title: Exploiting interoperable microservices in web objects enabled Internet of Things

abstract: The vision of internet of things is to connect every object beyond the boundaries of anywhere and whenever for the provision of services from the smart home user to fourth industrial revolution. The objects in the internet of things will be more heterogeneous because of their specialty and accuracy in their domain. Now a days in the Internet of things the data and its machine understanding gets more importance and remain the focal point rather than the objects that generate that data changes frequently due to innovations and their wears and tears. To make the machine understanding, exchanging and sharing information and knowledge; these objects needs a light weight and novel platform for the provision of IoT services in the future. The Web of Objects is a platform for IoT services because of its service modularity with the support of interoperable microservices and virtualization and granularity of heterogeneous objects through composite virtual objects and virtual objects. To realize the cross domain IoT applications we proposed WoO enabled interoperable microservices architecture and present a reference use case for the implementation.


Multi-Domain Neural Machine Translation through Unsupervised Adaptation


title: Leaf vein segmentation using Odd Gabor filters and morphological operations

abstract: Leaf vein forms the basis of leaf characterization and classification. Different species have different leaf vein patterns. It is seen that leaf vein segmentation will help in maintaining a record of all the leaves according to their specific pattern of veins thus provide an effective way to retrieve and store information regarding various plant species in database as well as provide an effective means to characterize plants on the basis of leaf vein structure which is unique for every species. The algorithm proposes a new way of segmentation of leaf veins with the use of Odd Gabor filters and the use of morphological operations for producing a better output. The Odd Gabor filter gives an efficient output and is robust and scalable as compared with the existing techniques as it detects the fine fiber like veins present in leaves much more efficiently.


title: Design and process optimization for dual row QFN

abstract: The continuous advancement in technology and miniaturization of electronic components, hand held and communication devices require superior thermal-electrical performance and miniature packages. An advanced and complicated Integrated Circuit (IC) device often demands increase in number of I/O's while maintaining its small size, footprint and weight. Dual Row Quad Flat No lead (DR-QFN) is an ideal solution for such demanding applications; however, despite the simplicity of its package structure, it possesses challenges in various assembly processes. This paper describes the problems that are associated with the different concepts of both 'saw' and 'punch' singulated DR-QFN and the corresponding solutions to overcome the barriers. Lead isolation or separation of 1st and 2nd row of leads has always been the major challenge for saw singulated DR-QFN (DR-QFN- S) where solder burr and leadfinger delamination are inherent. Shorting between inner and outer leads during SMT is also apparent. This paper demonstrates the study on different leadframe designs and how the solutions were derived. Through modeling, the impact on solder joint reliability for the different leadframe design and the respective surface mount behavior was also explored. The primary focus in designing punch singulated DR-QFN (DR-QFN-P) is to do away with lead isolation, which is to maintain its simplicity and cost effectiveness. The key factor is the design of the leadframe and the etching capability of the manufacturer. But despite the absence of lead isolation process, DR-QFN-P similarly comes with challenges such as solder bridging and lead-to-lead short. Further in this paper describes the approach taken to overcome issues associated with this package.


title: Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features

abstract: In recent decades, plastic-mulched farmland has expanded rapidly in China as well as in the rest of the world because it results in marked increases of crop production. However, plastic-mulched farmland significantly influences the environment and has so far been inadequately investigated. Accurately monitoring and mapping plastic-mulched farmland is crucial for agricultural production, environmental protection, resource management, and so on. Monitoring plastic-mulched farmland using moderate-resolution remote sensing data is technically challenging because of spatial mixing and spectral confusion with other ground objects. This paper proposed a new scheme that combines spectral and textural features for monitoring the plastic-mulched farmland and evaluates the performance of a Support Vector Machine (SVM) classifier with different kernel functions using Landsat-8 Operational Land Imager (OLI) imagery. The textural features were extracted from multi-bands OLI data using a Grey Level Co-occurrence Matrix (GLCM) algorithm. Then, six combined feature sets were developed for classification. The results indicated that Landsat-8 OLI data are well suitable for monitoring plastic-mulched farmland; the SVM classifier with a linear kernel function is superior both to other kernel functions and to two other widely used supervised classifiers: Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC). For the SVM classifier with a linear kernel function, the highest overall accuracy was derived from combined spectral and textural features in the 90° direction (94.14%, kappa 0.92), followed by the combined spectral and textural features in the 45° (93.84%, kappa 0.92), 135° (93.73%, kappa 0.92), 0° (93.71%, kappa 0.92) directions, and the spectral features alone (93.57%, kappa 0.91). Spectral features make a more significant contribution to monitoring the plastic-mulched farmland; adding textural features from medium resolution imagery provide only limited improvement in accuracy.


Millimeter-Wave Microstrip Array Antenna with High Efficiency for Automotive Radar Systems


title: Non-Interactive Zero-Knowledge from Homomorphic Encryption

abstract: We propose a method for compiling a class of Σ-protocols (3-move public-coin protocols) into non-interactive zero-knowledge arguments. The method is based on homomorphic encryption and does not use random oracles. It only requires that a private/public key pair is set up for the verifier. The method applies to all known discrete-log based Σ-protocols. As applications, we obtain non-interactive threshold RSA without random oracles, and non-interactive zero-knowledge for NP more efficiently than by previous methods.


title: TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks

abstract: Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend the research over BS switching operations, which should match up with traffic load variations. Instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast, we firstly formulate the traffic variations as a Markov decision process. Afterwards, in order to foresightedly minimize the energy consumption of RANs, we design a reinforcement learning framework based BS switching operation scheme. Furthermore, to avoid the underlying curse of dimensionality in reinforcement learning, a transfer actor-critic algorithm (TACT), which utilizes the transferred learning expertise in historical periods or neighboring regions, is proposed and provably converges. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed TACT algorithm contributes to a performance jumpstart and demonstrates the feasibility of significant energy efficiency improvement at the expense of tolerable delay performance.


title: Sentence Compression Beyond Word Deletion

abstract: In this paper we generalise the sentence compression task. Rather than simply shorten a sentence by deleting words or constituents, as in previous work, we rewrite it using additional operations such as substitution, reordering, and insertion. We present a new corpus that is suited to our task and a discriminative tree-to-tree transduction model that can naturally account for structural and lexical mismatches. The model incorporates a novel grammar extraction method, uses a language model for coherent output, and can be easily tuned to a wide range of compression specific loss functions.


title: Fuse: a reproducible, extendable, internet-scale corpus of spreadsheets

abstract: Spreadsheets are perhaps the most ubiquitous form of end-user programming software. This paper describes a corpus, called F use , containing 2,127,284 URLs that return spreadsheets (and their HTTP server responses), and 249,376 unique spreadsheets, contained within a public web archive of over 26.83 billion pages. Obtained using nearly 60,000 hours of computation, the resulting corpus exhibits several useful properties over prior spreadsheet corpora, including reproducibility and extendability. Our corpus is unencumbered by any license agreements, available to all, and intended for wide usage by end-user software engineering researchers. In this paper, we detail the data and the spreadsheet extraction process, describe the data schema, and discuss the trade-offs of F use with other corpora.


title: Learning to detect chest radiographs containing lung nodules using visual attention networks

abstract: Abstract Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing availability of PACS (Picture Archiving and Communication System), is laying the technological foundations needed to make available large volumes of clinical data and images from hospital archives. Binary labels indicating whether a radiograph contains a pulmonary lesion can be extracted at scale, using natural language processing algorithms. In this study, we propose two novel neural networks for the detection of chest radiographs containing pulmonary lesions. Both architectures make use of a large number of weakly-labelled images combined with a smaller number of manually annotated x-rays. The annotated lesions are used during training to deliver a type of visual attention feedback informing the networks about their lesion localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the inferred position of a lesion against the true position when this information is available; a localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning; the reward function penalises the exploration of areas, within an image, that are unlikely to contain nodules. Using a repository of over 430,000 historical chest radiographs, we present and discuss the proposed methods over related architectures that use either weakly-labelled or annotated images only.


title: Near-Memory Address Translation

abstract: Memory and logic integration on the same chip is becoming increasingly cost effective, creating the opportunity to offload data-intensive functionality to processing units placed inside memory chips. The introduction of memory-side processing units (MPUs) into conventional systems faces virtual memory as the first big showstopper: without efficient hardware support for address translation MPUs have highly limited applicability. Unfortunately, conventional translation mechanisms fall short of providing fast translations as contemporary memories exceed the reach of TLBs, making expensive page walks common.In this paper, we are the first to show that the historically important flexibility to map any virtual page to any page frame is unnecessary in today's servers. We find that while limiting the associativity of the virtual-to-physical mapping incurs no penalty, it can break the translate-then-fetch serialization if combined with careful data placement in the MPU's memory, allowing for translation and data fetch to proceed independently and in parallel. We propose the Distributed Inverted Page Table (DIPTA), a near-memory structure in which the smallest memory partition keeps the translation information for its data share, ensuring that the translation completes together with the data fetch. DIPTA completely eliminates the performance overhead of translation, achieving speedups of up to 3.81x and 2.13x over conventional translation using 4KB and 1GB pages respectively.


title: Possible death mechanisms other than respiratory asphyxia in a suicidal hanging case

abstract: Hanging is the most common suicide method that has seen in the routine autopsy practice. Known mechanisms of death include asphyxiation due to airway obstruction, cerebral ischemia due to arterial blockage, vagal inhibition (reflex cardiac arrest) due to carotid sinus compression, congestion due to venous blockage, fractures in cervical vertebral column with the cord injury. A 84 year-old man with totally laryngectomy found hanging from a tree in the garden of his house, and referred to Council of Forensic Medicine to determine cause of death. On the autopsy upper airway found to be ligatured from the level of hypopharynx. Case has been breathing via laryngectomy hole on his neck before suicide attempt. Case has been determined to be an interesting example for mechanism of death other than respiratory asphyxia in hanging cases.


ArgMine : A Framework for Argumentation Mining


title: Experimental implementation of underactuated potential energy shaping on a powered ankle-foot orthosis

abstract: Traditional control methodologies of rehabilitation orthoses/exoskeletons aim at replicating normal kinematics and thus fall into the category of kinematic control. This control paradigm depends on pre-defined reference trajectories, which can be difficult to adjust between different locomotor tasks and human subjects. An alternative control category, kinetic control, enforces kinetic goals (e.g., torques or energy) instead of kinematic trajectories, which could provide a flexible learning environment for the user while freeing up therapists to make corrections. We propose that the theory of underactuated potential energy shaping, which falls into the category of kinetic control, could be used to generate virtual body-weight support for stroke gait rehabilitation. After deriving the nonlinear control law and simulating it on a human-like biped model, we implemented this controller on a powered ankle-foot orthosis that was designed specifically for testing torque control strategies. Experimental results with an able-bodied human subject demonstrate the feasibility of the control approach for both positive and negative virtual body-weight augmentation.


title: Learning to Query, Reason, and Answer Questions On Ambiguous Texts

abstract: A key goal of research in conversational systems is to train an interactive agent to help a user with a task. Human conversation, however, is notoriously incomplete, ambiguous, and full of extraneous detail. To operate effectively, the agent must not only understand what was explicitly conveyed but also be able to reason in the presence of missing or unclear information. When unable to resolve ambiguities on its own, the agent must be able to ask the user for the necessary clarifications and incorporate the response in its reasoning. Motivated by this problem we introduce QRAQ ("crack"; Query, Reason, and Answer Questions), a new synthetic domain, in which a User gives an Agent a short story and asks a challenge question. These problems are designed to test the reasoning and interaction capabilities of a learning-based Agent in a setting that requires multiple conversational turns. A good Agent should ask only non-deducible, relevant questions until it has enough information to correctly answer the User's question. We use standard and improved reinforcement learning based memory-network architectures to solve QRAQ problems in the difficult setting where the reward signal only tells the Agent if its final answer to the challenge question is correct or not. To provide an upper-bound to the RL results we also train the same architectures using supervised information that tells the Agent during training which variables to query and the answer to the challenge question. We evaluate our architectures on four QRAQ dataset types, and scale the complexity for each along multiple dimensions.


Building a Large English-Chinese Parallel Corpus from Comparable Patents and its Experimental Application to SMT


title: A gentle introduction to the universal algorithmic agent AIXI

abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff''s theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameter-free theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AIXI model can formally solve them. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI$tl$, which is still effectively more intelligent than any other time $t$ and space $l$ bounded agent. The computation time of AIXI$tl$ is of the order $t \cdot 2^l$. Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches.


title: Pedagogical content knowledge in action: its impromptu development by an expert practitioner

abstract: Research into pedagogical content knowledge (PCK) has advanced over the years. Yet, since most research has developed within specific subject areas, this paper aims to investigate how an expert teacher generates PCK by using various forms of knowledge. This study draws upon the case of an expert Japanese teacher, Mr T, an educational consultant who leads professional development and school reform efforts by utilising observation of and joint reflection on lessons. Mr T advances PCK through interaction of stock knowledge from written materials, networks and previous observations, and flow knowledge from the lesson that he observes. Mr T’s case further demonstrates how PCK can engender a democratic and inclusive learning culture within the classroom.


title: Spontaneous Symmetry Breaking in Neural Networks

abstract: We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory. Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights. Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking. This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers. In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken. The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar. Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena,such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck, the phase transition out of it and gradient variance explosion (Shwartz-Ziv&Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.


title: Automatic Construction of Discourse Corpora for Dialogue Translation

abstract: In this paper, a novel approach is proposed to automatically construct parallel discourse corpus for dialogue machine translation. Firstly, the parallel subtitle data and its corresponding monolingual movie script data are crawled and collected from Internet. Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts. We not only evaluate the mapping results, but also integrate speaker information into the translation. Experiments show our proposed method can achieve 81.79% and 98.64% accuracy on speaker and dialogue boundary annotation, and speaker-based language model adaptation can obtain around 0.5 BLEU points improvement in translation qualities. Finally, we publicly release around 100K parallel discourse data with manual speaker and dialogue boundary annotation.


RGB-D camera Based Navigation for the Visually Impaired


title: Psychotic-like Experiences and Substance Use in College Students.

abstract: Psychotic disorders, as well as psychotic-like experiences and substance use, have been found to be associated. The main goal of the present study was to analyse the relationship between psychoticlike experiences and substance use in college students. The simple comprised a total of 660 participants (M = 20.3 years, SD = 2.6). The results showed that 96% of the sample reported some delusional experience, while 20.3% reported at least one positive psychotic-like experience. Some substance use was reported by 41.1% of the sample, differing in terms of gender. Substance users reported more psychoticlike experiences than non-users, especially in the positive dimension. Also, alcohol consumption predicted in most cases extreme scores on measures of delusional ideation and psychotic experiences. The association between these two variables showed a differentiated pattern, with a stronger relationship between substance use and cognitive-perceptual psychotic-like experiences. To some extent, these findings support the dimensional models of the psychosis phenotype and contribute a better understanding of the links between psychoticlike experiences and substance use in young adults. Future studies should further explore the role of different risk factors for psychotic disorders and include models of the gene-environment interaction.


title: Enable device-to-device communications underlaying cellular networks: challenges and research aspects

abstract: Device-to-device communication underlaying a cellular network is a promising technology in future wireless networks to improve network capacity and user experience. While D2D communication has great potential to improve wireless system spectral and energy efficiency due to the proximity of communication parties and higher spectrum reuse gain, tremendous work is still ongoing to turn the promising technology into a reality. This article discusses D2D technical challenges as well as standards progress and important research aspects that enable D2D communications underlaying cellular networks. The key research areas addressed include interference management, multihop D2D communications, and D2D communications in heterogeneous networks. When enabling D2D communications underlaying cellular networks, D2D communications can use either cellular downlink or cellular uplink resources. The two resource sharing modes will create different interference scenarios. The performance evaluation on D2D communications underlaying cellular networks under these two different scenarios is provided.


title: Enhancing Depth-Perception with Flexible Volumetric Halos

abstract: Volumetric data commonly has high depth complexity which makes it difficult to judge spatial relationships accurately. There are many different ways to enhance depth perception, such as shading, contours, and shadows. Artists and illustrators frequently employ halos for this purpose. In this technique, regions surrounding the edges of certain structures are darkened or brightened which makes it easier to judge occlusion. Based on this concept, we present a flexible method for enhancing and highlighting structures of interest using GPU-based direct volume rendering. Our approach uses an interactively defined halo transfer function to classify structures of interest based on data value, direction, and position. A feature-preserving spreading algorithm is applied to distribute seed values to neighboring locations, generating a controllably smooth field of halo intensities. These halo intensities are then mapped to colors and opacities using a halo profile function. Our method can be used to annotate features at interactive frame rates.


Recommendation Using Textual Opinions


Towards a ‘ Science ’ of Corpus Annotation : A New Methodological Challenge for Corpus Linguistics


title: Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics

abstract: Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.


title: The effects of relationship quality and switching barriers on customer loyalty

abstract: Because the cost of attracting new customers is much higher than the cost of retaining old customers, keeping customers loyal is a crucial issue for service firms. This research explores how relationship quality and switching barriers influence customer loyalty. Relationship quality consists of two aspects: satisfaction and trust. Antecedents of satisfaction and trust are explored. To test the proposed research model, a survey research methodology was used. Paper survey was distributed to mobile phone users in Taiwan. A total of 311 valid questionnaires were returned. Structural equation modeling was used to test hypotheses. Satisfaction, trust, and switching barriers have positive effects on loyalty. In terms of antecedents, it was found that playfulness and service quality impact satisfaction while service quality and intimacy affect trust. From a managerial perspective, this research suggests that practitioner should not only keep improving service quality, but also provide playfulness to ensure customer satisfaction. Service providers should also build relationship quality and switching barriers to reduce the possibility of defection and enhance customer loyalty.


title: Navel Orange Blemish Identification for Quality Grading System

abstract: A novel automated blemish detection system for ripe and unripe oranges is proposed in this paper. The algorithm is unique in that it does not rely on the global variations between pixels depicting the colours of an orange. By utilizing a priori knowledge of the properties of rounded convex objects, we introduce a set of colour classes that effectively `peels-off' the orange skin in order of increasing intensity layers. These layers are then examined independently, allowing us to scrutinize the skin more accurately for any blemishes present locally at the layer's intensity variation range. The efficacy of the algorithm is demonstrated using 170 images captured with a commercial fruit sorting machine as the benchmarking test set. Our results show that the system correctly classified 96% of good oranges and 97% of blemished oranges. The proposed system does not require any training.


title: New Public Organizing : Towards collaborative innovation in the public sector

abstract: In the light of a public dissatisfaction with organizational practices associated with new public management, new organizational forms and processes are sought after. In a Swedish municipality, aim ...


title: GPU accelerated graph SLAM and occupancy voxel based ICP for encoder-free mobile robots

abstract: Learning a map of an unknown environment and localising a robot in it is a common problem in robotics, with solutions usually requiring an estimate of the robot's motion. In scenarios such as Urban Search and Rescue, motion encoders can be highly inaccurate, and weight and battery requirements often limit computing power. We have developed a GPU based algorithm using Iterative Closest Point position tracking and Graph SLAM that can accurately generate a map of an unknown environment without the need for motion encoders and requiring minimal computational resources. The algorithm is able to correct for drift in the position tracking by rapidly identifying loops and optimising the map. We present a method for refining the existing map when revisiting areas to increase the accuracy of the existing map and bound the run-time to the size of the environment.


title: Selection of Support Vector Machines based classifiers for credit risk domain

abstract: We propose an approach combining feature selection, classification and sliding window testing.We propose a technique based on linear Support Vector Machines and particle swarm optimization.Proposed technique is also focused on imbalanced learning.Proposed technique can be useful to identify "risky" companies which are represented as "minority" in imbalanced datasets.Experiment is performed on real world financial dataset from SEC EDGAR database. This paper describes an approach for credit risk evaluation based on linear Support Vector Machines classifiers, combined with external evaluation and sliding window testing, with focus on application on larger datasets. It presents a technique for optimal linear SVM classifier selection based on particle swarm optimization technique, providing significant amount of focus on imbalanced learning issue. It is compared to other classifiers in terms of accuracy and identification of each class. Experimental classification performance results, obtained using real world financial dataset from SEC EDGAR database, lead to conclusion that proposed technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network, and thus be can be an appealing option for future development of real credit risk evaluation models.


title: Clinical Data Mining: Problems, Pitfalls and Solutions

abstract: The wide spread of electronic data collection in medical environments leads to an exponential growth of clinical data extracted from heterogeneous patient samples. Collecting, managing, integrating and analyzing these data are essential activities in order to shed light on diseases and on related therapies. The major issues in clinical data analysis are the incompleteness (missing values), the different adopted measure scales, the integration of the disparate collection procedures. Therefore, the main challenges are in managing clinical data, in discovering patients interactions, and in integrating the different data sources. The final goal is to extract relevant information from huge amounts of clinical data. Therefore, the analysis of clinical data requires new effective and efficient methods to extract compact and relevant information: the interdisciplinary field of data mining, which guides the automated knowledge discovery process, is a natural way to approach the complex task of clinical data analysis. Data mining deals with structured and unstructured data, that are, respectively, data for which we can give a model or not. For example, in clinical contexts it is important to highlight those trials (variables) that are frequent in a particular disease diagnosis. The objective of this work is to study and apply methods to manage and retrieve relevant information in clinical data sets. A practical analysis from real patient data collected from several dementia clinical departments in Italy is reported as example of clinical data mining. The particular field of logic classification, where a data model is computed in form of propositional logic formulas, is investigated for clinical data mining and compared to other techniques, showing that it is a successful approach to compute a compact data model for clinical knowledge discovery.


title: Bond risk, bond return volatility, and the term structure of interest rates

abstract: This paper explores the time variation in the bond risk, as measured by the covariation of bond returns with stock returns and consumption growth, and in the volatility of bond returns. A robust stylized fact in empirical finance is that the spread between the yields on long- and short-term bonds forecasts future excess returns on bonds at varying horizons positively; in addition, the short-term nominal interest rate forecasts both the stock return volatility and the exchange rate volatility positively. This paper presents evidence that movements in both the short-term nominal interest rate and the yield spread are positively related to changes in the subsequent realized bond risk and bond return volatility. The yield spread appears to proxy for business conditions, while the short rate appears to proxy for inflation and economic uncertainty. A decomposition of bond betas into a real cash flow risk component and a discount rate risk component shows that yield spreads have offsetting effects in each component. A widening yield spread is correlated with a reduced cash-flow (or inflationary) risk for bonds, but it is also correlated with a larger discount rate risk for bonds. The short rate only forecasts the discount rate component of the bond beta.


title: EduWear: smart textiles as ways of relating computing technology to everyday life

abstract: In this paper, we report on the outcomes of the European project EduWear. The aim of the project was to develop a construction kit with smart textiles and to examine its impact on young people. The construction kit, including a suitable programming environment and a workshop concept, was adopted by children in a number of workshops. The evaluation of the workshops showed that designing, creating, and programming wearables with a smart textile construction kit allows for creating personal meaningful projects which relate strongly to aspects of young people's life worlds. Through their construction activities, participants became more self-confident in dealing with technology and were able to draw relations between their own creations and technologies present in their environment. We argue that incorporating such constructionist processes into an appropriate workshop concept is essential for triggering thought processes about the character of digital media beyond the construction process itself.


title: Inferring Mechanics of Web Censorship Around the World

abstract: While mechanics of Web censorship in China are well studied, those of other countries are less understood. Through a combination of personal contacts and Planet-Lab nodes, we conduct experiments to explore the mechanics of Web censorship in 11 countries around the world, including China. Our work provides insights into the diversity of modus operandi of censors around the world and can guide future work on censorship evasion.


title: A DC/DC Boosting Technique and Power Management for Ultralow-Voltage Energy Harvesting Applications

abstract: With the increasing use of low-voltage portable devices and growing requirements of functionalities embedded into such devices, efficient dc/dc conversion and power management techniques are needed. In this paper, an architecture for boosting extremely low voltages (about 100 mV) to the typical supply voltages of current integrated circuits is presented which is suitable for power harvesting applications too. Starting from a 120-mV supply voltage, the converter reaches an output voltage of 1.2 V, providing an output current of 220 μA and exhibiting a maximum power efficiency of about 30%. Along with the dc/dc converter, a power management circuit is presented, which can regulate the output voltage and improve the overall efficiency. A test chip was fabricated using a United Microelectronics Corporation 180-nm low-threshold CMOS process.


title: Conservation law models for traffic flow on a network of roads

abstract: The paper develops a model of traffic flow near an intersection, ::: where drivers seeking to enter a congested road wait in a ::: buffer of limited capacity. Initial data comprise the vehicle density on each road, ::: together with the percentage of drivers approaching the intersection ::: who wish to turn into each of the outgoing roads. ::: ::: If the queue sizes ::: within the buffer are known, then the initial-boundary value ::: problems become decoupled and can be independently solved ::: along each incoming road. ::: Three variational problems are introduced, related to different kind of ::: boundary conditions. From the ::: value functions, one recovers the traffic density ::: along each incoming or outgoing road by a Lax type formula. ::: ::: Conversely, ::: if these value functions are known, then the queue sizes can be determined ::: by balancing the boundary fluxes of all incoming and outgoing roads. ::: In this way one obtains a contractive transformation, whose fixed point ::: yields the unique solution of the Cauchy problem ::: for traffic flow in an neighborhood of the intersection. ::: ::: The present model accounts for ::: backward propagation of queues along roads leading to a crowded intersection, ::: it achieves well-posedness for general $L^\infty $ data, and continuity w.r.t. weak convergence of the initial densities.


title: Object-Level Motion Detection From Moving Cameras

abstract: It is important for a moving observer to be able to identify his/her surrounding objects and determine whether these objects are moving or stationary, which is called object-level motion detection. Detecting object-level motion from moving cameras is a difficult problem to solve for collision-free navigation due to the dual motion introduced by the mixture of the camera motion and the object motion. This paper presents a novel technique that detects object-level motion from a freely moving camera using only two consecutive video frames. A context-aware motion descriptor (CMD) is designed based on the object’s moving speed and moving direction relative to that of the moving camera. The CMD employs the contextual information, e.g., the optical flow of the image background surrounding the moving object of interest, which describes the object motion behavior better than other contexts such as the camera’s GPS and direction. The inconsistency between the histogram of oriented optical flow of the object and its surrounding background is measured for the object-level motion detection. The proposed technique has been evaluated over two types of widely studied objects, i.e., vehicles and humans that are captured with different sizes, moving speeds, and image backgrounds using a moving camera. Experiments on challenging real-world videos show promising performance in object-level motion detection.


title: Sensor Network Architectures for Monitoring Underwater Pipelines

abstract: This paper develops and compares different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. These architectures are underwater wired sensor networks, underwater acoustic wireless sensor networks, RF (Radio Frequency) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper also discusses the reliability challenges and enhancement approaches for these network architectures. The reliability evaluation, characteristics, advantages, and disadvantages among these architectures are discussed and compared. Three reliability factors are used for the discussion and comparison: the network connectivity, the continuity of power supply for the network, and the physical network security. In addition, the paper also develops and evaluates a hierarchical sensor network framework for underwater pipeline monitoring.


title: RAND-WALK: A Latent Variable Model Approach to Word Embeddings

abstract: Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. ::: This paper proposes a new generative model, a dynamic version of the log-linear topic model of~\citet{mnih2007three}. The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by~\citet{mikolov2013efficient} and many subsequent papers. ::: Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.


title: Correlation between Facebook use for educational purposes and academic performance of students

abstract: In this paper a research on the application of Facebook in education is presented. A total of 139 students of the Faculty of Education in Sombor (Serbia) participated in the research. The results establish a positive correlation between the academic performance of students and the frequency of use of Facebook for educational purposes. The factor of perceived usefulness greatly affected the decision of students to use Facebook as a learning aid. No significant difference in the frequency of use of Facebook for general purposes has been reported between the groups of students divided by the academic performance. Frequency of educational use of Facebook positively influenced the academic performance.Perceived usefulness affected students use of Facebook as a learning aid.Facebook use for general purposes does not affect the academic performance.


title: Is cognitive case formulation science or science fiction

abstract: As with all systematic models of therapy, cognitive therapy distills a theory to the understanding of particular cases through the case formulation method. This article sets out criteria to evaluate whether cognitive case formulation follows the process of scientific inquiry, and it questions whether the formulation method meets these criteria. In terms of the evidence base for the cognitive theory that underpins cognitive case formulation, the research suggests that although the descriptive elements of cognitive theory are substantiated, the explanatory elements have received less support. In terms of the scientific status of the cognitive case formulation process, current evidence for the reliability of the cognitive case formulation method is modest, at best. There is a striking paucity of research examining the validity of cognitive case formulations or the impact of cognitive case formulation on therapy outcome. Implications for the clinical use of cognitive case formulation within a scientist-practitioner model are discussed, and potential programs of research to evaluate the case formulation method are described.


title: Massively parallel a* search on a GPU

abstract: A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion. Our experiments have demonstrated that the GPU-accelerated A* search is efficient in solving multiple real-world search tasks, including combinatorial optimization problems, pathfinding and game solving. Compared to the traditional sequential CPU-based A* implementation, our GPU-based A* algorithm can achieve a significant speedup by up to 45x on largescale search problems.


title: Efficiency evaluation of a small number of DMUs: an approach based on Li and Reeves's model

abstract: This paper deals with the evaluation of Decision Making Units (DMU) when their number is not large enough to allow the use of classic Data Envelopment Analysis (DEA) models. To do so, we take advantage of the TRIMAP software when used to study the Li and Reeves MultiCriteria DEA (MCDEA) model. We introduce an evaluation measure obtained with the integration of one of the objective functions along the weight space. This measure allows the DMUs joint evaluation. This approach is exemplified with numerical data from some Brazilian electrical companies.


title: A Parallel Sparse Direct Solver via Hierarchical DAG Scheduling

abstract: We present a parallel sparse direct solver for multicore architectures based on Directed Acyclic Graph (DAG) scheduling. Recently, DAG scheduling has become popular in advanced Dense Linear Algebra libraries due to its efficient asynchronous parallel execution of tasks. However, its application to sparse matrix problems is more challenging as it has to deal with an enormous number of highly irregular tasks. This typically results in substantial scheduling overhead both in time and space, which causes overall parallel performance to be suboptimal. We describe a parallel solver based on two-level task parallelism: tasks are first generated from a parallel tree traversal on the assembly tree; next, those tasks are further refined by using algorithms-by-blocks to gain fine-grained parallelism. The resulting fine-grained tasks are asynchronously executed after their dependencies are analyzed. Our approach is distinct from others in that we adopt two-level task scheduling to mirror the two-level parallelism. As a result, we reduce scheduling overhead, and increase efficiency and flexibility. The proposed parallel sparse direct solver is evaluated for the particular problems arising from the hp-Finite Element Method where conventional sparse direct solvers do not scale well.


Product metrics in agile software development


title: An AMOLED Panel Test System Using Universal Data Driver ICs for Various Pixel Structures

abstract: In this paper, we propose a test system for active-matrix organic light emitting diode (AMOLED) panels with various pixel structures with the external compensation method. The proposed AMOLED panel test system employs universal data drive ICs to measure the current of a driving thin-film transistor (TFT) and the anode voltage of the OLED in various pixels by only programming the field-programmable gate array in the proposed test system. The universal data driver IC are fabricated and implemented in the proposed AMOLED panel test system whose test board is assembled with a 55-in full-high-definition AMOLED panel. A fabricated universal data driver IC includes 640 data channels with a 12-b linear gamma digital-to-analog converter and 12-bit variable current sources. To evaluate the repeatability error of the proposed panel test system, the current of the driving TFT is repeatedly measured and the measured maximum repeatability error is 9.8 nA. In addition, to evaluate the measurement accuracy of the proposed panel test system, the variation in the currents of the driving TFTs is measured and compensated for, and its maximum value after compensation is 26 nA.


title: Distributed Computation of Large-scale Graph Problems

abstract: Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a number of fundamental graph problems in the message-passing model, where we have k machines that jointly perform computation on an arbitrary n-node (typically, n G k) input graph. The graph is assumed to be randomly partitioned among the k ≥ 2 machines (a common implementation in many real world systems). The communication is point-to-point, and the goal is to minimize the time complexity i.e., the number of communication rounds, of solving various fundamental graph problems. ::: ::: We present lower bounds that quantify the fundamental time limitations of distributively solving graph problems. We first show a lower bound of Ω(n/k) rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST), breadth-first tree (BFS), and shortest paths tree (SPT). We also show an Ω(n/k2) lower bound for connectivity ST verification and other related problems. Our lower bounds develop and use new bounds in random-partition communication complexity. ::: ::: To complement our lower bounds, we also give algorithms for various fundamental graph problems, e.g., PageRank, MST, connectivity, ST verification, shortest paths, cuts, spanners, covering problems, densest subgraph, subgraph isomorphism, finding triangles, etc. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in O(n/k) time (the notation O hides polylog(n) factors and an additive polylog(n) term); this shows that one can achieve almost linear (in k) speedup, whereas for shortest paths, we present algorithms that run in O(n/Ok) time (for (1 + e)-factor approximation) and in O(n/k) time (for O(log n)-factor approximation) respectively. ::: ::: Our results are a step towards understanding the complexity of distributively solving large-scale graph problems.


Monday mornings are my fave : ) #not Exploring the Automatic Recognition of Irony in English tweets


A Signal Processing Approach To Malware Analysis


title: Is Genicular Nerve Radiofrequency Ablation Safe? A Literature Review and Anatomical Study.

abstract: Genicular nerve radiofrequency ablation (RFA) has recently gained popularity as an intervention for chronic knee pain in patients who have failed other conservative or surgical treatments. Long-term efficacy and adverse events are still largely unknown. Under fluoroscopic guidance, thermal RFA targets the lateral superior, medial superior, and medial inferior genicular nerves, which run in close proximity to the genicular arteries that play a crucial role in supplying the distal femur, knee joint, meniscus, and patella. RFA targets nerves by relying on bony landmarks, but fails to provide visualization of vascular structures. Although vascular injuries after genicular nerve RFA have not been reported, genicular vascular complications are well documented in the surgical literature. This article describes the anatomy, including detailed cadaveric dissections and schematic drawings, of the genicular neurovascular bundle. The present investigation also included a comprehensive literature review of genicular vascular injuries involving those arteries which lie near the targets of genicular nerve RFA. These adverse vascular events are documented in the literature as case reports. Of the 27 cases analyzed, 25.9% (7/27) involved the lateral superior genicular artery, 40.7% (11/27) involved the medial superior genicular artery, and 33.3% (9/27) involved the medial inferior genicular artery. Most often, these vascular injuries result in the formation of pseudoaneurysm, arteriovenous fistula (AVF), hemarthrosis, and/or osteonecrosis of the patella. Although rare, these complications carry significant morbidities. Based on the detailed dissections and review of the literature, our investigation suggests that vascular injury is a possible risk of genicular RFA. Lastly, recommendations are offered to minimize potential iatrogenic complications.


title: A Random-Finite-Set Approach to Bayesian SLAM

abstract: This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the number and location of the features, as well as the vehicle trajectory. More concisely, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive, thereby incorporating both data association and feature management into a single recursion. Furthermore, the Bayes optimality of the proposed approach is established. A first-order solution, which is coined as the probability hypothesis density (PHD) SLAM filter, is derived, which jointly propagates the posterior PHD of the map and the posterior distribution of the vehicle trajectory. A Rao-Blackwellized (RB) implementation of the PHD-SLAM filter is proposed based on the Gaussian-mixture PHD filter (for the map) and a particle filter (for the vehicle trajectory). Simulated and experimental results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.


title: Entry Strategies Under Competing Standards: Hybrid Business Models in the Open Source Software Industry

abstract: The paper analyzes the strategies of software firms that have entered the open source (OS) field. The notion of the OS business model is discussed in the light of a substantial body of theoretical literature concerning strategic management and the economics of innovation, as well as specialized literature on OS. Empirical evidence based on a survey of 146 Italian software firms shows that firms have adapted to an environment dominated by incumbent standards by combining the offering of proprietary and OS software under different licensing schemes, thus choosing a hybrid business model. The paper examines the determinants of the degree of openness toward OS and discusses the stability of hybrid models in the evolution of the industry.


title: Lightning impulse test of field-aged PV modules and simulation partial discharge within MATLAB

abstract: Impulse voltage withstand test is an important method to identify that the insulating encapsulation of a PV module can withstand overvoltage due to the effects of direct or indirect lightning strikes from atmosphere. In this work, a comparison of testing and simulating on the partial discharge in the PV module produced by lightning impulse voltage source is given. For the experiment, the field aged PV module was applied by 1.2/50 □s impulse voltage generator up to 4 kV between terminal box and frame of PV module. It was found the measured impulse voltage waveform had a number of voltage spikes and oscillations superimposed on the impulse voltage. As to the explanation of this phenomenon, a novel mathematical model of partial discharge inside the void of PV insulation was implemented in MATLAB-Simulink program tool. The simulated result deduced that the ringing oscillations caused by partial discharge in the defects of the deterioration PV modules. In addition, some discussions are given for testing and simulating results.


Ridesharing : the State-ofthe-art and Future Directions


title: A Review on Graph-based Image Classification

abstract: Graphs are increasingly important in modelling complicated structures, such as circuits, images, chemical compounds, protein structures, biological networks, social networks and the web. Graph-based data representations are an important research topic due to the suitability of this kind of data structure to model entities and the complex relations among them. We are proposing to combine a powerful graph-based image representation and frequent approximate sub-graph (FAS) mining algorithms in order to classify images. Among the many approaches in performing image classification, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. This paper critically reviews existing important frequent subgraph mining algorithms This review not only reveals the pros in each method and category but also explores its limitations.


title: Sensor-based analysis of high-precision insertion tasks

abstract: This paper investigates the mechanism of a high-precision insertion process from the viewpoint of sensory information. Tolerance considered in the present study is approximately 5 /spl mu/m. Currently the assembly task is being conducted by skilled human workers. In order to analyze the insertion task, we focus on force and moment information and inclination measurements. A conventional approach, i.e. a position-based analysis, is not appropriate for such a narrow dimensional tolerance because it is unlikely that human operators conduct the task based on position information. This paper presents a newly developed, high-sensitivity moment sensor and experimental results in high-precision insertion tasks.


title: Natural Language Question/Answering: Let Users Talk With The Knowledge Graph

abstract: The ever-increasing knowledge graphs impose an urgent demand of providing effective and easy-to-use query techniques for end users. Structured query languages, such as SPARQL, offer a powerful expression ability to query RDF datasets. However, they are difficult to use. Keywords are simple but have a very limited expression ability. Natural language question (NLQ) is promising on querying knowledge graphs. A huge challenge is how to understand the question clearly so as to translate the unstructured question into a structured query. In this paper, we present a data + oracle approach to answer NLQs over knowledge graphs. We let users verify the ambiguities during the query understanding. To reduce the interaction cost, we formalize an interaction problem and design an efficient strategy to solve the problem. We also propose a query prefetch technique by exploiting the latency in the interactions with users. Extensive experiments over the QALD dataset demonstrate that our proposed approach is effective as it outperforms state-of-the-art methods in terms of both precision and recall.


title: Influence of amino acids on gastric emptying in young pigs.

abstract: The effects of amino acids on gastric emptying and secretion were studied in four young conscious pigs provided with a chronic gastric cannula. A basal test meal of 500 ml containing 10 g citrus pectin, 17.5 g sucrose, 100 mg phenol red alone or with glycine, L- or DL-tryptophan, DL-methionine or L-glutamic acid was poured into the stomach and recovered 20 min later. Glycine at concentrations of 26.7--106.8 mM/l did significantly affect gastric emptying, although at the highest concentration gastric emptying appeared to slow down. L-lysine (41.0 mM/l), DL-methionine (40.3 mM/l or L-tryptophan (29.4 mM/l did not significantly affect the rate of stomach emptying; L-tryptophan increased the amount of Cl- and H+ secreted as compared with the basal diet alone. The effects of addition of the essential amino acids, L-lysine and DL-methionine, were compared with those of the nonessential glycine and L-glutamic acid. There was no significant difference in the rate of gastric emptying or secretion between them. The L and DL isomers of lysine and trypotphan were not found to differ significantly in their effects on gastric emptying and secretion.


title: The Neutralization Sensitivity of Viruses Representing Human Immunodeficiency Virus Type 1 Variants of Diverse Subtypes from Early in Infection is Dependent on Producer Cell, as well as Characteristics of the Specific Antibody and Envelope Variant

abstract: Neutralization properties of human immunodeficiency virus (HIV-1) are often defined using pseudoviruses grown in transformed cells, which are not biologically relevant HIV-1 producer cells. Little information exists on how these viruses compare to viruses produced in primary lymphocytes, particularly for globally relevant HIV-1 strains. Therefore, replication-competent chimeras encoding envelope variants from the dominant HIV-1 subtypes (A, C, and D) obtained early after infection were generated and the neutralization properties explored. Pseudoviruses generated in 293T cells were the most sensitive to antibody neutralization. Replicating viruses generated in primary lymphocytes were most resistant to neutralization by plasma antibodies and most monoclonal antibodies (b12, 4E10, 2F5, VRC01). These differences were not associated with differences in envelope content. Surprisingly, the virus source did not impact neutralization sensitivity of most viruses to PG9. These findings suggest that producer cell type has a major effect on neutralization sensitivity, but in an antibody dependent manner.


title: Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images

abstract: In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 million sentences describing over 40 million images crawled and downloaded from publicly available Pins (i.e. an image with sentence descriptions uploaded by users) on Pinterest. This dataset is more than 200 times larger than MS COCO, the standard large-scale image dataset with sentence descriptions. In addition, we construct an evaluation dataset to directly assess the effectiveness of word embeddings in terms of finding semantically similar or related words and phrases. The word/phrase pairs in this evaluation dataset are collected from the click data with millions of users in an image search system, thus contain rich semantic relationships. Based on these datasets, we propose and compare several Recurrent Neural Networks (RNNs) based multimodal (text and image) models. Experiments show that our model benefits from incorporating the visual information into the word embeddings, and a weight sharing strategy is crucial for learning such multimodal embeddings. The project page is: http://www.stat.ucla.edu/~junhua.mao/multimodal_embedding.html (The datasets introduced in this work will be gradually released on the project page.).


title: Inverse kinematics for reduced deformable models

abstract: Articulated shapes are aptly described by reduced deformable models that express required shape deformations using a compact set of control parameters. Although sufficient to describe most shape deformations, these control parameters can be ill-suited for animation tasks, particularly when reduced deformable models are inferred automatically from example shapes. Our algorithm provides intuitive and direct control of reduced deformable models similar to a conventional inverse-kinematics algorithm for jointed rigid structures. We present a fully automated pipeline that transforms a set of unarticulated example shapes into a controllable, articulated model. With only a few manipulations, an animator can automatically and interactively pose detailed shapes at rates independent of their geometric complexity.


title: A Counter-WMD Strategy for the Future

abstract: The past year has witnessed a renewed emphasis by US government agencies addressing the threat of weapons of mass destruction (WMD). In December 2009, the Obama Administration released its second presidential policy directive, a "National Strategy to Counter Biological Threats," which addressed the challenge of combating infectious diseases, regardless of whether they were natural or manmade. In February 2010, the Quadrennial Defense Review stressed how the proliferation of WMD "continues to undermine global security." In April, the Nuclear Posture Review was released for the first time as an unclassified document, along with a newly signed Strategic Arms Reduction Treaty, reducing the deployable number of Russian and US nuclear weapons. In May, representatives from across the globe met to renew the Nuclear Nonproliferation Treaty, which attempts to reduce (and eventually eliminate) the total number of nuclear-owning weapon states in the world. It has been a busy spring. During talks related to these initiatives, President Barack Obama directly connected the threat of nuclear terrorism to the success of nuclear proliferation efforts. He declared, "The greatest threat to US and global security is no longer a nuclear exchange between nations, but nuclear terrorism by violent extremists and nuclear proliferation to an increasing number of states." (1) The current focus on nonproliferation activities, however, does not stop terrorists from seeking and potentially obtaining nuclear and biological materials, technology, and devices. For that matter, the emphasis on combating terrorism has not resulted in a reduction of terrorist ambitions to obtain these materials, either. The US government, and the Department of Defense (DOD) in particular, needs to review its strategy to combat weapons of mass destruction. The combating WMD framework is based on a counterproliferation strategy developed in response to the threat of nuclear, biological, and chemical (NBC) weapons to military forces in the 1990s, however, its scope was broadened after September 2001 to address concerns relating to homeland security. For all the talk about "the most dangerous weapons in the hands of the most dangerous people," there has been little discussion on whether the combating WMD strategy is adequate against current and future threats. This article will review the development of the combating WMD strategy from its initiation in the 1990s, as a result of the post-conflict analysis of the Persian Gulf War in 1991. It will outline the creation of the combating WMD strategy during the George W. Bush Administration. The article focuses on challenges in interpretation, largely due to the thesis that terrorists were actively seeking WMD materials and technology from "rogue states" that had developed this capability (or were in the process of doing so). Last, it will offer suggestions on how to improve the framework, largely by defining the strategy to counter nation-state WMD programs as distinct from the strategy to counter terrorist pursuit of WMD. If the US government clearly articulates these two strategies as separate but related, as opposed to being one single strategy to counter WMD, the agencies responsible for executing these strategies will be much more effective. Genesis of DOD's Combating WMD Strategy In 1993, the Office of the Secretary of Defense (OSD) initiated a Defense Counterproliferation Initiative with the image of ill-prepared US forces facing Iraq's chemical and biological (CB) weapons still fresh in its mind. There was some initial concern by the nonproliferation community that DOD was attempting to usurp its role that a focus on developing offensive and defensive capabilities to counter adversarial use of CB weapons would come at the cost of reducing nonproliferation efforts. (2) After a few years of discussion, OSD tasked the Joint Staff in 1996 to develop a counterproliferation strategy, stating in Defense Planning Guidance that "all US forces must be prepared to conduct wartime operations against adversaries armed with chemical or biological weapons. …


title: Abstract ARTICLE IN PRESS Fighting cybercrime: a review and the Taiwan experience

abstract: Cybercrime is becoming ever more serious. Findings from the 2002 Computer Crime and Security Survey show an upward trend that demonstrates a need for a timely review of existing approaches to fighting this new phenomenon in the information age. In this paper, we define different types of cybercrime and review previous research and current status of fighting cybercrime in different countries that rely on legal, organizational, and technological approaches. We focus on a case study of fighting cybercrime in Taiwan and discuss problems faced. Finally, we propose several recommendations to advance the work of fighting eybercrime.


title: Z-Forcing: Training Stochastic Recurrent Networks

abstract: Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently proposed architectures into a stochastic recurrent model: each step in the sequence is associated with a latent variable that is used to condition the recurrent dynamics for future steps. Training is performed with amortized variational inference where the approximate posterior is augmented with a RNN that runs backward through the sequence. In addition to maximizing the variational lower bound, we ease training of the latent variables by adding an auxiliary cost which forces them to reconstruct the state of the backward recurrent network. This provides the latent variables with a task-independent objective that enhances the performance of the overall model. We found this strategy to perform better than alternative approaches such as KL annealing. Although being conceptually simple, our model achieves state-of-the-art results on standard speech benchmarks such as TIMIT and Blizzard and competitive performance on sequential MNIST. Finally, we apply our model to language modeling on the IMDB dataset where the auxiliary cost helps in learning interpretable latent variables. Source Code: \url{this https URL}


title: New modular high voltage LDMOS technology based on Deep Trench Isolation and 0.18um CMOS platform

abstract: This paper presents the challenges of integrating 70V and 45V lateral DMOS transistor modules into a 0.18um base line process. This integration is achieved with minimal impact on baseline process and circuit IP's. Multi-epitaxial stack and Deep Trench Isolation (DTI) modules assure up to 140V isolation capability between different areas in the chip.


title: Adversarially Regularized Autoencoders

abstract: Deep latent variable models, trained using variational autoencoders or generative adversarial networks, are now a key technique for representation learning of continuous structures. However, applying similar methods to discrete structures, such as text sequences or discretized images, has proven to be more challenging. In this work, we propose a flexible method for training deep latent variable models of discrete structures. Our approach is based on the recently-proposed Wasserstein autoencoder (WAE) which formalizes the adversarial autoencoder (AAE) as an optimal transport problem. We first extend this framework to model discrete sequences, and then further explore different learned priors targeting a controllable representation. This adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space. Finally we show that the latent representation can be trained to perform unaligned textual style transfer, giving improvements both in automatic/human evaluation compared to existing methods.


title: A systematic literature review on attractiveness and learnability factors in Web applications

abstract: User interface design is an essential element for consideration in Web application development. Usability (ISO/IEC 9126-1:2001) or operability (ISO/IEC 25010:2011) is one of non-functional requirements which must be taken into consideration when designing a user interface. Attractiveness and learnability are among the sub criteria under the operability factor. Analysis of these criteria may be used to evaluate user interface design. This study has adopted a systematic literature review method to study existing works concerning the two factors of operability. The outcome of the review provides insight into existing works on attractiveness and learnability factors in software applications generally and Web applications specifically.


Ontology Development for Machine Translation: Ideology and Methodology


An Automatic Speech Recognition for the Filipino Language using the HTK System


title: Discrete Structural Planning for Neural Machine Translation

abstract: Structural planning is important for producing long sentences, which is a missing part in current language generation models. In this work, we add a planning phase in neural machine translation to control the coarse structure of output sentences. The model first generates some planner codes, then predicts real output words conditioned on them. The codes are learned to capture the coarse structure of the target sentence. In order to obtain the codes, we design an end-to-end neural network with a discretization bottleneck, which predicts the simplified part-of-speech tags of target sentences. Experiments show that the translation performance are generally improved by planning ahead. We also find that translations with different structures can be obtained by manipulating the planner codes.


title: Emotion Analysis in Code-Switching Text With Joint Factor Graph Model

abstract: Previous research on emotions analysis has placed much emphasis in monolingual instead of bilingual text. However, emotions on social media platforms are often found in bilingual or code-switching posts. Different from monolingual text, emotions in code-switching text can be expressed in both monolingual and bilingual forms. Moreover, more than one emotion can be expressed within a single post; yet they tend to be related in some ways which offers some implications. It is thus necessary to consider the correlation between different emotions. In this paper, a joint factor graph model is proposed to address this issue. In particular, attribute functions of the factor graph model are utilized to learn both monolingual and bilingual information from each post, factor functions are used to explore the relationship among different emotions, and a belief propagation algorithm is employed to learn and predict the model. Empirical studies demonstrate the importance of emotion analysis in code-switching text and the effectiveness of our proposed joint learning model.


title: Force-length characteristics of in vivo human skeletal muscle.

abstract: In the present study, the in vivo force-length relations of the human soleus (SOL) and tibialis anterior (TA) muscles were estimated. Measurements were taken in six men at ankle angles from 30 degrees of dorsiflexion to 45 degrees of plantarflexion in steps of 15 degrees, and involved dynamometry, electrical stimulation, ultrasonography and magnetic resonance imaging (MRI). For each muscle and ankle angle studied the following three measurements were carried out: (1) dynamometry-based measurement of maximal voltage tetanic moment, (2) ultrasound-based measurement of pennation angle and fibre length and (3) MRI-based measurement of tendon moment arm length. Tendon forces were calculated dividing moments by moment arm lengths, and muscle forces were calculated dividing tendon forces by the cosine of pennation angles. In the transition from 30 degrees of dorsiflexion to 45 degrees of plantarflexion the SOL muscle fibre length decreased from 3.8 to 2.4 cm and its force decreased from 3330 to 290 N. Over the same range of ankle angles the TA muscle fibre length increased from 3.7 to 6 cm and its force increased from 157 to 644 N. Over the longest muscle fibre lengths reached the force of both muscles remained approximately constant. These results indicate that the intact human SOL and TA muscles operate in the ascending limb and plateau region of the force-length relationship. Similar conclusions were reached when calculating the theoretical operating range of the two muscle sarcomeres in the study.


title: Equivalence conditions for the finite volume and finite element methods in spherical coordinates

abstract: A numerical technique for the solution of the compressible flow equations over unstructured grids in a spherical reference is presented. The proposed approach is based on a mixed finite volume/finite element discretization in space. Equivalence conditions relating the finite volume and the finite element metrics in spherical coordinates are derived. Numerical simulations of the explosion and implosion problems for inviscid compressible flows are carried out to evaluate the correctness of the numerical scheme and compare fairly well to one-dimensional simulations over very fine grids.


title: Ultrawideband Double Rhombus Antenna With Stable Radiation Patterns for Phased Array Applications

abstract: A broadband microstrip-fed printed antenna is proposed for phased antenna array systems. The antenna consists of two parallel-modified dipoles of different lengths. The regular dipole shape is modified to a quasi-rhombus shape by adding two triangular patches. Using two dipoles helps maintain stable radiation patterns close to their resonance frequencies. A modified array configuration is proposed to further enhance the antenna radiation characteristics and usable bandwidth. Scanning capabilities are studied for a four-element array. The proposed antenna provides endfire radiation patterns with high gain, high front-to-back (F-to-B) ratio, low cross-polarization level, wide beamwidth, and wide scanning angles in a wide bandwidth of 103%


Verifying Music Tag Annotation Via Association Analysis


title: Design of a Biologically Accurate Prosthetic Hand

abstract: A high fidelity biomimetic hand actuated by 9 stepper motors packaged within forearm casing was manufactured for less than 350 USD; it has 18 mechanical degrees of freedom, is 38 cm long, weighs 2.2 kg. The hand model has 3D printed replicas of human bones and laser cut tendons and ligaments. The user intent is deduced from EEG and EMG signals, obtained by Neurosky and Myoware commercial sensors, respectively. Three distinct EEG patterns trigger pinch, hook, and point actions. EMG signals are used for finer motor control, e.g. strength of grip. A pilot test study on three subjects showed that EMG can actuate the hand with an 80% success rate, while EEG allows for a 68% success rate. The system proved its robustness at the 2017 Cambridge Science Festival, using EEG signals alone. Out of approximately 30 visitors the majority could generate a “peace” sign after 1 to 2 minutes.


title: Software Process Models and Analysis on Failure of Software Development Projects

abstract: The software process model consists of a set of activities undertaken to design, develop and maintain software systems. A variety of software process models have been designed to structure, describe and prescribe the software development process. The software process models play a very important role in software development, so it forms the core of the software product. Software project failure is often devastating to an organization. Schedule slips, buggy releases and missing features can mean the end of the project or even financial ruin for a company. Oddly, there is disagreement over what it means for a project to fail. In this paper, discussion is done on current process models and analysis on failure of software development, which shows the need of new research.


title: Articulation and Clarification of the Dendritic Cell Algorithm

abstract: The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings.


title: Modified One-Cycle Controlled Bidirectional High-Power-Factor AC-to-DC Converter

abstract: AC-to-DC converters based on one-cycle control exhibit instability in current control at light load conditions as well as when they are operating in the inverting mode. In this paper, a modified one-cycle controller for bidirectional AC-to-DC converter is proposed. A fictitious current component in phase with the utility voltage is synthesized. The sum of this current component and the actual load current is compared with the sawtooth waveform to generate the gating pulses for the switches. This modification not only renders stability to the converter at light load conditions and the inverting mode of operations but also enables the converter to seamlessly transfer its operation from the rectifying mode to the inverting mode and vice versa. Detailed simulation studies are carried out to verify the effectiveness of the proposed scheme. To validate the viability of the scheme, detailed experimental studies are carried out on a 2-kW laboratory prototype.


title: Siamese Networks for Chromosome Classification

abstract: Karyotying is the process of pairing and ordering 23 pairs of human chromosomes from cell images on the basis of size, centromere position, and banding pattern. Karyotyping during metaphase is often used by clinical cytogeneticists to analyze human chromosomes for diagnostic purposes. It requires experience, domain expertise and considerable manual effort to efficiently perform karyotyping and diagnosis of various disorders. Therefore, automation or even partial automation is highly desirable to assist technicians and reduce the cognitive load necessary for karyotyping. With these motivations, in this paper, we attempt to develop methods for chromosome classification by borrowing the latest ideas from deep learning. More specifically, we perform straightening on chromosomes and feed them into Siamese Networks to push the embeddings of samples coming from similar labels closer. Further, we propose to perform balanced sampling from the pairwise dataset while selecting dissimilar training pairs for Siamese Networks, and an MLP based prediction on top of the embeddings obtained from the trained Siamese Networks. We perform our experiments on a real world dataset of healthy patients collected from a hospital and exhaustively compare the effect of different straightening techniques, by applying them to chromosome images prior to classification. Results demonstrate that the proposed methods speed up both training and prediction by 83 and 3 folds, respectively; while surpassing the performance of a very competitive baseline created utilizing deep convolutional neural networks.


title: The Influence of Various Upper Electrodes on Fatigue Properties of Perovskite Pb(Zr,Ti)O3 Thin Films

abstract: Ferroelectric thin films of Pb(Zr,Ti)O3 (PZT) were prepared by chemical solution deposition (CSD). The top electrodes of platinum (Pt), SrRuO3 (SRO), and IrO2 thin films are deposited on the PZT thin films by RF magnetron sputtering (RF) and the pulsed laser deposition (PLD) methods, respectively. The polarization fatigue of ferroelectric thin-film capacitors is investigated. As a result, the remanent polarization value of the PZT thin films of SRO/PZT/PT/Pt/TiOx/SiO2/Si and IrO2/PZT/PT/Pt/TiOx/SiO2/Si remains more than 1011 switching cycles at constant value and the value of the PZT thin films of Pt/PZT/PT/Pt/TiOx/SiO2/Si decreases to approximately 50% of its initial value at 108 cycles. It is confirmed that polarization fatigue is improved using SRO and IrO2 thin-film electrodes as top electrodes.


title: The 7piece perfect play lookup database for the game of checkers

abstract: Many research teams and individuals have computed endgame databases for the game of chess which use the distance-to-mate metric, enabling their software to forecast the number of moves remaining until the game is over. This is not the case for the game of checkers. Only one programming team has generated a checkers database capable of announcing the distance to the terminal position. This paper examines the benefits and detriments associated with computing three different types of checkers endgames databases, demonstrates the solutions to the longest wins in the 7-piece checkers database, presents tables of longest wins for positions including all permutations of four pieces and fewer against three pieces and fewer, and offers major improvements to some previously published play.


title: Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

abstract: Abstract The concentration of people in densely populated urban areas, especially in developing countries, calls for the use of monitoring systems like remote sensing. Such systems along with spatial analysis techniques like digital image processing and geographical information system (GIS) can be used for the monitoring and planning purposes as these enable the reporting of overall sprawl at a detailed level. In the present work, urban sprawl of the Ajmer city (situated in Rajasthan State of India) has been studied at a mid scale level, over a period of 25 years (1977–2002), to extract the information related to sprawl, area of impervious surfaces and their spatial and temporal variability. Statistical classification approaches have been used for the classification of the remotely sensed images obtained from various sensors viz. Landsat MSS, TM, ETM+ and IRS LISS-III. Urban sprawl and its spatial and temporal characteristics have been derived from the classified satellite images. The Shannon's entropy and landscape metrics (patchiness and map density) have been computed in terms of spatial phenomenon, in order to quantify the urban form (impervious area). Further, multivariate statistical techniques have been used to establish the relationship between the urban sprawl and its causative factors. Results reveal that land development (160.8%) in Ajmer is more than three times the population growth (50.1%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the urban sprawl over a period of last 25 years.


title: On Digital Differential Analyzer (DDA) Circle Generation for Computer Graphics

abstract: This correspondence comments on the method of circle generation described by Armstrong [1], and discusses the methods by which satisfactory circles for computer graphics may be obtained with digital differential analyzers (DDA's) using a single-phase Adam's procedure.


title: Shoe-last design innovation for better shoe fitting

abstract: Shoe-last, a 3D mould used for making footwear, influence the shape, size and fitting of footwear. Current shoe-last design software has focused mainly on reverse engineering of existing shoe-last and modification. Shoe-last designers have generally preferred to design the shoe-last manually due to limitations of design software. In order to solve these problems, a new software based on CATIA platform was developed. The shoe-last model is based on foot shape measurement data and foot biomechanics. Using the existing shoe-last design standards and the sections from existing shoe-lasts, design tables and relationship equations enables the design of shoe-last with different toe type, heel height and custom shoe-last. The design includes comfort and fit aspects as well as design aspect, therefore enables design of aesthetical comfortable shoes. Since the design can be modified instantaneously, the designers could visualize design changes leading to a reduction in shoe-last design cycle.


title: CRISP: A computational model of fixation durations in scene viewing.

abstract: Eye-movement control during scene viewing can be represented as a series of individual decisions about where and when to move the eyes. While substantial behavioral and computational research has been devoted to investigating the placement of fixations in scenes, relatively little is known about the mechanisms that control fixation durations. Here, we propose a computational model (CRISP) that accounts for saccade timing and programming and thus for variations in fixation durations in scene viewing. First, timing signals are modeled as continuous-time random walks. Second, difficulties at the level of visual and cognitive processing can inhibit and thus modulate saccade timing. Inhibition generates moment-by-moment changes in the random walk’s transition rate and processing-related saccade cancellation. Third, saccade programming is completed in 2 stages: an initial, labile stage that is subject to cancellation and a subsequent, nonlabile stage. Several simulation studies tested the model’s adequacy and generality. An initial simulation study explored the role of cognitive factors in scene viewing by examining how fixation durations differed under different viewing task instructions. Additional simulations investigated the degree to which fixation durations were under direct moment-to-moment control of the current visual scene. The present work further supports the conclusion that fixation durations, to a certain degree, reflect perceptual and cognitive activity in scene viewing. Computational model simulations contribute to an understanding of the underlying processes of gaze control.


Strategical Argumentative Agent for Human Persuasion.


title: Continuous speech recognition by statistical methods

abstract: Statistical methods useful in automatic recognition of continuous speech are described. They concern modeling of a speaker and of an acoustic processor, extraction of the models' statistical parameters and hypothesis search procedures and likelihood computations of linguistic decoding. Experimental results are presented that indicate the power of the methods.


Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch


title: Memory enhancement for emotional words: Are emotional words more vividly remembered than neutral words?

abstract: Individuals are more likely to remember negative information than neutral information. In the experiments reported here, we examined whether individuals were also more likely to remember details of the presentation of negative words, as compared with neutral words. In Experiment 1, the remember-know procedure was used to examine the effect of emotion on the vividness of an individual’s memory, showing thatremember responses were more frequently assigned to negative words than neutral words. In Experiment 2, a source memory paradigm was used, and again, evidence that individuals’ memories were more detailed for negative than for neutral words was found. In Experiments 3–6, we examined the relative contribution of valence and arousal, finding that both dimensions increased the vividness of remembered information (i.e., items with valence only and those that elicited arousal were better remembered than neutral information) but that the effect was greater for words that evoked arousal than for those with valence only. The results support a qualitative, as well as a quantitative, memory benefit for emotional, as compared with neutral, words.


title: Stealthy malware detection and monitoring through VMM-based “out-of-the-box” semantic view reconstruction

abstract: An alarming trend in recent malware incidents is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based antimalware systems is that they run inside the very hosts they are protecting (“in-the-box”), making them vulnerable to counter detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM (“out-of-the-box”). However, they gain tamper resistance at the cost of losing the internal semantic view of the host, which is enjoyed by “in-the-box” approaches. This poses a technical challenge known as the semantic gap. In this article, we present the design, implementation, and evaluation of VMwatcher—an “out-of-the-box” approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM nonintrusively from the outside. More specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. Furthermore, we extend guest view casting to reconstruct details of system call events (e.g., the process that makes the system call as well as the system call number, parameters, and return value) in the VM, enriching the semantic view. With the semantic gap effectively narrowed, we identify three unique malware detection and monitoring capabilities: (i) view comparison-based malware detection and its demonstration in rootkit detection; (ii) “out-of-the-box” deployment of off-the-shelf anti malware software with improved detection accuracy and tamper-resistance; and (iii) nonintrusive system call monitoring for malware and intrusion behavior observation. We have implemented a proof-of-concept VMwatcher prototype on a number of VMM platforms. Our evaluation experiments with real-world malware, including elusive kernel-level rootkits, demonstrate VMwatcher's practicality and effectiveness.


title: Synthetic Aperture Radar Processing with GPGPU

abstract: This article focuses on methodologies with recurrent use to code examples that try to couple with the flow of the main steps of the SAR processing. The possibility to be comprehensive was prevented by the wide scenario of variations of the focusing algorithm as well as the spread of applications. The reader should look at this work as a sample of possibilities offered by this new technology and a collection of suggestions and considerations that may guide to new applications and horizons.


title: Users of Different Travel Modes Differ in Journey Satisfaction and Habit Strength but not Environmental Worldviews: A Large-Scale Survey of Drivers, Walkers, Bicyclists and Bus Users Commuting to a UK University

abstract: People who travel to the same university workplace by bicycle, bus, car, and walking were compared in a survey (N = 1609). Data are presented on environmental worldviews, journey affective appraisals, and habit strength. Unexpectedly, findings showed comparable levels of environmental worldview across modes. This might reflect the role of attitudes on behaviour, or question the validity of the established environmental worldview scale used here. Results also replicated previous work on affective appraisal, and suggested that whilst walking, bicycling and bus use have distinctive affective appraisals associated with each mode, car driving was affectively neutral, generating no strong response on any dimension – a finding tentatively explained with reference to the normative status of driving. The survey also showed users of active travel modes reported stronger habit strength than car or public transport users, with possible links to the role of affect in formulating habit strength in line with habit theory.


title: Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

abstract: Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and nonstationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ‘nearestneighbours’ method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) shortterm rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone.