title: Using emulation to understand and improve wireless networks and applications

abstract: Researchers have long faced a fundamental tension between the experimental realism of wireless testbeds on one hand, and the control and repeatability of simulation on the other hand. To overcome the stark tradeoff of these traditional alternatives, we are developing a wireless emulator that enables both realistic and repeatable experimentation by leveraging physical layer emulation. We discuss the design and implementation of a prototype wireless emulator, and show how this emulator can be leveraged to provide insight into wireless network and application behaviour. Our experience shows that, compared to simulation, our emulator-based approach provides us with a better understanding of real-world wireless network performance, and enables us to quickly deploy our research into an operational wireless network, while still allowing us to enjoy the benefits of a controlled experimental environment.


title: Automatic Keyphrase Extractor from Arabic Documents

abstract: The keyphrase is a sentence or a part of a sentence that contains a sequence of words that expresses the meaning and the purpose of any given paragraph. Keyphrase extraction is the task of identifying the possible keyphrases from a given document. Many applications including text summarization, indexing, and characterization use keyphrase extraction. Also, it is an essential task to improve the performance of any information retrieval system. The internet contains a massive amount of documents that may have been manually assigned keyphrases or not. The Arabic language is an important language in the world. Nowadays the number of online Arabic documents is growing rapidly; and most of them have no manually assigned keyphrases, so the user will scan the whole retrieved web documents. To avoid scanning the entire retrieved document, we need keyphrases assigned to each web document manually or automatically. This paper addresses the problem of identifying keyphrases in Arabic documents automatically. In this work, we provide a novel algorithm that identified keyphrases from Arabic text. The new algorithm, Automatic Keyphrases Extraction from Arabic (AKEA), extracts keyphrases from Arabic documents automatically. In order to test the algorithm, we collected a dataset containing 100 documents from Arabic wiki; also, we downloaded another 56 agricultural documents from Food and Agricultural Organization of the United Nations (F.A.O.). The evaluation results show that the system achieves 83% precision value in identifying 2-word and 3-word keyphrases from agricultural domains.


Life Experience with Limb Trauma : A Thematic Analysis


title: Parental Mediation, Online Activities, and Cyberbullying

abstract: Abstract Cyberbullying, the use of information and communication technologies to intentionally harm others, has become an important area of research. Studies have begun to investigate the extent of cyberbullying and its victims' personality characteristics. Less is known about the effect of specific online activities and the role of parental mediation on the likelihood of being bullied. This study attempts to fill this gap in the literature conducting a secondary analysis of a representative sample of the U.S. youth population, the Teens and Parents survey conducted by the Pew and American Life Project (n = 935). The results indicate that the risk of youth being bullied is higher for adolescents who have an active profile on social networking sites and participate in chat rooms but not in playing games online. Gender differences emerge in risk factors. A few parental mediation techniques are protective, but most are not. The results indicate the need for more parental participation to reduce risks to yout...


title: TF-Replicator: Distributed Machine Learning for Researchers

abstract: We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same models can be effortlessly deployed to different cluster architectures (i.e. one or many machines containing CPUs, GPUs or TPU accelerators) using synchronous or asynchronous training regimes. To demonstrate the generality and scalability of TF-Replicator, we implement and benchmark three very different models: (1) A ResNet-50 for ImageNet classification, (2) a SN-GAN for class-conditional ImageNet image generation, and (3) a D4PG reinforcement learning agent for continuous control. Our results show strong scalability performance without demanding any distributed systems expertise of the user. The TF-Replicator programming model will be open-sourced as part of TensorFlow 2.0 (see this https URL).


title: Solid State Gas Sensor Research in Germany – a Status Report

abstract: This status report overviews activities of the German gas sensor research community. It highlights recent progress in the field of potentiometric, amperometric, conductometric, impedimetric, and field effect-based gas sensors. It is shown that besides step-by-step improvements of conventional principles, e.g. by the application of novel materials, novel principles turned out to enable new markets. In the field of mixed potential gas sensors, novel materials allow for selective detection of combustion exhaust components. The same goal can be reached by using zeolites for impedimetric gas sensors. Operando spectroscopy is a powerful tool to learn about the mechanisms in n-type and in p-type conductometric sensors and to design knowledge-based improved sensor devices. Novel deposition methods are applied to gain direct access to the material morphology as well as to obtain dense thick metal oxide films without high temperature steps. Since conductometric and impedimetric sensors have the disadvantage that a current has to pass the gas sensitive film, film morphology, electrode materials, and geometrical issues affect the sensor signal. Therefore, one tries to measure directly the Fermi level position either by measuring the gas-dependent Seebeck coefficient at high temperatures or at room temperature by applying a modified miniaturized Kelvin probe method, where surface adsorption-based work function changes drive the drain-source current of a field effect transistor.


A Variational Observation Model of 3 D Object for Probabilistic Semantic SLAM


title: Global downscaling of remotely sensed soil moisture using neural networks

abstract: Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., ::: of the order of 1 km) is necessary in order to quantify its role in regional ::: feedbacks between the land surface and the atmospheric boundary layer. ::: Moreover, several applications such as agricultural management can benefit ::: from soil moisture information at fine spatial scales. Soil moisture ::: estimates from current satellite missions have a reasonably good temporal ::: revisit over the globe (2–3-day repeat time); however, their finest spatial ::: resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has ::: estimated soil moisture at two different spatial scales of 36 and 9 km since ::: April 2015. In this study, we develop a neural-network-based downscaling ::: algorithm using SMAP observations and disaggregate soil moisture to 2.25 km ::: spatial resolution. Our approach uses the mean monthly Normalized Differenced ::: Vegetation Index (NDVI) as ancillary data to quantify the subpixel ::: heterogeneity of soil moisture. Evaluation of the downscaled soil moisture ::: estimates against in situ observations shows that their accuracy is better ::: than or equal to the SMAP 9 km soil moisture estimates.


title: BALANCE SCORECARD OF DAVID'S STRATEGIC MODELLING AT INDUSTRIAL BUSINESS FOR NATIONAL CONSTRUCTION CONTRACTOR OF INDONESIA

abstract: The condition of contractor services development to decrease, global economic impact, and the indication of lower capacity and competitive power of construction services or contractor in global market are caused by the limitation of strategic planning applied by the national contractor industries in formulating company’s business strategies. The purpose of this research is to establish the suitable business strategy modelling for contractor industry in Indonesia. The analysis method applied is factor analysis. Since one of the purposes of construction business refers to a profit oriented business, therefore, this research will apply the 4 (four) BSC (Balance Score Card) model perspectives combined with the David’s model. The samples of the research are those contractors of 6 and 7 grades. Questionnaire is applied for data collecting method. Data obtained results will be applied to determine the company’s position and condition by factor analysis at 4 perspectives, namely customers, finance, business processes, learning and growth. The data results will be applied to analyse the company’s position and condition as an input stage to establish IFE, EFE, CP matrixes. The matrix of input stage will be applied to the next analysis at matching stage. This stage produces TOWS, SPACE, BCG, IE, and GS matrixes by analysing the loading factor with factor analysis. The said factor analysis results will further establish the QSPM matrix. This matrix will establish the business strategy. This business strategy selection will become the objective strategy to be applied by the contractor in operating the company’s activities into its accountable projects. Based on the real case performed in one of the national constructing companies, it is concluded that the suitable strategic model is the alliance strategic model.


title: The purification of thyroxine-binding globulin and thyroxine-binding prealbumin

abstract: Abstract Methods have been described for the purification of the two principal thyroxine binding proteins in human serum, thyroxine-binding globulin (TBG) and thyroxine binding prealbumin (TBPA). Cohn Fraction IV-9 was used for the large-scale preparation of TBG. Unfractionated human serum or Cohn Fraction IV-9, containing tracer amounts of 131 I-labelled l -thyroxine, were subjected to the following procedures to yield fractions of increasing 131 I/protein ratios: column electrophoresis on cellulose powder, starch gel electrophoresis, gel filtration and ion-exchange chromatography. The identity and homogeneity of preparations from different purification steps were established by the following procedures: ultracentrifugal analysis, immunoelectrophoresis, zone electrophoresis on paper, cellulose acetate and starch gel in various buffers, immunodiffusion and autoradiography to locate the radioactive thyroxine. Although not pure, the final TBG and TBPA preparations were not contaminated mutually or by any other thyroxine-binding protein. An unidentified α-globulin ( S 20, w = 7.0−7.2) with moderate thyroxine-binding properties was separated from TBG ( S 20, w = 3.5) by ion-exchange chromatography. The importance of using multiple criteria in establishing the identity and homogeneity of TBG and TBPA is discussed.


title: Diffusion curves: a vector representation for smooth-shaded images

abstract: We describe a new vector-based primitive for creating smooth-shaded images, called the diffusion curve. A diffusion curve partitions the space through which it is drawn, defining different colors on either side. These colors may vary smoothly along the curve. In addition, the sharpness of the color transition from one side of the curve to the other can be controlled. Given a set of diffusion curves, the final image is constructed by solving a Poisson equation whose constraints are specified by the set of gradients across all diffusion curves. Like all vector-based primitives, diffusion curves conveniently support a variety of operations, including geometry-based editing, keyframe animation, and ready stylization. Moreover, their representation is compact and inherently resolution-independent. We describe a GPU-based implementation for rendering images defined by a set of diffusion curves in realtime. We then demonstrate an interactive drawing system for allowing artists to create artworks using diffusion curves, either by drawing the curves in a freehand style, or by tracing existing imagery. The system is simple and intuitive: we show results created by artists after just a few minutes of instruction. Furthermore, we describe a completely automatic conversion process for taking an image and turning it into a set of diffusion curves that closely approximate the original image content.


title: TASKer: behavioral insights via campus-based experimental mobile crowd-sourcing

abstract: While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowd-sourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TASKer. We enhanced the TA$Ker platform to support several new features (e.g., task bundling, differential pricing and cheating analytics) and experimentally investigated these features via a two-month deployment of TA$Ker, involving 900 real users on the SMU campus who performed over 30,000 tasks. Our studies (i) show the benefits of bundling tasks as a combined package, (ii) reveal the effectiveness of differential pricing strategies and (iii) illustrate key aspects of cheating (false reporting) behavior observed among workers.


title: Metalearning with Hebbian Fast Weights

abstract: We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule implement one-shot binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank one-shot learning benchmarks, our model achieves state-of-the-art results.


title: Mechanised wire-wise verification of Handel-C synthesis

abstract: The compilation of Handel-C programs into net-list descriptions of hardware components has been extensively used in commercial tools but never formally verified. In this paper, we first introduce an extension of the compilation schema that allows the synthesis of the prioritised choice construct. Then we present a variation of the existing semantic model for Handel-C compilation that is amenable to mechanical proof and detailed enough for analysing properties of the hardware generated. We use this model to prove the correctness of the wiring schema used to interconnect the components at the hardware level and propagate control signals among them. Finally, we present the most interesting aspects of the mechanisation of the model and the correctness proofs in the HOL theorem prover.


title: Design aesthetics leading to m-loyalty in mobile commerce

abstract: Researchers have previously examined the technology acceptance model (TAM) in many contexts, including the Internet. More recently TAM has been enhanced to include a hedonic component of enjoyment but the effect has rarely been investigated in a mobile commerce context. In addition, specific antecedents of TAM related to design aesthetics have not been examined within the mobile domain. Our research filled these gaps, and discovered that visual design aesthetics did significantly impact perceived usefulness, ease of use, and enjoyment, all of which ultimately influenced users' loyalty intentions towards a mobile service.


title: Online information quality and consumer satisfaction: The moderating roles of contextual factors - A meta-analysis

abstract: A meta-analysis is conducted of the relation between online IQ and satisfaction.Contextual moderators for the online IQ and satisfaction relation are explored.Website type moderates the relation between online IQ and satisfaction.IQ categories used moderate the relation between online IQ and satisfaction.Respondent characteristics do not moderate the online IQ and satisfaction relation. A meta-analysis involving 42 articles was performed to study the existence of potential contextual moderators of the relationship between information quality (IQ) and consumer satisfaction in the online environment. A moderator analysis involving website type (retail or e-services), sample characteristics (respondent type and respondent origin) and the IQ categories used (representational and non-representational) in articles revealed that whereas website type and IQ categories moderate the relationship between perceived online IQ and consumer satisfaction, sample characteristics do not. Implications of the results for researchers and practitioners and directions of future research are presented.


title: Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense

abstract: Along with the popularity of mobile social networks (MSNs) is the increasing danger of privacy breaches due to user location exposures. In this work, we take an initial step towards quantifying location privacy leakage from MSNs by matching the users’ shared locations with their real mobility traces. We conduct a three-week real-world experiment with 30 participants and discover that both direct location sharing (e.g., Weibo or Renren) and indirect location sharing (e.g., Wechat or Skout) can reveal a small percentage of users’ real points of interests (POIs). We further propose a novel attack to allow an external adversary to infer the demographics (e.g., age, gender, education) after observing users’ exposed location profiles. We implement such an attack in a large real-world dataset involving 22,843 mobile users. The experimental results show that the attacker can effectively predict demographic attributes about users with some shared locations. To resist such attacks, we propose SmartMask, a context-based system-level privacy protection solution, designed to automatically learn users’ privacy preferences under different contexts and provide a transparent privacy control for MSN users. The effectiveness and efficiency of SmartMask have been well validated by extensive experiments.


title: Moving object tracking using Laplacian-DCT based perceptual hash

abstract: Moving-object tracking is one of the basic and hot research domains in the computer vision area. This work presents a novel and effective method to track moving objects under a static background. Proposed method first executes the pre-processing tasks to remove noise from video frames. Then, with the help of rectangular window, we select the target object region in the first video frame (reference frame). Next, it applies the Laplacian operator on the selected target objects for sharpening and edge detection. The algorithm then applies the DCT and selects the few high energy coefficients. Subsequently, it computes the perceptual hash of the selected target objects with the help of mean of all the AC values of the block. Using perceptual hash of a target object, we find the similar object in subsequent frames of the video. The proposed method is correct for tracking the moving target object with varying object size and significant amount of noise. This work has been formulated, implemented and tested on real indoor-outdoor video sequences and the results are found to be adequate as it proved from the performance evaluation.


title: Comparison of 14 different families of classification algorithms on 115 binary datasets

abstract: We tested 14 very different classification algorithms (random forest, gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer neural nets, extreme learning machines, k-nearest neighbors and a bagging of knn, naive Bayes, learning vector quantization, elastic net logistic regression, sparse linear discriminant analysis, and a boosting of linear classifiers) on 115 real life binary datasets. We followed the Demsar analysis and found that the three best classifiers (random forest, gbm and RBF SVM) are not significantly different from each other. We also discuss that a change of less then 0.0112 in the error rate should be considered as an irrelevant change, and used a Bayesian ANOVA analysis to conclude that with high probability the differences between these three classifiers is not of practical consequence. We also verified the execution time of "standard implementations" of these algorithms and concluded that RBF SVM is the fastest (significantly so) both in training time and in training plus testing time.


title: The SlimSAR: A small, multi-frequency, Synthetic Aperture Radar for UAS operation

abstract: The SlimSAR is a small, low-cost, Synthetic Aperture Radar (SAR) and represents a new advancement in highperformance SAR. ARTEMIS employed a unique design methodology that exploits previous developments in designing the Slim-SAR to be smaller, lighter, and more flexible while consuming less power than typical SAR systems. With an L-band core, and frequency block converters, the system is very suitable for use on a number of small UAS's. Both linear-frequency-modulated continuous-wave (LFM-CW), which achieves high signal-to-noise ratio while transmitting with less power, and pulsed mode have been tested. The flexible control software allows us to change the radar parameters in flight. The system has a built-in high quality GPS/IMU motion measurement solution and can also be packaged with a small data link and a gimbal for high frequency antennas. Multi-frequency SAR provides day and night imaging through smoke, dust, rain, and clouds with the advantages of additional capabilites at different frequencies (i.e. dry ground and foliage penetration at low frequencies, and change detection at high frequencies.)


title: Face Recognition and Age Estimation Implications of Changes in Facial Features: A Critical Review Study

abstract: Facial features are considered as one of the important personal characteristics. This can be used in many applications, such as face recognition and age estimation. The value of these applications depends in several areas, such as security applications, law enforcement applications, and attendance systems. In addition, facial features are particularly the key usage in the finding of lost child. Present applications have achieved a high level of accuracy. This paper provides a survey of face recognition, including the age estimation, which was discussed. Moreover, the research outlines several challenges faced in face recognition area that had been explored. The research also provides a landscape mapping based on integrating into a critical and coherent taxonomy. In the methodology sections, the exploration the accomplished via a deep focused in every single article in “Face Recognition”, then “Age Estimation”, and later in “Facial Features”. The “Articles extraction” is mining from diverse sources, such as Web of Science, ACM, IEEE, Science Direct, and Springer databases. The research covers overall 72 articles; 32/72 articles were face recognition. Moreover, 39/72 of the articles were for age estimation. A comparison based on the objectives of the approaches is presented to underline the taxonomy. Ending by research conclusion on face techniques contributes to the understanding of the recognition approaches, which can be used in future researches. The research concluded that face techniques’ performance is distinct from one data set to another. This paper contributes to display gaps for other researchers to join this line of research.


title: An ontology-based approach for exploring knowledge in fundamental particles of Physics

abstract: Particle physics is a branch of physics that studies the elementary constituents of matter and radiation, and the interactions between them. Particle physicists have described the fundamental structure of matter using the fundamental forces of physics in the form of an elegant series of equations. However, fundamental particle of physics often faces problems from a shortage of specialists. The knowledge base in the field of fundamental particles is also very large and widely dispersed, which makes it difficult for humans to achieve expertise. This is clearly proved from the smaller number of research studies in fundamental particle of physics. The specific knowledge of particle physics is not widespread, so techniques to manage and share knowledge are necessary. This work proposes an ontology-based approach for describing networking physics domains related to fundamental particles of physics. This networking ontology is employed to implement the recommender web service system to help users to recommend fundamental particles related to the networking physics domain. The experiments on the system are conducted with particle physics specialists. Moreover, ontologies are developed using the HOZO tool in OWL format which can be easily integrated with any other ontology in computer science and engineering.


title: Impact of hardware impairments on mmWave MIMO systems with hybrid precoding

abstract: A notable commonality in the existing studies on millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is the assumption of perfect hardware during communication. However, this is a strong hypothesis since residual tranceiver hardware impairments (RTHIs) are inevitable in reality. Hence, this paper proposes a mmWave MIMO system that generalizes the state of the art by considering the inevitable RTHIs. We shed light on the impact of three major hardware impairments, namely, the multiplicative phase noise (PN), the amplified thermal noise (ATN), and the residual additive transceiver hardware impairments (RATHIs). In particular, we derive the spectral efficiency of the system under the presence of RTHIs. Additionally, we provide a detailed analysis to quantify the degradation caused to the spectral efficiency of the system by each separate impairment. Among the noteworthy outcomes is that the multiplicative PN is the most critical of the three impairments. Furthermore, we verify that the merits of the mmWave MIMO system are improved in line with large MIMO, which needs to be deployed in a cost-efficient manner.


title: A low-cost software-defined UHF RFID reader with active transmit leakage cancellation

abstract: A monostatic UHF RFID reader targeting personal applications based on recently introduced commercial-off-the-shelf (COTS) software-defined radio (SDR) application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) is proposed. The result is an RFID reader architecture whose principal components cost less than half of those of existing readers, but is flexible as a result of the programmable nature of the FPGA. The reader implements all mandatory EPC Gen 2 commands (except kill) and can perform inventory, targeted programming, blank tag programming, and singletag analysis via a smartphone interface. The proposed RFID reader architecture adaptively cancels the large CW transmit signal using a reflected power canceller (RPC). The RPC is terminated by a novel subranging tunable microwave network comprising digitally tunable capacitors capable of attenuating transmit leakage at the receiver by over 50dB. The reader achieves −73dBm of sensitivity at the antenna port when attached to a channel model and connectorized tag and achieves an open area tag read range of 2.6m with a 1.2dBi dipole antenna and 15.2m with a 12.5dBi patch antenna.


title: Thumbs Up? Sentiment Classification Using Machine Learning Techniques

abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.


title: The HCl scheduler: going all-in on heterogeneity

abstract: Heterogeneity is a growing concern for scheduling on the cloud. Hardware is increasingly heterogeneous (e.g., GPUs, FPGAs, diverse I/O devices), emphasizing the need to build schedulers that identify the internal structure of applications and utilize available hardware resources to their full potential. In this paper we present our initial efforts to build a scheduler that tackles heterogeneity (in hardware and in software) as a primary concern. Our scheduler, HCl (Heterogeneous Cluster), models applications as annotated directed acyclic graphs (DAGs), where each node represents a task. It maps tasks onto hardware nodes, also organized in DAGs. Initial results using application models based on TPC-DS queries running on Apache Spark show that HCl can improve significantly upon approaches that do not consider heterogeneity and generate schedules that approach the critical path in length.


Treatment of Acne Vulgaris with 1064 nm Nd:YAG Laser


title: Diagnosis and treatment of acute myocardial infarction in tertiary and secondary care hospitals in Estonia

abstract: AIM ::: To compare validity of AMI diagnosis and treatment of AMI patients between tertiary and secondary care hospitals in Estonia. ::: ::: ::: METHODS ::: Two tertiary and seven secondary care hospitals responsible for the treatment of most AMI patients in Estonia were included in the analysis. A random sample of 520 patients admitted to these hospitals with AMI in 2001 was taken from the Estonian Health Insurance Fund database. Medical records were reviewed by trained experts using a standardized data collection form. ::: ::: ::: RESULTS ::: Forty cases were excluded due to selection errors by the Health Insurance Fund. Of the remaining cases, a diagnosis of AMI was confirmed in 93.3% of cases in tertiary care hospitals and in 83.5% of cases in secondary care hospitals (p < 0.001). A total of 210 cases from tertiary and 213 cases from secondary care hospitals with confirmed AMI diagnoses were included in subsequent analysis. Utilization of beta-blockers, aspirin, and reperfusion therapy was similar in both types of hospitals. In tertiary care hospitals, ACE inhibitors and statins were more frequently used during hospital stay and recommended at discharge compared with secondary care hospitals. In-hospital mortality was similar in both types of hospitals both before and after adjustment. ::: ::: ::: CONCLUSIONS ::: Tertiary care physicians adhered more strictly to the current definition and guidelines for the management of AMI than did secondary care physicians. However, there is still a need for further improvement in both hospital settings according to international guidelines.


title: Agents in E-Commerce: State of the Art

abstract: This paper surveys the state of the art of agent-mediated electronic commerce (e-commerce), especially in business-to-consumer (B2C) e-commerce and business-to-business (B2B) e-commerce. From the consumer buying behaviour perspective, the roles of agents in B2C e-commerce are: product brokering, merchant brokering, and negotiation. The applications of agents in B2B e-commerce are mainly in supply chain management. Mobile agents, evolutionary agents, and data-mining agents are some special techniques which can be applied in agent-mediated e-commerce. In addition, some technologies for implementation are briefly reviewed. Finally, we conclude this paper by discussions on the future directions of agent-mediated e-commerce.


SHUNT CAPACITOR BANK SWITCHING TRANSIENTS : A TUTORIAL AND CASE STUDY


title: Translation techniques in cross-language information retrieval

abstract: Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval (IR). Like IR, CLIR is centered on the search for documents and for information contained within those documents. Unlike IR, CLIR must reconcile queries and documents that are written in different languages. The usual solution to this mismatch involves translating the query and/or the documents before performing the search. Translation is therefore a pivotal activity for CLIR engines. Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation. This article presents an overview of those techniques, with a special emphasis on recent developments.


title: Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd

abstract: Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.


title: Off-line Identification With Handwritten Signature Images: Survey and Perspectives

abstract: The first part of this paper presents a survey of the literature on automatic handwritten signature verification systems using binary or gray-level images, and focuses primarily on preprocessing techniques, feature or primitive extraction methods, comparison processes, and performance evaluation. With these previous studies in mind, we propose, in the second part of this paper, an image-understanding system based on the extraction of a novel representation of handwritten signature images. This approach is text insensitive. A structural match between a reference primitive set Pr and a test primitive set Pt takes into account the geometric shape and spatial relations between primitives. Finally, the local comparison of gray levels between pairs of primitives next to each node of the static solution path N results in a pseudodynamic similarity measure ϑ d (Pr, Pt). This scheme allows the elimination, with a certain degree of success, of skilled forgeries such as tracings and photocopies, showing marked gray-level dissimilarity along the signature line.


title: A Sensor Fusion Method for Tracking Vertical Velocity and Height Based on Inertial and Barometric Altimeter Measurements

abstract: A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04–0.24 m/s; height RMSE was in the range 5–68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.


title: Removing Node Overlaps Using Multi-sphere Scheme

abstract: In this paper, we consider the problem of removing overlaps of labels in a given layout by changing locations of some of the overlapping labels, and present a new method for the problem based on a packing approach, called multi-sphere scheme . More specifically, we study two new variations of the label overlap problem, inspired by real world applications, and provide a solution to each problem. Our new approach is very flexible to support various operations such as translation, translation with direction constraints , and rotation . Further, our method can support labels with arbitrary shapes in both 2D and 3D layout settings. Our extensive experimental results show that our new approach is very effective for removing label overlaps.


title: Employee turnover: a novel prediction solution with effective feature selection

abstract: This study proposed to address a new method that could select subsets more efficiently. In addition, the reasons why employers voluntarily turnover were also investigated in order to increase the classification accuracy and to help managers to prevent employers' turnover. The mixed feature subset selection used in this study combined Taguchi method and Nearest Neighbor Classification Rules to select feature subset and analyze the factors to find the best predictor of employer turnover. All the samples used in this study were from industry A, in which the employers left their job during 1st of February, 2001 to 31st of December, 2007, compared with those incumbents. The results showed that through the mixed feature subset selection method, total 18 factors were found that are important to the employers. In addition, the accuracy of correct selection was 87.85% which was higher than before using this feature subset selection method (80.93%). The new feature subset selection method addressed in this study does not only provide industries to understand the reasons of employers' turnover, but also could be a long-term classification prediction for industries.


title: Critical discourse analysis and social cognition : evidence from business media discourse

abstract: This article aims at reconciling Critical Discourse Analysis (CDA) and cognitive linguistics, particularly metaphor research. Although the two disciplines are compatible, efforts to discuss metaphor as a cognitive phenomenon have been scarce in the CDA tradition. By contrast, cognitive metaphor research has recently developed to emphasize the embodied, i.e. neural, origins of metaphor at the expense of its sociodiscursive impact. This article takes up the concept of social cognition, arguing that it organizes the modification of, and access to, cognitive resources, with metaphoric models playing a particularly salient role in the constitution of ideology. In a cyclical process, ideology will help particular models gain prominence in discourse, which will, in turn, impact on cognition. To illustrate the point, the article draws on an extensive corpus of business magazine texts on mergers and acquisitions, showing how that particular discourse centres on an ideologically vested metaphoric model of evolutionary struggle.


title: Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation

abstract: Color image segmentation is a very emerging topic for image processing research. Since it has the ability to present the result in a way that is much more close to the human eyes perceive, so today's more research is going on this area. Choosing a proper color space is a very important issue for color image segmentation process. Generally L*A*B* and HSV are the two frequently chosen color spaces. In this paper a comparative analysis is performed between these two color spaces with respect to color image segmentation. For measuring their performance, we consider the parameters: mse and psnr . It is found that HSV color space is performing better than L*A*B*.


title: Towards fair and efficient SMP virtual machine scheduling

abstract: As multicore processors become prevalent in modern computer systems, there is a growing need for increasing hardware utilization and exploiting the parallelism of such platforms. With virtualization technology, hardware utilization is improved by encapsulating independent workloads into virtual machines (VMs) and consolidating them onto the same machine. SMP virtual machines have been widely adopted to exploit parallelism. For virtualized systems, such as a public cloud, fairness between tenants and the efficiency of running their applications are keys to success. However, we find that existing virtualization platforms fail to enforce fairness between VMs with different number of virtual CPUs (vCPU) that run on multiple CPUs. We attribute the unfairness to the use of per-CPU schedulers and the load imbalance on these CPUs that incur inaccurate CPU allocations. Unfortunately, existing approaches to reduce unfairness, e.g., dynamic load balancing and CPU capping, introduce significant inefficiencies to parallel workloads. In this paper, we present Flex, a vCPU scheduling scheme that enforces fairness at VM-level and improves the efficiency of hosted parallel applications. Flex centers on two key designs: (1) dynamically adjusting vCPU weights (FlexW) on multiple CPUs to achieve VM-level fairness and (2) flexibly scheduling vCPUs (FlexS) to minimize wasted busy-waiting time. We have implemented Flex in Xen and performed comprehensive evaluations with various parallel workloads. Results show that Flex is able to achieve CPU allocations with on average no more than 5% error compared to the ideal fair allocation. Further, Flex outperforms Xen's credit scheduler and two representative co-scheduling approaches by as much as 10X for parallel applications using busy-waiting or blocking synchronization methods.


title: An Analysis on the Schemes for Detecting and Preventing ARP Cache Poisoning Attacks

abstract: The address resolution protocol (ARP) is used by computers to map network addresses (IP) to physical addresses (MAC). The protocol has proved to work well under regular circumstances, but it was not designed to cope with malicious hosts. By performing ARP cache poisoning or ARP spoofing attacks, an intruder can impersonate another host (man-in-the-middle attack) and gain access to sensitive information. Several schemes to mitigate, detect and prevent these attacks have been proposed, but each has its limitations. In this paper we analyze each of these schemes, identify their strengths and weaknesses, and propose guidelines for the design of an alternative and (arguably) better solution to the problem of ARP cache poisoning.


title: Fast Reading Comprehension with ConvNets

abstract: State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelization within an instances and often becomes the bottleneck for deploying such models to latency critical scenarios. This is particularly problematic for longer texts. Here we present a convolutional architecture as an alternative to these recurrent architectures. Using simple dilated convolutional units in place of recurrent ones, we achieve results comparable to the state of the art on two question answering tasks, while at the same time achieving up to two orders of magnitude speedups for question answering.


title: Education and the Brain: A Bridge Too Far

abstract: terms of informing classroom practice. There is, however, a science of mind, cognitive science, that can serve as a basic science for the development of an applied science of learning and instruction. Practical, well-founded examples of putting cognitive science into practice already exist in numerous schools and classrooms. Teachers would be better off looking at these examples than at speculative applications of neuroscience.


title: Adversarial neural networks for basal membrane segmentation of microinvasive cervix carcinoma in histopathology images

abstract: In this paper, a deep learning based method is proposed to perform the basal membrane segmentation, which is a crucial prerequisite step in the diagnosis of Microinvasive Carcinoma of the Cervix (MIC). We regard this as an object contour segmentaion problem and modify the recently proposed contour segmentation deep learning model with adversarial training strategy. Traditional adversarial networks contain a generator and a discrminator. We replace the generator with a fully convolutional networks model while keeping the discriminator part to perform the adversarial training. We evaluate our method on a challenging basal membrane segmentation dataset which is collected by experienced pathologists and the comparative experiments demonstrate the superior performance of adversarial networks in keeping the integrality and robustness of the basal membrane segmentation.


title: Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

abstract: We evaluate the performance of a spoken dialogue system that provides substantive dynamic responses to automatically detected user affective states. We then present a detailed system error analysis that reveals challenges for real-time affect detection and adaptation. This research is situated in the tutoring domain, where the user is a student and the spoken dialogue system is a tutor. Our adaptive system detects uncertainty in each student turn via a model that combines a machine learning approach with hedging phrase heuristics; the learned model uses acoustic-prosodic and lexical features extracted from the speech signal, as well as dialogue features. The adaptive system varies its content based on the automatic uncertainty and correctness labels for each turn. Our controlled experimental evaluation shows that the adaptive system yields higher global performance than two non-adaptive control systems, but the difference is only significant for a subset of students. Our system error analysis indicates that noisy affect labeling is a major performance bottleneck, yielding fewer than expected adaptations thus lower than expected performance. However, the percentage of received adaptation correlates with higher performance over all students. Moreover, when uncertainty is accurately recognized and adapted to, local performance is significantly improved.


title: A Signature Analysis Technique for the Identification of Failing Vectors with Application to Scan-BIST ∗

abstract: We present a new technique for uniquely identifying a single failing vector in an interval of test vectors. This technique is applicable to combinational circuits and for scan-BIST in sequential circuits with multiple scan chains. The proposed method relies on the linearity properties of the MISR and on the use of two test sequences, which are both applied to the circuit under test. The second test sequence is derived from the first in a straightforward manner and the same test pattern source is used for both test sequences. If an interval contains only a single failing vector, the algebraic analysis is guaranteed to identify it. We also show analytically that if an interval contains two failing vectors, the probability that this case is interpreted as one failing vector is very low. We present experimental results for the ISCAS benchmark circuits to demonstrate the use of the proposed method for identifying failing test vectors.


title: Socioeconomic Status and Child Development

abstract: ▪ Abstract Socioeconomic status (SES) is one of the most widely studied constructs in the social sciences. Several ways of measuring SES have been proposed, but most include some quantification of family income, parental education, and occupational status. Research shows that SES is associated with a wide array of health, cognitive, and socioemotional outcomes in children, with effects beginning prior to birth and continuing into adulthood. A variety of mechanisms linking SES to child well-being have been proposed, with most involving differences in access to material and social resources or reactions to stress-inducing conditions by both the children themselves and their parents. For children, SES impacts well-being at multiple levels, including both family and neighborhood. Its effects are moderated by children's own characteristics, family characteristics, and external support systems.


title: Millimeter Wave Communications With OAM-SM Scheme for Future Mobile Networks

abstract: The orbital angular momentum (OAM) technique provides a new degree of freedom for information transmissions in millimeter wave communications. Considering the spatial distribution characteristics of OAM beams, a new OAM spatial modulation (OAM-SM) millimeter wave communication system is first proposed for future mobile networks. Furthermore, the capacity, average bit error probability, and energy efficiency of OAM-SM millimeter wave communication systems are analytically derived for performance analysis. Compared with the OAM-based multi-input multi-output (MIMO) millimeter wave communication systems, the maximum energy efficiency of OAM-SM millimeter wave communication systems is improved by 227.2%. Moreover, numerical results indicate that the proposed OAM-SM millimeter wave communication systems are more robust to path-loss attenuations than the conventional MIMO millimeter wave communication systems, which makes it suitable for long-range transmissions. Therefore, OAM-SM millimeter wave communication systems provide a great growth space for future mobile networks.


title: Keeping up to date: An academic researcher's information journey

abstract: Keeping up to date with research developments is a central activity of academic researchers, but researchers face difficulties in managing the rapid growth of available scientific information. This study examined how researchers stay up to date, using the information journey model as a framework for analysis and investigating which dimensions influence information behaviors. We designed a 2-round study involving semistructured interviews and prototype testing with 61 researchers with 3 levels of seniority PhD student to professor. Data were analyzed following a semistructured qualitative approach. Five key dimensions that influence information behaviors were identified: level of seniority, information sources, state of the project, level of familiarity, and how well defined the relevant community is. These dimensions are interrelated and their values determine the flow of the information journey. Across all levels of professional expertise, researchers used similar hard formal sources to access content, while soft interpersonal sources were used to filter information. An important "pain point" that future information tools should address is helping researchers filter information at the point of need.


title: Internet Addiction Symptoms, Disordered Eating, and Body Image Avoidance

abstract: Abstract Internet addiction is an increasing concern among young adults. Self-presentational theory posits that the Internet offers a context in which individuals are able to control their image. Little is known about body image and eating concerns among pathological Internet users. The aim of this study was to explore the association between Internet addiction symptoms, body image esteem, body image avoidance, and disordered eating. A sample of 392 French young adults (68 percent women) completed an online questionnaire assessing time spent online, Internet addiction symptoms, disordered eating, and body image avoidance. Fourteen men (11 percent) and 26 women (9.7 percent) reported Internet addiction. Body image avoidance was associated with Internet addiction symptoms among both genders. Controlling for body–mass index, Internet addiction symptoms, and body image avoidance were both significant predictors of disordered eating among women. These findings support the self-presentational theory of Internet...


title: Baby-Step Giant-Step Algorithms for the Symmetric Group

abstract: We study discrete logarithms in the setting of group actions. Suppose that $G$ is a group that acts on a set $S$. When $r,s \in S$, a solution $g \in G$ to $r^g = s$ can be thought of as a kind of logarithm. In this paper, we study the case where $G = S_n$, and develop analogs to the Shanks baby-step / giant-step procedure for ordinary discrete logarithms. Specifically, we compute two sets $A, B \subseteq S_n$ such that every permutation of $S_n$ can be written as a product $ab$ of elements $a \in A$ and $b \in B$. Our deterministic procedure is optimal up to constant factors, in the sense that $A$ and $B$ can be computed in optimal asymptotic complexity, and $|A|$ and $|B|$ are a small constant from $\sqrt{n!}$ in size. We also analyze randomized"collision"algorithms for the same problem.


title: Constructing an Ideal Hash Function from Weak Ideal Compression Functions

abstract: We introduce the notion of a weak ideal compression function, which is vulnerable to strong forms of attack, but is otherwise random. We show that such weak ideal compression functions can be used to create secure hash functions, thereby giving a design that can be used to eliminate attacks caused by undesirable properties of compression functions. ::: ::: We prove that the construction we give, which we call the "zipper hash," is ideal in the sense that the overall hash function is indistinguishable from a random oracle when implemented with these weak ideal building blocks. ::: ::: The zipper hash function is relatively simple, requiring two compression function evaluations per block of input, but it is not streamable. We also show how to create an ideal (strong) compression function from ideal weak compression functions, which can be used in the standard iterated way to make a streamable hash function.


title: A method for business process reengineering based on enterprise ontology

abstract: Business Process Reengineering increases enterprise's chance to survive in competition among organizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology. Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment


title: Identification of Ion-Selectivity Determinants in Heavy-Metal Transport P1B-type ATPases

abstract: P1B-type ATPases transport a variety of metals (Cd2+, Zn2+, Pb2+, Co2+, Cu2+, Ag+, Cu+) across biomembranes. Characteristic sequences CP[C/H/S] in transmembrane fragment H6 were observed in the putative transporting metal site of the founding members of this subfamily (initially named CPx-ATPases). In spite of their importance for metal homeostasis and biotolerance, their mechanisms of ion selectivity are not understood. Studies of better-characterized P(II)-type ATPases (Ca-ATPase and Na,K-ATPase) have identified three transmembrane segments that participate in ion binding and transport. Testing the hypothesis that metal specificity is determined by conserved amino acids located in the equivalent transmembrane segments of P1B-type ATPases (H6, H7, and H8), 234 P1B-ATPase protein sequences were analyzed. This showed that although H6 contains characteristic CPX or XPC sequences, conserved amino acids in H7 and H8 provide signature sequences that predict the metal selectivity in each of five P1B-ATPase subgroups identified. These invariant amino acids contain diverse side chains (thiol, hydroxyl, carbonyl, amide, imidazolium) that can participate in transient metal coordination during transport and consequently determine the particular metal selectivity of each enzyme. Each subgroup shares additional structural characteristics such as the presence (or absence) of particular amino-terminal metal-binding domains and the number of putative transmembrane segments. These differences suggest unique functional characteristics for each subgroup in addition to their particular metal specificity.


title: A survey on predicting user behavior based on web server log files in a web usage mining

abstract: A presently internet is most imperative part of human life. The internet is an growing day by day, so online users are also escalating. The interesting information for knowledge of extracting from such enormous data demands for new logic and the new method. Every user spends their most of the time on the internet and their behavior is different from one and another. The application of data mining techniques service to need of web-based applications. Web usage mining is leading research area in Web Mining concerned about the web user's behavior. This paper gives an attention on Web usage mining to predict the behavior of web users based on web server log files. Users using web pages, a frequent access path's and frequent access pages, links are stored in web server log files. A Web log along with the individuality of the user captures their browsing behavior on a website and discussing regarding the behavior from analysis of different algorithms and different methods.


title: Atrial natriuretic peptide gene promoter polymorphism is associated with left ventricular hypertrophy in hypertension

abstract: Recent studies suggest that the ANP (atrial natriuretic peptide)/NPRA (type A natriuretic peptide receptor) system modulates ventricular remodelling and cardiac hypertrophy in hypertension in Western populations. In the present study, we tested for any association between two SNPs (single nucleotide polymorphisms) in the ANP gene (one in the promoter and one exonic) with cardiac hypertrophy. We tested the hypothesis in 2118 hypertensive patients, including 945 with LVH [LV (left ventricular) hypertrophy] and 1173 without LVH, as well as 816 healthy control subjects. All subjects were genotyped for the −A2843G and A188G polymorphisms. We found that the GG genotype at position −2843 conferred a 2.2-fold risk for LVH compared with the AA or AG genotypes, including septal wall thickness (11.8±1.4 mm for GG compared with 10.9±1.4 and 10.7±1.3 mm for AA and AG respectively; P P 2.7 for GG compared with 57.9±8.6 and 57.8±8.4 g/m 2.7 for AA and AG respectively; P P P P >0.05). No significant association between the A188G genotype and echocardiographic variables was found in either hypertensive patients or controls ( P >0.05). In conclusion, our findings indicate that the −A2843G polymorphism in the ANP gene promoter might be a genetic risk factor for the development of LVH in patients with hypertension.


title: Biometrics and their use in e-passports

abstract: A successful design, deployment and operation of biometric systems depends highly on the results for existing biometrical technologies and components. These existing technologies as well as new solutions need to be evaluated on their performance. However it is often forgotten that the biometric (iris, finger, face e.g.) is only one part of a fully deployed application. As biometric (sub)systems are often not designed with security and or privacy in mind, system integrators will need to address the requirements of the deployed application in this light. The fears and concerns of a significant segment of the user population need to be addressed as early as possible in the design process, to ensure that appropriate mechanisms are in place to reassure such users. These concerns may relate to privacy or to safety issues, which may be addressed in part through legal and regulatory measures. This article discusses the requirements, design and application scenario's of biometrical systems in general and the introduction of a new biometrical passport in The Netherlands in particular. On the one hand it is based on one of the authors' (BS) lecture notes of the Second International Summer School on ''Biometrics for Secure Authentication'', Alghero (IT), Summer 2005 (The summer school is co-organized by the BioSecure network of Excellence in Biometrics and supported by the European Biometrics Forum). On the other hand it is based on the other authors' (BJ) experiences as external advisor of the Ministry of Internal Affairs in The Netherlands - which is responsible for the introduction of the new passport.


title: Graph Coloring Based Pilot Allocation to Mitigate Pilot Contamination for Multi-Cell Massive MIMO Systems

abstract: A massive multiple-input multiple-output (MIMO) system, which utilizes a large number of base station (BS) antennas to serve a set of users, suffers from pilot contamination due to the inter-cell interference (ICI). In this letter, a graph coloring based pilot allocation (GC-PA) scheme is proposed to mitigate pilot contamination for multi-cell massive MIMO systems. Specifically, by exploiting the large-scale characteristics of fading channels, an interference graph is firstly constructed to describe the potential ICI relationship of all users. Then, with the limited pilot resource, the proposed GC-PA scheme aims to mitigate the potential ICI by efficiently allocating pilots among users in the interference graph. The performance gain of the proposed scheme is verified by simulations.


title: Deep learning of knowledge graph embeddings for semantic parsing of Twitter dialogs

abstract: This paper presents a novel method to learn neural knowledge graph embeddings. The embeddings are used to compute semantic relatedness in a coherence-based semantic parser. The approach learns embeddings directly from structured knowledge representations. A deep neural network approach known as Deep Structured Semantic Modeling (DSSM) is used to scale the approach to learn neural embeddings for all of the concepts (pages) of Wikipedia. Experiments on Twitter dialogs show a 23.6% reduction in semantic parsing errors compared to the state-of-the-art unsupervised approach.


title: Waste polyethylene terephthalate as an aggregate in concrete

abstract: This paper reports the strength behaviour of concrete containing three types of recycled polyethylene terephthalate (PET) aggregate. Results are also analysed to determine the PET-aggregate's effect on the relationship between the flexural and splitting tensile strengths and compressive strength and to know whether the relationships between compressive strength and other strength characteristics given in European design codes are applicable to concrete made with PET-aggregates. The compressive strength development of concrete containing all types of PET-aggregate behaves like in conventional concrete, though the incorporation of any type of PET-aggregate significantly lowers the compressive strength of the resulting concrete. The PET-aggregate incorporation improves the toughness behaviour of the resulting concrete. This behaviour is dependent on PET-aggregate's shape and is maximised for concrete containing coarse, flaky PET-aggregate. The splitting tensile and flexural strength characteristics are proportional to the loss in compressive strength of concrete containing plastic aggregates.


title: Equivalence and efficiency of image alignment algorithms

abstract: There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additive approach), the second an incremental warp (the compositional approach). We first prove that these two formulations are equivalent. A very efficient algorithm was proposed by Hager and Belhumeur (1998) using the additive approach that unfortunately can only be applied to a very restricted class of warps. We show that using the compositional approach an equally efficient algorithm (the inverse compositional algorithm) can be derived that can be applied to any set of warps which form a group. While most warps used in computer vision form groups, there are a certain warps that do not. Perhaps most notable is the set of piecewise affine warps used in flexible appearance models (FAMs). We end this paper by extending the inverse compositional algorithm to apply to FAMs.


title: Controlled AC-DC converter fed permanent magnet DC motor using single-phase matrix converter

abstract: This paper presents simulation of AC to DC converter fed permanent magnet DC motor (PMDC) using single phase matrix converter (SPMC) topology. The use of AC-DC converter has a setback in that it draws non-sinusoidal supply current waveform with high harmonic contents. As a result it contributes to high total harmonic distortion (THD) level and low total effective supply power factor affecting the quality of the power supply system. Concern with this problem, Active Power Filter (APF) function is also presented in order to achieve high power factor and low total harmonic distortion (THD) level. Selected simulation results using MATLAB/Simulink are presented to verify the proposed operations.


title: ERONTO: a tool for extracting ontologies from extended E/R diagrams

abstract: Realization of Semantic Web requires structuring of web data using domain ontologies. Most data intensive websites are powered by relational databases whose design process involves developing conceptual model using E/R or Extended E/R diagrams. This paper discusses the implementation details of a tool that builds domain ontologies in OWL (Ontology Web Language) from Extended E/R diagrams. Ontology development being a knowledge intensive task, our tool would be helpful in reducing the developmental efforts by automating the process. We bring out the differences and the similarities between the expressive capabilities of the two conceptual modeling methods, namely OWL and Extended E/R diagrams.


title: An Empirical Study of Bugs in Machine Learning Systems

abstract: Many machine learning systems that include various data mining, information retrieval, and natural language processing code and libraries are used in real world applications. Search engines, internet advertising systems, product recommendation systems are sample users of these algorithm-intensive code and libraries. Machine learning code and toolkits have also been used in many recent studies on software mining and analytics that aim to automate various software engineering tasks. With the increasing number of important applications of machine learning systems, the reliability of such systems is also becoming increasingly important. A necessary step for ensuring reliability of such systems is to understand the features and characteristics of bugs occurred in the systems. A number of studies have investigated bugs and fixes in various software systems, but none focuses on machine learning systems. Machine learning systems are unique due to their algorithm-intensive nature and applications to potentially large-scale data, and thus deserve a special consideration. In this study, we fill the research gap by performing an empirical study on the bugs in machine learning systems. We analyze three systems, Apache Mahout, Lucene, and OpenNLP, which are data mining, information retrieval, and natural language processing tools respectively. We look into their bug databases and code repositories, analyze a sample set of bugs and corresponding fixes, and label the bugs into various categories. Our study finds that 22.6% of the bugs belong to the algorithm/method category, 15.6% of the bugs belong to the non-functional category, and 13% of the bugs belong to the assignment/initialization category. We also report the relationship between bug categories and bug severities, the time and effort needed to fix the bugs, and bug impacts. We highlight several bug categories that deserve attention in future research.


title: Corporate career presences on social network sites: an analysis of hedonic and utilitarian value

abstract: Due to the shortage of skilled workforce and the increasing usage of social network sites, companies increasingly apply social network sites to attract potential applicants. This paper explores how corporate career presences on network sites should be realized in order to attract potential applicants. Therefore, we tested the impact of seven individual characteristics (namely Appointments, Daily Working Routine, Jobs, Corporate News, Entertainment, Media Format, and Features) of these corporate career presences that we extracted by a comprehensive pre-study on users' perceived hedonic and utilitarian value of these presences on social network sites. Based on an online survey with 470 participants, the results reveal a highly significant impact of five characteristics that corporate career presences provide both a hedonic as well as a utilitarian value to the user.


A New Metric To Evaluate Ontology Modularization


title: Analytic modeling of multitier Internet applications

abstract: Since many Internet applications employ a multitier architecture, in this article, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, tier replication, load imbalances across replicas, and caching at intermediate tiers. We validate our model using real multitier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. Furthermore, our model successfully handles a comprehensive range of resource utilization---from 0 to near saturation for the CPU---for two separate tiers. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95p confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests/minute, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the tiers by factors of 2 and 3.5, respectively.


title: Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization

abstract: This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic optimization and robust optimization approaches. The proposed real-time price-based DR management application can be imbedded into smart meters and automatically executed on-line for determining the optimal operation of residential appliances within 5-minute time slots while considering uncertainties in real-time electricity prices. Operation tasks of residential appliances are categorized into deferrable/non-deferrable and interruptible/non-interruptible ones based on appliances' DR preferences as well as their distinct spatial and temporal operation characteristics. The stochastic optimization adopts the scenario-based approach via Monte Carlo (MC) simulation for minimizing the expected electricity payment for the entire day, while controlling the financial risks associated with real-time electricity price uncertainties via the expected downside risks formulation. Price uncertainty intervals are considered in the robust optimization for minimizing the worst-case electricity payment while flexibly adjusting the solution robustness. Both approaches are formulated as mixed-integer linear programming (MILP) problems and solved by state-of-the-art MILP solvers. The numerical results show attributes of the two approaches for solving the real-time optimal DR management problem for residential appliances.


Accelerating Capsule Networks with Tensor Comprehensions


title: A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce

abstract: E-commerce platforms surface interesting products largely through product recommendations that capture users' styles and aesthetic preferences. Curating recommendations as a complete complementary set, or assortment, is critical for a successful e-commerce experience, especially for product categories such as furniture, where items are selected together with the overall theme, style or ambiance of a space in mind. In this paper, we propose two visually-aware recommender systems that can automatically curate an assortment of living room furniture around a couple of pre-selected seed pieces for the room. The first system aims to maximize the visual-based style compatibility of the entire selection by making use of transfer learning and topic modeling. The second system extends the first by incorporating text data and applying polylingual topic modeling to infer style over both modalities. We review the production pipeline for surfacing these visually-aware recommender systems and compare them through offline validations and large-scale online A/B tests on Overstock. Our experimental results show that complimentary style is best discovered over product sets when both visual and textual data are incorporated.


title: A flexible model for training action localization with varying levels of supervision

abstract: Spatio-temporal action detection in videos is typically addressed in a fully-supervised setup with manual annotation of training videos required at every frame. Since such annotation is extremely tedious and prohibits scalability, there is a clear need to minimize the amount of manual supervision. In this work we propose a unifying framework that can handle and combine varying types of less-demanding weak supervision. Our model is based on discriminative clustering and integrates different types of supervision as constraints on the optimization. We investigate applications of such a model to training setups with alternative supervisory signals ranging from video-level class labels over temporal points or sparse action bounding boxes to the full per-frame annotation of action bounding boxes. Experiments on the challenging UCF101-24 and DALY datasets demonstrate competitive performance of our method at a fraction of supervision used by previous methods. The flexibility of our model enables joint learning from data with different levels of annotation. Experimental results demonstrate a significant gain by adding a few fully supervised examples to otherwise weakly labeled videos.


title: A Framework for Unique Ring Signatures

abstract: We propose a simple, general, and unified framework for constructing unique ring signatures that simplify and capture the spirit of linkable ring signatures. The framework, which can be efficiently instantiated in the random oracle and the standard model, is obtained by generalizing the Bellare-Goldwasser “PRF made public” paradigm. Security of the first instantiation can be tightly related to the DDH problem. The scheme leads to the most efficient linkable/unique ring signature in the random oracle model, for a given level of provable security. The second one based on stronger assumptions partly simplifies and slightly improves sublinear size traceable ring signature of Fujisaki. Both of the improvements would be difficult without the general framework in hand.


title: Knowledge Based Image Enhancement Using Neural Networks

abstract: In this paper we combine the concept of adaptive filters with neural networks in order to be able to include high level knowledge about the contents of the image in the filtering process. Adaptive image enhancement algorithms often utilize low level knowledge like gradient information to guide filtering parameters. The advantage is that these filters do not need any specific knowledge and can thus be applied to a broad spectrum of images. However, for many problems this low level information is not sufficient to achieve good results. For example in medical imaging it is often very important that some features are preserved while others are suppressed. Usually these features cannot be distinguished by low level information. Therefore we propose a method to incorporate high level knowledge in the filtering process in order to adjust the parameters of any given filter thus creating a guided filter. We present a scheme for acquiring this high level knowledge which allows us to apply our method to all kinds of images using pattern recognition and special preprocessing techniques. The design of the guided filter itself is easy as for the high level knowledge only some sample pixels including their neighborhood and the desired parameters for these pixels are necessary.


title: Methanol-essential growth of Escherichia coli

abstract: Methanol represents an attractive substrate for biotechnological applications. Utilization of reduced one-carbon compounds for growth is currently limited to methylotrophic organisms, and engineering synthetic methylotrophy remains a major challenge. Here we apply an in silico-guided multiple knockout approach to engineer a methanol-essential Escherichia coli strain, which contains the ribulose monophosphate cycle for methanol assimilation. Methanol conversion to biomass was stoichiometrically coupled to the metabolization of gluconate and the designed strain was subjected to laboratory evolution experiments. Evolved strains incorporate up to 24% methanol into core metabolites under a co-consumption regime and utilize methanol at rates comparable to natural methylotrophs. Genome sequencing reveals mutations in genes coding for glutathione-dependent formaldehyde oxidation (frmA), NAD(H) homeostasis/biosynthesis (nadR), phosphopentomutase (deoB), and gluconate metabolism (gntR). This study demonstrates a successful metabolic re-routing linked to a heterologous pathway to achieve methanol-dependent growth and represents a crucial step in generating a fully synthetic methylotrophic organism.


title: Tree Kernels for Semantic Role Labeling

abstract: The availability of large scale data sets of manually annotated predicate-argument structures has recently favored the use of machine learning approaches to the design of automated semantic role labeling (SRL) systems. The main research in this area relates to the design choices for feature representation and for effective decompositions of the task in different learning models. Regarding the former choice, structural properties of full syntactic parses are largely employed as they represent ways to encode different principles suggested by the linking theory between syntax and semantics. The latter choice relates to several learning schemes over global views of the parses. For example, re-ranking stages operating over alternative predicate-argument sequences of the same sentence have shown to be very effective. ::: ::: In this article, we propose several kernel functions to model parse tree properties in kernel-based machines, for example, perceptrons or support vector machines. In particular, we define different kinds of tree kernels as general approaches to feature engineering in SRL. Moreover, we extensively experiment with such kernels to investigate their contribution to individual stages of an SRL architecture both in isolation and in combination with other traditional manually coded features. The results for boundary recognition, classification, and re-ranking stages provide systematic evidence about the significant impact of tree kernels on the overall accuracy, especially when the amount of training data is small. As a conclusive result, tree kernels allow for a general and easily portable feature engineering method which is applicable to a large family of natural language processing tasks.


title: Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes

abstract: The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems. Since macro-actions last for stochastic durations, multiple agents executing decentralized policies in cooperative environments must act asynchronously. We present an algorithm that modifies generalized advantage estimation for temporally extended actions, allowing a state-of-the-art policy optimization algorithm to optimize policies in Dec-POMDPs in which agents act asynchronously. We show that our algorithm is capable of learning optimal policies in two cooperative domains, one involving real-time bus holding control and one involving wildfire fighting with unmanned aircraft. Our algorithm works by framing problems as “event-driven decision processes,” which are scenarios in which the sequence and timing of actions and events are random and governed by an underlying stochastic process. In addition to optimizing policies with continuous state and action spaces, our algorithm also facilitates the use of event-driven simulators, which do not require time to be discretized into time-steps. We demonstrate the benefit of using event-driven simulation in the context of multiple agents taking asynchronous actions. We show that fixed time-step simulation risks obfuscating the sequence in which closely separated events occur, adversely affecting the policies learned. In addition, we show that arbitrarily shrinking the time-step scales poorly with the number of agents.


title: In vitro efficacy of allicin on chicken Eimeria tenella sporozoites

abstract: Chicken coccidiosis is a major parasitic disease caused by Eimeria spp. It is controlled and treated using chemical anticoccidial agents. Development of partial or complete resistance toward these anticoccidials is considered a major problem in poultry industry. Allicin is an organosulfur compound produced as a result of the reaction between alliin and alliinase after hacking of garlic. In this study, tenfold dilution from 180 mg/ml to 1.8 ng/ml of allicin in distilled water was tested against E. tenella in vitro. The percent of inhibition in allicin was from 99.9 to 71.53 % using 180 mg/ml and 180 ng/ml, respectively. The percent of inhibition was 56.24 % using 1.8 ng/ml. We used allicin as a treatment from plants against chicken coccidiosis; however, in vivo study should be performed to confirm these results.


title: Treatment outcomes using CBT-IA with Internet-addicted patients

abstract: Background and aims: Internet Gaming Disorder, a subtype of Internet Addiction, is now classified in Section 3 of the DSM-5. Cognitive behavioral therapy (CBT) has been suggested in treating Internet addiction as this modality has been shown to be an effective treatment for similar impulse control disorders. Given the daily and necessary use of the Internet and technology in general compared to other compulsive syndromes, a specialized form of CBT has been developed called Cognitive-Behavioral Therapy for Internet Addiction (CBT-IA). CBT-IA is a comprehensive three phase approach that includes behavior modification to control compulsive Internet use, cognitive restructuring to identify, challenge, and modify cognitive distortions that lead to addictive use, and harm reduction techniques to address and treat co-morbid issues associated with the disorder. Methods: As the first model of its kind, this study examines 128 clients to measure treatment outcomes using CBT-IA. Clients were evaluated using the Internet Addiction Test (IAT) to classify subjects and were administered twelve weekly sessions of CBT-IA. Treatment outcomes were measured at the end of the twelve weeks, one-month, three months and at six month post-treatment. Results: Results showed that over 95% of clients were able to manage symptoms at the end of the twelve weeks and 78% sustained recovery six months following treatment. Discussion and Conclusions: Results found that CBT-IA was effective at ameliorating symptoms associated with Internet addiction after twelve weekly sessions and consistently over one-month, three months, and six months after therapy. Further research implications such as investigating long-term outcome effects of the model with larger client populations and treatment differences among the subtypes of Internet addiction or with other cultural populations using CBT-IA are discussed.


Internet-scale Probing of CPS: Inference, Characterization and Orchestration Analysis.


title: Our algorithm for nasal reconstruction

abstract: Summary Nasal reconstruction is always challenging for plastic surgeons. Its midfacial localisation and the relationship between convexities and concavities of nasal subunits make impossible to hide any sort of deformity without a proper reconstruction. Nasal tissue defects can be caused by tumor removal, trauma or by any other insult to the nasal pyramid, like cocaine abuse, developing an irreversible sequela. Due to the special characteristics of the nasal pyramid surface, the removal of the lesion or the debridement must be performed according to nasal subunits as introduced by Burget. Afterwards, the reconstructive technique or a combination of them must be selected according to the size and the localisation of the defect created, and tissue availability to fulfil the procedure. An anatomical reconstruction must be completed as far as possible, trying to restore the nasal lining, the osteocartilaginous framework and the skin cover. In our department, 35 patients were operated on between 2000 and 2002: three bilobed flaps, five nasolabial flaps, two V-Y advancement flaps from the sidewall, three dorsonasal flaps modified by Ohsumi, 19 paramedian forehead flaps, three cheek advancement flaps, three costocondral grafts, two full-thickness skin grafts and two auricular helix free flaps for alar reconstruction. All flaps but one free flap survived with no postoperative complications. After 12–24 months of follow-up, all reconstructions remained stable from cosmetic and functional point of view. Our aim is to present our choice for nasal reconstruction according to the size and localization of the defect, and donor tissue availability.


PODCASTING: AN EFFECTIVE TOOL FOR HONING LANGUAGE STUDENTS' PRONUNCIATION?


title: 3D positioning algorithm based on multiple quasi-monostatic IR-UWB radar sensors

abstract: In this paper, we propose a 3D positioning algorithm based on multiple quasi-monostatic impulse radio ultra-wideband (IR-UWB) radar sensors. Unlike the conventional algorithms such as approximate maximum likelihood (AML) or two stage maximum likelihood (TSML) that find maximum likelihood (ML) positioning solutions using mathematical approximations, the proposed algorithm basically pursues a maximum likelihood (ML) solution based on iterative algorithm using spatial division. The proposed algorithm can be applied to a quasi-monostatic structure in which Tx and Rx are separated, and can be applied to a situation where the distribution of the distance error of each radar is not the same. In addition, the boundary of the target error is used as an important parameter of the algorithm, so that the calculation complexity of the algorithm is fixed. This has the advantage that it can be set appropriately for applications that do not require high accuracy, or for limited computation resource environments such as embedded environments. To verify the performance of the proposed algorithm, we performed simulations.


title: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

abstract: Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. ::: The dataset is freely available at this https URL .


Fast and Robust Ellipse Extraction from Complicated Images


title: Analysis and evaluation of unstructured data: text mining versus natural language processing

abstract: Nowadays, most of information saved in companies are as unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the storage efficiency and unstructured data analysis. Merrill Lynch recently estimated that more than 80% of all potentially useful business information is unstructured data. The large number and complexity of unstructured data opens up many new possibilities for the analyst. We analyze both structured and unstructured data individually and collectively. Text mining and natural language processing are two techniques with their methods for knowledge discovery form textual context in documents. In this study, text mining and natural language techniques will be illustrated. The aim of this work comparison and evaluation the similarities and differences between text mining and natural language processing for extraction useful information via suitable themselves methods.


title: Constructivist Grounded Theory?

abstract: In meinem Beitrag greife ich zuruck auf den ausgezeichneten und inspirierenden Artikel von CHARMAZ zu konstruktivistischer Grounded Theory, um an diesem Beispiel zu diskutieren, dass und warum die Grounded Theory kein konstruktivistisches Unterfangen ist. Ich versuche zu zeigen, dass "konstruktivistische Daten" bzw. konstruktivistische Anwendungen der Grounded Theory, sofern sie uberhaupt existieren bzw. sinnvoll sein konnten, nur einen verschwindend kleinen Teil der Grounded Theory ausmachen. ::: URN: urn:nbn:de:0114-fqs0203125


title: Certainty-Driven Consistency Loss for Semi-supervised Learning

abstract: The recently proposed semi-supervised learning methods exploit consistency loss between different predictions under random perturbations. Typically, a student model is trained to predict consistently with the targets generated by a noisy teacher. However, they ignore the fact that not all training data provide meaningful and reliable information in terms of consistency. For misclassified data, blindly minimizing the consistency loss around them can hinder learning. In this paper, we propose a novel certainty-driven consistency loss (CCL) to dynamically select data samples that have relatively low uncertainty. Specifically, we measure the variance or entropy of multiple predictions under random augmentations and dropout as an estimation of uncertainty. Then, we introduce two approaches, i.e. Filtering CCL and Temperature CCL to guide the student learn more meaningful and certain/reliable targets, and hence improve the quality of the gradients backpropagated to the student. Experiments demonstrate the advantages of the proposed method over the state-of-the-art semi-supervised deep learning methods on three benchmark datasets: SVHN, CIFAR10, and CIFAR100. Our method also shows robustness to noisy labels.


title: A Performance Comparison of Loss Functions for Deep Face Recognition

abstract: The emergence of biometric tools and its increased usage in day to day devices has brought simplicity in the authentication process for the users as compared to the passwords and pattern locks being used. The ease of use of biometric reduces the manual work and helps in faster and automatic authentication. Among different biometric traits, the face is one which can be captured without much cooperation of users. Moreover, face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of Computer Vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. The loss functions play an important role in CNN training. Basically, it generates a huge loss, if the network does not perform well using the current parameter setting. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing. The training and test results are analyzed in this paper.


title: A Markov decision process approach to multi-category patient scheduling in a diagnostic facility

abstract: Objectives: To develop a mathematical model for multi-category patient scheduling decisions in computed tomography (CT), and to investigate associated tradeoffs from economic and operational perspectives. Methods: We modeled this decision-problem as a finite-horizon Markov decision process (MDP) with expected net CT revenue as the performance metric. The performance of optimal policies was compared with five heuristics using data from an urban hospital. In addition to net revenue, other patient-throughput and service-quality metrics were also used in this comparative analysis. Results: The optimal policy had a threshold structure in the two-scanner case - it prioritized one type of patient when the queue-length for that type exceeded a threshold. The net revenue gap between the optimal policy and the heuristics ranged from 5% to 12%. This gap was 4% higher in the more congested, single-scanner system than in the two-scanner system. The performance of the net revenue maximizing policy was similar to the heuristics, when compared with respect to the alternative performance metrics in the two-scanner case. Under the optimal policy, the average number of patients that were not scanned by the end of the day, and the average patient waiting-time, were both nearly 80% smaller in the two-scanner case than in the single-scanner case. The net revenue gap between the optimal policy and the priority-based heuristics was nearly 2% smaller as compared to the first-come-first-served and random selection schemes. Net revenue was most sensitive to inpatient (IP) penalty costs in the single-scanner system, whereas to IP and outpatient revenues in the two-scanner case. Conclusions: The performance of the optimal policy is competitive with the operational and economic metrics considered in this paper. Such a policy can be implemented relatively easily and could be tested in practice in the future. The priority-based heuristics are next-best to the optimal policy and are much easier to implement.


title: Stochastic Model Checking ?

abstract: This tutorial presents an overview of model checking for both discrete and continuous-time Markov chains (DTMCs and CTMCs). Model checking algorithms are given for verifying DTMCs and CTMCs against specifications written in probabilistic extensions of temporal logic, including quantitative properties with rewards. Example properties include the probability that a fault occurs and the expected number of faults in a given time period. We also describe the practical application of stochastic model checking with the probabilistic model checker PRISM by outlining the main features supported by PRISM and three real-world case studies: a probabilistic security protocol, dynamic power management and a biological pathway.