare our system to humans trained with simple criteria. Our system is intended for potential clinical use to augment the capabilities of healthcare providers. While domain-specific, we believe the methods invoked in this work will be useful in applying CNNs across domains that suffer from limited data availability.


title: PERSONAGE: Personality Generation for Dialogue

abstract: Over the last fifty years, the “Big Five” model of personality traits has become a standard in psychology, and research has systematically documented correlations between a wide range of linguistic variables and the Big Five traits. A distinct line of research has explored methods for automatically generating language that varies along personality dimensions. We present PERSONAGE (PERSONAlity GEnerator), the first highly parametrizable language generator for extraversion, an important aspect of personality. We evaluate two personality generation methods: (1) direct generation with particular parameter settings suggested by the psychology literature; and (2) overgeneration and selection using statistical models trained from judge’s ratings. Results show that both methods reliably generate utterances that vary along the extraversion dimension, according to human judges.


title: Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs

abstract: Cyberphysical systems (CPSs) refer to the embedding of widespread sensing, networking, computation, and control into physical spaces with the goal of making them safer, more efficient, and reliable. Driven by the miniaturization and integration of sensing, communication, and computation in cost-efficient devices, CPSs are bound to transform industries such as aerospace, transportation, built environments, energy, health care, and manufacturing, to name a few. This great opportunity, unfortunately, is matched by even greater challenges. Taming the complexity of design and analysis of these systems poses a fundamental problem as a new paradigm is needed to bridge various scientific domains, which, through the years, have developed significantly different formalisms and methodologies. In addition, while the use of dedicated communication networks has so far sheltered systems from the outside world, use of off-the-shelf networking and computing, combined with the unattended operation of a plethora of devices, provides several opportunities for malicious entities to inject attacks on CPSs. A wide variety of motivations exists for launching an attack on CPSs, ranging from economic reasons, such as obtaining a financial gain, all the way to terrorism, for instance, threatening an entire population by manipulating life-critical resources. Any attack on safety-critical CPSs may significantly hamper the economy and lead to the loss of human lives. While the threat of attacks on CPSs tends to be underplayed at times, the Stuxnet worm provided a clear example of the possible future to come. This malware, targeting a uranium enriching facility in Iran, managed to reach the supervisory control and data acquisition (SCADA) system controlling the centrifuges used in the enrichment process. Stuxnet modified the control system, increasing pressure in the centrifuges in a first version of the worm and spinning centrifuges in an erratic fashion in a second version. As a result, Stuxnet caused significant damage to the plant [1]. For details, see "The Stuxnet Attack."


title: An on-chip CMOS relaxation oscillator with power averaging feedback using a reference proportional to supply voltage

abstract: Recently, on-chip reference oscillators are required for low-cost single-chip applications including biomedical sensors, microcomputers, high-speed interfaces such as DDR I/F and HDMI (for initial negotiation), and SoCs. RC oscillators (including relaxation oscillators) were developed to realize on-chip oscillators with standard CMOS processes. In this paper, a power-averaging feedback (PAF) concept for accurate oscillators with low power and small area is presented.


FAUNAL TURNOVER AND DEVELOPMENT OF FOSSIL AVIFAUNAS IN SOUTH AMERICA


title: A NoSQL Data Model for Scalable Big Data Workflow Execution

abstract: While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.


title: OntoEdit: Collaborative Ontology Development For The Semantic Web

abstract: Ontologies now play an important role for enabling the semantic web. They provide a source of precisely defined terms e.g. for knowledge-intensive applications. The terms are used for concise communication across people and applications. Typically the development of ontologies involves collaborative efforts of multiple persons. OntoEdit is an ontology editor that integrates numerous aspects of ontology engineering. This paper focuses on collaborative development of ontologies with OntoEdit which is guided by a comprehensive methodology.


title: Deep Colorization

abstract: This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labelled color scribbles on the grayscale target image or a careful selection of colorful reference images (e.g., capturing the same scene in the grayscale target image). Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method. With the assumption of a perfect patch matching technique, the use of an extremely large-scale reference database (that contains sufficient color images) is the most reliable solution to the colorization problem. However, patch matching noise will increase with respect to the size of the reference database in practice. Inspired by the recent success in deep learning techniques which provide amazing modeling of large-scale data, this paper re-formulates the colorization problem so that deep learning techniques can be directly employed. To ensure artifact-free quality, a joint bilateral filtering based post-processing step is proposed. Numerous experiments demonstrate that our method outperforms the state-of-art algorithms both in terms of quality and speed.


title: A Rigorous Approach to Uncovering Security Policy Violations in UML Designs

abstract: There is a need for rigorous analysis techniquesthat developers can use to uncover security policyviolations in their UML designs. There are a few UMLanalysis tools that can be used for this purpose, butthey either rely on theorem-proving mechanisms thatrequire sophisticated mathematical skill to useeffectively, or they are based on model-checkingtechniques that require a “closed-world” view of thesystem (i.e., a system in which there are no inputs fromexternal sources). In this paper we show how alightweight, scenario-based UML design analysisapproach we developed can be used to rigorouslyanalyze a UML design to uncover security policyviolations.In the method, a UML design class model, in whichsecurity policies and operation specifications areexpressed in the Object Constraint Language (OCL), isanalyzed against a set of scenarios describingbehaviors that adhere to and that violate securitypolicies. The method includes a technique forgenerating scenarios. We illustrate how the methodcan be applied through an example involvingrole-based access control policies.


A conceptual framework for the design of organizational control mechanisms


title: Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home

abstract: This paper presents a robust location-aware activity recognition approach for establishing ambient intelligence applications in a smart home. With observations from a variety of multimodal and unobtrusive wireless sensors seamlessly integrated into ambient-intelligence compliant objects (AICOs), the approach infers a single resident's interleaved activities by utilizing a generalized and enhanced Bayesian Network fusion engine with inputs from a set of the most informative features. These features are collected by ranking their usefulness in estimating activities of interest. Additionally, each feature reckons its corresponding reliability to control its contribution in cases of possible device failure, therefore making the system more tolerant to inevitable device failure or interference commonly encountered in a wireless sensor network, and thus improving overall robustness. This work is part of an interdisciplinary Attentive Home pilot project with the goal of fulfilling real human needs by utilizing context-aware attentive services. We have also created a novel application called ldquoActivity Maprdquo to graphically display ambient-intelligence-related contextual information gathered from both humans and the environment in a more convenient and user-accessible way. All experiments were conducted in an instrumented living lab and their results demonstrate the effectiveness of the system.


title: An efficient authentication system of smart device using multi factors in mobile cloud service architecture

abstract: Summary ::: ::: Mobile cloud computing environments have overcome the performance limitation of mobile devices and provide use environments not restricted by places. However, user information protection mechanisms are required because of both the security vulnerability of mobile devices and the security vulnerability of cloud computing. In this paper, a multifactor mobile device authentication system is proposed to provide safety, efficiency, and user convenience for mobile device use in cloud service architectures. This system improves security by reinforcing the user authentication required before using cloud computing services. Furthermore, to reinforce user convenience, the system proposed increases the strength of authentication keys by establishing multiple factors for authentication. For efficient entries in mobile device use environments, this system combines mobile device identification number entries, basic ID/password type authentication methods, and the authentication of diverse user bio-information. This system also enhances authentication efficiency by processing the authentication factors of a user's authentication attempt in a lump instead of one by one in the cloud computing service environment. These authentication factors can be continuously added, and this authentication system provides authentication efficiency even when authentication factors are added. The main contribution is to improve high security level by through authentication of mobile devices with multifactors simultaneously and to use the mobile cloud service architecture for its efficient processing with respect to execution time of it. Copyright © 2013 John Wiley & Sons, Ltd.


title: Zero-IF receivers for phased array radars

abstract: This paper considers the suitability of zero-IF receivers for inclusion into the transmit/receive (T/R) modules in modern phased array radars. The main performance measures are the overall system dynamic range and tuning range. The achievable dynamic range is estimated based on currently available components and compared against the results of a prototype system. The overall results indicate that a dynamic range of more than 60 dB is achievable in a 50 MHz bandwidth.


title: NGS-Based Genotyping, High-Throughput Phenotyping and Genome-Wide Association Studies Laid the Foundations for Next-Generation Breeding in Horticultural Crops

abstract: Demographic trends and changes to climate require a more efficient use of plant genetic resources in breeding programs. Indeed, the release of high-yielding varieties has resulted in crop genetic erosion and loss of diversity. This has produced an increased susceptibility to severe stresses and a reduction of several food quality parameters. Next generation sequencing (NGS) technologies are being increasingly used to explore “gene space” and to provide high-resolution profiling of nucleotide variation within germplasm collections. On the other hand, advances in high-throughput phenotyping are bridging the genotype-to-phenotype gap in crop selection. The combination of allelic and phenotypic data points via genome-wide association studies is facilitating the discovery of genetic loci that are associated with key agronomic traits. In this review, we provide a brief overview on the latest NGS-based and phenotyping technologies and on their role to unlocking the genetic potential of vegetable crops; then, we discuss the paradigm shift that is underway in horticultural crop breeding.


Towards Constraint Satisfaction through Logic Programs and the Stable Model Semantics


title: Adult Norms for the Rey-Osterrieth Complex Figure Test and for Supplemental Recognition and Matching Trials from the Extended Complex Figure Test

abstract: The Rey-Osterrieth Complex Figure Test (“the Rey”; Osterrieth, 1944; Rey, 1941) has accumulated a considerable literature as a test of visual-spatial perception/construction and memory. The Extended Complex Figure Test (ECFT; Fastenau, 1996a, in press-a; Fastenau & Manning, 1992) supplements the Rey with Recognition and Matching trials that follow Copy, Immediate Recall, and Delayed Recall. The Rey and ECFT were administered to 211 healthy adults. Age ranged from 30 years to 85 years (M = 62.9, SD = 14.2), education ranged from 12 years to 25 years (M = 14.9, SD = 2.6), 55% were women, and over 95% were Caucasian. Age and education effects were evident on all trials (Multiple R ranged .23 to .50, p < .05), but education explained minimal variance (usually 2-3%) on copy and memory trials. Gender effects were negligible, if present. Age-appropriate norms are presented using Osterrieth's 36-point scoring, overlapping cells, and convenient tables for converting raw scores to scaled scores.


title: Intention Analysis for Sales, Marketing and Customer Service

abstract: In recent years, social media has become a customer touch-point for the business functions of marketing, sales and customer service. We aim to show that intention analysis might be useful to these business functions and that it can be performed effectively on short texts (at the granularity level of a single sentence). We demonstrate a scheme of categorization of intentions that is amenable to automation using simple machine learning techniques that are language-independent. We discuss the grounding that this scheme of categorization has in speech act theory. In the demonstration we go over a number of usage scenarios in an attempt to show that the use of automatic intention detection tools would benefit the business functions of sales, marketing and service. We also show that social media can be used not just to convey pleasure or displeasure (that is, to express sentiment) but also to discuss personal needs and to report problems (to express intentions). We evaluate methods for automatically discovering intentions in text, and establish that it is possible to perform intention analysis on social media with an accuracy of 66.97%±0.10%.


title: On Organizational Becoming: Rethinking Organizational Change

abstract: Traditional approaches to organizational change have been dominated by assumptions privileging stability, routine, and order. As a result, organizational change has been reified and treated as exceptional rather than natural. In this paper, we set out to offer an account of organizational change on its own terms--to treat change as the normal condition of organizational life. The central question we address is as follows: What must organization(s) be like if change is constitutive of reality? Wishing to highlight the pervasiveness of change in organizations, we talk about organizational becoming. Change, we argue, is the reweaving of actors' webs of beliefs and habits of action to accommodate new experiences obtained through interactions. Insofar as this is an ongoing process, that is to the extent actors try to make sense of and act coherently in the world, change is inherent in human action, and organizations are sites of continuously evolving human action. In this view, organization is a secondary accomplishment, in a double sense. Firstly, organization is the attempt to order the intrinsic flux of human action, to channel it towards certain ends by generalizing and institutionalizing particular cognitive representations. Secondly, organization is a pattern that is constituted, shaped, and emerging from change. Organization aims at stemming change but, in the process of doing so, it is generated by it. These claims are illustrated by drawing on the work of several organizational ethnographers. The implications of this view for theory and practice are outlined.


Efficient , Safe , and Probably Approximately Complete Learning of Action Models


title: The role of visual attention in saccadic eye movements

abstract: The relationship between saccadic eye movements and covert orienting of visual spatial attention was investigated in two experiments. In the first experiment, subjects were required to make a saccade to a specified location while also detecting a visual target presented just prior to the eye movement. Detection accuracy was highest when the location of the target coincided with the location of the saccade, suggesting that subjects use spatial attention in the programming and/or execution of saccadic eye movements. In the second experiment, subjects were explicitly directed to attend to a particular location and to make a saccade to the same location or to a different one. Superior target detection occurred at the saccade location regardless of attention instructions. This finding shows that subjects cannot move their eyes to one location and attend to a different one. The results of these experiments suggest that visuospatial attention is an important mechanism in generating voluntary saccadic eye movements.


title: Internet of Vehicles (IoV) for traffic management

abstract: Internet of Things (IoT) is a world-wide network connecting all the smart objects together. It is the way in which all things are enabled to talk with each other. Whenever those smart things being connected over internet are restricted to only vehicles, then it is called as Internet of Vehicles (IoV). With continuously increasing urban population and rapidly expanding cities, vehicle ownership has been increasing at an exponential rate. Hence, traffic management has become a great problem in our day today life. This paper provides IoV based traffic management solution to overcome the problem that is prevailing in our daily life.


title: The DNA-stabilising nucleoside 7-iodo-2′-deoxytubercidin: its structure in the solid state and in solution

abstract: The crystal structure of 7-iodo-2′-deoxytubercidin 2 has been determined and was compared with those of 2′-deoxytubercidin 3 and 2′-deoxyadenosine 4. The bulky 7-iodo substituent lies 13.2 pm below and the nitrogen of the 6-amino group 5.5 pm above the 7-deazapurine plane. The puckering of compound 2 is 3′E while compound 3 shows a 2′T3′ sugar pucker. The conformation in aqueous solution, determined by 1H NMR spectroscopy, is only slightly different, showing a 2′E conformation. The glycosylic bond torsion angle is anti in all cases.


title: Linearization Techniques for CMOS Low Noise Amplifiers: A Tutorial

abstract: This tutorial catalogues and analyzes previously reported CMOS low noise amplifier (LNA) linearization techniques. These techniques comprise eight categories: a) feedback; b) harmonic termination; c) optimum biasing; d) feedforward; e) derivative superposition (DS); f) IM2 injection; g) noise/distortion cancellation; and h) post-distortion. This paper also addresses broadband-LNA-linearization issues for emerging reconfigurable multiband/multistandard and wideband transceivers. Furthermore, we highlight the impact of CMOS technology scaling on linearity and outline how to design a linear LNA in a deep submicrometer process. Finally, general design guidelines for high-linearity LNAs are provided.


title: Inconsistency Management in Software Engineering: Survey and Open Research Issues

abstract: The development of complex software systems is a complex and lengthy activity that involves the participation and collaboration of many stakeholders (e.g. customers, users, analysts, designers, and developers). This results in many partial models of the developing system. These models can be inconsistent with each other since they describe the system from different perspectives and reflects the views of the stakeholders involved in their construction. Inconsistent software models can have negative and positive effects in the software development life-cycle. On the negative side, inconsistencies can delay and increase the cost of system development; do not guarantee some properties of the system, such as safety and reliability; and generate difficulties on system maintenance. On the positive side, inconsistencies can facilitate identification of some aspects of the system that need further analysis, assist with the specification of alternatives for the development of the system, and support elicitation of information about it. The software engineering community has proposed many techniques and methods to support the management of inconsistencies in various software models. In this paper, we present a survey of these techniques and methods. The survey is organized according to a conceptual framework which views inconsistency management as a process composed of six activities. These activities are the detection of overlaps, detection of inconsistencies, diagnosis of inconsistencies, handling of inconsistencies, tracking of inconsistencies, and specification and application of a management policy for inconsistencies. This paper also presents the main contributions of the research work that has been conducted to support each of the above activities and identifies the issues which are still open to further research.


title: Atypical antipsychotic agents: a critical review.

abstract: The pharmacology, efficacy, and adverse effects of atypical antipsychotic agents when used to treat schizophrenia and other disorders are reviewed. Atypical antipsychotic agents were developed in response to problems with typical agents, including lack of efficacy in some patients, lack of improvement in negative symptoms, and troublesome adverse effects, especially extrapyramidal symptoms (EPSs) and tardive dyskinesia CTD). Atypical antipsychotics differ from typical psychotics in their "limbic-specific" dopamine type 2 (D2)-receptor binding and high ratio of serotonin type 2 (5-HT2)-receptor binding to D2 binding. In clinical trials in patients with non-treatment-resistant schizophrenia, risperidone and olanzapine were superior to placebo for positive and negative symptoms. Risperidone and olanzapine were superior to haloperidol on some measures. Quetiapine was better than placebo but was not better than typical antipsychotics. Head-to-head comparisons of atypical antipsychotics in non-treatment-resistant schizophrenia have been inconclusive. Clozapine remains the standard agent for treatment-resistant schizophrenia. Atypical agents are substantially more expensive than their typical antipsychotic counterparts. To fully determine the overall efficiency of these drugs, other potential benefits, such as improved quality of life, need to be factored in. Atypical antipsychotics are associated with a decreased capacity to cause EPSs, TD, neuroleptic malignant syndrome, and hyperprolactinemia. Clozapine carries a risk of agranulocytosis; the white blood cell count must be monitored. Atypical antipsychotics are increasingly being used for indications other than schizophrenia, such as the management of aggression, mania, and depression. Atypical antipsychotics are often considered first-line agents for treating schizophrenia and are promising treatment alternatives for other psychiatric and neurologic conditions.


title: The Inventory of Depressive Symptomatology (IDS) : psychometric properties

abstract: The psychometric properties of the 28- and 30-item versions of the Inventory of Depressive Symptomatology, Clinician-Rated (IDS-C) and Self-Report (IDS-SR) are reported in a total of 434 (28-item) and 337 (30-item) adult out-patients with current major depressive disorder and 118 adult euthymic subjects (15 remitted depressed and 103 normal controls). Cronbach's α ranged from 0.92 to 0.94 for the total sample and from 0.76 to 0.82 for those with current depression. Item total correlations, as well as several tests of concurrent and discriminant validity are reported. Factor analysis revealed three dimensions (cognitive/mood, anxiety/arousal and vegetative) for each scale. Analysis of sensitivity to change in symptom severity in an open-label trial of fluoxetine (N = 58) showed that the IDS-C and IDS-SR were highly related to the 17-item Hamilton Rating Scale for Depression. Given the more complete item coverage, satisfactory psychometric properties, and high correlations with the above standard ratings, the 30-item IDS-C and IDS-SR can be used to evaluate depressive symptom severity. The availability of similar item content for clinician-rated and self-reported versions allows more direct evaluations of these two perspectives.


title: Taxonomy Induction Using Hierarchical Random Graphs

abstract: This paper presents a novel approach for inducing lexical taxonomies automatically from text. We recast the learning problem as that of inferring a hierarchy from a graph whose nodes represent taxonomic terms and edges their degree of relatedness. Our model takes this graph representation as input and fits a taxonomy to it via combination of a maximum likelihood approach with a Monte Carlo Sampling algorithm. Essentially, the method works by sampling hierarchical structures with probability proportional to the likelihood with which they produce the input graph. We use our model to infer a taxonomy over 541 nouns and show that it outperforms popular flat and hierarchical clustering algorithms.


title: Event detection in field sports video using audio-visual features and a support vector Machine

abstract: In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable.


title: A Design Science Research Methodology for Information Systems Research

abstract: The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). DS is of importance in a discipline oriented to the creation of successful artifacts. Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.


On the Role of Seed Lexicons in Learning Bilingual Word Embeddings


title: EXPERIMENTAL AND NUMERICAL ANALYSIS OF THE DOE REFERENCE MODEL 1 HORIZONTAL AXIS HYDROKINETIC TURBINE

abstract: We present two studies, one experimental and another numerical, of a Horizontal Axis Hydrokinetic Turbine (HAHT) based on the DOE Reference Model 1. In the experiments, the performance and wake of a small array of three turbines was analyzed with measurements conducted on a 45:1 scale physical model of the DOE RM1 rotor. The influence of the Reynolds number dissimilarity between the laboratory model and the full scale is described. The numerical solution of the RANS equations solved around a computational model of the full scale geometry yield the details of the rotor flow field and wake evolution. The influence of turbulent closure models on the full scale Reynolds number flow is considered, as is the use of high resolution simulations in lower fidelity models for fast turn around, low computational cost, numerical design efforts.


title: Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR

abstract: Although Augmented Reality technology was first developed over forty years ago, there has been little survey work giving an overview of recent research in the field. This paper reviews the ten-year development of the work presented at the ISMAR conference and its predecessors with a particular focus on tracking, interaction and display research. It provides a roadmap for future augmented reality research which will be of great value to this relatively young field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area.


Transformable Convolutional Neural Network for Text Classification


title: A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections

abstract: High-level shape understanding and technique evaluation on large repositories of 3D shapes often benefit from additional information known about the shapes. One example of such information is the semantic segmentation of a shape into functional or meaningful parts. Generating accurate segmentations with meaningful segment boundaries is, however, a costly process, typically requiring large amounts of user time to achieve high quality results. In this paper we present an active learning framework for large dataset segmentation, which iteratively provides the user with new predictions by training new models based on already segmented shapes. Our proposed pipeline consists of three novel components. First, we a propose a fast and relatively accurate feature-based deep learning model to provide dataset-wide segmentation predictions. Second, we propose an information theory measure to estimate the prediction quality and for ordering subsequent fast and meaningful shape selection. Our experiments show that such suggestive ordering helps reduce users time and effort, produce high quality predictions, and construct a model that generalizes well. Finally, we provide effective segmentation refinement features to help the user quickly correct any incorrect predictions. We show that our framework is more accurate and in general more efficient than state-of-the-art, for massive dataset segmentation with while also providing consistent segment boundaries.


title: WOVSQLI: Detection of SQL Injection Behaviors Using Word Vector and LSTM

abstract: The Structured Query Language Injection Attack (SQLIA) is one of the most serious and popular threats of web applications. The results of SQLIA include the data loss or complete host takeover. Detection of SQLIA is always an intractable challenge because of the heterogeneity of the attack payloads. In this paper, a novel method to detect SQLIA based on word vector of SQL tokens and LSTM neural networks is described. In the proposed method, SQL query strings were firstly syntactically analyzed into tokens, and then likelihood ratio test is used to build the word vector of SQL tokens, ultimately, an LSTM model is trained with sequences of token word vectors. We developed a tool named WOVSQLI, which implements the proposed technique, and it was evaluated with a dataset from several sources. The results of experiments demonstrate that WOVSQLI can effectively identify SQLIA.


title: Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

abstract: While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter settings interact in a way that can render this separation suboptimal. Likewise, we demonstrate that the common practice of using very few epochs during the main NAS and much larger numbers of epochs during a post-processing step is inefficient due to little correlation in the relative rankings for these two training regimes. To combat both of these problems, we propose to use a recent combination of Bayesian optimization and Hyperband for efficient joint neural architecture and hyperparameter search.