title: Biological recognition of graphene nanoflakes

abstract: The systematic study of nanoparticle–biological interactions requires particles to be reproducibly dispersed in relevant fluids along with further development in the identification of biologically relevant structural details at the materials–biology interface. Here, we develop a biocompatible long-term colloidally stable water dispersion of few-layered graphene nanoflakes in the biological exposure medium in which it will be studied. We also report the study of the orientation and functionality of key proteins of interest in the biolayer (corona) that are believed to mediate most of the early biological interactions. The evidence accumulated shows that graphene nanoflakes are rich in effective apolipoprotein A-I presentation, and we are able to map specific functional epitopes located in the C-terminal portion that are known to mediate the binding of high-density lipoprotein to binding sites in receptors that are abundant in the liver. This could suggest a way of connecting the materials' properties to the biological outcomes.


title: A Global Teaming Model for Global Software Development Governance: A Case Study

abstract: Global software development (GSD) is increasingly the norm. As firms expand into new markets overseas, acquire companies in different countries, and recruit talented developers in new locations, projects become distributed by necessity. As projects become increasingly distributed, and include external participants such as outsourcing vendors, conventional top-down management oversight and control becomes more difficult. How can organizations ensure that the activities of all parties involved are aligned with the strategic objectives and values of a software development undertaking? The Global Teaming Model is a framework that specifies practices and recommendations for Global Software Development. As such, it serves as a means to organize GSD practices relevant to Software Development Governance. We conducted a case study of a small team engaged in Global Software Development, identifying governance shortcomings. Then, we used the Global Teaming Model to identify governance practices that would address those shortcomings. We identified several aspects of the team's interactions with other teams in the company that would be improved by recommendations from the Global Teaming Governance Model. The Global Teaming Model provides a blueprint for Software Development Governance that organizations seeking to globalize their current development projects in a controlled way can use to implement good Software Development Governance.


title: What do you want to do next: a novel approach for intent prediction in gaze-based interaction

abstract: Interaction intent prediction and the Midas touch have been a longstanding challenge for eye-tracking researchers and users of gaze-based interaction. Inspired by machine learning approaches in biometric person authentication, we developed and tested an offline framework for task-independent prediction of interaction intents. We describe the principles of the method, the features extracted, normalization methods, and evaluation metrics. We systematically evaluated the proposed approach on an example dataset of gaze-augmented problem-solving sessions. We present results of three normalization methods, different feature sets and fusion of multiple feature types. Our results show that accuracy of up to 76% can be achieved with Area Under Curve around 80%. We discuss the possibility of applying the results for an online system capable of interaction intent prediction.


title: Negative Self-Efficacy and Goal Effects Revisited

abstract: The authors address the verification of the functional properties of self-efficacy beliefs and document how self-efficacy beliefs operate in concert with goal systems within a sociocognitive theory of self-regulation in contrast to the focus of control theory on discrepancy reduction. Social cognitive theory posits proactive discrepancy production by adoption of goal challenges working in concert with reactive discrepancy reduction in realizing them. Converging evidence from diverse methodological and analytic strategies verifies that perceived self-efficacy and personal goals enhance motivation and performance attainments. The large body of evidence, as evaluated by 9 meta-analyses for the effect sizes of self-efficacy beliefs and by the vast body of research on goal setting, contradicts findings (J. B. Vancouver, C. M. Thompson, & A. A. Williams, 2001; J. B. Vancouver, C. M. Thompson, E. C. Tischner, & D. J. Putka, 2002) that belief in one’s capabilities and personal goals is self-debilitating. Social cognitive theory is rooted in an agentic perspective in which people function as anticipative, purposive, and selfevaluating proactive regulators of their motivation and actions (Bandura, 2001). A theory embodying feed-forward selfregulation differs from control theories rooted solely in a negative feedback control system aimed at error correction. Among the mechanisms of human agency, none is more central or pervasive than beliefs of personal efficacy. Whatever other factors serve as guides and motivators, they are rooted in the core belief that one has the power to produce desired effects; otherwise one has little incentive to act or to persevere in the face of difficulties. Selfefficacy beliefs regulate human functioning through cognitive, motivational, affective, and decisional processes (Bandura, 1997). They affect whether individuals think in self-enhancing or selfdebilitating ways, how well they motivate themselves and persevere in the face of difficulties, the quality of their emotional well-being and their vulnerability to stress and depression, and the choices they make at important decisional points.


title: Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach

abstract: In this paper, we propose to leverage the emerging deep learning techniques for spatiotemporal modeling and prediction in cellular networks, based on big system data. First, we perform a preliminary analysis for a big dataset from China Mobile, and use traffic load as an example to show non-zero temporal autocorrelation and non-zero spatial correlation among neighboring Base Stations (BSs), which motivate us to discover both temporal and spatial dependencies in our study. Then we present a hybrid deep learning model for spatiotemporal prediction, which includes a novel autoencoder-based deep model for spatial modeling and Long Short-Term Memory units (LSTMs) for temporal modeling. The autoencoder-based model consists of a Global Stacked AutoEncoder (GSAE) and multiple Local SAEs (LSAEs), which can offer good representations for input data, reduced model size, and support for parallel and application-aware training. Moreover, we present a new algorithm for training the proposed spatial model. We conducted extensive experiments to evaluate the performance of the proposed model using the China Mobile dataset. The results show that the proposed deep model significantly improves prediction accuracy compared to two commonly used baseline methods, ARIMA and SVR. We also present some results to justify effectiveness of the autoencoder-based spatial model.


title: Multi-sensor data fusion for hand tracking using Kinect and Leap Motion

abstract: Often presented as competing products on the market of low cost 3D sensors, the Kinect™ and the Leap Motion™ (LM) can actually be complementary in some scenario. We promote, in this paper, the fusion of data acquired by both LM and Kinect sensors to improve hand tracking performances. The sensor fusion is applied to an existing augmented reality system targeting the treatment of phantom limb pain (PLP) in upper limb amputees. With the Kinect we acquire 3D images of the patient in real-time. These images are post-processed to apply a mirror effect along the sagittal plane of the body, before being displayed back to the patient in 3D, giving him the illusion that he has two arms. The patient uses the virtual reconstructed arm to perform given tasks involving interactions with virtual objects. Thanks to the plasticity of the brain, the restored visual feedback of the missing arm allows, in some cases, to reduce the pain intensity. The Leap Motion brings to the system the ability to perform accurate motion tracking of the hand, including the fingers. By registering the position and orientation of the LM in the frame of reference of the Kinect, we make our system able to accurately detect interactions of the hand and the fingers with virtual objects, which will greatly improve the user experience. We also show that the sensor fusion nicely extends the tracking domain by supplying finger positions even when the Kinect sensor fails to acquire the depth values for the hand.


title: Energy-efficient acceleration of big data analytics applications using FPGAs

abstract: A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization. Considering the time dedicated to create such hardware implementations, an analysis that estimates how much benefit we gain in terms of speed and energy efficiency, through offloading various functions to hardware would be necessary. This work analyzes data mining and machine learning algorithms, which are utilized extensively in big data applications in a heterogeneous CPU+FPGA platform. We select and offload the computational intensive kernels to the hardware accelerator to achieve the highest speed-up and best energy-efficiency. We use the latest Xilinx Zynq boards for implementation and result analysis. We also perform a first order comprehensive analysis of communication and computation overheads to understand how the speedup of each application contributes to its overall execution in an end-to-end Hadoop MapReduce environment. Moreover, we study how other system parameters such as the choice of CPU (big vs little) and the number of mapper slots affect the performance and power-efficiency benefits of hardware acceleration. The results show that a kernel speedup of upto χ 321.5 with hardware+software co-design can be achieved. This results in χ2.72 speedup, 2.13χ power reduction, and 15.21χ energy efficiency improvement (EDP) in an end-to-end Hadoop MapReduce environment.


title: Execution replay of multiprocessor virtual machines

abstract: Execution replay of virtual machines is a technique which has many important applications, including debugging, fault-tolerance, and security. Execution replay for single processor virtual machines is well-understood, and available commercially. With the advancement of multi-core architectures, however, multiprocessor virtual machines are becoming more important. Our system, SMP-ReVirt, is the first system to log and replay a multiprocessor virtual machine on commodity hardware. We use hardware page protection to detect and accurately replay sharing between virtual cpus of a multi-cpu virtual machine, allowing us to replay the entire operating system and all applications. We have tested our system on a variety of workloads, and find that although sharing under SMP-ReVirt is expensive, for many workloads and applications, including debugging, the overhead is acceptable.


title: Class-Based N-Gram Models Of Natural Language

abstract: We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithms for assigning words to classes based on the frequency of their co-occurrence with other words. We find that we are able to extract classes that have the flavor of either syntactically based groupings or semantically based groupings, depending on the nature of the underlying statistics.


title: SATzilla: Portfolio-based Algorithm Selection for SAT

abstract: It has been widely observed that there is no single"dominant"SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.


title: Multilayer Substrate Integrated Waveguide (SIW) Filters With Higher-Order Mode Suppression

abstract: This letter presents a multilayer approach for suppressing the higher order modes of substrate integrated waveguide (SIW) bandpass filters. Rectangular SIW resonators in multilayer substrates can be vertically coupled with magnetic and/or electric coupling by the proposed apertures etched on the middle metal layer. Harmonic passbands produced by the first higher-order mode TE ::: 102 ::: can be suppressed thanks to the proposed weak coupling or mixed coupling schemes in connection with the TE ::: 102 ::: mode. Compared with conventional horizontally coupled filter, the proposed filters also show a compact physical size. To demonstrate the proposed design method, two double-layered SIW bandpass filters Filter (I and II) are fabricated and measured. The experimental results show that the maximum rejection levels at the TE ::: 102 ::: mode frequencies are below -30 dB and -20 dB for Filter I and II, respectively. Furthermore, simulated and measured results also show a good agreement.


title: An 18 nA, 87% Efficient Solar, Vibration and RF Energy-Harvesting Power Management System With a Single Shared Inductor

abstract: We present a modular power management system that can harvest energy from three sources simultaneously, with available power levels of 25 nW to $100~\mu \text {W}$ , with one inductor. The DC-DC converter is clocked with energize and dump pulses, and the pulse-widths are generated for constant peak inductor current and for no reversal, without current sensing. We use a comparator to reach the open-circuit-voltage (OCV)-based maximum power point (MPP), and train an oscillator to mimic the comparator output. The oscillator frequency is tuned through a successive-approximation algorithm within 11 comparator cycles. The 180 nm chip has a maximum efficiency of 87% at an input available power of $20~\mu \text {W}$ (input voltage of 0.6 V), and has an output voltage of 1.5 V.


title: Placenta Accreta Spectrum

abstract: Key Clinical Points Placenta Accreta Spectrum The incidence of placenta accreta spectrum has increased by a factor of approximately 8 since the 1970s, probably owing to increases in cesarean delivery. Women with major risk factors, such as placenta previa, previous cesarean delivery, endometrial ablation, or other uterine surgery, should undergo obstetrical sonography in the middle-to-late second trimester to assess for possible placenta accreta spectrum. Patients with suspected placenta accreta spectrum should be referred to a center with multidisciplinary expertise and experience. Recommended management of suspected placenta accreta spectrum is planned cesarean hysterectomy with the placenta left in situ. However, surgical management may be individualized. In most cases, planned preterm delivery at 34 weeks of gestation is recommended to best balance maternal and neonatal risks. Earlier delivery may be warranted in women with labor, bleeding, or other complications.


title: Detection of self-paced reaching movement intention from EEG signals

abstract: Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user's intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1-1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing robot-assisted neurorehabilitation.


title: Dynamic optimization of batch processes II . Role of measurements in handling uncertainty

abstract: Abstract The main bottleneck in using optimization at the industrial level is the presence of uncertainty in the form of model mismatch and disturbances. The way uncertainty can be handled constitutes the subject of this series of two papers. The first part dealt with the characterization of the nominal solution and proposed an approach to separate the constraint-seeking from the sensitivity-seeking components of the inputs. This second part reviews various strategies for optimization under uncertainty, namely the robust and measurement-based optimization schemes. A novel scheme is proposed, where optimality is achieved by tracking the necessary conditions of optimality. The different approaches are compared via the simulation of a bioreactor for penicillin production.


title: An Overview of Event Extraction from Text

abstract: One common application of text mining is event extraction, which encompasses deducing specific knowledge concerning incidents referred to in texts. Event extraction can be applied to various types of written text, e.g., (online) news messages, blogs, and manuscripts. This literature survey reviews text mining techniques that are employed for various event extraction purposes. It provides general guidelines on how to choose a particular event extraction technique depending on the user, the available content, and the scenario of use.


title: Transition-based Knowledge Graph Embedding with Relational Mapping Properties

abstract: Many knowledge repositories nowadays contain billions of triplets, i.e. (head-entity, relationship, tail-entity), as relation instances. These triplets form a directed graph with entities as nodes and relationships as edges. However, this kind of symbolic and discrete storage structure makes it difficult for us to exploit the knowledge to enhance other intelligenceacquired applications (e.g. the QuestionAnswering System), as many AI-related algorithms prefer conducting computation on continuous data. Therefore, a series of emerging approaches have been proposed to facilitate knowledge computing via encoding the knowledge graph into a low-dimensional embedding space. TransE is the latest and most promising approach among them, and can achieve a higher performance with fewer parameters by modeling the relationship as a transitional vector from the head entity to the tail entity. Unfortunately, it is not flexible enough to tackle well with the various mapping properties of triplets, even though its authors spot the harm on performance. In this paper, we thus propose a superior model called TransM to leverage the structure of the knowledge graph via pre-calculating the distinct weight for each training triplet according to its relational mapping property. In this way, the optimal function deals with each triplet depending on its own weight. We carry out extensive experiments to compare TransM with the state-of-the-art method TransE and other prior arts. The performance of each approach is evaluated within two different application scenarios on several benchmark datasets. Results show that the model we proposed significantly outperforms the former ones with lower parameter complexity as TransE.


title: Topic-dependent sentiment analysis of financial blogs

abstract: While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches.


title: Deep Neural Networks Under Stress

abstract: In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression rate. By introducing perturbations to image descriptions extracted from a deep convolutional neural network, we change their precision and number of dimensions, measuring how it affects the final score. We show that deep features are more robust to these disturbances when compared to classical approaches, achieving a compression rate of 98.4%, while losing only 0.88% of their original score for Pascal VOC 2007.


When abstract moralizing runs amok.


title: Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

abstract: In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.


title: A variable rate channel quality feedback scheme for 3G wireless packet data systems

abstract: An expanded effort is underway to support the evolution of the UMTS and cdma2000-1x standards to meet the rapidly developing needs associated with wireless Internet applications. A number of performance enhancing technologies are proposed to ensure high peak and average packet data rates while supporting circuit-switched voice and packet data on the same spectrum. These techniques include adaptive modulation and coding (AMC), hybrid ARQ (H-ARQ) and fat-pipe scheduling. In order to enable these techniques downlink channel quality feedback (CQF) through explicit uplink signaling is necessary. Frequent CQF results in good estimates of downlink channel quality at the base station, which, in turn, improves downlink system performance. However, this comes at the expense of larger uplink signaling overhead, thereby impacting the overall uplink capacity. Infrequent CQF, on the other hand, reduces this signaling overhead in the uplink at the expense of larger errors in the channel quality estimates available at the base station, thereby leading to system performance degradation. In this paper, we present a variable rate CQF scheme that significantly reduces uplink signaling overhead without affecting downlink system performance. In addition to simulation results demonstrating the performance of the proposed scheme, we also compare the reduction in uplink signaling overhead of the said scheme with other known methods.


title: Challenges and solutions in distributed software development project management: A systematic literature review

abstract: This paper presents a systematic literature review of the challenges, best practices, models, and tools in Distributed Software Development (DSD) Project Management. The objective is to collect and systematize reported knowledge in terms of what are the difficulties in managing DSD projects, what are the best practices to overcome these difficulties, and how existing models and tools support these practices. We found 54 works related to DSD project management, published between 1998 and 2009. Using the data systematically extracted from these works, we propose an evidence-based DSD project management improvement model. Our contention is that this model can support practitioners and researchers to better understand the landscape of DSD project challenges and devise more effective solutions to improve project management in a distributed setting.


title: BigBench V2: The New and Improved BigBench

abstract: Benchmarking Big Data solutions has been gaining a lot of attention from research and industry. BigBench is one of the most popular benchmarks in this area which was adopted by the TPC as TPCx-BB. BigBench, however, has key shortcomings. The structured component of the data model is the same as the TPC-DS data model which is a complex snowflake-like schema. This is contrary to the simple star schema Big Data models in real life. BigBench also treats the semi-structured web-logs more or less as a structured table. In real life, web-logs are modeled as key-value pairs with unknown schema. Specific keys are captured at query time - a process referred to as late binding. In addition, eleven (out of thirty) of the BigBench queries are TPC-DS queries. These queries are complex SQL applied on the structured part of the data model which again is not typical of Big Data workloads. In this paper1, we present BigBench V2 to address the aforementioned limitations of the original BigBench. BigBench V2 is completely independent of TPC-DS with a new data model and an overhauled workload. The new data model has a simple structured data model. Web-logs are modeled as key-value pairs with a substantial and variable number of keys. BigBench V2 mandates late binding by requiring query processing to be done directly on key-value web-logs rather than a pre-parsed form of it. A new scale factor-based data generator is implemented to produce structured tables, key-value semistructured web-logs, and unstructured data. We implemented and executed BigBench V2 on Hive. Our proof of concept shows the feasibility of BigBench V2 and outlines different ways of implementing late binding.


title: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

abstract: Several researchers characterized the activation function under which multilayer feedforward networks can act as universal approximators. We show that most of all the characterizations that were reported thus far in the literature are special cases of the following general result: A standard multilayer feedforward network with a locally bounded piecewise continuous activation function can approximate any continuous function to any degree of accuracy if and only if the network's activation function is not a polynomial. We also emphasize the important role of the threshold, asserting that without it the last theorem does not hold.


title: Key-Threshold based spiking neural network

abstract: In the paper a novel model of Key-Threshold based Spiking Neural Network (KTSNN) is proposed. This neural network consists of quasi-neurons oriented to recognize any key-spikes distributed in time (sequence of spikes) or in space (in synapses). Every neuron aims to recognize key (template of spikes) stored in its memory and to react by output spike at successfully detection. Software implementation of this model is suggested. Possible methods of learning, implementation and usage of this model are discussed.


title: Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches

abstract: In dialogical argumentation it is often assumed that the involved parties always correctly identify the intended statements posited by each other, realize all of the associated relations, conform to the three acceptability states (accepted, rejected, undecided), adjust their views when new and correct information comes in, and that a framework handling only attack relations is sufficient to represent their opinions. Although it is natural to make these assumptions as a starting point for further research, removing them or even acknowledging that such removal should happen is more challenging for some of these concepts than for others. Probabilistic argumentation is one of the approaches that can be harnessed for more accurate user modelling. The epistemic approach allows us to represent how much a given argument is believed by a given person, offering us the possibility to express more than just three agreement states. It is equipped with a wide range of postulates, including those that do not make any restrictions concerning how initial arguments should be viewed, thus potentially being more adequate for handling beliefs of the people that have not fully disclosed their opinions in comparison to Dung's semantics. The constellation approach can be used to represent the views of different people concerning the structure of the framework we are dealing with, including cases in which not all relations are acknowledged or when they are seen differently than intended. Finally, bipolar argumentation frameworks can be used to express both positive and negative relations between arguments. In this paper we describe the results of an experiment in which participants judged dialogues in terms of agreement and structure. We compare our findings with the aforementioned assumptions as well as with the constellation and epistemic approaches to probabilistic argumentation and bipolar argumentation.


title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

abstract: This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.


A 115-mW, 0.5- m CMOS GPS Receiver with Wide Dynamic-Range Active Filters


title: Spatio-temporal Constrained Adaptive Sinogram Restoration for Low-dose Dynamic Cerebral Perfusion CT Imaging

abstract: Dynamic cerebral perfusion computed tomography (PCT) imaging shows its potential in acute stroke diagnosis by visualizing and quantifying hemodynamic information of cerebral tissue and vessels. However, high radiation dose imposed on patients during dynamic PCT scanning has aroused growing concerns. To address this issue, we proposed a spatio-temporal constrained adaptive sinogram restoration (ST-ASR) method to improve the quality of dynamic cerebral perfusion CT imaging with low-dose protocol. The proposed ST-ASR method explores the spatio-temporal correlation in dynamic cerebral PCT projections to develop a new spatio-temporal prior model for sinogram restoration, and conducts an adaptive sinogram weighting according to the noise variance of sinogram data. Experimental results with clinical patient data demonstrate that the proposed ST-ASR method can achieve noticeable improvements over existing adaptive sinogram restoration method in terms of noise reduction and quantitative evaluations for low-dose dynamic cerebral PCT imaging.


title: Checking an Expert Systems Knowledge Base for Consistency and Completeness

abstract: In this paper we describe a program that verifies the consistency and completeness of expert system knowledge bases which utilize the Lookheed Expert System (LES) framework. The algorithms described here are not specific to LES and can be applied to most rule-based systems. The program, called CHECK, combines logical principles as well as specific information about the knowledge representation formalism of LES. The program checks for redundant rules, conflicting rules, subsumed rules, missing rules, circular rules, unreachable clauses, and deadend clauses. It also generates a dependency chart which shows the dependencies among the rules and between the rules and the goals. CHECK can help the knowledge engineer to detect many programming errors even before the knowledge base testing phase. It also helps detect gaps in the knowledge base which the knowledge engineer and the expert might have overlooked. A wide variety of knowledge bases have been analyzed using CHECK.


A Lateration-localizing Algorithm for Energy-efficient Target Tracking in Wireless Sensor Networks.


Risk factors associated with childhood asthma: A case control study


ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets


title: A Note on Super-Gain Antenna Arrays

abstract: Numerical calculations have been made for linear broadside super-gain arrays. Using arrays having an over-all length of a quarter wavelength as an example, it is shown that as the required directive gain is increased, tremendous currents are required to produce only a small radiated field. For a 9-element array which produces a power gain of 8.5, the currents must be adjusted to their correct value to an accuracy of better than 1 part in 1011. The efficiency is less than 10-14 per cent.


title: Matrix transformers for renewable energy integration

abstract: this paper investigates the concept of using matrix transformers to achieve renewable energy integration. Matrix transformers can provide great design flexibility because they can be compatible with a variety of renewable energy sources as well as energy storage devices. Several key techniques raised by this concept are illustrated, and a high efficiency multiple-input and single output converter was designed to show how to use matrix transformers. Simulation and experimental results are presented that validate the approach presented in this paper.


title: Text analysis and knowledge mining system

abstract: Large text databases potentially contain a great wealth of knowledge. However, text represents factual information (and information about the author's communicative intentions) in a complex, rich, and opaque manner. Consequently, unlike numerical and fixed field data, it cannot be analyzed by standard statistical data mining methods. Relying on human analysis results in either huge workloads or the analysis of only a tiny fraction of the database. We are working on text mining technology to extract knowledge from very large amounts of textual data. Unlike information retrieval technology that allows a user to select documents that meet the user's requirements and interests, or document clustering technology that organizes documents, we focus on finding valuable patterns and rules in text that indicate trends and significant features about specific topics. By applying our prototype system named TAKMI (Text Analysis and Knowledge Mining) to textual databases in PC help centers, we can automatically detect product failures; determine issues that have led to rapid increases in the number of calls and their underlying reasons; and analyze help center productivity and changes in customers' behavior involving a particular product, without reading any of the text. We have verified that our framework is also effective for other data such as patent documents.


title: Putting Their Best Foot Forward: Emotional Disclosure on Facebook

abstract: Abstract Facebook has become a widely used online self-representation and communication platform. In this research, we focus on emotional disclosure on Facebook. We conducted two studies, and results from both self-report and observer rating show that individuals are more likely to express positive relative to negative emotions and present better emotional well-being on Facebook than in real life. Our study is the first to demonstrate impression management on Facebook through emotional disclosure. We discuss important theoretical and practical implications of our study.


title: H-LEACH: Hybrid-low energy adaptive clustering hierarchy for wireless sensor networks

abstract: Wireless sensor networks are one of the fast growing technologies in communication field, because of their compact size and ability to work in rugged conditions where human reach is not possible. This paper discusses about H-LEACH, which is used to solve problems of energy considerations while electing a channel head. H-LEACH considers residual and maximum energy of nodes for every round while electing a channel head using threshold condition. H-LEACH is compared with LEACH and HEED protocol, and proved that it is efficient than both methods. Simulations for LEACH, HEED and H-LEACH are done using MATLAB.


title: [Kidney transplantation in the rat: An institutional technique].

abstract: Kidney transplantation in rats is a classic experimental model used in transplant research. As several methods have been previously reported without definition of a gold standard, we describe herein a simple institutional technique that has been used in more than 500 procedures and taught to medical students or surgeons without previous microsurgical experience. Our step-by-step illustrated report would allow French-speaking trainees to learn how to perform kidney transplantation in rats in a reproducible manner and add this valuable tool to their research protocols.


title: Generalized Design Equations for Class-E Power Amplifiers with Finite DC Feed Inductance

abstract: In literature, it is widely accepted that the design of class-E power amplifier (PA) with finite dc feed inductance requires a long iterative solution procedure. To avoid such iterative solution methods, analytical design equations should be known. The problem associated with the finite dc feed inductance class-E PA is usually ascribed to the fact that the circuit element values are transcendental functions of the input parameters which is assumed to prevent the derivation of exact or fully analytical design equations. Using a proper analytical method, exact design solutions for class-E PA with any inductor value can however be derived. A mathematically exact analysis of the idealized class-E PA with finite dc feed inductance has been done and analytical expressions showing the relation between the circuit elements and the input parameters are found. These analytical expressions have been simplified to obtain explicit, relatively simple design equations. In this paper, it presents these relatively simple design equations. Using these design equations, class-E PA with finite dc feed inductance can be designed without iterative design procedures. The current paper discusses these simplified versions of the exact solution of general class-E PA with finite dc feed inductance, and discusses some implications of these design equations


title: Structuring Linked Data Search Results Using Probabilistic Soft Logic

abstract: On-the-fly generation of integrated representations of Linked Data (LD) search results is challenging because it requires successfully automating a number of complex subtasks, such as structure inference and matching of both instances and concepts, each of which gives rise to uncertain outcomes. Such uncertainty is unavoidable given the semantically heterogeneous nature of web sources, including LD ones. This paper approaches the problem of structuring LD search results as an evidence-based one. In particular, the paper shows how one formalism (viz., probabilistic soft logic (PSL)) can be exploited to assimilate different sources of evidence in a principled way and to beneficial effect for users. The paper considers syntactic evidence derived from matching algorithms, semantic evidence derived from LD vocabularies, and user evidence, in the form of feedback. The main contributions are: sets of PSL rules that model the uniform assimilation of diverse kinds of evidence, an empirical evaluation of how the resulting PSL programs perform in terms of their ability to infer structure for integrating LD search results, and, finally, a concrete example of how populating such inferred structures for presentation to the end user is beneficial, besides enabling the collection of feedback whose assimilation further improves search result presentation.


title: M1 Stage Subdivision and Treatment Outcome of Patients With Bone-Only Metastasis of Nasopharyngeal Carcinoma

abstract: Background.The current M1 stage in nasopharyngeal carcinoma (NPC) does not differentiate patients based on metastatic site and number of metastases. This study aims to subdivide the M1 stage of NPC patients with bone-only metastases and to identify the patients who may benefit from combined chemoradiotherapy (CRT). Methods. Between 1998 and 2007, 312 patients diagnosed with bone-only metastasis at Sun Yat-sen University Cancer Centerwereenrolled.VariouspossiblesubdivisionsofM1stage were considered, including by the time order of metastasis (synchronous vs. metachronous), involvement of specific bone metastaticsite,thenumberofmetastaticsites,andthenumber of metastases.The correlation of the subdivisions of M1 stage with overall survival (OS) was determined by Cox regression. Results.The median OS was 23.4 months. Patients with more than three metastatic sites had significantly poorer OS than patients with three or fewer metastatic sites (16.2 vs. 32.4 months; p , .001). Metastasis to the spine was significantly associated with unfavorable OS (20.4 vs. 37.9 months; p , .001). Multivariate analysis showed that number of metastatic sites (more than three vs. three or fewer), spine involvement (present vs. absent), and treatment modality (CRT vs. chemotherapy or radiotherapy only) were independentprognosticfactorsforOS.Instratifiedanalysis,compared with chemotherapy or radiotherapy alone, combined chemoradiotherapy could significantly benefit the patients with single bone metastasis (hazard ratio: 0.21; 95% confidence interval: 0.09–0.50). Conclusion. Metastasis to the spine and having more than three bone metastatic sites are independent unfavorable predictors for OS in NPC patients with bone-only metastasis. Combined chemoradiotherapy should be considered for patients with single bone metastasis. The Oncologist 2015; 20:291–298 Implication for Practice: Establishing rigorous subdivision of the M1 stage of bone-only metastatic nasopharyngeal carcinoma (NPC)patients will facilitatedisease assessmentand treatment planning forclinicians. In ourstudy, we found thatthe numberof metastatic sites and spine involvement were independent prognostic factors for overall survival of NPC patients with bone-only metastasis;combinedchemoradiotherapy(CRT)couldpotentiallybenefitthepatientswithsinglebonemetastasis.Bysubdividing theM1stageofthepatientsaccordingtothetwoprognosticfactors,wecanbetterpredicttheprognosisofpatientsandfacilitate treatment planning. Moreover, combined CRT is recommended for NPC patients with single bone metastasis.


title: Toward Simple Strategy for Optimal Tracking and Localization of Robots With Adaptive Particle Filtering

abstract: The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and simple adaptive strategy to increase the efficiency and drastically reduce the computational effort in particle filters (PFs). The purpose of the adaptive approach (dispersion-based adaptive particle filter - DAPF) is to provide higher number of particles during the initial searching state (when the localization presents greater uncertainty) and fewer particles during the subsequent state (when the localization exhibits less uncertainty). With the aim of studying the dynamical PF behavior regarding others and putting the proposed algorithm into practice, we designed a methodology based on different target applications and a Kinect sensor. The various experiments conducted for both color tracking and mobile robot localization problems served to demonstrate that the DAPF algorithm can be further generalized. As a result, the DAPF approach significantly improved the computational performance over two well-known filtering strategies: 1) the classical PF with fixed particle set sizes, and 2) the adaptive technique named Kullback-Leiber distance.


title: Treatment of the Post-ICU Patient in an Outpatient Setting

abstract: The care of a patient in the intensive care unit extends well beyond his or her hospitalization. Evaluation of a patient after leaving the intensive care unit involves a review of the hospital stay, including principal diagnosis, exposure to medications, period spent in the intensive care unit, and history of prolonged mechanical ventilation. Fatigue should prompt evaluation for possible anemia, nutritional deficits, sleep disturbance, muscular deconditioning, and neurologic impairment. Other common problems include poor appetite with possible weight loss, falls, and sexual dysfunction. Psychological morbidities, posttraumatic stress disorder, anxiety disorder, and depression also often occur in the post-intensive care unit patient. These conditions are more common among patients with a history of delirium, prolonged sedation, mechanical ventilation, and acute respiratory distress syndrome. The physician should gain an understanding of the patient's altered quality of life, including employment status, and the state of his or her relationships with loved ones or the primary caregiver. As in many aspects of medicine, a multidisciplinary treatment approach is most beneficial to the post-intensive care unit patient.


title: The Interplay of Institutional Logics in it Public-Private Partnerships

abstract: Public-private partnerships (PPPs) offer a popular means by which the public sector can obtain infor-mation technology (IT) innovations and management know-how from private firms. However, these IT PPPs are extremely difficult to realize, especially considering the divergent interests of public- and private-side stake-holders. Our case study of an IT PPP reveals public- and private-side differences that initially impeded the establishment of a partnership; using institutional logics theory as meta-theoretical lens, we propose a model that explains how public and private parties managed to negotiate their mode of collaboration by balancing their competing institutional norms and practices which ultimately resulted in the convergence of the two divergent logics. Our paper contributes to theory and practice by (1) elucidating the theoretical foundations and role of institutional logics for IT project management that we found dominated by public and private norms and practices, (2) explaining why collaboration in IT PPPs is so diffi-cult, and (3) how eventually an IT PPP can be estab-lished. We discuss theoretical and practical implica-tions in the paper.


Tag Based Client Side Detection of Content Sniffing Attacks with File Encryption and File Splitter Technique


title: A model for improved association of radar and camera objects in an indoor environment

abstract: This paper presents a model for the deviation of distances measured by radar and by optical sensors (3D point clouds). The measured 3D point clouds are typically clustered to objects and the cluster centers are then associated with the radar targets. However, the physical extent and the object geometry cause a divergence of the measured radar range from the cluster centers of the 3D point cloud. Existing solutions either ignore this deviation and compare radar ranges and cluster centers directly without any correction or use object class specific models for the correction. On the contrary, the presented parametric model is independent of the object class. All necessary properties (i.e. object size, flatness and aspect angle) can be obtained online from the covariance matrix of the 3D-point cloud. The model weights are fitted offline using a representative dataset of indoor objects with known radar and cluster correspondences. Furthermore, it is described how this model can be used to localize radar reflections in thermal infrared (TIR) images for a firefighting application. In this particular application, structure-from-motion (SfM) is used to obtain the 3D point cloud from a single moving camera. Experiments with measured data show that the proposed model allows a better data association compared to a direct association.


title: Attack-Resistant Wireless Localization Using an Inclusive Disjunction Model

abstract: Wireless positioning is vulnerable to malicious attacks due to the nature of its open medium. This study proposes an attack-resistant fingerprinting localization algorithm based on a probabilistic inclusive disjunction model. This model allows an attacked observation to play a less significant role during the localization process, thus achieving more robust location estimations under security threats. This study included experiments conducted in an actual Wi-Fi network. Experimental results demonstrated that this approach apparently achieves more robust estimation than cluster-based, median-based, and sensor-selection methods under various attacks on RSS.


title: Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

abstract: We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. During testing, scene patch descriptors are matched against a database of synthetic model view patches and cast 6D object votes which are subsequently filtered to refined hypotheses. We evaluate on three datasets to show that our method generalizes well to previously unseen input data, delivers robust detection results that compete with and surpass the state-of-the-art while being scalable in the number of objects.


title: Challenges of applying DTN routing protocols in realistic disaster scenarios

abstract: Delay Tolerant Networks (DTNs) are used to deliver messages in cases of intermittent connectivity. Disaster scenarios with multiple Mobile Ad hoc Networks formed by the communication equipment of first responders are one example for such networks. In this paper, we evaluate different delay tolerant routing schemes based on realistic mobility models and traffic patterns. Both options are crucial as the routing schemes usually exploit the movement of the nodes in order to deliver the messages. Our results show that the choice of a routing protocol has an impact on the experienced delay, but a DTN-based communication system should also provide the necessary contact opportunities. Therefore, we present a solution to integrate additional nodes and efficient routing to address this.


Simple Fast and Adaptive Lossless Image Compression Algorithm


title: Computational models of affordance in robotics: a taxonomy and systematic classification

abstract: J. J. Gibson’s concept of affordance, one of the central pillars of ecological psychology, is a truly remarkable idea that provides a concise theory of animal perception predicated on environmental...


title: Mobile game-based learning in secondary education: engagement, motivation and learning in a mobile city game

abstract: Using mobile games in education combines situated and active learning with fun in a potentially excellent manner. The effects of a mobile city game called Frequency 1550, which was developed by The Waag Society to help pupils in their first year of secondary education playfully acquire historical knowledge of medieval Amsterdam, were investigated in terms of pupil engagement in the game, historical knowledge, and motivation for History in general and the topic of the Middle Ages in particular. A quasi-experimental design was used with 458 pupils from 20 classes from five schools. The pupils in 10 of the classes played the mobile history game whereas the pupils in the other 10 classes received a regular, project-based lesson series. The results showed those pupils who played the game to be engaged and to gain significantly more knowledge about medieval Amsterdam than those pupils who received regular project-based instruction. No significant differences were found between the two groups with respect to motivation for History or the Middle Ages. The impact of location-based technology and game-based learning on pupil knowledge and motivation are discussed along with suggestions for future research.


title: Evaluating a Pump Controlled Open Circuit Solution

abstract: Today’s mobile machines most often contain hydraulic valve controlled drives in an open loop-circuit. For the purpose of saving energy the constant pressure pumps have, in the past, often been repl ...


title: Clinical Observations of Bilateral Sudden Deafness

abstract: A series of 12 cases of bilateral sudden deafness was reported. 7 cases of them had simultaneous loss of hearing in both ears and the other 5 cases had alternate onsets in certain intervals ranging from 10 months to 42 years.The former cases showed severe deafness and similar audiograms in both ears and their prognosis was bad. On the other hand hearing of the latter cases recovered at least in the recently affected side.The pathogenesis of bilateral sudden deafness was discussed and it was considered that sudden deafness could always be bilateral and the rare bilateral case might be the severer type of the unilateral one.


Combination of Marx and Capacitor Diode Voltage Multiplier for Pulsed Power Applications


title: The spread of true and false news online

abstract: We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.


title: Prediction methodology of single event effect sensitivity and application on SRAM device

abstract: This work reports a contribution to the evaluation of Single Event Effect (SEE) sensitivity. This prediction should allow designers to assess radiation sensitivity of Application Specific Integrated Circuit (ASIC) during the design stage in order to harden it efficiently. Such tools already exist but not a complete method starting from the whole IC design up to the SEE reliability prediction. A detailed study of these tools allows the identification of the methods already used to evaluate the failure risk corresponding to specific types of SEE. A comparative study will allow the determination of their relevance. The implementation of the most suitable tool suite in an engineering software is a long and complex process. To demonstrate its feasibility, a list of selected models was made to build a simple method. It was used to perform an SEE simulation on a realistic model of SRAM device.


title: Pediatric residents' perceptions of communication competencies: Implications for teaching.

abstract: BACKGROUND ::: Medical regulatory organizations worldwide require competency in communication skills. Pediatric communication competencies are unique, and little is known about pediatric residents' perceptions regarding these skills. ::: ::: ::: AIM ::: The purpose of this study was to examine pediatric residents' attitudes about communication skills, their perceptions of the importance of learning 15 specific communication skills relevant to pediatrics, confidence in these skills, and relevant program supports. ::: ::: ::: METHODS ::: We developed a 47-item cross-sectional questionnaire to study pediatric residents' attitudes and perceptions regarding communication competencies. 104 pediatric housestaff in a university-affiliated program in the US were asked to complete the questionnaire. Scale variables were created and evaluated for reliability. Data were analysed using descriptive and univariate statistics. ::: ::: ::: RESULTS ::: Response rate was 86% (89/104). Cronbach's alpha reliabilities of the Importance Scale (r = 0.92) and Confidence Scale (r = 0.90) were excellent. Ninety nine percent of the participants agreed that learning to communicate effectively with patients was a priority. All agreed it is important to demonstrate empathy and caring, and to teach medical students to communicate effectively with patients. Pediatric residents agreed that the 15 communication competencies studied were important to learn. Most reported confidence in core communication competencies (interviewing, listening, building rapport, demonstrating caring and empathy), but only half or fewer were confident in 7 more advanced communication skills (ability to discuss end-of-life issues, speaking with children about serious illness, giving bad news, dealing with the 'difficult' patient/parent, cultural awareness/sensitivity, understanding psychosocial aspects, and understanding patients' perspectives). Few reported the availability of relevant program supports for learning these skills. ::: ::: ::: CONCLUSIONS ::: Pediatric residents perceive communication competencies as important and a priority for learning, yet report a lack of confidence in advanced communication skills and insufficient program supports. Our measurement scales can add to the evaluation of residency programs, and may provide suggestions for pediatric curricular content in core and advanced communication skills.


title: Relevant and Robust: A Response to Marcus and Davis (2013)

abstract: The authors discuss computational models in psychology. They are exact, fully stated scientific hypothesis. Probabilistic models specifically formalize hypotheses about the beliefs of agents, their knowledge and assumptions about the world, utilizing the structured collection of probabilities regarded as priors and likelihoods.


title: Programmable low-complexity linear frequency discriminator for microwave photonic links (invited paper)

abstract: We report a unique highly-linear, low-complexity, selectable slope, optical electric field frequency discriminator (FD) filter as novel phase-modulated/frequency-modulated-tointensity- modulated (PM/FM-to-IM) converter. We refer to this FD as Sin-Sin2 FD filter. Here, we review its different aspects, including its (i) surprisingly simple device design, (ii) excellent linearity performance, and (iii) unique programmable features. Compared with other linear FD filters having 6 to 11 Mach– Zehnder interferometer (MZI) elements, Sin-Sin2 uses only two MZIs and one Y-branch combiner. Its unique programmable features include (i) selectable dual-slope functionality via dynamic control of device’s path length, (ii) selectable dual-slope functionality via tuning the wavelength of laser source, (iii) switchable positive/negative slope signs, and (iv) inherent compensation property. We also introduce simplified link architectures that maximize these features.


title: Postprandial levels of the natural vitamin E tocotrienol in human circulation.

abstract: Compared to tocopherols, tocotrienols are poorly understood. The postabsorptive fate of tocotrienol isomers and their association with lipoprotein subfractions was examined. Normocholesterolemic women were subjected to an oral fat challenge supplemented with vitamin E (capsule containing 77 mg alpha-tocotrienol, 96 mg alpha-tocotrienol, 3 mg gamma-tocotrienol, 62 mg alpha-tocopherol, and 96 mg gamma-tocopherol). Plasma samples were collected at every 2 h intervals for up to 8 h following a one-time supplementation. Lipoproteins were measured by NMR spectroscopy, and subfractions of lipoproteins were isolated by density gradient ultracentrifugation. The maximal alpha-tocotrienol concentrations in supplemented individuals averaged approximately 3 microM in blood plasma, 1.7 microM in LDL, 0.9 microM in triglyceride-rich lipoprotein, and 0.5 microM in HDL. The peak plasma level corresponded to 12- to 30-fold more than the concentration of alpha-tocotrienol required to completely prevent stroke-related neurodegeneration. Tocotrienols were detected in the blood plasma and all lipoprotein subfractions studied postprandially.


title: Automatic Discovery of Speech Act Categories in Educational Games

abstract: In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker’s intentions (the task of speech act classification) which in turn is crucial to providing adequate feedback and scaffolding. A key step in the speech act classification task is defining the speech act categories in an underlying speech act taxonomy. Most research to date has relied on taxonomies which are guided by experts’ intuitions, which we refer to as an extrinsic design of the speech act taxonomies. A pure data-driven approach would discover the natural groupings of dialogue utterances and therefore reveal the intrinsic speech act categories. To this end, this paper presents a fully-automated data-driven method to discover speech act taxonomies based on utterance clustering. Experiments were conducted on three datasets from three online educational games. This work is a step towards building speech act taxonomies based on both extrinsic (expert-driven) and intrinsic aspects (datadriven) of the target domain.


title: Why Do Computers Stop And What Can Be Done About It?

abstract: An analysis of the failure statistics of a commercially available fault-tolerant system shows that administration and software are the major contributors to failure. Various approaches to software fault-tolerance are then discussed -- notably process-pairs, transactions and reliable storage. It is pointed out that faults in production software are often soft (transient) and that a transaction mechanism combined with persistent processpairs provides fault-tolerant execution -- the key to software fault-tolerance.


title: AssocGEN: Engine for analyzing metadata based associations in digital evidence

abstract: Traditionally, sources of digital evidence are analyzed by individually examining the various artifacts contained therein and using the artifact metadata to validate authenticity and sequence them. However, when artifacts from forensic images, folders, log files, and network packet dumps have to be analyzed, the examination of the artifacts and the metadata in isolation presents a significant challenge. Ideally, when a source is examined, it is a valuable task to determine correlations between the artifacts and group the related artifacts. Such a grouping can simplify the task of analysis by minimizing the need for human intervention. By virtue of the value that metadata bring to an investigation and its ubiquitous nature, metadata based associations is the first step in realizing such correlations automatically during analysis. In this paper, we present the AssocGEN analysis engine which uses the metadata to determine associations between artifacts that belong to files, logs and network packet dumps, and identifies metadata associations to group the related artifacts. A metadata association can represent any type of value match 1 or relationship that is deemed relevant in the context of an investigation. We have conducted preliminary evaluation of AssocGEN on the classical ownership problem to highlight the benefits of incorporating this


title: Nursing bedside clinical handover - an integrated review of issues and tools.

abstract: AIMS AND OBJECTIVES ::: This article reviews the available literature that supports implementing bedside clinical handover in nursing clinical practice and then seeks to identify key issues if any. ::: ::: ::: BACKGROUND ::: Clinical handover practices are recognised as being an essential component in the effective transfer of clinical care between health practitioners. It is recognised that the point where a patient is 'handed over' from one clinician to another is significant in maintaining continuity of care and that doing this poorly can have significant safety issues for the patient. ::: ::: ::: DESIGN ::: An integrated literature review. ::: ::: ::: METHOD ::: A literature review of 45 articles was undertaken to understand bedside clinical handover and the issues related to the implementation of this process. ::: ::: ::: RESULTS ::: It was identified that there are a number of clinical handover mnemonics available that provide structure to the process and that areas such as confidentiality, inclusion of the patient/carer and involving the multidisciplinary team remain topical issues for practitioners in implementing good clinical handover practices. ::: ::: ::: CONCLUSIONS ::: This literature review identified a lack of literature available about the transfer of responsibility and accountability during clinical handover and auditing practices of the clinical handover process. The nurses were more concerned about confidentiality issues than were patients. The use of a structured tool was strongly supported; however, no one singular tool was considered suitable for all clinical areas. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: Nursing clinicians seeking to implement best practice within their professional speciality should consider some of the issues raised within this article and seek to address these issues by developing strategies to overcome them.


Lipiodol Injection : A Cost Effective Alternative for Liver Cancer Treatment


title: High-voltage pulsed power based on series-connected full-bridge and solid-state switches

abstract: This paper presents a high-voltage pulsed power (HVPP) employing series-connected full-bridge converter and solid-state switch, which has modularity feature to boost the output voltage. To optimize the static conduction loss and peak current, this paper utilizes stacked SiC-MOSFETs instead of classical Si-MOSFETs as the core component of solid-state switches (SSS). Meanwhile, in order to solve the voltage tail influence due to the drain-source parasitic capacitance across SSS, a push-pull circuit configuration is designed. Finally, the corresponding experimental results are provided to validate the performance of the proposed HVPP circuit.


title: Survey on Internet of Things (IoT) security issues & solutions

abstract: Internet of things (IOT) is a rising wireless technology that connects different things to the internet. Things can be “anything” that is around us. ‘Over 50 billion one-of-a-kind gadgets can be related to the IoT through 2020’ said through Cisco. Contemporary security technologies are very constrained as they may be unable to fulfil safety requirements of IoT devices. As Cisco said, in coming years the growth of IoT can be progressive but at the equal time we need to offer a strong provision for security of user's data. In this paper, we can consciousness on specific vulnerabilities to the security of IoT devices and provide possible solution to them.


title: Theme-Weighted Ranking of Keywords from Text Documents Using Phrase Embeddings

abstract: Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases. Keywords from text documents are primarily extracted using supervised and unsupervised approaches. In this paper, we present an unsupervised technique that uses a combination of theme-weighted personalized PageRank algorithm and neural phrase embeddings for extracting and ranking keywords. We also introduce an efficient way of processing text documents and training phrase embeddings using existing techniques. We share an evaluation dataset derived from an existing dataset that is used for choosing the underlying embedding model. The evaluations for ranked keyword extraction are performed on two benchmark datasets comprising of short abstracts (Inspec), and long scientific papers (SemEval 2010), and is shown to produce results better than the state-of-the-art systems.


title: Decentralized path planning for multi-agent teams with complex constraints

abstract: This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.


title: Combining Word-Level and Character-Level Models for Machine Translation Between Closely-Related Languages

abstract: We propose several techniques for improving statistical machine translation between closely-related languages with scarce resources. We use character-level translation trained on n-gram-character-aligned bitexts and tuned using word-level BLEU, which we further augment with character-based transliteration at the word level and combine with a word-level translation model. The evaluation on Macedonian-Bulgarian movie subtitles shows an improvement of 2.84 BLEU points over a phrase-based word-level baseline.


title: Holy basil (Ocimum sanctum Linn.) leaf extract enhances specific cognitive parameters in healthy adult volunteers: A placebo controlled study.

abstract: Introduction: Ocimum sanctum (OS), known as Holy basil, has been documented to possess neuroprotective, cognition-enhancing and stress relieving effects in animal models. However there is paucity of clinical studies to document these effects. Materials and methods: Effect of OS on parameters related to cognition and stress in humans was evaluated with administration of 300 milligram capsules of ethanolic leaf extracts of Ocimum sanctum (EtOS) or placebo per day, over 30 days. Results: Intra-group comparison of Sternberg and Stroop test showed improvement in both the placebo and EtOS groups, however, the improvement stabilized after day 15 in the placebo group. Intergroup comparison revealed a significant improvement of the following cognitive parameters in the EtOS as compared to the placebo: reaction time (RT) and error rate (ER) of Sternberg test, RT of neutral task of Stroop, RT and ER of interference task of Stroop. The intra-group comparison of P300 latency, salivary cortisol, and State-Trait Anxiety Inventory showed improvement over time in the EtOS group alone, though the inter-group difference was significant in the P300 latency alone. There were no changes in heart rate (HR), ΔHR, or galvanic skin response (GSR) or ΔGSR. Conclusion: Ocimum sanctum leaf extract seems to have potential cognition-enhancing properties in humans.


title: A graphene quantum dot photodynamic therapy agent with high singlet oxygen generation

abstract: Photosensitisers are used in cancer therapy to promote the formation of reactive oxygen species on irradiation with light. Here, the authors present a graphene quantum dot photosensitiser with a singlet oxygen quantum yield of approximately 1.3, and investigate its in vitro and in vivo applications


title: Automatic construction of polygonal maps from point cloud data

abstract: This paper presents a novel approach to create polygonal maps from 3D point cloud data. The gained map is augmented with an interpretation of the scene. Our procedure produces accurate maps of indoor environments fast and reliably. These maps are successfully used by different robots with varying sensor configurations for reliable self localization.


title: PENS-wheel (one-wheeled self balancing vehicle) balancing control using PID controller

abstract: Pens-Wheel is an electric vehicle which uses one wheel that able to balance itself so the rider does not fall forward or backward while riding it. This vehicle uses one brushless DC motor as actuator which capable to rotate in both directions symmetrically. The vehicle uses a combination of accelerometer and gyroscope contained in IMU (Inertial Measurement Unit) for balancing sensor. The controlled motion on the vehicle occurs only on the x-axis (pitch angle), in forward and backward directions. The PID (Proportional Integral Derivative) control algorithm is used to maintain the balance and movement of the vehicle. From the simulation and application in the real vehicle, the use of PID control is capable driving the vehicle in maintaining the balance condition within ±10° tilt angle boundary on flat surface, bumpy road, and inclining road up to 15° slope.


title: Synthetic body wave seismograms for three- dimensional laterally varying media

abstract: Summary. Two methods of computing body wave synthetic seismograms in three-dimensional laterally varying media are discussed. Both these methods are based on the summation of Gaussian beams. In the first, the initial beam parameters are chosen at the source, in the second at the beam endpoints. Both these variants eliminate the ray method singularities. The expansion of th- wavefield into plane waves may be considered as the limiting case of the first approach and the Chapman-Maslov method as the limiting case of the second approach. Computer algorithms are briefly described and numerical examples presented. In the first numerical example, the comparisons of the two approaches, based on summing Gaussian beams, with the reflectivity method indicate that the computed synthetic seismograms are satisfactorily accurate even in the caustic region. The next example suggests that the two methods discussed can be simply and effectively applied to 3-D laterally inhomogeneous structures.


title: Towards building ontology for community-based fire management

abstract: Participation of the public in fire situations is vital to reduce the loss of the community. However, due to the complexity and variability of the fire information, achieving this goal is quite a challenge. The ability of fire management plays a crucial role when a fire accident happens in a community. In this paper, we present community based fire ontology by researching different Sources such as emergency management guidelines, the community, ontology theory, which have taken into account the different concepts of the fire domain. We think that the proposed ontology addresses the information of the community-based fire management by providing the accessibility for the community.


Production of Gibberellic Acid by Bacillus siamensis BE 76 Isolated from Banana Plant ( musa spp )


title: Cyberattack detection in mobile cloud computing: A deep learning approach

abstract: With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.


title: A Comparison of Trojan Virus Behavior in Linux and Windows Operating Systems

abstract: Trojan virus attacks pose one of the most serious threats to computer security. A Trojan horse is typically separated into two parts - a server and a client. It is the client that is cleverly disguised as significant software and positioned in peer-to-peer file sharing networks, or unauthorized download websites. The most common means of infection is through email attachments. The developer of the virus usually uses various spamming techniques in order to distribute the virus to unsuspecting users. Malware developers use chat software as another method to spread their Trojan horse viruses such as Yahoo Messenger and Skype. The objective of this paper is to explore the network packet information and detect the behavior of Trojan attacks to monitoring operating systems such as Windows and Linux. This is accomplished by detecting and analyzing the Trojan infected packet from a network segment -which passes through email attachment- before attacking a host computer. The results that have been obtained to detect information and to store infected packets through monitoring when using the web browser also compare the behaviors of Linux and Windows using the payload size after implementing the Wireshark sniffer packet results. Conclusions of the figures analysis from the packet captured data to analyze the control bit, and check the behavior of the control bits, and the usability of the operating systems Linux and Windows.


title: State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems

abstract: State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.


title: Bucket elimination: a unifying framework for reasoning

abstract: Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many problem-solving and reasoning tasks. Algorithms such as directional-resolution for propositional satisfiability, adaptive-consistency for constraint satisfaction, Fourier and Gaussian elimination for solving linear equalities and inequalities, and dynamic programming for combinatorial optimization, can all be accommodated within the bucket elimination framework. Many probabilistic inference tasks can likewise be expressed as bucket-elimination algorithms. These include: belief updating, finding the most probable explanation, and expected utility maximization. These algorithms share the same performance guarantees; all are time and space exponential in the inducedwidth of the problem’s interaction graph. While elimination strategies have extensive demands on memory, a contrasting class of algorithms called “conditioning search” require only linear space. Algorithms in this class split a problem into subproblems by instantiating a subset of variables, called a conditioning set ,o r acutset. Typical examples of conditioning search algorithms are: backtracking (in constraint satisfaction), and branch and bound (for combinatorial optimization). The paper presents the bucket-elimination framework as a unifying theme across probabilistic and deterministic reasoning tasks and show how conditioning search can be augmented to systematically trade space for time. © 1999 Elsevier Science B.V. All rights reserved.


title: Internet of Entities (IoE): a Blockchain-based Distributed Paradigm to Security

abstract: The exponential growth of wireless-based solutions, such as those related to the mobile smart devices (e.g., smart-phones and tablets) and Internet of Things (IoT) devices, has lead to countless advantages in every area of our society. Such a scenario has transformed the world a few decades back, dominated by latency, into a new world based on an efficient real-time interaction paradigm.Recently, cryptocurrency have contributed to this technological revolution, the fulcrum of which are a decentralization model and a certification function offered by the so-called blockchain infrastructure, which make it possible to certify the financial transactions, anonymously. However, it should be observed how this challenging scenario has generated new security problems directly related to the involved new technologies (e.g., e-commerce frauds, mobile bot-net attacks, blockchain DoS attacks, cryptocurrency scams, etc.). In this context, we can acknowledge that the scientific community efforts are usually oriented toward specific solutions, instead to exploit all the available technologies, synergistically, in order to define more efficient security paradigms. This paper aims to indicate a possible approach able to improve the security of people and things by introducing a novel paradigm to security defined Internet of Entities (IoE). It is a mechanism for the localization of people and things, which exploits both the huge number of existing wireless-based devices and the blockchain-based distributed ledger technology, overcoming the limits of traditional localization approaches, but without jeopardizing the user privacy. Its operation is based on two core elements with interchangeable roles, entities and trackers, which can be very common elements such as smart-phones, tablets, and IoT devices, and its implementation requires minimal efforts thanks to the existing infrastructures and devices.


title: A deep reinforcement learning approach to character segmentation of license plate images

abstract: Automated license plate recognition (ALPR) has been applied to identify vehicles by their license plates and is critical in several important transportation applications. In order to achieve the recognition accuracy levels typically required in the market, it is necessary to obtain properly segmented characters. A standard method, projection-based segmentation, is challenged by substantial variation across the plate in the regions surrounding the characters. In this paper a reinforcement learning (RL) method is adapted to create a segmentation agent that can find appropriate segmentation paths that avoid characters, traversing from the top to the bottom of a cropped license plate image. Then a hybrid approach is proposed, leveraging the speed and simplicity of the projection-based segmentation technique along with the power of the RL method. The results of our experiments show significant improvement over the histogram projection currently used for character segmentation.


title: A lifespan database of adult facial stimuli

abstract: Faces constitute a unique and widely used category of stimuli. In spite of their importance, there are few collections of faces for use in research, none of which adequately represent the different ages of faces across the lifespan. This lack of a range of ages has limited the majority of researchers to using predominantly young faces as stimuli even when their hypotheses concern both young and old participants. We describe a database of 575 individual faces ranging from ages 18 to 93. Our database was developed to be more representative of age groups across the lifespan, with a special emphasis on recruiting older adults. The resulting database has faces of 218 adults age 18–29, 76 adults age 30–49, 123 adults age 50–69, and 158 adults age 70 and older. These faces may be acquired for research purposes fromhttp://agingmind.cns.uiuc.edu/facedb/. This will allow researchers interested in using facial stimuli access to a wider age range of adult faces than has previously been available.


title: MetaAware: Identifying Metamorphic Malware

abstract: Detection of malicious software (malware) by the use of static signatures is often criticized for being overly simplistic. Available methods of obfuscating code (so-called metamorphic malware) will invalidate the use of a fixed signature, without changing the harmful effects of the software. This paper presents a new approach for recognizing metamorphic malware. The method uses fully automated static analysis of executables to summarize and compare program semantics, based primarily on the pattern of library or system functions which are called. The proposed method has been prototyped and evaluated using randomized benchmark programs, instances of known malware program variants, and utility software available in multiple releases. The results demonstrate three important capabilities of the proposed method: (a) it does well at identifying metamorphic variants of common malware; (h) it distinguishes easily between programs that are not related; and, (c) it can identify and detect program variations, or code reuse. Such variations can be due to insertion of malware (such as viruses) into the executable of a host program. We argue that this method of metamorphic code detection will be difficult for malware writers to bypass.


title: A Survey of Keystroke Dynamics Biometrics

abstract: Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up-to-date extensive survey and evaluation is not yet available. The objective of this paper is to provide an insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades, as well as offering suggestions and possible future research directions.


title: Evolutionary game theory and multi-agent reinforcement learning

abstract: In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory.


title: Sensing Visual Attention by Sequential Patterns

abstract: A method for sensing human visual attention is proposed. The method is based on the analysis of sequential image patterns of faces and irises observed at regular time intervals. The basic concept is to represent the set of image patterns produced by the action of gazing at a certain area as a nonlinear subspace in a high-dimensional pattern vector space. Such a space is called an attention subspace. In this framework, an input subspace from an unknown action is classified into an attention subspace of gazing at a certain area or into attention subspaces of gazing at other areas (named non-attention subspaces) by measuring the canonical angles between the input subspace and pre-computed dictionary subspaces. To maintain performance even in the presence of head movement, two mechanisms are introduced: 1) the kernel orthogonal mutual subspace method, which is suitable for classifying sets of multiple images, and 2) a kernel function for considering the head position in addition to a kernel function for pixel values. The stable performance of the proposed method including situations with head movements is demonstrated through experiments.


title: Wrench-feasible workspace generation for cable-driven robots

abstract: This paper presents a method for analytically generating the boundaries of the wrench-feasible workspace (WFW) for cable robots. This method uses the available net wrench set, which is the set of all wrenches that a cable robot can apply to its surroundings without violating tension limits in the cables. The geometric properties of this set permit calculation of the boundaries of the WFW for planar, spatial, and point-mass cable robots. Complete analytical expressions for the WFW boundaries are detailed for a planar cable robot and a spatial point-mass cable robot. The analytically determined boundaries are verified by comparison with numerical results. Based on this, several workspace properties are shown for point-mass cable robots. Finally, it is shown how this workspace-generation approach can be used to analytically formulate other workspaces


title: FingerNet: An unified deep network for fingerprint minutiae extraction

abstract: Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: this https URL


title: A graph based clustering technique for tweet summarization

abstract: Twitter is a very popular online social networking site, where hundreds of millions of tweets are posted every day by millions of users. Twitter is now considered as one of the fastest and most popular communication mediums, and is frequently used to keep track of recent events or news-stories. Whereas tweets related to a particular event / news-story can easily be found using keyword matching, many of the tweets are likely to contain semantically identical information. If a user wants to keep track of an event / news-story, it is difficult for him to have to read all the tweets containing identical or redundant information. Hence, it is desirable to have good techniques to summarize large number of tweets. In this work, we propose a graph-based approach for summarizing tweets, where a graph is first constructed considering the similarity among tweets, and community detection techniques are then used on the graph to cluster similar tweets. Finally, a representative tweet is chosen from each cluster to be included into the summary. The similarity among tweets is measured using various features including features based on WordNet synsets which help to capture the semantic similarity among tweets. The proposed approach achieves better performance than Sumbasic, an existing summarization technique.


ANALYSIS OF DISCRETE ILL-POSED PROBLEMS BY MEANS OF THE L-CURVE


title: Tracking systems for satellite communications

abstract: Satellites in nominally geostationary orbits possess diurnal motion which causes the apparent position of the spacecraft to wander in the sky as seen by an earth station. If uncompensated this causes a variation in the performance of the communications link. The motion of the satellite therefore has to be tracked by the earth station antenna. During the past twenty years or so a considerable amount of work has gone into the development of tracking schemes for satellite communication systems. Here a review of the various techniques is presented. For each method used the principle of operation is described and the various salient features extracted. Comparisons are made where appropriate and practical implementations of the systems discussed. The application of intelligent control algorithms to tracking is also considered. The areas covered include, orbit determination, optimal estimation techniques, performance, and practical implementations. Recently introduced techniques are considered and likely future developments projected.


title: Emerging infrastructure and technology challenges in 5G wireless networks

abstract: Next generation technologies are being tried to develop for 5G wireless cellular networks nowadays by many researchers. Some key technologies provide significant improvements for 5G systems in terms of huge capacity, higher data rate, signaling overhead on the network and energy-spectral efficiency. etc., But these technologies also bring along critical challenges for 5G systems. In this study, these major challenges of 5G networks are discussed in terms of scarcity radio frequency (RF) spectrum, inter-antenna synchronous, antenna deployments in cells, network and traffic management, cost and workload. Also these challenges are expressed, presented and discussed in each section to provide prior knowledge and perspective for 5G network designers and researchers.