title: Geolocation using TDOA and FDOA measurements

abstract: Passive geolocaton of communication emitters provides great benefits to military and civilian surveillance and security operations. Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurement combination for stationary emitters may be obtained by sensors mounted on mobile platforms, for example on a pair of UAVs. Complex Ambiguity Function (CAF) of received complex signals can be efficiently calculated to provide required TDOA / FDOA measurement combination. TDOA and FDOA measurements are nonlinear in the sense that the emitter uncertainty given measurements in the Cartesian domain is non-Gaussian. Multiple non-linear measurements of emitter location need to be fused to provide the geolocation estimates. Gaussian Mixture Measurement (GMM) filter fuses nonlinear measurements as long as the uncertainty of each measurement in the surveillance (Cartesian) space is modeled by a Gaussian Mixture. Simulation results confirm this approach and compare it with geolocation using Bearings Only (BO) measurements.


title: Image classification with caffe deep learning framework

abstract: Image classification is one of the important problems in the field of machine learning. Deep learning architectures are used in many machine learning applications such as image classification and object detection. The ability to manipulate large image clusters and implement them quickly makes deep learning a popular method in classifying images. This study points out the success of the convolutional neural networks which is the architecture of deep learning, in solving image classification problems. In the study, the convolutional neural network model of the winner of ilsvrc12 competition is implemented. The method distinguishes 1.2 million images with 1000 categories in success. The application is performed with the caffe library, and the image classification process is employed. In the application that uses the speed facility provided by GPU, the test operation is performed by using the images in Caltech-101 dataset.


title: A Comparative Evaluation of Two Algorithms for Windows Registry Anomaly Detection

abstract: We present a component anomaly detector for a host-based intrusion detection system (IDS) for Microsoft Windows. The core of the detector is a learning-based anomaly detection algorithm that detects attacks on a host machine by looking for anomalous accesses to the Windows Registry. We present and compare two anomaly detection algorithms for use in our IDS system and evaluate their performance. One algorithm called PAD, for Probabilistic Anomaly Detection, is based upon a probability density estimation while the second uses the Support Vector Machine framework. The key idea behind the detector is to first train a model of normal Registry behavior on a Windows host, even when noise may be present in the training data, and use this model to detect abnormal Registry accesses. At run-time the model is used to check each access to the Registry in real-time to determine whether or not the behavior is abnormal and possibly corresponds to an attack. The system is effective in detecting the actions of malicious software while maintaining a low rate of false alarms. We show that the probabilistic anomaly detection algorithm exhibits better performance in accuracy and in computational complexity over the support vector machine implementation under three different kernel functions.


title: Healthcare information system integration: a service oriented approach

abstract: System integration has been always the most important issue for the development of informatization in healthcare industry. The formulation and popularization of HL7 and DICOM, the healthcare data exchange standards, have greatly improved sharing extent of healthcare information, but they are not enough to solve the problem of workflow integration. The IHE technical framework has promoted the workflow integration by specifying the implementation mode of HL7 and DICOM, and its essential idea and the initiative, designed for hospital workflow and dataflow, provide a practical guidelines, but we could not be forced to conform to the way IHE specified in many real cases. Based on the IHE technical framework, the authors try to present a service oriented way of healthcare information integration in this paper.


title: Battery-Free Connected Machine Vision with WISPCam

abstract: Sustained exponential improvements in the energy efficiency of microelectronics has recently enabled us to build battery-free camera systems that are powered entirely by propagating radio waves. This paper describes primitive machine vision applications built using this highly constrained, battery-free camera system. After describing the WISPCam system and its constraints, we show how to use it to capture (relatively) high-resolution images of faces, without ever capturing a full frame at high resolution. This example application illustrates the issues that arise in partitioning a demanding vision application across mobile hardware that is highly constrained in power, storage, computation and communication.


title: Analytical Configuration of Wheeled Robotic Locomotion

abstract: Through their ability to navigate and perform tasks in unstructured environments, robots have made their way into applications like farming, earth moving, waste clean-up and exploration. All mobile robots use locomotion that generates traction, negotiates terrain and carries payload. Well-designed robotic locomotion also stabilizes a robot's frame, smooths the motion of sensors and accommodates the deployment and manipulation of work tools. Because locomotion is the physical interface between a robot and its environment, it is the means by which it reacts to gravitational, inertial and work loads. Locomotion is the literal basis of a mobile robot's performance. ::: Despite its significance, locomotion design and its implications to robotic function have not been addressed. In fact, with the exception of a handful of case studies, the issue of how to synthesize robotic locomotion configurations remains a topic of ad hoc speculation and is commonly pursued in a way that lacks rationalization. This thesis focuses on the configuration of wheeled robotic locomotion through the formulation and systematic evaluation of analytical expressions called configuration equations. These are mathematical functions which capture quantitative relationships among configuration parameters (e.g., wheel diameter, chassis articulation location), performance parameters (e.g. drawbar pull, maximum gradeable slope) and environmental/task parameters (e.g. soil geophysical properties, density and size of obstacles). Solutions to the configuration equations are obtained in parametric form to allow for comprehensive characterization of variant locomotion concepts as opposed to searching for point designs. Optimal configuration parameters are sought in the context of three indices of performance: trafficability, maneuverability and terrainability. ::: The derivation of configuration equations, the estimation and optimization of configuration parameters and predictions of performance are performed in a computational framework called Locomotion Synthesis (LocSyn). LocSyn offers a practical approach to rationalizing configuration design of robotic locomotion through quantitative studies. ::: The configuration of Nomad, a planetary prototype robot for exploration of barren terrain is a case illustrating the implementation and evaluation of the Locomotion Synthesis (LocSyn) framework put forth by this thesis.


title: Why Do Developers Get Password Storage Wrong?: A Qualitative Usability Study

abstract: Passwords are still a mainstay of various security systems, as well as the cause of many usability issues. For end-users, many of these issues have been studied extensively, highlighting problems and informing design decisions for better policies and motivating research into alternatives. However, end-users are not the only ones who have usability problems with passwords! Developers who are tasked with writing the code by which passwords are stored must do so securely. Yet history has shown that this complex task often fails due to human error with catastrophic results. While an end-user who selects a bad password can have dire consequences, the consequences of a developer who forgets to hash and salt a password database can lead to far larger problems. In this paper we present a first qualitative usability study with 20 computer science students to discover how developers deal with password storage and to inform research into aiding developers in the creation of secure password systems.


title: ICBS: improved conflict-based search algorithm for multi-agent pathfinding

abstract: Conflict-Based Search (CBS) and its enhancements, Meta-Agent CBS and bypassing conflicts are amongst the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper introduces two new improvements to CBS and incorporates them into a coherent, improved version of CBS, namely ICBS. Experimental results show that each of these improvements further reduces the runtime over the existing CBS-based approaches. When all improvements are combined, an even larger improvement is achieved, producing state-of-the art results for a number of domains.


qSpell: Spelling Correction of Web Search Queries using Ranking Models and Iterative Correction


title: Fully Homomorphic Encryption over the Integers Revisited

abstract: Two main computational problems serve as security foundations of current fully homomorphic encryption schemes: Regev’s Learning With Errors problem (\(\mathrm {LWE}\)) and Howgrave-Graham’s Approximate Greatest Common Divisor problem (\(\mathrm {AGCD}\)). Our first contribution is a reduction from \(\mathrm {LWE}\) to \(\mathrm {AGCD}\). As a second contribution, we describe a new \(\mathrm {AGCD}\)-based fully homomorphic encryption scheme, which outperforms all prior \(\mathrm {AGCD}\)-based proposals: its security does not rely on the presumed hardness of the so-called Sparse Subset Sum problem, and the bit-length of a ciphertext is only \(\widetilde{O}(\lambda )\), where \(\lambda \) refers to the security parameter.


title: Learning to Decompose and Disentangle Representations for Video Prediction

abstract: Our goal is to predict future video frames given a sequence of input frames. Despite large amounts of video data, this remains a challenging task because of the high-dimensionality of video frames. We address this challenge by proposing the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework that combines structured probabilistic models and deep networks to automatically (i) decompose the high-dimensional video that we aim to predict into components, and (ii) disentangle each component to have low-dimensional temporal dynamics that are easier to predict. Crucially, with an appropriately specified generative model of video frames, our DDPAE is able to learn both the latent decomposition and disentanglement without explicit supervision. For the Moving MNIST dataset, we show that DDPAE is able to recover the underlying components (individual digits) and disentanglement (appearance and location) as we would intuitively do. We further demonstrate that DDPAE can be applied to the Bouncing Balls dataset involving complex interactions between multiple objects to predict the video frame directly from the pixels and recover physical states without explicit supervision.


title: Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks

abstract: Artificial neural networks tend to learn only what they need for a task. A manipulation of the training data can counter this phenomenon. In this paper, we study the effect of different alterations of the training data, which limit the amount and position of information that is available for the decision making. We analyze the accuracy and robustness against semantic and black box attacks on the networks that were trained on different training data modifications for the particular example of morphing attacks. A morphing attack is an attack on a biometric facial recognition system where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image.


title: Local Cloud Internet of Things Automation: Technology and Business Model Features of Distributed Internet of Things Automation Solutions

abstract: The hype concerning digitalization is increasing the demand for new generations of automation systems. Concepts like Reference Architecture Model Industry 4.0 (RAMI 4.0) give us models but do not tell us how to facilitate actual implementations. This article discusses the transition from legacy automation technology as defined by ISA-95 to highly distributed Internet of Things (IoT)- and system of systems (SoS)- based automation systems that fully utilize Internet technologies, thus enabling the implementation of Industry 4.0 and RAMI 4.0 models. Distributed IoT automation systems have a number of general requirements concerning real-time performance, security, engineering cost, scalability, and interoperability. Meeting these requirements is necessary to enable possibilities for a realworld implementation of IoT automation. A key concept is local automation clouds. The discussion is based on a particular example of such an automation integration platform, the Arrowhead Framework.


title: Design for conversation: lessons from Cognoter

abstract: Abstract When studying the use of Cognoter, a multi-user idea organizing tool, we noticed that users encountered unexpected communicative breakdowns. Many of these difficulties stemmed from an incorrect model of conversation implicit in the design of the software. Drawing on recent work in psychology and sociology, we were able to create a more realistic model of the situation our users faced and apply it to the system to understand the breakdowns. We discovered that users encountered difficulties coordinating their conversational actions. They also had difficulty determining that they were talking about the same objects and actions in the workspace. This work led to the redesign of the tool and to the identification of areas for further exploration.


title: Smartphone-Based Gaze Gesture Communication for People with Motor Disabilities

abstract: Current eye-tracking input systems for people with ALS or other motor impairments are expensive, not robust under sunlight, and require frequent re-calibration and substantial, relatively immobile setups. Eye-gaze transfer (e-tran) boards, a low-tech alternative, are challenging to master and offer slow communication rates. To mitigate the drawbacks of these two status quo approaches, we created GazeSpeak, an eye gesture communication system that runs on a smartphone, and is designed to be low-cost, robust, portable, and easy-to-learn, with a higher communication bandwidth than an e-tran board. GazeSpeak can interpret eye gestures in real time, decode these gestures into predicted utterances, and facilitate communication, with different user interfaces for speakers and interpreters. Our evaluations demonstrate that GazeSpeak is robust, has good user satisfaction, and provides a speed improvement with respect to an e-tran board; we also identify avenues for further improvement to low-cost, low-effort gaze-based communication technologies.


title: Things fall apart: maintenance, repair, and technology for education initiatives in rural Namibia

abstract: Repair and maintenance haunt the margins of ICT and development ('ICTD') and broader information school scholarship, but have rarely received central theoretical or empirical attention in the field. This paper attempts to fill this gap. Theoretically, it explores ideas from the growing but scattered body of social science work around infrastructure, maintenance and repair, and argues for maintenance and repair as key sites of difference, innovation, power, and sustainability in ICTD settings. Empirically, the paper examines patterns and tensions in maintenance and repair in Rundu and the wider Kavango region in northeastern Namibia. We conclude with key findings and lessons for future ICTD and iSchool scholarship.


title: BlockChain for IoT Security and Management: Current Prospects, Challenges and Future Directions

abstract: The paper presents an in detailed case study of integrating BlockChain (BC) in IoT ecosystems in order to achieve security and privacy. Since the use in cryptocurrency, BlockChain has gained tremendous attraction due to the versatile application prospects it raises. As internet of things (IoT) is emerging towards the smart city requirements, device or data security seems to be a major concern. Therefore, BlockChain based decentralized and distributed system can meet the privacy preserving management in IoT ecosystems. In this paper, we discuss the necessity of utilizing BlockChain for IoT security, privacy, management. We present a literature overview of current progresses and security enhancement in sensor networks using BlockChain; their scope, prospects and limitations as well. Finally, we suggest some future directions to indicate further areas of improvement.


title: Deep Graph Translation

abstract: Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been proposed to generate discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the graphs being generated. Differently, in this paper, we are interested in a new problem named \emph{Deep Graph Translation}: given an input graph, we want to infer a target graph based on their underlying (both global and local) translation mapping. Graph translation could be highly desirable in many applications such as disaster management and rare event forecasting, where the rare and abnormal graph patterns (e.g., traffic congestions and terrorism events) will be inferred prior to their occurrence even without historical data on the abnormal patterns for this graph (e.g., a road network or human contact network). To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs. GT-GAN consists of a graph translator where we propose new graph convolution and deconvolution layers to learn the global and local translation mapping. A new conditional graph discriminator has also been proposed to classify target graphs by conditioning on input graphs. Extensive experiments on multiple synthetic and real-world datasets demonstrate the effectiveness and scalability of the proposed GT-GAN.


title: P-Q and P-V Control of Photovoltaic Generators in Distribution Systems

abstract: In this paper, simultaneous control of active power and volt/var is explored with photovoltaic (PV) generators in distribution systems. The PV active power output can be controlled in the load-following mode when sufficient solar power is available to supply a local load, or the maximum power point tracking (MPPT) mode when a local load is large or injection to the system is allowed. Two selected control approaches, P-Q control in the load-following mode and P-V control in the MPPT mode, are investigated in this paper. The P-Q control is implemented with a relatively simple approach, while the P-V control demands an extra MPPT logic, which is solved based on a power balance between the dc and ac sides in a two-stage PV configuration. The control algorithms are tested with the IEEE 13-bus distribution feeder with various system conditions like the presence of multiple PV generators, imbalance, harmonics, and faults. The MATLAB and SimPowerSystems simulation results clearly demonstrate the capability of the proposed control in maintaining the P/V bus as either a P-Q or P-V bus depending on different applications.


title: Measuring E-Marketing Mix Elements for Online Business

abstract: E-marketing strategy is normally based and built upon the traditional 4 P’s (Product, Price, Promotion, and Place)that forms the classic marketing mix; e-marketing’s uniqueness is created using a series of specific and relational functions that are combined with the 4P’s to form the e-marketing mix elements, each of which contain associated e-marketing mix tools that are provided on business web sites to facilitate sales transactions. This research analyses the importance of each e-marketing tool related to its supporting e-marketing mix element. Furthermore, the composite score of each e-marketing mix element is determined. This research concludes with a discussion of the relative weights of e-marketing tools.


title: Varactor-Tunable Second-Order Bandpass Frequency-Selective Surface With Embedded Bias Network

abstract: A varactor-tunable second-order bandpass frequency-selective surface (FSS) for microwave frequencies is presented in this article. The FSS is composed of three stacked metallic layers. The wire grid in each layer in combination with metallic vias provides the bias for the varactors. This configuration eliminates the need for a dedicated bias network for the varactors, and thus avoids undesirable responses associated with the added bias grid. An equivalent circuit model together with an analytical design method is provided to simplify the design procedure of the FSS. The performance of the proposed structure is experimentally validated in a parallel-plate waveguide setup. Measurements show that by changing the varactor capacitance from 0.12 to 0.38 pF, the center frequency of the filter is tuned from 5.2 to 3.7 GHz with a consistent fractional bandwidth of 9% and with an insertion loss between 3 and 6 dB.


title: Cooperative Multi-agent Control Using Deep Reinforcement Learning

abstract: This work considers the problem of learning cooperative policies in complex, partially observable domains without explicit communication. We extend three classes of single-agent deep reinforcement learning algorithms based on policy gradient, temporal-difference error, and actor-critic methods to cooperative multi-agent systems. To effectively scale these algorithms beyond a trivial number of agents, we combine them with a multi-agent variant of curriculum learning. The algorithms are benchmarked on a suite of cooperative control tasks, including tasks with discrete and continuous actions, as well as tasks with dozens of cooperating agents. We report the performance of the algorithms using different neural architectures, training procedures, and reward structures. We show that policy gradient methods tend to outperform both temporal-difference and actor-critic methods and that curriculum learning is vital to scaling reinforcement learning algorithms in complex multi-agent domains.


title: High Current Ripple for Power Density and Efficiency Improvement in Wide Bandgap Transistor-Based Buck Converters

abstract: With the recent development and availability of wide bandgap devices in the market, more and more power converters are being designed with such devices. Given their fast commutation, when compared to their equivalent Si-based counterparts, these new devices allow increasing the converter's efficiency and/or power density. However, in order to fully avail these new devices, one should precisely know their switching characteristics and exploit it the best way possible. This paper recalls our own precise method to measure separately turn-on and turn-off energies of wide bandgap devices. This method is applied to commercially available SiC and GaN transistors and results show that they present much lower turn-off than turn-on energies. For that reason, we show that a SiC-based buck converter must have high current ripple in the output filter inductor in order to decrease transistor losses. Analysis of these losses as well as experimental results are presented. Finally, the precise design of a 2-kW SiC-based buck converter for aircraft applications is performed for different current ripples and switching frequencies. We show that current ripple higher than 250% of the dc load current significantly decreases the converter's losses, and consequently allows the increase of the switching frequency, which reduces the system volume and weight.


title: 3D Printing All-Aromatic Polyimides using Mask-Projection Stereolithography: Processing the Nonprocessable

abstract: High-performance, all-aromatic, insoluble, engineering thermoplastic polyimides, such as pyromellitic dianhydride and 4,4′-oxydianiline (PMDA–ODA) (Kapton), exhibit exceptional thermal stability (up to ≈600 °C) and mechanical properties (Young's modulus exceeding 2 GPa). However, their thermal resistance, which is a consequence of the all-aromatic molecular structure, prohibits processing using conventional techniques. Previous reports describe an energy-intensive sintering technique as an alternative technique for processing polyimides with limited resolution and part fidelity. This study demonstrates the unprecedented 3D printing of PMDA–ODA using mask-projection stereolithography, and the preparation of high-resolution 3D structures without sacrificing bulk material properties. Synthesis of a soluble precursor polymer containing photo-crosslinkable acrylate groups enables light-induced, chemical crosslinking for spatial control in the gel state. Postprinting thermal treatment transforms the crosslinked precursor polymer to PMDA–ODA. The dimensional shrinkage is isotropic, and postprocessing preserves geometric integrity. Furthermore, large-area mask-projection scanning stereolithography demonstrates the scalability of 3D structures. These unique high-performance 3D structures offer potential in fields ranging from water filtration and gas separation to automotive and aerospace technologies.


title: Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

abstract: In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.


title: Loss of HMW1 and HMW3 in noncytadhering mutants of Mycoplasma pneumoniae occurs post-translationally

abstract: The genomic sequence of Mycoplasma pneumoniae establish this cell-wall-less prokaryote as among the smallest known microorganisms capable of self-replication. However, this genomic simplicity and corresponding biosynthetic austerity are sharply contrasted by the complex terminal structure found in this species. This tip structure (attachment organelle) directs colonization of the human respiratory mucosa, leading to bronchitis and atypical pneumonia. Furthermore, formation of a second tip structure appears to precede cell division, implying temporal regulation. However, the organization, regulation, and assembly of the attachment organelle in M. pneumoniae are poorly understood, and no counterparts have been identified among the walled bacteria. M. pneumoniae possesses a cytoskeleton-like structure required to localize adhesin proteins to the attachment organelle. The cytadherence-associated proteins HMW1, HMW2, and HMW3 are components of the mycoplasma cytoskeleton, with HMW1 localizing strictly along the filamentous extensions from the cell body and HMW3 being a key structural element of the terminal organelle. Disruptions in hmw2 result in the loss of HMW1 and HMW3. However, the hmw1 and hmw3 genes were transcribed and translated at wild-type levels in hmw2 mutants. HMW1 and HMW3 were relatively stable in the wild-type background over 8 h but disappeared in the mutants over this time period. Evaluation of recombinant HMW1 levels in mycoplasma mutants suggested a requirement for the C-terminal domain of HMW1 for turnover. Finally, an apparent defect in the processing of the precursor for the adhesin protein P1 was noted in the HMW- mutants.


An Ensemble Approach to Intrusion Detection Based on Improved Multi-Objective Genetic Algorithm


title: A review and framework for understanding the potential impact of poor solid waste management on health in developing countries

abstract: The increase in solid waste generated per capita in Africa has not been accompanied by a commensurate growth in the capacity and funding to manage it. It is reported that less than 30% of urban waste in developing countries is collected and disposed appropriately. The implications of poorly managed waste on health are numerous and depend on the nature of the waste, individuals exposed, duration of exposure and availability of interventions for those exposed. To present a framework for understanding the linkages between poor solid waste management, exposure and associated adverse health outcomes. The framework will aid understanding of the relationships, interlinkages and identification of the potential points for intervention. Development of the framework was informed by a review of literature on solid waste management policies, practices and its impact on health in developing countries. A configurative synthesis of literature was applied to develop the framework. Several iterations of the framework were reviewed by experts in the field. Each linkage and outcomes are described in detail as outputs of this study. The resulting framework identifies groups of people at a heightened risk of exposure and the potential health consequences. Using the iceberg metaphor, the framework illustrates the pathways and potential burden of ill-health related to solid waste that is hidden but rapidly unfolding with our inaction. The existing evidence on the linkage between poor solid waste management and adverse health outcomes calls to action by all stakeholders in understanding, prioritizing, and addressing the issue of solid waste in our midst to ensure that our environment and health are preserved. A resulting framework developed in this study presents a clearer picture of the linkages between poor solid waste management and could guide research, policy and action.


title: Novel compact circularly polarized square microstrip antenna

abstract: A novel compact circular-polarization (CP) operation of the square microstrip antenna with four slits and a pair of truncated corners is proposed and investigated. Experimental results show that the proposed compact CP design can have an antenna-size reduction of about 36% as compared to the conventional corner-truncated square microstrip antenna at a given operating frequency. Also, the required size of the truncated corners for CP operation is much greater than that for the conventional CP design using a simple square microstrip patch, providing a relaxed manufacturing tolerance for the proposed compact CP design. Details of the experimental results are presented and discussed.


title: A data-driven approach for real-time full body pose reconstruction from a depth camera

abstract: In recent years, depth cameras have become a widely available sensor type that captures depth images at real-time frame rates. Even though recent approaches have shown that 3D pose estimation from monocular 2.5D depth images has become feasible, there are still challenging problems due to strong noise in the depth data and self-occlusions in the motions being captured. In this paper, we present an efficient and robust pose estimation framework for tracking full-body motions from a single depth image stream. Following a data-driven hybrid strategy that combines local optimization with global retrieval techniques, we contribute several technical improvements that lead to speed-ups of an order of magnitude compared to previous approaches. In particular, we introduce a variant of Dijkstra's algorithm to efficiently extract pose features from the depth data and describe a novel late-fusion scheme based on an efficiently computable sparse Hausdorff distance to combine local and global pose estimates. Our experiments show that the combination of these techniques facilitates real-time tracking with stable results even for fast and complex motions, making it applicable to a wide range of inter-active scenarios.


Feature Extraction Methods for Time Series Data in SAS ® Enterprise Miner TM


Distance Leadership Practices - What Works in Higher Education


title: Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

abstract: This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.


title: A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis

abstract: Cardiovascular disease is a condition of interest to clinicians around the world and is one of the chief causes of death. In Korea, cardiovascular disease is the second largest cause of death following cancer, and is classified as one of the four major diseases, making an early diagnosis of cardiovascular disease essential for proper and timely treatment. Although the risk of cardiovascular disease is recognized in Korea, there is a lack of quality studies regarding the disease. In the present study, a prediction model was developed which allows for the calculation of the risk of cardiovascular disease among Korean patients. The prediction model for cardiovascular disease was developed using a discriminant analysis based on the data set of Korean National Health and Nutrition Examinations Survey V (KNHANES V).


title: A low-swing AC- and DC- coupled voltage-mode driver with pre-emphasis

abstract: This paper describes a 2-tap voltage-mode driver with an auxiliary AC-coupled driver. Tap weight control and impedance matching are accomplished by a dual-loop regulator. The AC-coupling technique is employed to enhance the edge rates and reduce the burden of the post-tap equalizer. The proposed transmitter is fabricated using a 0.13-µm CMOS technology. It is shown that when 200mVp-p 5-Gb/s signals are sent over 10″ FR4 channels, the eye of the received data has 0.87 UI timing margin, 114 mV voltage margin.


title: Enhanced Teaching Model (ETM) for Teaching Programming Languages

abstract: Expectations from academics and the industry, to have students and employees who are independent and capable of quickly writing code to resolve work-related issues, are growing high. However, teaching and learning programming is certainly not easy and very challenging. Literature shows that a lot of work has been done to improve this. Nonetheless, it is evident that little effect of this work has had impact on the actual practice of teaching and learning of Software Development programming skills. This gap has been addressed in this paper to enhance the teaching and learning process of programming to students. Furthermore, teaching programming literature research has been classified into 3 categories; teaching approach, teaching model and teaching tool. As a result, this paper proposes the following objectives to tackle this problem: Identify what research has found out about how to teach and learn programming and other aspects of Software Development Investigate how and why this research has not been applied to teaching Software Development How more use could be made of it to improve teaching? Finally, an Enhanced Teaching Model (ETM) has been proposed, which combines several teaching approaches and models from literature. In addition, this model uses teaching tools to provide goal-focused exercises, assess students' performance and obtain feedback from the learning community. Last but not least, a discussion about the future work required in order to assess the model and thus improve it.


title: Evaluation of classification algorithms for intrusion detection in MANETs

abstract: Mobile Ad hoc Networks (MANETs) are wireless networks without fixed infrastructure based on the cooperation of independent mobile nodes. The proliferation of these networks and their use in critical scenarios (like battlefield communications or vehicular networks) require new security mechanisms and policies to guarantee the integrity, confidentiality and availability of the data transmitted. Intrusion Detection Systems used in wired networks are inappropriate in this kind of networks since different vulnerabilities may appear due to resource constraints of the participating nodes and the nature of the communication. This article presents a comparison of the effectiveness of six different classifiers to detect malicious activities in MANETs. Results show that Genetic Programming and Support Vector Machines may help considerably in detecting malicious activities in MANETs.


title: Basic Emotions, Natural Kinds, Emotion Schemas, and a New Paradigm

abstract: Research on emotion flourishes in many disciplines and specialties, yet experts cannot agree on its definition. Theorists and researchers use the term emotion in ways that imply different processes and meanings. Debate continues about the nature of emotions, their functions, their relations to broad affective dimensions, the processes that activate them, and their role in our daily activities and pursuits. I will address these issues here, specifically in terms of basic emotions as natural kinds, the nature of emotion schemas, the development of emotion—cognition relations that lead to emotion schemas, and discrete emotions in relation to affective dimensions. Finally, I propose a new paradigm that assumes continual emotion as a factor in organizing consciousness and as an influence on mind and behavior. The evidence reviewed suggests that a theory that builds on concepts of both basic emotions and emotion schemas provides a viable research tool and is compatible with more holistic or dimensional approaches.


title: The fickle P value generates irreproducible results

abstract: The reliability and reproducibility of science are under scrutiny. However, a major cause of this lack of repeatability is not being considered: the wide sample-to-sample variability in the P value. We explain why P is fickle to discourage the ill-informed practice of interpreting analyses based predominantly on this statistic.


title: 6K Effective Resolution with 4K HEVC Decoding Capability for OMAF-compliant 360° Video Streaming

abstract: The recent Omnidirectional MediA Format (OMAF) standard specifies delivery of 360° video content. OMAF supports only equirectangular (ERP) and cubemap projections and their region-wise packing with a limitation on video decoding capability to the maximum resolution of 4K (e.g., 4096x2048). Streaming of 4K ERP content allows only a limited viewport resolution, which is lower than the resolution of many current head-mounted displays (HMDs). In order to take the full advantage of those HMDs, this work proposes a specific mixed-resolution packing of 6K (6144x3072) ERP content and its realization in tile-based streaming, while complying with the 4K-decoding constraint and the High Efficiency Video Coding (HEVC) standard. Experimental results indicate that, using Zonal-PSNR test methodology, the proposed layout decreases the streaming bitrate up to 32% in terms of BD-rate, when compared to mixed-quality viewport-adaptive streaming of 4K ERP as an alternative solution.


title: Crowdfunding Success: A Perspective from Social Media and E-Commerce

abstract: Crowdfunding as a new way of financing in the web 2.0 has increased over the last years, but only little is known how project initiators increase their chances of successful fundraising through on-page and off-page communication activities. Using a dataset of 740 projects published on one of the dominant crowdfunding platforms in Switzerland, we test communication-related determinants of fundraising success in terms of (1) number of donations (2) average amount of donations and (3) total campaign success. Our results show that high media richness in the project presentation and a high frequency of project updates leverage fundraising success. In contrast, no beneficial effects of the simple application of social media channels could be observed. The implications for research on crowdfunding and practice are discussed.


title: A Robotic Prosthesis for an Amputee Drummer

abstract: The design and evaluation of a robotic prosthesis for a drummer with a transradial amputation is presented. The principal objective of the prosthesis is to simulate the role fingers play in drumming. This primarily includes controlling the manner in which the drum stick rebounds after initial impact. This is achieved using a DC motor driven by a variable impedance control framework in a shared control system. The user's ability to perform with and control the prosthesis is evaluated using a musical synchronization study. A secondary objective of the prosthesis is to explore the implications of musical expression and human-robotic interaction when a second, completely autonomous, stick is added to the prosthesis. This wearable robotic musician interacts with the user by listening to the music and responding with different rhythms and behaviors. We recount some empirical findings based on the user's experience of performing under such a paradigm.


title: User Interest Modeling in Twitter with Named Entity Recognition

abstract: Considering wide use of Twitter as the source of information, reaching an interesting tweet for a user among a bunch of tweets is challenging. In this work we propose a Named Entity Recognition (NER) based user profile modeling for Twitter users and employ this model to generate personalized tweet recommendations. E↵ectiveness of the proposed method is shown through a set of experiments.


title: A Decision Tree Approach to Predicting Recidivism in Domestic Violence

abstract: Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.


Model Predictive Control of a Mobile Robot Using Linearization


title: Web-to-Application Injection Attacks on Android: Characterization and Detection

abstract: Vulnerable Android applications (or apps) are traditionally exploited via malicious apps. In this paper, we study an underexplored class of Android attacks which do not require the user to install malicious apps, but merely to visit a malicious website in an Android browser. We call them web-to-app injection (or W2AI) attacks, and distinguish between different categories of W2AI side-effects. To estimate their prevalence, we present an automated W2AIScanner to find and confirm W2AI vulnerabilities. We analyze real apps from the official Google Play store and found 286 confirmed vulnerabilities in 134 distinct applications. This findings suggest that these attacks are pervasive and developers do not adequately protect apps against them. Our tool employs a novel combination of static analysis, symbolic execution and dynamic testing. We show experimentally that this design significantly enhances the detection accuracy compared with an existing state-of-the-art analysis.


title: Microstrip bandstop filter using spurline and defected ground structures(DGS)

abstract: Microstrip filters are widely used in microwave circuit, This paper briefly describes the design principle of microstrip bandstop filter (BSF). A compact wide band high rejection BSF is presented. This filter consists of two parts: defected ground structures filter (DGS) and spurline filter. Due to the inherently compact characteristics of the spurline and DGS, the proposed filter shows a better rejection performance than open stub BSF in the same circuit size. The results of simulation and optimization given by HFSS12 prove the correctness of the design.


title: Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction

abstract: Maneuvering vessel detection and tracking (VDT), incorporated with state estimation and trajectory prediction, are important tasks for vessel navigational systems (VNSs), as well as vessel traffic monitoring and information systems (VTMISs) to improve maritime safety and security in ocean navigation. Although conventional VNSs and VTMISs are equipped with maritime surveillance systems for the same purpose, intelligent capabilities for vessel detection, tracking, state estimation, and navigational trajectory prediction are underdeveloped. Therefore, the integration of intelligent features into VTMISs is proposed in this paper. The first part of this paper is focused on detecting and tracking of a multiple-vessel situation. An artificial neural network (ANN) is proposed as the mechanism for detecting and tracking multiple vessels. In the second part of this paper, vessel state estimation and navigational trajectory prediction of a single-vessel situation are considered. An extended Kalman filter (EKF) is proposed for the estimation of vessel states and further used for the prediction of vessel trajectories. Finally, the proposed VTMIS is simulated, and successful simulation results are presented in this paper.


title: Longitudinal Study of Procrastination, Performance, Stress, and Health: The Costs and Benefits of Dawdling

abstract: Procrastination is variously described as harmful, innocuous, or even beneficial Two longitudinal studies examined procrastination among students Procrastinators reported lower stress and less illness than nonprocrastinators early in the semester, but they reported higher stress and more illness late in the term, and overall they were sicker Procrastinators also received lower grades on all assignments Procrastination thus appears to be a self-defeating behavior pattern marked by short-term benefits and long-term costs


title: The Role of Intrinsic Pathway in Apoptosis Activation and Progression in Peyronie's Disease

abstract: Peyronie's disease (PD) is characterized with formation of fibrous plaques which result in penile deformity, pain, and erectile dysfunction. The aim of this study was to investigate the activation of the intrinsic apoptotic pathway in plaques from PD patients. Tunica albuginea from either PD or control patients was assessed for the expression of bax, bcl-2 and caspases 9 and 3 using immunohistochemistry and by measurement of apoptotic cells using TUNEL assay. Bax overexpression was observed in metaplastic bone tissue, in fibroblasts, and in myofibroblast of plaques from PD patients. Little or no bcl-2 immunostaining was detected in samples from either patients or controls. Caspase 3 immunostaining was very strong in fibrous tissue, in metaplasic bone osteocytes, and in primary ossification center osteoblasts. Moderate caspase 9 immunostaining was seen in fibrous cells plaques and in osteocytes and osteoblasts of primary ossification centers from PD patients. Control samples were negative for caspase 9 immunostaining. In PD patients the TUNEL immunoassay showed intense immunostaining of fibroblasts and myofibroblasts, the absence of apoptotic cells in metaplasic bone tissue and on the border between fibrous and metaplastic bone tissue. Apoptosis occurs in stabilized PD plaques and is partly induced by the intrinsic pathway.


hexylresorcinol exerts antitumor effects via suppression of calcium oscillation and its antitumor effects are inhibited by calcium channel blockers


title: Learning Abstract Classes using Deep Learning

abstract: Humans are generally good at learning abstract concepts about objects and scenes (e.g. spatial orientation, relative sizes, etc.). Over the last years convolutional neural networks have achieved almost human performance in recognizing concrete classes (i.e. specific object categories). This paper tests the performance of a current CNN (GoogLeNet) on the task of differentiating between abstract classes which are trivially differentiable for humans. We trained and tested the CNN on the two abstract classes of horizontal and vertical orientation and determined how well the network is able to transfer the learned classes to other, previously unseen objects.


title: Scalable indexing of HD Video

abstract: Video indexing technique is crucial in multimedia applications. In the case of HD (high definition) video, the principle of scalability is of great importance. The wavelet decomposition used in the MJPEG2000 provides this property. In this paper, two descriptors for indexing of HD video are presented. The first one is based on the GM (global motion) of the video and the correlation matrix is used as similarity measure for matching. The second one is object-oriented and uses histograms in the wavelet domain as indexes ; similarity measure is then based on histogram intersection. The complete process of extraction of objects of interest and to determine GM are also detailed.


title: On predicting the popularity of newly emerging hashtags in Twitter

abstract: Because of Twitter’s popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naive bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.


Application of Data Mining Using Artificial Neural Network: Survey


title: Ultra-wideband channel model for communication around the human body

abstract: Using ultra-wideband (UWB) wireless sensors placed on a person to continuously monitor health information is a promising new application. However, there are currently no detailed models describing the UWB radio channel around the human body making it difficult to design a suitable communication system. To address this problem, we have measured radio propagation around the body in a typical indoor environment and incorporated these results into a simple model. We then implemented this model on a computer and compared experimental data with the simulation results. This paper proposes a simple statistical channel model and a practical implementation useful for evaluating UWB body area communication systems.


title: Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

abstract: Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms.


title: Conversion rate optimization through evolutionary computation

abstract: Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited number of combinations through multivariate testing, making it possible to evaluate only a small fraction of designs in a vast design space. This paper describes Sentient Ascend, an automatic conversion optimization system that uses evolutionary optimization to create effective web interface designs. Ascend makes it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. Moreover, evaluation of design candidates is done in parallel online, i.e. with a large number of real users interacting with the system. A case study on an existing media site shows that significant improvements (i.e. over 43%) are possible beyond human design. Ascend can therefore be seen as an approach to massively multivariate conversion optimization, based on a massively parallel interactive evolution.


title: Designing a Cloud-Based Infrastructure for Spectrum Sensing: A Case Study for Indoor Spaces

abstract: We argue that spectrum sensing on mobile clients will be both necessary and feasible if we wish to manage the white space spectrum optimally in indoor spaces. We demonstrate the necessity with a set of empirical measurements showing the need for fine grained sensing. We demonstrate the feasibility by building a spectrum sensing infrastructure that collects measurements from sensing devices to analyze and better use spectrum resources. The infrastructure consists of mobile spectrum sensors that are built using DTV receiver dongles interfaced with Android-based mobile devices and a cloud-based central server to manage such sensing devices. We also show results about resource consumption (energy, network overhead) involved in operating such sensors. The vision is ultimately creating a system where mobile devices perform part-time spectrum sensing in a coordinated fashion under the control of a central spectrum manager. We lay out the research challenges based on our initial prototyping and benchmarking experience.


Less Rhyme, More Reason: Knowledge-based Poetry Generation with Feeling, Insight and Wit


title: Sky detection and log illumination refinement for PDE-based hazy image contrast enhancement

abstract: This report presents the results of a sky detection technique used to improve the performance of a previously developed partial differential equation (PDE)-based hazy image enhancement algorithm. Additionally, a proposed alternative method utilizes a function for log illumination refinement to improve de-hazing results while avoiding over-enhancement of sky or homogeneous regions. The algorithms were tested with several benchmark and calibration images and compared with several standard algorithms from the literature. Results indicate that the algorithms yield mostly consistent results and surpasses several of the other algorithms in terms of colour and contrast enhancement in addition to improved edge visibility.


title: High-level synthesis for micro-electrode-dot-array digital microfluidic biochips

abstract: A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. However, today's DMFBs suffer from several limitations: (i) constraints on droplet size and the inability to vary droplet volume in a fine-grained manner; (ii) the lack of integrated sensors for real-time detection; (iii) the need for special fabrication processes and reliability/yield concerns. To overcome the above problems, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have recently been demonstrated. However, due to the inherent differences between today's DMFBs and MEDA, existing synthesis solutions cannot be utilized for MEDA-based biochips. We present the first biochip synthesis approach that can be used for MEDA. The proposed synthesis method targets operation scheduling, module placement, routing of droplets of various sizes, and diagonal movement of droplets in a two-dimensional array. Simulation results using benchmarks and experimental results using a fabricated MEDA biochip demonstrate the effectiveness of the proposed co-optimization technique.


title: Overlapping Community Detection by Local Community Expansion

abstract: Community structure is the key aspect of complex network analysis and it has important practical significance. While in real networks, some nodes may belong to multiple communities, so overlapping community detection attracts more and more attention. But most of the existing overlapping community detection algorithms increase the time complexity in some extent. In order to detect overlapping community structures in complex network more effectively, we propose a novel overlapping community detection method by local community expansion called OCDLCE. The proposed algorithm firstly partitions the network into small local communities using the local structural information, and then merges these communities to the final overlapping community structures. We present the concept of community connectivity as the criterion of community combination in the second stage of the proposed algorithm. The experimental results on both synthetic and real networks demonstrate that our algorithm improves the community detection performance, and at the same time, its time efficiency is better than the state-of-the-art methods.


title: Dependent Gated Reading for Cloze-Style Question Answering

abstract: We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.


title: Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data

abstract: Hyperspectral remote sensing methods are advancing rapidly and offer the promise of estimation of pigment, biochemical, and water content dynamics. The recent Earth Observer 1 (EO-1) Hyperion mission, and associated field campaigns, has allowed a range of biophysical and biochemistry attributes of eucalypt foliage to be analyzed in conjunction with remotely sensed spectra. This paper reports on a study at Tumbarumba (Bago-Maragle State Forest), Australia, which has a wide variety of eucalypt species, ranging in productivity and age. EO-1 Hyperion imagery was obtained in April 2001, and a field program was undertaken involving the establishment of plots, collection of standard forestry inventory data, and green leaf samples. Leaf nitrogen (N) content was measured from leaf samples using wet chemistry techniques and canopy N concentration estimated using leaf mass and proportional species leaf area index data. A number of models were developed from Hyperion reflectance, absorbance, and derivate transformations using partial least squares regression and multiple linear regression. The most significant calibration model predicted N with a correlation coefficient (r)=0.9 (82% variance explained) and a validation r/sup 2/=0.62 (P<0.01). The standard error of the estimate of foliar N was 0.16% equating to 13% of the mean observed %N at the site. These initial results indicate that predictions of canopy foliar N using Hyperion spectra is possible for native multispecies eucalypt forest. Similar studies worldwide, particular those associated with the flux tower network, will allow these findings to be placed in context with other biomes and functional types.


title: Direct torque control of PWM inverter-fed AC motors - a survey

abstract: This paper presents a review of recently used direct torque and flux control (DTC) techniques for voltage inverter-fed induction and permanent-magnet synchronous motors. A variety of techniques, different in concept, are described as follows: switching-table-based hysteresis DTC, direct self control, constant-switching-frequency DTC with space-vector modulation (DTC-SVM). Also, trends in the DTC-SVM techniques based on neuro-fuzzy logic controllers are presented. Some oscillograms that illustrate properties of the presented techniques are shown.


title: How can i improve my app? Classifying user reviews for software maintenance and evolution

abstract: App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).


title: TNF-α transgenic and knockout models of CNS inflammation and degeneration

abstract: Abstract Tumour necrosis factor- α (TNF- α ) plays a central role in inflammatory events including those taking place in the central nervous system (CNS), and has been implicated as a key pathogenicc mediator in several human inflammatory, infectious and autoimmune CNS disorders. Using transgenic and gene knockout mice we have investigated the role of deregulated TNF- α production in the CNS. We show that the overexpression of wild-type murine or human TNF- α transgenes by resident CNS astrocytes or neurons in sufficient to trigger a neurological disorder characterised by ataxia, seizures and paresis, with histopathological features of chronic CNS inflammation and white matter degeneration. Furthermore, we show that transmembrane human TNF- α is sufficient to trigger CNS inflammation and degeneration when overexpressed by astrocytes but not by neurons, indicating that target cells mediating the neuroinflammatory activities of TNF- α localise in the vicinity of astrocytes rather than neurons. Our results establish that both soluble and transmembrane molecular forms of TNF- α can play critical roles in vivo in the pathogenesis of CNS inflammation and demyelination, and validate TNF- α transgenic and mutant mice as important models for the further study of related human CNS diseases.


title: Comparison of Omega-K and backprojection regarding spatial resolution for squinted spotlight SAR with motion errors

abstract: The image quality of an image processing technique in a real airborne spotlight Synthetic Aperture Radar scenario mainly depends on its capability of correcting motion error effects. Therefore we compare the frequency domain imaging technique rotated Omega-K with explicit range cell migration correction with the time domain algorithm global Backprojection in their imaging quality by taking motion errors and scene size as well as the squint and the depression angles into account. The numerical investigations show that for a squint angle of 20° and a depression angle of 25° up to a limit of 5m motion error magnitude Omega-K yields good image quality in terms of Integrated Side Lobe Ratio for point targets with 200 m ground range distance to the spot center. For larger motion errors or larger scene sizes the Backprojection algorithm should be used to guarantee image quality with the disadvantage of higher computational costs.


title: Diversification as a public good: Community effects in portfolio choice

abstract: A DRAM includes a test mode circuit. Test mode circuit generates respective test mode signals of an L level and an H level by detecting first and second power supply voltages in response to first and second test mode shift signals, respectively. A control circuit controls peripheral circuits to input and output data for executing a special test to and from a plurality of memory cells in response to receiving of the test mode signals of an L level and an H level. Consequently, a semiconductor memory device can enter the test mode in a module.


title: An analysis of time-dependent planning

abstract: This paper presents a framework for exploring issues in time-dependent planning: planning in which the time available to respond to predicted events varies, and the decision making required to formulate effective responses is complex. Our analysis of time-dependent planning suggests an approach based on a class of algorithms that we call anytime algorithms. Anytime algorithms can be interrupted at any point during computation to return a result whose utility is a function of computation time. We explore methods for solving time-dependent planning problems based on the properties of anytime algorithms.


title: A Novel, Planar, and Compact Crossover Design for Dual-Band Applications

abstract: This paper presents, for the first time, the novel design of a dual-band crossover. The proposed circuit features planar implementation with moderate line impedance, compact layout, and widely separated frequency bands. Moreover, simple and explicit closed-form expressions are available for the exact evaluation of the line impedances. For verification, both simulated and measured results of a microstrip crossover operating at 800 MHz and 1.2 GHz are given.


title: ATLAS – The multilingual language processing platform

abstract: The work reported here was carried out within the Applied Technology for Language-Aided CMS project co-funded by the European Commission under the Information and Communications Technologies (ICT) Policy Support Programme (Grant Agreement No 250467).


title: Semantic Clustering of Search Engine Results

abstract: This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision.


title: Texture Mapping as a Fundamental Drawing Primitive

abstract: Texture mapping has traditionally been used to add realism to computer graphics images. In recent years, this technique has moved from the domain of software rendering systems to that of high performance graphics hardware. But texture mapping hardware can be used for many more applications than simply applying diffuse patterns to polygons. We survey applications of texture mapping including simple texture mapping, projective textures, and image warping. We then describe texture mapping techniques for drawing anti-aliased lines, air-brushes, and anti-aliased text. Next we show how texture mapping may be used as a fundamental graphics primitive for volume rendering, environment mapping, color interpolation, contouring, and many other applications. CR


title: Addressing the Data Sparsity Issue in Neural AMR Parsing

abstract: Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we de- scribe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural atten- tion model and our results are also compet- itive against state-of-the-art systems that do not use extra linguistic resources.


title: Cut Points on 0–10 Numeric Rating Scales for Symptoms Included in the Edmonton Symptom Assessment Scale in Cancer Patients: A Systematic Review

abstract: CONTEXT ::: To improve the management of cancer-related symptoms, systematic screening is necessary, often performed by using 0-10 numeric rating scales. Cut points are used to determine if scores represent clinically relevant burden. ::: ::: ::: OBJECTIVES ::: The aim of this systematic review was to explore the evidence on cut points for the symptoms of the Edmonton Symptom Assessment Scale. ::: ::: ::: METHODS ::: Relevant literature was searched in PubMed, CINAHL®, Embase, and PsycINFO®. We defined a cut point as the lower bound of the scores representing moderate or severe burden. ::: ::: ::: RESULTS ::: Eighteen articles were eligible for this review. Cut points were determined using the interference with daily life, another symptom-related method, or a verbal scale. For pain, cut point 5 and, to a lesser extent, cut point 7 were found as the optimal cut points for moderate pain and severe pain, respectively. For moderate tiredness, the best cut point seemed to be cut point 4. For severe tiredness, both cut points 7 and 8 were suggested frequently. A lack of evidence exists for nausea, depression, anxiety, drowsiness, appetite, well-being, and shortness of breath. Few studies suggested a cut point below 4. ::: ::: ::: CONCLUSION ::: For many symptoms, there is no clear evidence as to what the optimal cut points are. In daily clinical practice, a symptom score ≥4 is recommended as a trigger for a more comprehensive symptom assessment. Until there is more evidence on the optimal cut points, we should hold back using a certain cut point in quality indicators and be cautious about strongly recommending a certain cut point in guidelines.


title: Modeling of Lithium Ion battery with nonlinear transfer resistance

abstract: This paper discusses the Lithium Ion battery modeling by using equivalent circuit model with nonlinear transfer resistance. The modeling is done by using MATLAB/Simulink. A constant current pulse test is used to extract the model's parameters and the procedures are presented in details. Then the voltage responses of the conventional model, proposed model and MATLAB Li-ion cell model are compared. From the comparison, it reveals that the proposed model producing a better accuracy compared to conventional equivalent circuit model.


title: Resistant and Recurrent Late Reaction to Hyaluronic Acid-Based Gel.

abstract: BACKGROUND ::: Late reactions to hyaluronic acid (HA)-based fillers have a recorded rate of 0.02%. The recent experience with a newly introduced filler in the tear trough area and the lips showed higher rate of reactions with a tendency to persistency and recurrences. ::: ::: ::: OBJECTIVE ::: To delineate the features of reactions to this newly introduced filler. ::: ::: ::: MATERIALS AND METHODS ::: Medical records of 400 patients (360 women and 40 men; average age = 49.6 years) were examined in this retrospective study. Juvéderm Volbella (HA-Vb) filler was injected only into the tear trough area or lips. Other HA-based products were used in other areas of the face. ::: ::: ::: RESULTS ::: Seventeen patients (4.25%) developed prolonged (up to 11 months) and recurrent (average: 3.17 episodes) late (average onset: 8.41 weeks after the injection) inflammatory cutaneous reactions. ::: ::: ::: CONCLUSION ::: The incidence of late reactions to HA-based fillers varies between products. The authors are reporting an exceptionally high rate of cutaneous reactions for this newly introduced filler. In the authors' experience, broad-spectrum antibiotics in conjunction with repeated high-dose hyaluronidase injections into the inflammatory nodules were effective treatments.


title: Learning End-to-End Goal-Oriented Dialog

abstract: Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging success recently obtained in chit-chat dialog may not carry over to goal-oriented settings. This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications. Set in the context of restaurant reservation, our tasks require manipulating sentences and symbols, so as to properly conduct conversations, issue API calls and use the outputs of such calls. We show that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations. We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (Henderson et al., 2014a). We show similar result patterns on data extracted from an online concierge service.


title: Research paper recommendation with topic analysis

abstract: With the collaborative filtering techniques becoming more and more mature, recommender systems are widely used nowadays, especially in electronic commerce and social networks. However, the utilization of recommender system in academic research itself has not received enough attention. A research paper recommender system would greatly help researchers to find the most desirable papers in their fields of endeavor. Due to the textual nature of papers, content information could be integrated into existed recommendation methods. In this paper, we proposed that by using topic model techniques to make topic analysis on research papers, we could introduce a thematic similarity measurement into a modified version of item-based recommendation approach. This novel recommendation method could considerable alleviate the cold start problem in research paper recommendation. Our experiment result shows that our approach could recommend highly relevant research papers.


title: From machu_picchu to "rafting the urubamba river": anticipating information needs via the entity-query graph

abstract: We study the problem of anticipating user search needs, based on their browsing activity. Given the current web page p that a user is visiting we want to recommend a small and diverse set of search queries that are relevant to the content of p, but also non-obvious and serendipitous. We introduce a novel method that is based on the content of the page visited, rather than on past browsing patterns as in previous literature. Our content-based approach can be used even for previously unseen pages. We represent the topics of a page by the set of Wikipedia entities extracted from it. To obtain useful query suggestions for these entities, we exploit a novel graph model that we call EQGraph (Entity-Query Graph), containing entities, queries, and transitions between entities, between queries, as well as from entities to queries. We perform Personalized PageRank computation on such a graph to expand the set of entities extracted from a page into a richer set of entities, and to associate these entities with relevant query suggestions. We develop an efficient implementation to deal with large graph instances and suggest queries from a large and diverse pool. We perform a user study that shows that our method produces relevant and interesting recommendations, and outperforms an alternative method based on reverse IR.


title: FASTER: A Concurrent Key-Value Store with In-Place Updates

abstract: Over the last decade, there has been a tremendous growth in data-intensive applications and services in the cloud. Data is created on a variety of edge sources, e.g., devices, browsers, and servers, and processed by cloud applications to gain insights or take decisions. Applications and services either work on collected data, or monitor and process data in real time. These applications are typically update intensive and involve a large amount of state beyond what can fit in main memory. However, they display significant temporal locality in their access pattern. This paper presents FASTER, a new key-value store for point read, blind update, and read-modify-write operations. FASTER combines a highly cache-optimized concurrent hash index with a hybrid log: a concurrent log-structured record store that spans main memory and storage, while supporting fast in-place updates of the hot set in memory. Experiments show that FASTER achieves orders-of-magnitude better throughput - up to 160M operations per second on a single machine - than alternative systems deployed widely today, and exceeds the performance of pure in-memory data structures when the workload fits in memory.


title: Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications

abstract: An approach to optimize the deployment of microservices-based applications using containers in multi-cloud architectures is presented. The optimization objectives are three: cloud service cost, network latency among microservices, and time to start a new microservice when a provider becomes unavailable. The decision variables are: the scale level of the microservices; their allocation in the virtual machines; the provider and virtual machine type selection; and the number of virtual machines. The experiments compare the optimization results between a Greedy First-Fit and a Non-dominated Sorting Genetic Algorithm II (NSGA-II). NSGA-II with a two-point crossover operator and three mutation operators obtained an overall improvement of 300% in regard to the greedy algorithm.


title: Randomized Iterative Methods for Linear Systems

abstract: We develop a novel, fundamental and surprisingly simple randomized iterative method for solving consistent linear systems. Our method has six different but equivalent interpretations: sketch-and-project, constrain-and-approximate, random intersect, random linear solve, random update and random fixed point. By varying its two parameters$-$a positive definite matrix (defining geometry), and a random matrix (sampled in an independently and identically distributed fashion in each iteration)$-$we recover a comprehensive array of well-known algorithms as special cases, including the randomized Kaczmarz method, randomized Newton method, randomized coordinate descent method and random Gaussian pursuit. We naturally also obtain variants of all these methods using blocks and importance sampling. However, our method allows for a much wider selection of these two parameters, which leads to a number of new specific methods. We prove exponential convergence of the expected norm of the error in a single theorem, from which existing complexity results for known variants can be obtained. However, we also give an exact formula for the evolution of the expected iterates, which allows us to give lower bounds on the convergence rate.


title: Automated IT system failure prediction: A deep learning approach

abstract: In mission critical IT services, system failure prediction becomes increasingly important; it prevents unexpected system downtime, and assures service reliability for end users. While operational console logs record rich and descriptive information on the health status of those IT systems, existing system management technologies mostly use them in a labor-intensive forensics approach, i.e., identifying what went wrong after the fact. Recent efforts on log-based system management take an automation approach with text mining techniques, such as term frequency — inverse document frequency (TF-IDF). However, those techniques lead to a high-dimensional feature space, and are not easily generalizable to heterogeneous log formats. In this paper, we present a novel system that automatically parses streamed console logs and detects early warning signals for IT system failure prediction. In particular, our solution includes a log pattern extraction method by clustering together logs with similar format and content. We then resemble the TF-IDF idea by considering each pattern as a word and the set of patterns in each discretized epoch as a document. This leads to a feature space with significantly lower dimensionality that can provide robust signals for the status of the system. As system failures tend to occur very rare, we apply a recurrent neural network, namely, Long Short-Term Memory (LSTM), to deal with the “rarity” of labeled data in the training process. LSTM is able to capture the long-range dependency across sequences, therefore outperforms traditional supervised learning methods in our application domain. We evaluated and compared our proposed technology with state-of-the-art machine learning approaches using real log traces from two large enterprise systems. The results showed the advantage and potentials of our system in prediction of complex IT failures. To our knowledge, our work is the first that employs LSTM for log-based system failure prediction.


title: Evolutionary ensembles with negative correlation learning

abstract: Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.


title: Structure determination of candidine, a violet indolic constituent from culture solutions of Candida lipolytica

abstract: Abstract A violet indolic constituent, candidine, isolated from culture solutions of Candida lipolytica has been identified as 6,12-dihydro-6-(3-oxoindolid-2-ene)-12-oxoindolo[2,1- b ]quinazoline by an independent synthesis (condensation of 6,12-dihydro-6,12-dioxoindolo[2,1- b ]quinazoline with 1-acetylindoxyl).


title: Recurrent neural networks for aortic image sequence segmentation with sparse annotations

abstract: Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct application of a segmentation algorithm to each time frame of a sequence may ignore the temporal continuity inherent in the sequence. In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task. A key challenge in training this network is that the available manual annotations are temporally sparse, which forbids end-to-end training. We address this challenge by performing non-rigid label propagation on the annotations and introducing an exponentially weighted loss function for training. Experiments on aortic MR image sequences demonstrate that the proposed method significantly improves both accuracy and temporal smoothness of segmentation, compared to a baseline method that utilises spatial information only. It achieves an average Dice metric of 0.960 for the ascending aorta and 0.953 for the descending aorta.


title: SILAC Mouse for Quantitative Proteomics Uncovers Kindlin-3 as an Essential Factor for Red Blood Cell Function

abstract: Stable isotope labeling by amino acids in cell culture (SILAC) has become a versatile tool for quantitative, mass spectrometry (MS)-based proteomics. Here, we completely label mice with a diet containing either the natural or the (13)C(6)-substituted version of lysine. Mice were labeled over four generations with the heavy diet, and development, growth, and behavior were not affected. MS analysis of incorporation levels allowed for the determination of incorporation rates of proteins from blood cells and organs. The F2 generation was completely labeled in all organs tested. SILAC analysis from various organs lacking expression of beta1 integrin, beta-Parvin, or the integrin tail-binding protein Kindlin-3 confirmed their absence and disclosed a structural defect of the red blood cell membrane skeleton in Kindlin-3-deficient erythrocytes. The SILAC-mouse approach is a versatile tool by which to quantitatively compare proteomes from knockout mice and thereby determine protein functions under complex in vivo conditions.


title: A Delaunay Refinement Algorithm for Quality 2-Dimensional Mesh Generation

abstract: We present a simple new algorithm for triangulating polygons and planar straightline graphs, It provides "shape" and "size" guarantees: ?All triangles have a bounded aspect ratio.?The number of triangles is within a constant factor of optimal. Such "quality" triangulations are desirable as meshes for the finite element method, in which the running time generally increases with the number of triangles, and where the convergence and stability may be hurt by very skinny triangles. The technique we use-successive refinement of a Delaunay triangulation-extends a mesh generation technique of Chew by allowing triangles of varying sizes. Compared with previous quadtree-based algorithms for quality mesh generation, the Delaunay refinement approach is much simpler and generally produces meshes with fewer triangles. We also discuss an implementation of the algorithm and evaluate its performance on a variety of inputs.


title: On-line fusion of trackers for single-object tracking

abstract: Abstract Visual object tracking is a fundamental function of computer vision that has been the object of numerous studies. The diversity of the proposed approaches leads to the idea of trying to fuse them and take advantage of their individual strengths while controlling the noise they may introduce in some circumstances. The work presented here describes a generic framework for combining and/or selecting on-line the different components of the processing chain of a set of trackers, and examines the impact of various fusion strategies. The results are assessed from a repertoire of 9 state-of-the-art trackers evaluated over 46 fusion strategies on the VOT 2013, VOT 2015 and OTB-100 datasets. A complementarity measure able to predict the overall performance of a given set of trackers is also proposed.


title: Determination of effective dose combination of Azadirachta indica leaf extracts and diminazene diaceturate in the treatment of experimental Trypanosoma brucei brucei infection in rats

abstract: This study investigated the effective dose of methanolic Azadirachta indica leaf extracts, MAILE, combined with diminazene diaceturate, DDA, in the treatment of experimental Trypanosoma brucei brucei infection in rats. Acute toxicity study of the drug and extract combinations was carried in non-infected rats. Eleven different groups of ten rats each were used. Ten out of the eleven groups were infected with T. brucei brucei and used to determine the effective dose of MAILE and DDA combination to be used in the treatment of the infection. All the infected rats were treated, viz, 7.0 mg/kg body weight (bw) DDA plus 500 mg/kg bw MAILE (group 1); 7.0 mg/kg body bw DDA plus 250 mg/kg bw MAILE (group 2); 7.0 mg/kg body bw DDA plus 125 mg/kg bw MAILE (group 3); 3.5 mg/kg body bw DDA plus 500 mg/kg bw MAILE (group 4); 3.5 mg/kg body bw DDA plus 250 mg/kg bw MAILE (group 5); 3.5 mg/kg body bw DDA plus 125 mg/kg bw MAILE (group 6); 1.8 mg/kg bw DDA plus 500 mg/kg bw MAILE (group 7); 1.8 mg/kg bw DDA plus 250 mg/kg bw MAILE (group 8); 1.8 mg/kg body bw DDA plus 125 mg/kg bw MAILE (group 9). Two other groups, infected untreated (group 10) and uninfected untreated (group 11), served as negative and positive control, respectively. The parameters assessed to determine the effective dose combination of the two were onset of parasitaemia (OP), level of parasitaemia (LOP), clearance of parasites post-treatment (COPPT), relapse of infection period (RIP), erythrocyte counts (EC), packed cell volume (PCV) and total leucocyte counts (TLC). There was no significant difference (p < 0.05) in OP between the groups. A day following treatment, the LOP of groups 1, 2, 3 and 4 was found to be significantly lower (p < 0.05) than that of groups 5 and 6 (p < 0.05) which in turn was lower (p < 0.05) than that of groups 7, 8 and 9, respectively. The mean COPPT of groups 5 and 6 was significantly (p < 0.05) longer than that of groups 1, 2, 3 and 4. There was no significant difference (p < 0.05) in the mean COPPT among groups 1, 2, 3 and 4. There was no clearance of parasites in groups 7, 8 and 9. The mean RIP of group 5 and 6 was significantly shorter (p < 0.05) than in group 4. There was no relapse of infection in group 1, 2 and 3 rats. Rats in groups 1, 2, 3 and 4 had significantly higher (p < 0.05) PCV, EC and TLC 10 days post-treatment, and that trend continued throughout the experimental period when compared to other infected groups. It was concluded that dose combination of 125 mg/kg bw extract plus 7 mg/kg bw DDA was the best dose combination judging from the parameters assessed.


title: An optoelectronic-inertial system for handheld GPR positioning

abstract: Ground penetrating radar (GPR) needs precise antenna positioning system for soil sections imaging. In the GPR located on wheeled vehicle or flying platform the data about its position are most often generated by standard measure devices like survey wheels or GPS. These solutions are useless for the light handheld systems especially in the hard terrain. In the paper a new method for precise positioning of handheld GPR is described. It uses micro electro-mechanical systems (MEMS) and optoelectronic device for precise 3D movement parameters estimation.


title: Two wheels caster type odometer for omni-directional vehicles

abstract: This paper proposes a new type of two wheels caster type odometer to estimate the current position and orientation of omni-directional vehicles. The proposed odometer is composed of two passive wheels and one passive rotational axis. The two wheels rotate independently, and the rotational axis changes the odometer's orientation as the omni-directional vehicle moves. Three rotary encoders using a gear train measure angular velocities of the two wheels and rotational axis. A mechanism of the gear train is proposed to prevent the encoder from being entangled with it. Using measured values of the three encoders, the current velocity vector of an omni-directional vehicle is calculated by kinematics of the odometer. Using the velocity vector, the current position and orientation of the omni-directional vehicle are estimated by dead reckoning. Results of localization experiments by the developed odometer are shown.


title: Error Log Analysis: Statistical Modeling and Heuristic Trend Analysis

abstract: Most error-log analysis studies perform a statistical fit to the data assuming a single underlying error process. The authors present the results of an analysis that demonstrates that the log is composed of at least two error processes: transient and intermittent. The mixing of data from multiple processes requires many more events to verify a hypotheses using traditional statistical analysis. Based on the shape of the interarrival time function of the intermittent errors observed from actual error logs, a failure-prediction heuristic, the dispersion frame technique (DFT), is developed. The DFT was implemented in a distributed system for the campus-wide Andrew file system at Carnegie Mellon University. Data collected from 13 file servers over a 22-month period were analyzed using both the DFT and conventional statistical methods. It is shown that the DFT can extract intermittent errors from the error log and uses only one fifth of the error-log entry points required by statistical methods for failure prediction. The DFT achieved a 93.7% success rate in predicting failures in both electromechanical and electronic devices. >


title: Multi-Path Feedback Recurrent Neural Network for Scene Parsing

abstract: In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images. MPF-RNN can enhance the capability of RNNs in modeling long-range context information at multiple levels and better distinguish pixels that are easy to confuse. Different from feedforward CNNs and RNNs with only single feedback, MPF-RNN propagates the contextual features learned at top layer through \textit{multiple} weighted recurrent connections to learn bottom features. For better training MPF-RNN, we propose a new strategy that considers accumulative loss at multiple recurrent steps to improve performance of the MPF-RNN on parsing small objects. With these two novel components, MPF-RNN has achieved significant improvement over strong baselines (VGG16 and Res101) on five challenging scene parsing benchmarks, including traditional SiftFlow, Barcelona, CamVid, Stanford Background as well as the recently released large-scale ADE20K.