title: Fast pose estimation with parameter-sensitive hashing

abstract: Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and high-dimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. Our algorithm extends locality-sensitive hashing, a recently developed method to find approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice of hash functions that are optimally relevant to a particular estimation problem. Experiments demonstrate that the resulting algorithm, which we call parameter-sensitive hashing, can rapidly and accurately estimate the articulated pose of human figures from a large database of example images.


title: Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

abstract: In this paper, we focus on the problem of answer triggering ad-dressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep in-teractions between the candidate answers and the question. Our result on F val-ue achieves 42.6%, which surpasses the baseline by over 10 %.


title: Large range dual-axis micro-stage driven by electrostatic comb-drive actuators

abstract: This paper presents a micro XY stage that employs electrostatic comb-drive actuators and achieves a bi-directional displacement range greater than 225 µm per motion axis. The proposed XY stage design comprises four rigid stages (ground, motion stage, and two intermediate stages) interconnected via flexure modules. The motion stage, which has two translational degrees of freedom, is connected to two independent single degree of freedom intermediate stages via respective parallelogram (P) transmission flexures. The intermediate stages are connected to the ground via respective clamped paired double parallelogram (C-DP-DP) guidance flexures. The C-DP-DP flexure, unlike conventional flexures such as the paired double parallelogram flexure (DP-DP), provides high bearing direction stiffness (Kb) while maintaining low motion direction stiffness (Km) over a large range of motion direction displacement. This helps delay the onset of sideways instability in the comb-drive actuators that are integrated with the intermediate stages, thereby offering a significantly greater actuation stroke compared to existing designs. The presented work includes closed-form stiffness analysis of the proposed micro-stage, finite elements simulation, and experimental measurements of its static and dynamic behavior.


title: Effect of different substrates on slotted Log Periodic Fractal Koch Antenna

abstract: This paper discussed about the effect of different substrates on slotted Log Periodic Fractal Koch Antenna (LPFKA). This log periodic antenna is comprised of ten radiating elements with titled slot angles and placed on both sides of the substrate in crisscross arrangement. This LPFKA was designed at frequency between 0.47 GHz and 0.79 GHz which focus on UHF TVWS bands. A comprehensive study between the performances of the proposed antenna on different substrates is carried out. With the gains from 7.07 dBi to 7.95 dBi and reflection coefficient below than ™10 dB over desired frequencies, this antenna is suitable and has a big potential for UHF TVWS applications.


title: Deep Multitask Learning for Railway Track Inspection

abstract: Railroad tracks need to be periodically inspected and monitored to ensure safe transportation. Automated track inspection using computer vision and pattern recognition methods has recently shown the potential to improve safety by allowing for more frequent inspections while reducing human errors. Achieving full automation is still very challenging due to the number of different possible failure modes, as well as the broad range of image variations that can potentially trigger false alarms. In addition, the number of defective components is very small, so not many training examples are available for the machine to learn a robust anomaly detector. In this paper, we show that detection performance can be improved by combining multiple detectors within a multitask learning framework. We show that this approach results in improved accuracy for detecting defects on railway ties and fasteners.


title: Stubborn Mining: Generalizing Selfish Mining and Combining with an Eclipse Attack

abstract: Selfish mining, originally discovered by Eyal et al. [9], is a well-known attack where a selfish miner, under certain conditions, can gain a disproportionate share of reward by deviating from the honest behavior. In this paper, we expand the mining strategy space to include novel "stubborn" strategies that, for a large range of parameters, earn the miner more revenue. Consequently, we show that the selfish mining attack is not (in general) optimal. Further, we show how a miner can further amplify its gain by non-trivially composing mining attacks with network-level eclipse attacks. We show, surprisingly, that given the attacker's best strategy, in some cases victims of an eclipse attack can actually benefit from being eclipsed!


title: Daytime Naps, Motor Memory Consolidation and Regionally Specific Sleep Spindles

abstract: Background ::: Increasing evidence demonstrates that motor-skill memories improve across a night of sleep, and that non-rapid eye movement (NREM) sleep commonly plays a role in orchestrating these consolidation enhancements. Here we show the benefit of a daytime nap on motor memory consolidation and its relationship not simply with global sleep-stage measures, but unique characteristics of sleep spindles at regionally specific locations; mapping to the corresponding memory representation.


title: Principles of Analytic Monitoring for Continuous Assurance

abstract: The advent of new enabling technologies and the surge in corporate scandals has combined to increase the supply, the demand, and the development of enabling technologies for a new system of continuous assurance and measurement. This paper positions continuous assurance (CA) as a methodology for the analytic monitoring of corporate business processes, taking advantage of the automation and integration of business processes brought about by information technologies. Continuous analytic monitoring‐based assurance will change the objectives, timing, processes, tools, and outcomes of the assurance process. The objectives of assurance will expand to encompass a wide set of qualitative and quantitative management reports. The nature of this assurance will be closer to supervisory activities and will involve intensive interchange with more of the firm's stakeholders than just its shareholders. The timing of the audit process will be very close to the event, automated, and will conform to the natural life cycle of...


title: Toxicity of Bacillus thuringiensis toward Aedes aegypti larvae.

abstract: Abstract Among six strains of Bacillus thuringiensis and five other species of Bacillus, only two strains of B. thuringiensis, strains HD-1 and BA-068, were toxic to Aedes aegypti larvae within 24 hr. The LC50s were 5.6 × 104 and 2.4 × 105 spores/ml for strains HD-1 and BA-068, respectively. The toxic factor(s) was heat sensitive and γ ray resistant and preliminary evidences indicated that it was associated with the crystalline body of B. thuringiensis.


title: Recursive estimation of emitter location using TDOA measurements from two UAVs

abstract: This paper considers the recursive estimation of emitter location using time difference of arrival measurements formed by the correlation of signals received by two unmanned aerial vehicles. The time difference of arrival measurement defines an hyperbola of possible emitter locations. This hyperbola is used as a measurement in a nonlinear Alter. The performance of two such filters, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF), is analysed for a stationary and moving emitter and compared with the Cramer-Rao lower bound. The UKF performs generally better than the EKF, but both algorithms suffer from diverged tracks.


title: Algorithm 777: HOMPACK90: a suite of Fortran 90 codes for globally convergent homotopy algorithms

abstract: HOMPACK90 is a Fortran 90 version of the Fortran 77 package HOMPACK (Algorithm 652), a collection of codes for finding zeros or fixed points of nonlinear systems using globally convergent probability-one homotopy algorithms. Three qualitatively different algorithms— ordinary differential equation based, normal flow, quasi-Newton augmented Jacobian matrix—are provided for tracking homotopy zero curves, as well as separate routines for dense and sparse Jacobian matrices. A high level driver for the special case of polynomial systems is also provided. Changes to HOMPACK include numerous minor improvements, simpler and more elegant interfaces, use of modules, new end games, support for several sparse matrix data structures, and new iterative algorithms for large sparse Jacobian matrices.


title: Force and Torque Analytical Models of a Reaction Sphere Actuator Based on Spherical Harmonic Rotation and Decomposition

abstract: This paper presents an analytical model for the force and torque developed by a reaction sphere actuator for satellite attitude control. The reaction sphere is an innovative momentum exchange device consisting of a magnetic bearings spherical rotor that can be electronically accelerated in any direction making all the three axes of stabilized spacecrafts controllable by a unique device. The spherical actuator is composed of an 8-pole permanent magnet spherical rotor and of a 20-coil stator. Force and torque analytical models are derived by solving the Laplace equation and applying the Lorentz force law. The novelty consists in exploiting powerful properties of spherical harmonic functions under rotation to derive closed-form linear expressions of forces and torques for all possible orientations of the rotor. Specifically, the orientation of the rotor is parametrized using seven decomposition coefficients that can be determined noniteratively and in a linear fashion by measuring the radial component of the magnetic flux density from at least seven different locations. Therefore, force and torque models for all possible orientations of the rotor are expressed in closed form as linear combination of mutually orthogonal force and torque characteristic matrices, which are computed offline. The proposed analytical models are experimentally validated using a developed laboratory prototype.


title: Is the Human Amygdala Critical for the Subjective Experience of Emotion? Evidence of Intact Dispositional Affect in Patients with Amygdala Lesions

abstract: It is thought that the human amygdala is a critical component of the neural substrates of emotional experience, involved particularly in the generation of fear, anxiety, and general negative affectivity. Although many neuroimaging studies demonstrate findings consistent this notion, little evidence of altered emotional experience following amygdala damage has been gathered in humans. In a preliminary test of the amygdala's role in phenomenal affective states, we assessed the extent of experienced positive and negative affective states in patients with amygdala damage and age-, sex-, and education-matched controls. To assess chronic changes in experienced affect, all groups were administered the Positive and Negative Affect Schedules (PANAS, Watson, Clark, & Tellegen, 1988). In the first study, we examined the effects of amygdala lesions on affective traits in 10 left and 10 right amygdala-damaged patients, 1 patient with bilateral amygdala damage (SP), and 20 control subjects. Subjects were asked to indicate the typicality of different experiential states of positive (e.g., inspired, excited) and negative (e.g., afraid, nervous) valence. In a second study, we examined more closely the effects of bilateral amygdala damage on the day-to-day generation of affective states by administering the PANAS daily for a 30-day period to patient SP and age-, sex-, and education-matched controls. In both experiments, no differences in the magnitude and frequency of self-reported positive or negative affect were found between control subjects and patients with amygdala damage. Moreover, principal components analyses of the covariation among different affects (across individuals in Study 1 and within individuals across days in Study 2) confirmed a two-factor (positive vs. negative) description of experienced affect in controls. A highly similar two-factor description of experienced affect was found in patients with amygdala lesions. This suggests that the underlying structure of affective states was intact following amygdala damage. It is concluded that the human amygdala may be recruited during phenomenal affective states in the intact brain, but is not necessary for the production of these states.


title: Self-improving reactive agents based on reinforcement learning, planning and teaching

abstract: To date, reinforcement learning has mostly been studied solving simple learning tasks. Reinforcement learning methods that have been studied so far typically converge slowly. The purpose of this work is thus two-fold: 1) to investigate the utility of reinforcement learning in solving much more complicated learning tasks than previously studied, and 2) to investigate methods that will speed up reinforcement learning. ::: ::: This paper compares eight reinforcement learning frameworks: adaptive heuristic critic (AHC) learning due to Sutton, Q-learning due to Watkins, and three extensions to both basic methods for speeding up learning. The three extensions are experience replay, learning action models for planning, and teaching. The frameworks were investigated using connectionism as an approach to generalization. To evaluate the performance of different frameworks, a dynamic environment was used as a testbed. The environment is moderately complex and nondeterministic. This paper describes these frameworks and algorithms in detail and presents empirical evaluation of the frameworks.


title: 5G Radio Access Network Design with the Fog Paradigm: Confluence of Communications and Computing

abstract: Cloud-based wireless networking system applies centralized resource pooling to improve operation efficiency. Fog-based wireless networking system reduces latency by placing processing units in the network edge. Confluence of fog and cloud design paradigms in 5G radio access network will better support diverse applications. In this article, we describe the recent advances in fog radio access network (F-RAN) research, hybrid fog-cloud architecture, and system design issues. Furthermore, the GPP platform facilitates the confluence of computational and communications processing. Through observations from GPP platform testbed experiments and simulations, we discuss the opportunities of integrating the GPP platform with F-RAN architecture.


title: An Effective RSU Allocation Strategy for Maximizing Vehicular Network Connectivity

abstract: Roadside Unit (RSU) in Vehicular Ad-hoc Network (VANET) is an essential unit usually placed at intersections to collect and analyze traffic data given from smart vehicles. Due to the costs of RSU setup, effective RSU allocation is one of important issues in VANET. In this paper, we provide an optimal RSU allocation algorithm with a concept of intersectionconnectivity between intersections. We initially find optimal RSU candidate intersections which can cover the entire intersections, in the meantime minimizing the overlapped transmission coverage of RSUs. Then we repeatedly eliminate RSU candidates by the value of intersection-connectivity between RSU candidate intersections. The intersection-connectivity between two intersections is basically measured by the number of vehicles with the same moving pattern such that pass through both intersections. Thus, a high value of intersectionconnectivity between two intersections stands for that the traffic information obtained at one intersection can be carried-and-forwarded to the other intersection with a high probability by those vehicles. With this feature, we repeatedly remove RSUs having high intersectionconnectivity with its neighbors. Finally, we provide simulated analyses of our algorithms using real urban roadmaps of JungGu and Seochogu in Seoul. We analyze how our algorithms work in different types of roadways with real traffic data, and find the optimal number and positions of RSUs in these areas.


title: Securing SIFT: Privacy-Preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data

abstract: Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner’s private information, such as the personal identity, locations, or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that the previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-the-art, and performs comparably with the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, and addition of noise and change in 3D viewpoint and illumination.


title: HoneyMesh: Preventing Distributed Denial of Service Attacks using Virtualized Honeypots

abstract: Today, internet and web services have become an inseparable part of our lives. Hence, ensuring continuous availability of service has become imperative to the success of any organization. But these services are often hampered by constant threats from myriad types of attacks. One such attack is called distributed denial of service attack that results in issues ranging from temporary slowdown of servers to complete non-availability of service. Honeypot, which is a sort of a trap, can be used to interact with potential attackers to deflect, detect or prevent such attacks and ensure continuous availability of service. This paper gives insights into the problems posed by distributed denial of service attacks, existing solutions that use honeypots and how a mesh of virtualized honeypots can be used to prevent distributed denial of service attacks.


title: WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs

abstract: Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F-scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively.


title: Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines

abstract: Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania–New Jersey–Maryland (PJM) interconnection day-ahead and real-time markets.


title: Process Innovations to Prevent Glass Substrate Fracture From RDL Stress and Singulation Defects

abstract: Glass is an ideal material for package substrates due to the excellent electrical properties, tailorable coefficient of thermal expansion, high mechanical rigidity, availability in large and thin panel form, and smooth surface for fine line fabrication. Glass does have challenges, arising mainly from the brittle nature of glass, and glass substrates with copper and polymer re-distribution layers can suffer brittle fracture after dicing processes. This paper demonstrates three methods to prevent cracking induced by redistribution layer (RDL) stress and dicing defect in glass substrates for RDL build-up up to $90~ {\mu }\text{m}$ polymer and $40~ {\mu }\text{m}$ copper. These methods are edge coating, two-step dicing, and laser ablation dicing. Edge coating is a protective layer of polymer located on the diced edge of the glass substrate. After dicing, edge coating puts the glass in compression and prevents moisture from reaching the glass and, in turn, prevents the glass cracking from RDL stresses and dicing defects. In two-step dicing, the first step is used to ablate the RDL using a laser and the second step is used for blade dicing within the ablated region to singulate the glass. The resulting structure from such a dicing process reduces stress at the free glass edge, ensuring that the glass does not crack during dicing. Two-step dicing also allows for thicker RDL build-ups while ensuring no glass cracking occurs from dicing defects. Laser ablation dicing uses a CO2 laser to singulate the glass panel into individual substrates instead of blade dicing, which reduces the dicing defect size and creates a heat affected zone. Use of laser ablation dicing allows for thicker RDL build-ups with minimal process change when compared to blade dicing.


title: Daily life explorers, social networks and landscape policies

abstract: The article sheds light on virtual networks’ capability of driving landscape changes, both at epistemological level and at geographical level. “Living in” the landscape brings out the main drivers of change at individual level, while the ICTs are offering complementary places, with respect to those ones already existing, for increasing people’s role in sharing values and meanings. Then, common visions might emerge and “inform” landscape policies, indirectly influencing the meaning of wellbeing. Specifically, the socioeconomic theory might constitute a missing link between regulatory issues and technological achievements, enhancing and combining the new opportunities for participation offered by the European Landscape Convention, and by the widespread diffusion of social networks on the web. Indeed, “living in” the landscape inspires a plurality of visions that people are able to describe and share on the web or send directly to the interested institutions. Geographers might collect these issues and explore the landscape by living in it in order to produce “ethic visions”. Integrated with political and economic issues using the Regulation Impact Analysis (RIA), their contents might contribute to inform landscape transformation policies. Landscape policies might be participated also in the implementation phase, involving people in the fund raising activities and delegating the realization of some interventions to the spontaneous action of the interested citizens and firms. More participation at political and at social level might strengthen the sense of community reinforcing the narratives that connect the human and natural elements of landscape, integrating equity and sustainability in the traditional meaning of wellbeing.


title: Design of a High-Efficiency Wireless Power Transfer System With Intermediate Coils for the On-Board Chargers of Electric Vehicles

abstract: In this paper, a high efficiency inductive wireless power transfer system for the on-board chargers of electric vehicles is proposed. In order to improve the power transfer efficiency, the proposed system adopts two additional intermediate coils with resonant capacitors, which increases the effective magnetizing impedance between the transmitter and receiver coils with no ferrites. The resonant tank of the proposed system is designed to operate the converter as a current source and as a voltage source at two different frequencies to implement the constant current (CC) mode charge and constant voltage (CV) charge, respectively. Since the proposed converter operates at a fixed frequency in each mode of charge operation, full soft switching of all the switching devices is possible and the zero phase angle condition can be achieved in both the CC and CV mode operations. A theoretical analysis based on a Thevenin model to come up with a suitable design for the battery charger and its closed-loop controller is presented and its superior performance is demonstrated by experimental results. A 6.6 kW prototype is implemented with a 200 mm air gap to demonstrate the validity of the proposed method. Experimental results show that the dc to dc conversion efficiency of the proposed system is 97.08% at 3.7 kW of output power in the CV mode charge.


title: Automated sub‐cortical brain structure segmentation combining spatial and deep convolutional features

abstract: Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting the need for robust automated segmentation methods. In this paper, we present a novel convolutional neural network based approach for accurate segmentation of the sub-cortical brain structures that combines both convolutional and prior spatial features for improving the segmentation accuracy. In order to increase the accuracy of the automated segmentation, we propose to train the network using a restricted sample selection to force the network to learn the most difficult parts of the structures. We evaluate the accuracy of the proposed method on the public MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different traditional and deep learning state-of-the-art methods. On the MICCAI 2012 dataset, our method shows an excellent performance comparable to the best participant strategy on the challenge, while performing significantly better than state-of-the-art techniques such as FreeSurfer and FIRST. On the IBSR 18 dataset, our method also exhibits a significant increase in the performance with respect to not only FreeSurfer and FIRST, but also comparable or better results than other recent deep learning approaches. Moreover, our experiments show that both the addition of the spatial priors and the restricted sampling strategy have a significant effect on the accuracy of the proposed method. In order to encourage the reproducibility and the use of the proposed method, a public version of our approach is available to download for the neuroimaging community.


title: Unpacking the social media phenomenon: towards a research agenda

abstract: In this paper, we highlight some of the challenges and opportunities that social media presents to researchers, and offer relevant theoretical avenues to be explored. To do this, we present a model that unpacks social media by using a honeycomb of seven functional building blocks. We then examine each of the seven building blocks and, through appropriate social and socio-technical theories, raise questions that warrant further in-depth research to advance the conceptualization of social media in public affairs research. Finally, we combine the individual research questions for each building block back into the honeycomb model to illustrate how the theories in combination provide a powerful macro-lens for research on social media dynamics.


title: Basic Concepts of Lexical Resource Semantics

abstract: Semanticists use a range of highly expressive logical languages to characterize the meaning of natural language expressions. The logical languages are usually taken from an inventory of standard mathematical systems, with which generative linguists are familiar. They are, thus, easily accessible beyond the borders of a given framework such as Categorial Grammar, Lexical Functional Grammar, or Government and Binding Theory. Linguists working in the HPSG framework, on the other hand, often use rather idiosyncratic and specialized semantic representations. Their choice is sometimes motivated by computational applications in parsing, generation, or machine translation. Naturally, the intended areas of application influence the design of semantic representations. A typical property of semantic representations in HPSG that is concerned with computational applications is underspecification, and other properties come from the particular unification or constraint solving algorithms that are used for processing grammars. While the resulting semantic representations have properties that are motivated by, and are adequate for, certain practical applications, their relationship to standard languages is sometimes left on an intuitive level. In addition, the theoretical and ontological status of the semantic representations is often neglected. This vagueness tends to be unsatisfying to many semanticists, and the idiosyncratic shape of the semantic representations confines their usage to HPSG. Since their entire architecture is highly dependent on HPSG, hardly anyone working outside of that framework is interested in studying them. With our work on Lexical Resource Semantics (LRS), we want to contribute to the investigation of a number of important theoretical issues surrounding semantic representations and possible ways of underspecification. While LRS is formulated in a constraint-based grammar environment and takes advantage of the tight connection between syntax proper and logical representations that can easily be achieved in HPSG, the architecture of LRS remains independent from that framework, and combines attractive properties of various semantic systems. We will explore the types of semantic frameworks which can be specified in Relational Speciate Re-entrant Language (RSRL), the formalism that we choose to express our grammar principles, and we evaluate the semantic frameworks with respect to their potential for providing empirically satisfactory analyses of typical problems in the semantics of natural languages. In LRS, we want to synthesize a flexible meta-theory that can be applied to different interesting semantic representation languages and make computing with them feasible. We will start our investigation with a standard semantic representation language from natural language semantics, Ty2 (Gallin, 1975). We are well aware of the debate about the appropriateness of Montagovian-style intensionality for the analysis of natural language semantics, but we believe that it is best to start with a semantic representation that most generative linguists are familiar with. As will become clear in the course of our discussion, the LRS framework is a meta-theory of semantic representations, and we believe that it is suitable for various representation languages, This paper can be regarded as a snapshot of our work on LRS. It was written as material for the authors’ course Constraint-based Combinatorial Semantics at the 15th European Summer School in Logic, Language and Information in Vienna in August 2003. It is meant as background reading and as a basis of discussion for our class. Its air of a work in progress is deliberate. As we see continued development in LRS, its application to a wider range of languages and empirical phenomena, and especially the implementation of an LRS module as a component of the TRALE grammar development environment; we expect further modifications and refinements to the theory. The implementation of LRS is realized in collaboration with Gerald Penn of the University of Toronto. We would like to thank Carmella Payne for proofreading various versions of this paper.


title: Static analysis of cable-driven manipulators with non-negligible cable mass

abstract: This paper addresses the static analysis of cable-driven robotic manipulators with non-negligible cable mass. An approach to computing the static displacement of a homogeneous elastic cable is presented. The resulting cable-displacement expression is used to solve the inverse kinematics of general cable-driven robotic manipulators. In addition, the sag-induced stiffness of the cables is derived. Finally, two sample robotic manipulators with dimensions and system parameters similar to a large scale cable-driven manipulator currently under development are analyzed. The results show that cable sag can have a significant effect on both the inverse kinematics and stiffness of such manipulators.


title: HTTPS traffic analysis and client identification using passive SSL/TLS fingerprinting

abstract: The encryption of network traffic complicates legitimate network monitoring, traffic analysis, and network forensics. In this paper, we present real-time lightweight identification of HTTPS clients based on network monitoring and SSL/TLS fingerprinting. Our experiment shows that it is possible to estimate the User-Agent of a client in HTTPS communication via the analysis of the SSL/TLS handshake. The fingerprints of SSL/TLS handshakes, including a list of supported cipher suites, differ among clients and correlate to User-Agent values from a HTTP header. We built up a dictionary of SSL/TLS cipher suite lists and HTTP User-Agents and assigned the User-Agents to the observed SSL/TLS connections to identify communicating clients. The dictionary was used to classify live HTTPS network traffic. We were able to retrieve client types from 95.4 % of HTTPS network traffic. Further, we discussed host-based and network-based methods of dictionary retrieval and estimated the quality of the data.


title: Text Readability and Intuitive Simplification: A Comparison of Readability Formulas.

abstract: Texts are routinely simplified for language learners with authors relying on a variety of approaches and materials to assist them in making the texts more comprehensible. Readability measures are one such tool that authors can use when evaluating text comprehensibility. This study compares the Coh-Metrix Second Language (L2) Reading Index, a readability formula based on psycholinguistic and cognitive models of reading, to traditional readability formulas on a large corpus of texts intuitively simplified for language learners. The goal of this study is to determine which formula best classifies text level (advanced, intermediate, beginner) with the prediction that text classification relates to the formulas’ capacity to measure text comprehensibility. The results demonstrate that the Coh-Metrix L2 Reading Index performs significantly better than traditional readability formulas, suggesting that the variables used in this index are more closely aligned to the intuitive text processing employed by authors when simplifying texts.


title: Adaptive contrast enhancement using gain-controllable clipped histogram equalization

abstract: Histogram equalization is a simple and effective method for contrast enhancement as it can automatically define the intensity transformation function based on statistical characteristics of the image. However, it tends to alter the brightness of the entire image, which it is not suitable for consumer electronic products, where preservation of the original brightness is essential to avoid annoying artifacts. This paper presents a new contrast enhancement method for generalization of the existing bihistogram equalization (BHE) and recursive mean-separate histogram equalization (RMSHE) methods. The proposed method is referred to gain-controllable clipped histogram equalization (GC-CHE) to provide both histogram equalization and brightness preservation. More specifically adaptive contrast enhancement is realized by using clipped histogram equalization with controllable gain. The clipping rate is determined based on the mean brightness, and the clipping threshold is determined based on the clipping rate. The clipping rate is adaptively controlled to enhance the contrast with preserving the mean brightness. It is mathematically proven that the mean brightness of the output image converges to that of the input image with adaptive controlled. Simulation results show that the proposed GC-CHE method outperforms existing histogram-based methods, such as HE, BHE, and RMSHE, in various situations.


title: Design and Implement of Spring Security-Based T-RBAC

abstract: Aimed at the problem of resource protection in Web system and it is not suitable for maintenance in the process of Web system development due to the tight interleaving of business logic and security logic, based on the open source security framework Spring Security, this paper proposes a task-role-based-access-control (T-RBAC) implementation scheme for Web systems. This scheme can realize the effective protection of Web resources and service methods, and can realize fine access control of Web resources. Through the security analysis and performance tests show that this scheme can manage permissions based on tasks and roles, and it meets the requirements of task based access control for Web systems, and has dynamic access control capability and good system performance. At the same time, the scheme is implemented on the basis of Spring Security framework, which can effectively develop the program, and has strong security. It has certain popularization value in practical application development.


title: On the Split-Block Realization of Millimeter-Wave Ridge Waveguide Components

abstract: A method to reduce RF leakage in split-block fabricated metallic ridged waveguide is presented. By placing a pin wall into the split-block seam, RF leakage and detrimental performance spikes are significantly reduced. Three different split-block double-ridge waveguide components of varying design complexity operating over 18–45-GHz bandwidth are fabricated and measured. It is shown that the addition of the pin wall improves the performance of each component such that results correlate well to simulated models with no split.


title: A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks

abstract: Malicious calls, i.e., telephony spams and scams, have been a long-standing challenging issue that causes billions of dollars of annual financial loss worldwide. This work presents the first machine learning-based solution without relying on any particular assumptions on the underlying telephony network infrastructures. The main challenge of this decade-long problem is that it is unclear how to construct effective features without the access to the telephony networks' infrastructures. We solve this problem by combining several innovations. We first develop a TouchPal user interface on top of a mobile App to allow users tagging malicious calls. This allows us to maintain a large-scale call log database. We then conduct a measurement study over three months of call logs, including 9 billion records. We design 29 features based on the results, so that machine learning algorithms can be used to predict malicious calls. We extensively evaluate different state-of-the-art machine learning approaches using the proposed features, and the results show that the best approach can reduce up to 90% unblocked malicious calls while maintaining a precision over 99.99% on the benign call traffic. The results also show the models are efficient to implement without incurring a significant latency overhead. We also conduct ablation analysis, which reveals that using 10 out of the 29 features can reach a performance comparable to using all features.


title: DPSNet: End-to-end Deep Plane Sweep Stereo

abstract: Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion. Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions. In this paper, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches for dense depth reconstruction. Rather than directly estimating depth and/or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume via a context-aware cost aggregation, and regressing the dense depth map from the cost volume. The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network. Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, DPSNet achieves state-of-the-art reconstruction results on a variety of challenging datasets.


A Systematic Review of the Use of Blockchain in Healthcare


title: Analyzing Disinformation and Crowd Manipulation Tactics on YouTube

abstract: YouTube, since its inception in 2005, has grown to become largest online video sharing website. It's massive userbase uploads videos and generates discussion by commenting on these videos. Lately, YouTube, akin to other social media sites, has become a vehicle for spreading fake news, propaganda, conspiracy theories, and radicalizing content. However, lack ineffective image and video processing techniques has hindered research on YouTube. In this paper, we advocate the use of metadata in identifying such malicious behaviors. Specifically, we analyze metadata of videos (e.g., comments, commenters) to study a channel on YouTube that was pushing content promoting conspiracy theories regarding World War III. Identifying signals that could be used to detect such deviant content (e.g., videos, comments) can help in stemming the spread of disinformation. We collected over 4,145 videos along with 16,493 comments from YouTube. We analyze user engagement to assess the reach of the channel and apply social network analysis techniques to identify inorganic behaviors.


Application of WEPP and GIS-GRASS to a small watershed in Indiana


title: DNS Tunneling Detection Techniques – Classification, and Theoretical Comparison in Case of a Real APT Campaign

abstract: Domain Name System (DNS) plays an important role as a translation protocol in everyday use of the Internet. The purpose of DNS is to translate domain names into IP addresses and vice versa. However, its simple architecture can easily be misused for malicious activities. One huge security threat concerning DNS is tunneling, which helps attackers bypass the security systems unnoticed. A DNS tunnel can be used for three purposes: as a command and control channel, for data exfiltration or even for tunneling another protocol through it. In this paper, we surveyed different techniques for DNS tunneling detection. We classified those first based on the type of data and then within the categories based on the type of analysis. We conclude with a comparison between the various detection techniques. We introduce one real Advanced Persistent Threat campaign that utilizes DNS tunneling, and theoretically compare how well the surveyed detection techniques could detect it.


title: Distribution-Specific Hardness of Learning Neural Networks

abstract: Although neural networks are routinely and successfully trained in practice using simple gradient-based methods, most existing theoretical results are negative, showing that learning such networks is difficult, in a worst-case sense over all data distributions. In this paper, we take a more nuanced view, and consider whether specific assumptions on the "niceness" of the input distribution, or "niceness" of the target function (e.g. in terms of smoothness, non-degeneracy, incoherence, random choice of parameters etc.), are sufficient to guarantee learnability using gradient-based methods. We provide evidence that neither class of assumptions alone is sufficient: On the one hand, for any member of a class of "nice" target functions, there are difficult input distributions. On the other hand, we identify a family of simple target functions, which are difficult to learn even if the input distribution is "nice". To prove our results, we develop some tools which may be of independent interest, such as extending Fourier-based hardness techniques developed in the context of statistical queries \cite{blum1994weakly}, from the Boolean cube to Euclidean space and to more general classes of functions.


title: Optimistic Atomic Multicast

abstract: Message ordering is one of the cornerstones of reliable distributed systems. However, some ordering guarantees, such as atomic order, are expensive to implement in terms of message delays. This paper presents Optimistic Atomic Multicast, a protocol that combines reduced latency and increased throughput. Messages can be delivered optimistically in a single communication step and conservatively in three communication steps. Differently from previous optimistic group communication protocols, Optimistic Atomic Multicast does not rely on spontaneous message ordering for fast delivery. In addition to presenting Optimistic Atomic Multicast, we provide detailed performance results comparing it to other ordering protocols in both local-area and wide-area networks.


title: NeTraMark: a network traffic classification benchmark

abstract: Recent research on Internet traffic classification has produced a number of approaches for distinguishing types of traffic. However, a rigorous comparison of such proposed algorithms still remains a challenge, since every proposal considers a different benchmark for its experimental evaluation. A lack of clear consensus on an objective and cientific way for comparing results has made researchers uncertain of fundamental as well as relative contributions and limitations of each proposal. In response to the growing necessity for an objective method of comparing traffic classifiers and to shed light on scientifically grounded traffic classification research, we introduce an Internet traffic classification benchmark tool, NeTraMark. Based on six design guidelines (Comparability, Reproducibility, Efficiency, Extensibility, Synergy, and Flexibility/Ease-of-use), NeTraMark is the first Internet traffic lassification benchmark where eleven different state-of-the-art traffic classifiers are integrated. NeTraMark allows researchers and practitioners to easily extend it with new classification algorithms and compare them with other built-in classifiers, in terms of three categories of performance metrics: per-whole-trace flow accuracy, per-application flow accuracy, and computational performance.


title: Alzheimer's disease

abstract: An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease. Thus, Alzheimer's disease represents a major public health concern and has been identified as a research priority. Although there are licensed treatments that can alleviate symptoms of Alzheimer's disease, there is a pressing need to improve our understanding of pathogenesis to enable development of disease-modifying treatments. Methods for improving diagnosis are also moving forward, but a better consensus is needed for development of a panel of biological and neuroimaging biomarkers that support clinical diagnosis. There is now strong evidence of potential risk and protective factors for Alzheimer's disease, dementia, and cognitive decline, but further work is needed to understand these better and to establish whether interventions can substantially lower these risks. In this Seminar, we provide an overview of recent evidence regarding the epidemiology, pathogenesis, diagnosis, and treatment of Alzheimer's disease, and discuss potential ways to reduce the risk of developing the disease.


title: Hairy Slices: Evaluating the Perceptual Effectiveness of Cutting Plane Glyphs for 3D Vector Fields

abstract: Three-dimensional vector fields are common datasets throughout the sciences. Visualizing these fields is inherently difficult due to issues such as visual clutter and self-occlusion. Cutting planes are often used to overcome these issues by presenting more manageable slices of data. The existing literature provides many techniques for visualizing the flow through these cutting planes; however, there is a lack of empirical studies focused on the underlying perceptual cues that make popular techniques successful. This paper presents a quantitative human factors study that evaluates static monoscopic depth and orientation cues in the context of cutting plane glyph designs for exploring and analyzing 3D flow fields. The goal of the study was to ascertain the relative effectiveness of various techniques for portraying the direction of flow through a cutting plane at a given point, and to identify the visual cues and combinations of cues involved, and how they contribute to accurate performance. It was found that increasing the dimensionality of line-based glyphs into tubular structures enhances their ability to convey orientation through shading, and that increasing their diameter intensifies this effect. These tube-based glyphs were also less sensitive to visual clutter issues at higher densities. Adding shadows to lines was also found to increase perception of flow direction. Implications of the experimental results are discussed and extrapolated into a number of guidelines for designing more perceptually effective glyphs for 3D vector field visualizations.


title: Synthesizing Obama: learning lip sync from audio

abstract: Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track. Our approach produces photorealistic results.


title: An Intrusion-Detection Model

abstract: A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. The model includes profiles for representing the behavior of subjects with respect to objects in terms of metrics and statistical models, and rules for acquiring knowledge about this behavior from audit records and for detecting anomalous behavior. The model is independent of any particular system, application environment, system vulnerability, or type of intrusion, thereby providing a framework for a general-purpose intrusion-detection expert system.


title: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

abstract: Abstract Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra- and inter-expert variability, many methods have been proposed to automatically segment lesions. The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.


title: Inselect: Automating the Digitization of Natural History Collections

abstract: The world’s natural history collections constitute an enormous evidence base for scientific research on the natural world. To facilitate these studies and improve access to collections, many organisations are embarking on major programmes of digitization. This requires automated approaches to mass-digitization that support rapid imaging of specimens and associated data capture, in order to process the tens of millions of specimens common to most natural history collections. In this paper we present Inselect—a modular, easy-to-use, cross-platform suite of open-source software tools that supports the semi-automated processing of specimen images generated by natural history digitization programmes. The software is made up of a Windows, Mac OS X, and Linux desktop application, together with command-line tools that are designed for unattended operation on batches of images. Blending image visualisation algorithms that automatically recognise specimens together with workflows to support post-processing tasks such as barcode reading, label transcription and metadata capture, Inselect fills a critical gap to increase the rate of specimen digitization.


title: Fracking Sarcasm using Neural Network

abstract: Precise semantic representation of a sentence and definitive information extraction are key steps in the accurate processing of sentence meaning, especially for figurative phenomena such as sarcasm, Irony, and metaphor cause literal meanings to be discounted and secondary or extended meanings to be intentionally profiled. Semantic modelling faces a new challenge in social media, because grammatical inaccuracy is commonplace yet many previous state-of-the-art methods exploit grammatical structure. For sarcasm detection over social media content, researchers so far have counted on Bag-of-Words(BOW), N-grams etc. In this paper, we propose a neural network semantic model for the task of sarcasm detection. We also review semantic modelling using Support Vector Machine (SVM) that employs constituency parsetrees fed and labeled with syntactic and semantic information. The proposed neural network model composed of Convolution Neural Network(CNN) and followed by a Long short term memory (LSTM) network and finally a Deep neural network(DNN). The proposed model outperforms state-of-the-art textbased methods for sarcasm detection, yielding an F-score of .92.


title: Development of Heat-treatment Furnace with Maximum Uniform Zone using Gas-phase Condensing Heat Exchange

abstract: A horizontal tube furnace with a wide uniform-temperature zone was developed using isothermal characteristics of a heat pipe. The heat pipe heating system consists of a concentric annular shaped stainless-steel container, sodium as a working fluid and a screen mesh wick structure. The performance test of the heat pipe revealed that temperature changes along seven consecutive positions of the heat pipe outer wall were less than, thereby ensuring the high isothermal property. The isothermal property of the heat pipe-adapted tube fumace was investigated and compared to a conventional non-heat pipe type tube furnace. The temperature distribution measurement showed that the uniform temperature zone, in which temperature change is less than1C, of the heat pipe employed tube furnace system was about three times longer compared to the conventional tube furnace system.


title: Differentiating soybean responses to Heterodera glycines races

abstract: Physiological variation among populations of Heterodera glycines Ichinohe, the soybean cyst nematode, has been a problem for soybean [Glycine max (L.) Merr.] growers and scientists. These populations have been classified into 16 races, but the system still leaves ambiguity, especially in the characterization of resistance. Two schemes are proposed: one to classify the nematode races and the other to designate the soybean response to the nematode(.)


title: Novel Design of Microstrip Bandpass Filters with a Controllable Dual-Passband Response: Description and Implementation

abstract: SUMMARY Novel microstrip dual-band bandpass filters with controllable fractional bandwidths and good in-between isolation are presented and implemented. A half-wavelength stepped-impedance resonator is firstly characterized, aiming at producing the two resonant frequencies at 2.4 and 5.2 GHz. Two types of coupled microstrip lines in the parallel and anti-parallel formats are then investigated in terms of unified equivalent J-inverter network. Extensive results are derived to quantitatively show their distinctive frequency-distributed coupling performances under different coupling lengths. The coupling degrees of these two coupled lines at the two resonances are properly adjusted to achieve the dual-passband response with varied or tunable bandwidths. In addition, the parallel coupled line is modeled to bring out a transmission zero between the two resonances so as to achieve the good in-between isolation. The three two-stage bandpass filters are initially designed to exhibit their dual-band response with changeable dual-band bandwidths. A three-stage dual-band filter is in final optimally designed and its predicted performance is confirmed in experi


title: Multi-class SVM classifier for english handwritten digit recognition using manual class segmentation

abstract: A new method for recognition of isolated handwritten English digits is presented here. This method is based on Support Vector Machines (SVMs). Mean and standard deviation of each digit is considered as the features. Using these features, multiple SVM classifiers are trained to separate different classes of digits. Support vector machine are based on the concept of decision planes that defines the decision boundaries. The decision plane is one that separates between the set of digits having different class membership. The approach works in four steps 1) Preprocessing 2) Feature extraction 3) Classification 4) detection. A database of 100 different representation of each digit is constructed for the training database. The digits are first manually segmented into 5 classes to minimize the time required to obtain the hyperplane. Then the input is again check against the two classes by 2-class SVM classifier. Experiments show that the proposed features can provide a very good recognition result using Support Vector Machines at a recognition rate 97%, compared with 91.25% obtained by MLP neural network classifier using the same features and test set.


title: Autopilot System for Kiteplane

abstract: This paper proposes an autopilot system for a small and light unmanned air vehicle called Kiteplane. The Kiteplane has a large delta-shaped main wing that is easily disturbed by the wind, which was minimized by utilizing trim flight with drift. The proposed control system for autonomous trajectory following with a wind disturbance included fuzzy logic controllers, a speed controller, a wind disturbance attenuation block, and low-level feedback controllers. The system was implemented onboard the aircraft. Experiments were performed to test the performance of the proposed system and the Kiteplane nearly succeeded in following the desired trajectory, under the wind disturbance. Although the path was not followed perfectly, the airplane was able to traverse the waypoints by utilizing a failsafe waypoint updating rule


Triplet Loss Based Cosine Similarity Metric Learning for Text-independent Speaker Recognition


title: Incorporating Word Embedding into Cross-Lingual Topic Modeling

abstract: In this paper, we address the cross-lingual topic modeling, which is an important technique that enables global enterprises to detect and compare topic trends across global markets. Previous works in cross-lingual topic modeling have proposed methods that utilize parallel or comparable corpus in constructing the polylingual topic model. However, parallel or comparable corpus in many cases are not available. In this research, we incorporate techniques of mapping cross-lingual word space and the topic modeling (LDA) and propose two methods: Translated Corpus with LDA (TC-LDA) and Post Match LDA (PM-LDA). The cross-lingual word space mapping allows us to compare words of different languages, and LDA enables us to group words into topics. Both TC-LDA and PM-LDA do not need parallel or comparable corpus and hence have more applicable domains. The effectiveness of both methods is evaluated using UM-Corpus and WS-353. Our evaluation results indicate that both methods are able to identify similar documents written in different language. In addition, PM-LDA is shown to achieve better performance than TC-LDA, especially when document length is short.


title: Design of a robotic upper extremity repetitive therapy device

abstract: Intensive repetitive therapy shows promise to improve motor function and quality of life for stroke patients. Intense therapies provided by individualized interaction between the patient and rehabilitation specialist to overcome upper extremity impairment are beneficial, however, they are expensive and difficult to evaluate quantitatively and objectively. The development of a pneumatic muscle (PM) driven therapeutic device, the RUPERT/spl trade/ has the potential of providing a low cost and safe take-home method of supplementing therapy in addition to in the clinic treatment. The device can also provide real-time, objective assessment of functional improvement from the therapy.


title: Gratitude and the Reduced Costs of Materialism in Adolescents

abstract: Materialistic youth seem to be languishing while grateful youth seem to be flourishing. High school students (N = 1,035) completed measures of materialism, gratitude, academic functioning, envy, depression, life satisfaction, social integration, and absorption. Using structural equation modeling, we found that gratitude, controlling for materialism, uniquely predicts all outcomes considered: higher grade point average, life satisfaction, social integration, and absorption, as well as lower envy and depression. In contrast, materialism, controlling for gratitude, uniquely predicts three of the six outcomes: lower grade point average, as well as higher envy and life satisfaction. Furthermore, when examining the relative strengths of gratitude and materialism as predictors, we found that gratitude is generally a stronger predictor of these six outcomes than is materialism.


How Does Social Media Interactivity Affect Brand Loyalty?


Using Model-based Intrusion Detection for SCADA Networks ∗


title: Motor Training of the Lumbar Paraspinal Muscles Induces Immediate Changes in Motor Coordination in Patients With Recurrent Low Back Pain

abstract: Abstract Recurrent low back pain (LBP) is associated with altered motor coordination of the lumbar paraspinal muscles. Whether these changes can be modified with motor training remains unclear. Twenty volunteers with unilateral LBP were randomly assigned to cognitively activate the lumbar multifidus independently from other back muscles (skilled training) or to activate all paraspinal muscles with no attention to any specific muscles (extension training). Electromyographic (EMG) activity of deep (DM) and superficial multifidus (SM) muscles were recorded bilaterally using intramuscular fine-wire electrodes and that of superficial abdominal and back muscles using surface electrodes. Motor coordination was assessed before and immediately after training as onsets of trunk muscle EMG during rapid arm movements, and as EMG amplitude at the mid-point of slow trunk flexion-extension movements. Despite different intentions of the training tasks, the pattern of activity was similar for both. After both training tasks, activation of the DM and SM muscles was earlier during rapid arm movements. However, during slow trunk movements, DM and SM activity was increased, and EMG activity of the superficial trunk muscles was reduced only after skilled training. These findings show the potential to alter motor coordination with motor training of the lumbar paraspinal muscles in recurrent LBP. Perspectives Changes in motor coordination differed between skilled and extension training during slows trunk movements. As identical patterns of muscle activity were observed between training protocols, the results suggest that training-induced changes in motor coordination are not simply related to the muscle activation, but appear to be related to the task.


title: High-Frequency Modeling of Power Transformers for Use in Frequency Response Analysis (FRA)

abstract: This paper presents an advanced model of the frequency response of a three-phase power transformer for use in conjunction with diagnostic measurements by the method of frequency response analysis (FRA). The model includes high- frequency behavior of the laminated core and the insulation through taking into account the frequency dependencies of the complex permittivity of insulation materials (paper, pressboard, and oil) and of the anisotropic complex permeability of magnetic core and conductors. A lumped parameter circuit model is used to simulate the frequency response of open-circuit impedance, short-circuit impedance, and impedance between primary and secondary windings, in which the characteristics of circuit elements are calculated by means of the finite-element method. The effect of correct representation of each circuit element on the FRA response is analyzed and discussed in comparison to measurement results on a real transformer.


title: A Novel Transmission Policy for Reliable eMBMS Download Delivery

abstract: Enhanced Multimedia Broadcast/Multicast Service (eMBMS) is a new function defined in 3GPP Release 8 specification, which supports download delivery and streaming delivery to group users in LTE mobile networks. For download delivery, there are two mechanisms implemented in different entities to improve reliability: (1) At eBMSC (Enhanced Broadcast/Multicast Service Center), Raptor code is implemented in software to provide packet-level protection in the application layer; (2) At eNB (Enhanced NodeB), HARQ is used to provide bit-level protection in the link-layer. In this paper, we first analyze the redundancy caused by the coexistence of HARQ and Raptor, and then propose a novel HARQ retransmission policy to reduce the redundancy. Simulation results, using the Matlab and OPNET, confirm significant improvement.


title: One Query, Many Clicks: Analysis of Queries with Multiple Clicks by the Same User

abstract: In this paper, we study multi-click queries - queries for which more than one click is performed by the same user within the same query session. Such queries may reflect a more complex information need, which leads the user to examine a variety of results. We present a comprehensive analysis that reveals unique characteristics of multi-click queries, in terms of their syntax, lexical domains, contextual properties, and returned search results page. We also show that a basic classifier for predicting multi-click queries can reach an accuracy of 75% over a balanced dataset. We discuss the implications of our findings for the design of Web search tools.


title: Using histograms to detect and track objects in color video

abstract: Two methods of detecting and tracking objects in color video are presented. Color and edge histograms are explored as ways to model the background and foreground of a scene. The two types of methods are evaluated to determine their speed, accuracy and robustness. Histogram comparison techniques are used to compute similarity values that aid in identifying regions of interest. Foreground objects are detected and tracked by dividing each video frame into smaller regions (cells) and comparing the histogram of each cell to the background model. Results are presented for video sequences of human activity.


title: An Automatic Framework for Detecting and Characterizing Performance Degradation of Software Systems

abstract: Software systems that run continuously over a long time have been frequently reported encountering gradual degradation issues. That is, as time progresses, software tends to exhibit degraded performance, deflated service capacity, or deteriorated QoS. Currently, the state-of-the-art approach of Mann-Kendall Test & Seasonal Kendall Test & Sen's Slope Estimator & Seasonal Sen's Slope Estimator (MKSK) detects and characterizes degradation via a combination of techniques in statistical trend analysis. Nevertheless, we pinpoint some drawbacks of MKSK in this paper: 1) MKSK cannot be automated for large scale software degradation analysis, 2) MKSK estimates the degradation trend of software in an oversimplified linear way, 3) MKSK is sensitive to noise, and 4) MKSK suffers from high computational complexity. To overcome all these limitations, we propose a more advanced approach called Modified Cox-Stuart Test & Iterative Hodrick-Prescott Filter (CSHP). The superiority of our CSHP approach over MKSK is validated through extensive Monte Carlo simulations, as well as a real performance dataset measured from 99 real-world web servers.


title: Defeating the Database Adversary Using Deception - A MySQL Database Honeypot

abstract: In today's knowledge society data plays a central role. Large parts of an organization's critical data currently resides in databases which makes them an attractive target for cyber-attacker. The increasing digitization and interconnection of organizations combined with growing complexity of IT systems led to increased attack surface of most organizations. At the same time cyber-adversaries increased their skill sets leading to sophisticated attacks in the recent past. While it is evident that the business behind cyber-crime is growing, the question for defenders arise how to protect their systems these days. Therefore, in this paper we firstly want to present the state-of-recent in the domain of database honeypots which makes use of the advantages of the defender and secondly want to highlight how a database honeypot can be created using open source technology such as MySQL.


title: Pop-up assembly of a quadrupedal ambulatory MicroRobot

abstract: Here we present the design of a 1.27g quadrupedal microrobot manufactured using “Pop-up book MEMS” the first such device capable of locomotion. Implementing popup assembly techniques enables manufacturing of the robot's exoskeleton and drivetrain transmissions from a single 23-layer laminate. Its demonstrated capabilities include payload capacity greater than 1.35g (106% of body mass), maneuverability on flat terrain, and high-speed locomotion up to 37cm/s. Additionally, locomotion performance is compared to a hand-assembled quadruped with similar design parameters. The results demonstrate that the pop-up manufacturing methodology enables more complex mechanisms while simultaneously increasing performance over hand-assembled alternatives.


title: Gradually Updated Neural Networks for Large-Scale Image Recognition

abstract: Depth is one of the keys that make neural networks succeed in the task of large-scale image recognition. The state-of-the-art network architectures usually increase the depths by cascading convolutional layers or building blocks. In this paper, we present an alternative method to increase the depth. Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner. The added orderings not only increase the depths and the learning capacities of the networks without any additional computation costs, but also eliminate the overlap singularities so that the networks are able to converge faster and perform better. Experiments show that the networks based on our method achieve the state-of-the-art performances on CIFAR and ImageNet datasets.


title: Findings From the Pittsburgh Youth Study: Cognitive Impulsivity and Intelligence as Predictors of the Age–Crime Curve

abstract: Abstract Objective This article first summarizes key research findings from the Pittsburgh Youth Study from 1987 to the present, and focuses on delinquency in 1,517 young men who have been followed up from late childhood into their 20s. Second, the article addresses how indicators of self-control prospectively predict later offending, and whether the prediction shows individual difference in the age–crime curve, particularly the up-slope, peak, and down-slope of that curve. Method Longitudinal analyses were conducted on a sample of boys in the middle sample of the Pittsburgh Youth Study (n = 422), whose cognitive impulsivity and intelligence were assessed at about age 12 years. Criminal records on the sample were until age 28. Results The results show that cognitive impulsivity and intelligence, measured between ages 12 and 13 by means of psychometric tests, predicted the age–crime curve. The age–arrest curve was substantially higher in boys with high cognitive impulsivity and in boys with low IQ. However, there was a significant interaction between cognitive impulsivity and intelligence. For boys with high IQ, cognitive impulsivity was associated with a greater escalation in the prevalence of offending during early adolescence, followed by a more rapid decline in offending as boys entered early adulthood with a slight subsequent increase in criminal offending then occurring late 20. In contrast, there was no evidence that cognitive impulsivity independently influenced criminal offending at any developmental period for boys with low IQ. Conclusions The results are discussed in terms of interventions to reduce individuals' delinquency from childhood through early adulthood and lower the age–crime curve for populations. However, the association was complex because it was moderated by both age and intelligence.


title: Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables

abstract: An important problem of matrix completion/approximation based on Matrix Factorization (MF) algorithms is the existence of multiple global optima; this problem is especially serious when the matrix is sparse, which is common in real-world applications such as personalized recommender systems. In this work, we clarify data sparsity by bounding the solution space of MF algorithms. We present the conditions that an MF algorithm should satisfy for reliable completion of the unobservables, and we further propose to augment current MF algorithms with extra constraints constructed by compressive sampling on the unobserved values, which is well-motivated by the theoretical analysis. Model learning and optimal solution searching is conducted in a properly reduced solution space to achieve more accurate and efficient rating prediction performances. We implemented the proposed algorithms in the Map-Reduce framework, and comprehensive experimental results on Yelp and Dianping datasets verified the effectiveness and efficiency of the augmented matrix factorization algorithms.


title: A Faster Scrabble Move Generation Algorithm

abstract: Appel and Jacobson1 presented a fast algorithm for generating every possible move in a given position in the game of Scrabble using a DAWG, a finite automaton derived from the trie of a large lexicon. This paper presents a faster algorithm that uses a GADDAG, a finite automaton that avoids the non-deterministic prefix generation of the DAWG algorithm by encoding a bidirectional path starting from each letter of each word in the lexicon. For a typical lexicon, the GADDAG is nearly five times larger than the DAWG, but generates moves more than twice as fast. This time/space trade-off is justified not only by the decreasing cost of computer memory, but also by the extensive use of move-generation in the analysis of board positions used by Gordon2 in the probabilistic search for the most appropriate play in a given position within realistic time constraints.


title: A Reservation-based Smart Parking System

abstract: Finding a parking space in most metropolitan areas, especially during the rush hours, is difficult for drivers. The difficulty arises from not knowing where the available spaces may be at that time; even if known, many vehicles may pursue very limited parking spaces to cause serious traffic congestion. In this paper, we design and implement a prototype of Reservation-based Smart Parking System (RSPS) that allows drivers to effectively find and reserve the vacant parking spaces. By periodically learning the parking status from the sensor networks deployed in parking lots, the reservation service is affected by the change of physical parking status. The drivers are allowed to access this cyber-physical system with their personal communication devices. Furthermore, we study state-of-the-art parking policies in smart parking systems and compare their performance. The experiment results show that the proposed reservation-based parking policy has the potential to simplify the operations of parking systems, as well as alleviate traffic congestion caused by parking searching.


title: Optimal Security Proofs for PSS and Other Signature Schemes

abstract: The Probabilistic Signature Scheme (PSS) designed by Bellare and Rogaway is a signature scheme provably secure against chosen message attacks in the random oracle model, whose security can be tightly related to the security of RSA. We derive a new security proof for PSS in which a much shorter random salt is used to achieve the same security level, namely we show that loge q sig bits suffice, where q sig is the number of signature queries made by the attacker. When PSS is used with message recovery, a better bandwidth is obtained because longer messages can now be recovered. In this paper, we also introduce a new technique for proving that the security proof of a signature scheme is optimal. In particular, we show that the size of the random salt that we have obtained for PSS is optimal: if less than loge q sig bits are used, then PSS is still provably secure but it cannot have a tight security proof. Our technique applies to other signature schemes such as the Full Domain Hash scheme and Gennaro-Halevi-Rabin's scheme, whose security proofs are shown to be optimal.


title: NFC Smart Tourist Card: Combining Mobile and Contactless Technologies towards a Smart Tourist Experience

abstract: According to several digital marketing experts Near Field Communication (NFC) represents the future of Mobile Technology. One of the main strengths of this technology is the possibility to enable smartphones and other mobile devices to become Mobile Wallet, simplifying and enhancing the user experience. In this paper we describe an application for Android smartphone, called NFC Smart Tourist Card, offering a wide range of services: information supply, mobile payment, mobile ticketing, device pairing, location based services, access authorization, management of loyalty, bonus and membership cards. This study has specific value for academics working in the field of Mobile Information Systems with practical implications for telecommunication operators and/or other companies interested in developing and selling mobile services for tourism 2.0.


title: Customer Satisfaction Measurement using Sentiment Analysis

abstract: Besides the traditional methods of targeting customers, social media presents its own set of opportunities. While companies look for a simple way with a large number of responses, social media platforms like Twitter can allow them to do just that. For example, by creating a hashtag and prompting followers to tweet their answers to some question they can quickly get a large number of answers about a question while simultaneously engaging their customers. Additionally, consumers share their opinions about services and products in public and with their social circles. This valuable data can be used to support business decisions. However, it is huge amounts of unstructured data that is difficult to extract meaningful information out of them. Social Media Analytics is the field which makes insights out of social media data and analyzes its sentiment rather than just reading and counting text. In this article, we used Twitter data to get insight from public opinion hidden in data. The support vector machine algorithm is used to classify sentiment of tweets whether it is positive or negative and the unigram applied as a feature extraction method. The experiments were conducted using large set of training dataset and the algorithm achieved high accuracy around 87%.


title: Physical-layer fingerprinting of LoRa devices using supervised and zero-shot learning

abstract: Physical-layer fingerprinting investigates how features extracted from radio signals can be used to uniquely identify devices. This paper proposes and analyses a novel methodology to fingerprint LoRa devices, which is inspired by recent advances in supervised machine learning and zero-shot image classification. Contrary to previous works, our methodology does not rely on localized and low-dimensional features, such as those extracted from the signal transient or preamble, but uses the entire signal. We have performed our experiments using 22 LoRa devices with 3 different chipsets. Our results show that identical chipsets can be distinguished with 59% to 99% accuracy per symbol, whereas chipsets from different vendors can be fingerprinted with 99% to 100% accuracy per symbol. The fingerprinting can be performed using only inexpensive commercial off-the-shelf software defined radios, and a low sample rate of 1 Msps. Finally, we release all datasets and code pertaining to these experiments to the public domain.


title: Development of Polydimethylsiloxane Substrates with Tunable Elastic Modulus to Study Cell Mechanobiology in Muscle and Nerve

abstract: Mechanics is an important component in the regulation of cell shape, proliferation, migration and differentiation during normal homeostasis and disease states. Biomaterials that match the elastic modulus of soft tissues have been effective for studying this cell mechanobiology, but improvements are needed in order to investigate a wider range of physicochemical properties in a controlled manner. We hypothesized that polydimethylsiloxane (PDMS) blends could be used as the basis of a tunable system where the elastic modulus could be adjusted to match most types of soft tissue. To test this we formulated blends of two commercially available PDMS types, Sylgard 527 and Sylgard 184, which enabled us to fabricate substrates with an elastic modulus anywhere from 5 kPa up to 1.72 MPa. This is a three order-of-magnitude range of tunability, exceeding what is possible with other hydrogel and PDMS systems. Uniquely, the elastic modulus can be controlled independently of other materials properties including surface roughness, surface energy and the ability to functionalize the surface by protein adsorption and microcontact printing. For biological validation, PC12 (neuronal inducible-pheochromocytoma cell line) and C2C12 (muscle cell line) were used to demonstrate that these PDMS formulations support cell attachment and growth and that these substrates can be used to probe the mechanosensitivity of various cellular processes including neurite extension and muscle differentiation.


title: A review of privacy and usability issues in mobile health systems: Role of external factors

abstract: The increased penetration of mobile devices has created opportunities in the health sector and led to emerging of mobile health systems. As much as the mobile health systems have registered tremend...


title: “It Gets Better”: Internet memes and the construction of collective identity:

abstract: In September 2010, a video titled “It Gets Better” was uploaded to YouTube, responding to suicides of gay teens who had suffered from homophobic bullying. Before long, thousands of Internet users added their own versions of the clip, creating a mass appeal to young people while simultaneously negotiating the norms of lesbian, gay, bisexual, transgender and queer (LGBTQ) collective identity. Conceptualizing this body of videos as an Internet meme, we examine the extent to which participants imitate or alter textual components presented in previous videos. A combined quantitative and qualitative analysis of 200 clips shows that in an arena ostensibly free of formal gatekeepers, participants tend to police themselves, toeing the line with conformist norms. We also identify domains of potential subversion, related not only to the content of the videos but mainly to the forms facilitated by digital media.


title: HTS Machines for Applications in All-Electric Aircraft

abstract: Environmental preservation has now become paramount as natural resources are rapidly consumed. A global reduction of the emissions of pollutants in the atmosphere is therefore needed and new technologies have to be considered. A large part of the emissions rejected in the atmosphere comes from transportation vehicles, including cars, trucks or airplanes, due to the nature of their combustion-based propulsion systems. To contribute to solving the problem, our team has been working for several years on the development of high power density superconducting motors for aircraft propulsion. This paper investigates the feasibility of all-electric aircraft based on currently available technology through the presentation of several designs of machines. Electric propulsion would require the development of high power density electric propulsion motors and generators. The requirements in terms of weight and volume of these components cannot be achieved with conventional technologies; however, the use of superconductors associated with hydrogen based power plants makes possible the design of power components reasonably light and would therefore enable the development of all-electric aero-vehicles. Many advantages would come from electrical propulsion such as better controllability of the propulsion, higher efficiency, higher availability and less maintenance needs. This paper shows that high temperature superconducting machines are mature enough to be used in aircraft.


title: Achieving k-anonymity in privacy-aware location-based services

abstract: Location-Based Service (LBS) has become a vital part of our daily life. While enjoying the convenience provided by LBS, users may lose privacy since the untrusted LBS server has all the information about users in LBS and it may track them in various ways or release their personal data to third parties. To address the privacy issue, we propose a Dummy- Location Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric, and then propose an enhanced-DLS algorithm, to make sure that the selected dummy locations are spread as far as possible. Evaluation results show that the proposed DLS algorithm can significantly improve the privacy level in terms of entropy. The enhanced-DLS algorithm can enlarge the cloaking region while keeping similar privacy level as the DLS algorithm. I. INTRODUCTION With the rapid development of mobile devices and social networks, Location-Based Service (LBS) has become a vital part in our daily activities in recent years. With smartphones or tablets, users can download location-based applications from Apple Store or Google Play Store. With the help of these


A Pump FMEA Approach to Improve Reliability Centered Maintenance Procedure : The Case of Centrifugal Pumps in Onshore Industry


Unsupervised Deep Autoencoders for Feature Extraction with Educational Data


title: Network segregation in a model of misinformation and fact checking

abstract: Misinformation under the form of rumor, hoaxes, and conspiracy theories spreads on social media at alarming rates. One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network. One possible antidote is fact checking which, in some cases, is known to stop rumors from spreading further. However, fact checking may also backfire and reinforce the belief in a hoax. Here we take into account the combination of network segregation, finite memory and attention, and fact-checking efforts. We consider a compartmental model of two interacting epidemic processes over a network that is segregated between gullible and skeptic users. Extensive simulation and mean-field analysis show that a more segregated network facilitates the spread of a hoax only at low forgetting rates, but has no effect when agents forget at faster rates. This finding may inform the development of mitigation techniques and overall inform on the risks of uncontrolled misinformation online.


title: ARMI 2.0: An online risk management simulation

abstract: Risk management is an important area in software engineering. An effective risk management process positively influences project success. ARMI is a physical card game which simulates risk management in software engineering. During the simulation, participants follow principles of risk management and aim to complete the cycles while economizing their expenses. ARMI 2.0 aims to improve shortcomings in the original such as difficulties in calculation of costs and semi-predetermined randomization as well as enhance user experiences. The development and experimental results of this enhanced simulation are described and discussed in this paper.


title: Cross Site Scripting Prevention with Dynamic Data Tainting and Static Analysis

abstract: Cross-site scripting (XSS) is an attack against web applications in which scripting code is injected into the output of an application that is then sent to a user’s web browser. In the browser, this scripting code is executed and used to transfer sensitive data to a third party (i.e., the attacker). Currently, most approaches attempt to prevent XSS on the server side by inspecting and modifying the data that is exchanged between the web application and the user. Unfortunately, it is often the case that vulnerable applications are not fixed for a considerable amount of time, leaving the users vulnerable to attacks. The solution presented in this paper stops XSS attacks on the client side by tracking the flow of sensitive information inside the web browser. If sensitive information is about to be transferred to a third party, the user can decide if this should be permitted or not. As a result, the user has an additional protection layer when surfing the web, without solely depending on the security of the web application.


title: Generalised Kernel Machines

abstract: The generalised linear model (GLM) is the standard approach in classical statistics for regression tasks where it is appropriate to measure the data misfit using a likelihood drawn from the exponential family of distributions. In this paper, we apply the kernel trick to give a non-linear variant of the GLM, the generalised kernel machine (GKM), in which a regularised GLM is constructed in a fixed feature space implicitly defined by a Mercer kernel. The MATLAB symbolic maths toolbox is used to automatically create a suite of generalised kernel machines, including methods for automated model selection based on approximate leave-one-out cross-validation. In doing so, we provide a common framework encompassing a wide range of existing and novel kernel learning methods, and highlight their connections with earlier techniques from classical statistics. Examples including kernel ridge regression, kernel logistic regression and kernel Poisson regression are given to demonstrate the flexibility and utility of the generalised kernel machine.


title: Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes

abstract: We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (; 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.


title: A multi-task framework for metric learning with common subspace

abstract: Metric learning has been widely studied in machine learning due to its capability to improve the performance of various algorithms. Meanwhile, multi-task learning usually leads to better performance by exploiting the shared information across all tasks. In this paper, we propose a novel framework to make metric learning benefit from jointly training all tasks. Based on the assumption that discriminative information is retained in a common subspace for all tasks, our framework can be readily used to extend many current metric learning methods. In particular, we apply our framework on the widely used Large Margin Component Analysis (LMCA) and yield a new model called multi-task LMCA. It performs remarkably well compared to many competitive methods. Besides, this method is able to learn a low-rank metric directly, which effects as feature reduction and enables noise compression and low storage. A series of experiments demonstrate the superiority of our method against three other comparison algorithms on both synthetic and real data.


title: STIMO: STIll and MOving video storyboard for the web scenario

abstract: In the current Web scenario a video browsing tool that produces on-the-fly storyboards is more and more a need. Video summary techniques can be helpful but, due to their long processing time, they are usually unsuitable for on-the-fly usage. Therefore, it is common to produce storyboards in advance, penalizing users customization. The lack of customization is more and more critical, as users have different demands and might access the Web with several different networking and device technologies. In this paper we propose STIMO, a summarization technique designed to produce on-the-fly video storyboards. STIMO produces still and moving storyboards and allows advanced users customization (e.g., users can select the storyboard length and the maximum time they are willing to wait to get the storyboard). STIMO is based on a fast clustering algorithm that selects the most representative video contents using HSV frame color distribution. Experimental results show that STIMO produces storyboards with good quality and in a time that makes on-the-fly usage possible.