zeta-alpha-ai/NanoSCIDOCS huggingface dataset:
Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements; they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, machine learning-based intrusion detection systems have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. This work aims to compare efficiency of machine learning methods in intrusion detection system, including artificial neural networks and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in machine learning-based intrusion detectors, we propose to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compare the accuracy, detection rate, false alarm rate for 4 attack types. The extensive experimental results on the KDD-cup intrusion detection benchmark dataset demonstrate that the proposed approach produces higher performance than KDD Winner, especially for U2R and U2L type attacks.
We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.
Despite of the progress achieved by deep learning in face recognition (FR), more and more people find that racial bias explicitly degrades the performance in realistic FR systems. Facing the fact that existing training and testing databases consist of almost Caucasian subjects, there are still no independent testing databases to evaluate racial bias and even no training databases and methods to reduce it. To facilitate the research towards conquering those unfair issues, this paper contributes a new dataset called Racial Faces in-the-Wild (RFW) database with two important uses, 1) racial bias testing: four testing subsets, namely Caucasian, Asian, Indian and African, are constructed, and each contains about 3000 individuals with 6000 image pairs for face verification, 2) racial bias reducing: one labeled training subset with Caucasians and three unlabeled training subsets with Asians, Indians and Africans are offered to encourage FR algorithms to transfer recognition knowledge from Caucasians to other races. For we all know, RFW is the first database for measuring racial bias in FR algorithms. After proving the existence of domain gap among different races and the existence of racial bias in FR algorithms, we further propose a deep information maximization adaptation network (IMAN) to bridge the domain gap, and comprehensive experiments show that the racial bias could be narrowed-down by our algorithm.
We present a technique for constructing random elds from a set of training samples. The learning paradigm builds increasingly complex elds by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the eld and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random eld models and techniques introduced in this paper di er from those common to much of the computer vision literature in that the underlying random elds are nonMarkovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classi cation in natural language processing.
RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.
Owing to the energy shortage and the increasingly serious environmental pollution, fuel cell electric vehicles (FCEV) with zero-emission and high-efficiency have been expected to be the most potential candidate to substitute the conventional vehicles. The DC/DC converter is the interface between the fuel cell (FC) and the driveline of FCEV. It not only needs high voltage gain to convert the wide FC voltage into an appropriate voltage level, but also needs the capacity of fault tolerance to enhance the reliability of the system. For this reason, floating interleaved boost converters (FIBC) seem to be the optimal selection. Despite this topology can continue operating without interruption under the action of the proper control scheme in the case of power switch open circuit fault (OCF), operating in degraded mode has adverse impacts on the component stress and the input current ripple. Hence, this paper aims to design an effective controller to maintain the dc bus voltage constant and to demonstrate thorough theoretical analysis and simulation verification of these undesirable effects.
This paper presents an ultra-broadband ultracompact Butler Matrix design scheme. The design employs stacked transformer based couplers and lumped LC π-network phase shifters for substantial size reduction. As a proof-of-concept design, a 4×4 Butler Matrix is implemented in a standard 130nm bulk CMOS process at a center frequency of 2.0 GHz. Compared with reported fully integrated 2.0 GHz 4×4 Butler Matrix designs in CMOS, the proposed design achieves the lowest insertion loss of 1.10dB, the smallest amplitude mismatch of 0.3 dB, the largest fractional bandwidth of 34.6%, and the smallest chip core area of 0.635×1.122 mm2. Based on the measured S-parameters, the four concurrent electrical array patterns of the Butler Matrix achieve array peak-to-null ratio (PNR) of 29.5 dB at 2.0 GHz and better than 15.0 dB between 1.55 GHz and 2.50 GHz.
Long-term ECG monitoring is desirable in many daily healthcare situations where a wearable device that can continuously record ECG signals is needed. In this work, we proposed a wearable heart rate belt for ambulant ECG monitoring which can be comfortably worn on the chest or the waist. Active textile electrodes were designed for ECG recording. And a battery powered circuit board was developed consisting of ECG signal conditioning circuits, a 3-axis accelerator for body motion detection, a 12-bit AD converter, a DSP for signal processing and a SD card for data storage. The system also includes a wireless communication module that can transmit heart rate data to a sport watch for displaying. Experiments were carried out which shows that the proposed system is unobtrusive and can be comfortably worn by the user during daily activities. When wearing on the waist, ECG signals with reasonably good quality were acquired in rest and walking situations. The proposed system shows promise for long-term ambulant ECG monitoring.
The sampling frequency and quantity of time series data collected from water distribution systems has been increasing in recent years, giving rise to the potential for improving system knowledge if suitable automated techniques can be applied, in particular, machine learning. Novelty (or anomaly) detection refers to the automatic identification of novel or abnormal patterns embedded in large amounts of ‘‘normal’’ data. When dealing with time series data (transformed into vectors), this means abnormal events embedded amongst many normal time series points. The support vector machine is a data-driven statistical technique that has been developed as a tool for classification and regression. The key features include statistical robustness with respect to non-Gaussian errors and outliers, the selection of the decision boundary in a principled way, and the introduction of nonlinearity in the feature space without explicitly requiring a nonlinear algorithm by means of kernel functions. In this research, support vector regression is used as a learning method for anomaly detection from water flow and pressure time series data. No use is made of past event histories collected through other information sources. The support vector regression methodology, whose robustness derives from the training error function, is applied
Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to non-image data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.
Reconfigurable reflectarray antennas operating in Ku-band are presented in this paper. First, a novel multilayer unit-cell based on polarization turning concept is proposed to achieve the single-bit phase shift required for reconfigurable reflectarray applications. The principle of the unit-cell is discussed using the current model and the space match condition, along with simulations to corroborate the design and performance criteria. Then, an offset-fed configuration is developed to verify performance of the unit-cell in antenna application, and its polarization transformation property is elaborated. Finally, an offset-fed reflectarray with 10 × 10 elements is developed and fabricated. The dual-polarized antenna utilizes the control code matrices to accomplish a wide angle beam-scanning. A full-wave analysis is applied to the reflectarray, and detailed results are presented and discussed. This electronically steerable reflectarray antenna has significant potential for satellite applications, due to its wide operating band, simple control and beam-scanning capability.
We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the groundtruth trajectory of camera poses. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Applying Organizational Routines in understanding organizational change Markus Becker, Nathalie Lazaric, Richard Nelson, Sidney G. Winter
In this study, the authors examined the findings and implications of the research on trust in leadership that has been conducted during the past 4 decades. First, the study provides estimates of the primary relationships between trust in leadership and key outcomes, antecedents, and correlates (k = 106). Second, the study explores how specifying the construct with alternative leadership referents (direct leaders vs. organizational leadership) and definitions (types of trust) results in systematically different relationships between trust in leadership and outcomes and antecedents. Direct leaders (e.g., supervisors) appear to be a particularly important referent of trust. Last, a theoretical framework is offered to provide parsimony to the expansive literature and to clarify the different perspectives on the construct of trust in leadership and its operation.
An understanding of culture is important to the study of information technologies in that culture at various levels, including national, organizational, and group, can influence the successful implementation and use of information technology. Culture also plays a role in managerial processes that may directly, or indirectly, influence IT. Culture is a challenging variable to research, in part because of the multiple divergent definitions and measures of culture. Notwithstanding, a wide body of literature has emerged that sheds light on the relationship of IT and culture. This paper sets out to provide a review of this literature in order to lend insights into our understanding of the linkages between IT and culture. We begin by conceptualizing culture and laying the groundwork for a values-based approach to the examination of IT and culture. Using this approach, we then provide a comprehensive review of the organizational and cross-cultural IT literature that conceptually links these two traditionally separate streams of research. From our analysis, we develop six themes of IT-culture research emphasizing culture's impact on IT, IT's impact on culture, and IT culture. Building upon these themes, we then develop a theory of IT, values, and conflict. Based upon the theory, we develop propositions concerning three types of cultural conflict and the results of these conflicts. Ultimately, the theory suggests that the reconciliation of these conflicts results in a reorientation of values. We conclude with the particular research challenges posed in this line of inquiry.
Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.
Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.
This paper describes the main features of a multilayer antenna panel for a low cost Ka-band Array Antenna for on-the-move applications. The LOCOMO satcom terminal is based on a dual-polarized low-profile antenna with trasmit/receive and RHCP/LHCP switching capability.
We present a design for an electronic-steerable holographic antenna with polarization control composed of a radial array of Ku-band, electronically-steerable, surface-wave waveguide (SWG) artificial-impedance-surface antennas (AISA). The antenna operates by launching surface waves into each of the SWGs via a central feed network. The surface-wave impedance is electronically controlled with varactor-tuned impedance patches. The impedance is adjusted to scan the antenna in elevation, azimuth and polarization. The radial symmetry allows for 360° azimuthal steering. If constructed with previously-demonstrated SWG AISAs, it is capable of scanning in elevation from -75° to 75° with gain variation of less than 3 dB. The polarization can be switched among V-Pol, H-Pol, LHCP and RHCP at will.
As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.
We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.
We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.
We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.
In this paper, we report the feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand-crafted image features, Fisher Vectors with HoG and Color patches. In the experiments, we have achieved 72.26% as the top-1 accuracy and 92.00% as the top-5 accuracy for the 100-class food dataset, UEC-FOOD100, which outperforms the best classification accuracy of this dataset reported so far, 59.6%, greatly.
In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used .
Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Earlier attempts to grammatical error correction involve rule-based and classifier approaches which are limited to correcting only some particular type of errors in a sentence. As sentences may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this report, we investigate GEC as a translation task from incorrect to correct English and explore some machine translation approaches for developing end-to-end GEC systems for all error types. We apply Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) approaches to GEC and show that they can correct multiple errors of different types in a sentence when compared to the earlier methods which focus on individual errors. We also discuss some of the weakness of machine translation approaches. Finally, we also experiment on a candidate re-ranking technique to re-rank the hypotheses generated by machine translation systems. With regression models, we try to predict a grammaticality score for each candidate hypothesis and re-rank them according to the score.
Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals. The popularity of such methods has grown significantly in recent years. However, a limitation of HMC methods is the required gradient computation for simulation of the Hamiltonian dynamical system—such computation is infeasible in problems involving a large sample size or streaming data. Instead, we must rely on a noisy gradient estimate computed from a subset of the data. In this paper, we explore the properties of such a stochastic gradient HMC approach. Surprisingly, the natural implementation of the stochastic approximation can be arbitrarily bad. To address this problem we introduce a variant that uses second-order Langevin dynamics with a friction term that counteracts the effects of the noisy gradient, maintaining the desired target distribution as the invariant distribution. Results on simulated data validate our theory. We also provide an application of our methods to a classification task using neural networks and to online Bayesian matrix factorization.
The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.
Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be optimized with a discriminator or an encoder. The embedding is Lipschitz continuous to deformations so that generators transform linear interpolations between input white noise vectors into deformations between output images. This embedding is computed with a wavelet Scattering transform. Numerical experiments demonstrate that the resulting Scattering generators have similar properties as GANs or VAEs, without learning a discriminative network or an encoder.
Architectures form the backbone of complete robotic systems. The right choice of architecture can go a long way in facilitating the specification, implementation and validation of robotic systems. Conversely, of course, the wrong choice can make one's life miserable. We present some of the needs of robotic systems, describe some general classes of robot architectures, and discuss how different architectural styles can help in addressing those needs. The paper, like the field itself, is somewhat preliminary, yet it is hoped that it will provide guidance for those who use, or develop, robot architectures.
The substrate integrated waveguide (SIW) allows the construct of some types of planar antennas which cannot be conventionally integrated on the substrate. However, owing to some technology restrictions, designed SIW horn antennas normally work in the frequency above 10 GHz. This paper proposes a 6.8GHz low-profile H-plane horn antenna based on the ridged SIW, which allows the substrates thinner than λ0/10. Far field radiation patterns are reported to reveal the good performance of proposed antenna. Comparisons among the ridged SIW horn antennas with different number of ridges are also made to shown the matching improvements.
The scholars in the research domains of innovation and strategic management concerned about the appropriability for about 30 years or more. They focused on appropriability research and constantly evolving. In this paper, we analyze 30 years (1986–2016) literature on appropriability studies from Web of Science Core Collection database. A cited reference clustering map of different periods and terms co-occurrence map have been generated using bibliometric analysis and content analysis. Based on this, we study the evolutionary trajectory, mechanisms and theoretical architecture of appropriability research and explore further research directions. The results indicate that the essence of the appropriability research evolution is the perception changes in opening and sharing, value creation and value growth, and future research is focusing on role of appropriability in the platform governance, generative appropriability and the evolution of the problem-solving mechanisms.
Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its lowresolution corrupted observation. When the scaling ratio is small, point estimates achieve impressive performance, but soon they suffer from the regression-to-themean problem, result of their inability to capture the multi-modality of this conditional distribution. Modeling high-dimensional image and audio distributions is a hard task, requiring both the ability to model complex geometrical structures and textured regions. In this paper, we propose to use as conditional model a Gibbs distribution, where its sufficient statistics are given by deep convolutional neural networks. The features computed by the network are stable to local deformation, and have reduced variance when the input is a stationary texture. These properties imply that the resulting sufficient statistics minimize the uncertainty of the target signals given the degraded observations, while being highly informative. The filters of the CNN are initialized by multiscale complex wavelets, and then we propose an algorithm to fine-tune them by estimating the gradient of the conditional log-likelihood, which bears some similarities with Generative Adversarial Networks. We evaluate experimentally the proposed approach in the image superresolution task, but the approach is general and could be used in other challenging ill-posed problems such as audio bandwidth extension.
We present a novel l1 regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computational complexity. The algorithmic framework underlying ROTD integrates two key ideas: off-policy convergent gradient TD methods, such as TDC, and a convex-concave saddle-point formulation of non-smooth convex optimization, which enables first-order solvers and feature selection using online convex regularization. A detailed theoretical and experimental analysis of RO-TD is presented. A variety of experiments are presented to illustrate the off-policy convergence, sparse feature selection capability and low computational cost of the RO-TD algorithm.
In this paper we have presented a brief current state of the Art for Arabic text representation and classification methods. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC.
This paper focuses on the non-inverting buck- boost converter operated as either buck or boost converter. It is shown that a pulse-width modulation (PWM) discontinuity around buck/boost mode transition can result in substantial increases in output voltage ripple. The effect of the PWM nonlinearity is studied using periodic steady state analysis to quantify the worst case ripple voltage in terms of design parameters. Furthermore, a bifurcation analysis shows that the PWM discontinuity leads to quasi- periodic route to chaos, which results in erratic operation around the buck/boost mode transition. The increased ripple is a very significant problem when the converter is used as a power supply for an RF power amplifier, as is the case in WCDMA handsets. An approach is proposed to remove the discontinuity which results in reduced output voltage ripple, at the expense of reduced efficiency, as demonstrated on an experimental prototype.
A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 mile/h. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5 mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed.
In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control. Category: Ubiquitous computing
Building trustless cross-blockchain trading protocols is challenging. Therefore, centralized liquidity providers remain the preferred route to execute transfers across chains — which fundamentally contradicts the purpose of permissionless ledgers to replace trusted intermediaries. Enabling crossblockchain trades could not only enable currently competing blockchain projects to better collaborate, but seems of particular importance to decentralized exchanges as those are currently limited to the trade of digital assets within their respective blockchain ecosystem. In this paper we systematize the notion of cryptocurrencybacked tokens, an approach towards trustless cross-chain communication. We propose XCLAIM, a protocol for issuing, trading, and redeeming e.g. Bitcoin-backed tokens on Ethereum. We provide implementations for three possible protocol versions and evaluate their security and on-chain costs. With XCLAIM, it costs at most USD 1.17 to issue an arbitrary amount of Bitcoinbacked tokens on Ethereum, given current blockchain transaction fees. Our protocol requires no modifications to Bitcoin’s and Ethereum’s consensus rules and is general enough to support other cryptocurrencies.
We review accuracy estimation methods and compare the two most commonmethods cross validation and bootstrap Recent experimen tal results on arti cial data and theoretical re sults in restricted settings have shown that for selecting a good classi er from a set of classi ers model selection ten fold cross validation may be better than the more expensive leave one out cross validation We report on a large scale experiment over half a million runs of C and a Naive Bayes algorithm to estimate the e ects of di erent parameters on these al gorithms on real world datasets For cross validation we vary the number of folds and whether the folds are strati ed or not for boot strap we vary the number of bootstrap sam ples Our results indicate that for real word datasets similar to ours the best method to use for model selection is ten fold strati ed cross validation even if computation power allows using more folds
Microsoft Research Redmond participated for the first time in TREC this year, focusing on the question answering track. There is a separate report in this volume on the Microsoft Research Cambridge submissions for the filtering and Web tracks (Robertson et al., 2002). We have been exploring data-driven techniques for Web question answering, and modified our system somewhat for participation in TREC QA. We submitted two runs for the main QA track (AskMSR and AskMSR2).
A novel ultra-wideband (UWB) microstrip-to-CPS (coplanar stripline) transition has been developed. This transition or balun structure has several attractive advantages such as good impedance transformation, compact size and wide bandwidth. After the parallel-coupled line section between the microstrip line and CPS is investigated under varied traversal dimensions, a wide transmitting band is well achieved with the emergence of two transmission poles. Next, such a single transition circuit is optimally designed to cover the whole UWB band (3.1 GHz to 10.6 GHz). To verify the predicted results in experiment, the two back-to-back transitions with the same 50 Omega microstrip feed lines are fabricated and tested. Measured results exhibit the return loss close to 10.0 dB over a band from 3.5 GHz to 10.0 GHz.
This paper deals with the implementation of mobile measuring station in greenhouse environment navigated using potential field method. The function of a greenhouse is to create the optimal growing conditions for the full life of the plants. Using autonomous measuring systems helps to monitor all the necessary parameters for creating the optimal environment in the greenhouse. The robot equipped with sensors is capable of driving to the end and back along crop rows inside the greenhouse. It introduces a wireless sensor network that was used for the purpose of measuring and controlling the greenhouse application. Continuous advancements in wireless technology and miniaturization have made the deployment of sensor networks to monitor various aspects of the environment increasingly flexible.
We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.
Humans, due to aging and hormonal changes are prone to pains in their limbs. As a result of which, the fundamental activity of humans i.e., movement pattern of limbs also known as gait is affected. By exerting unequal weight on both limbs in-order to avoid pain in one leg, humans slowly develop an abnormal gait pattern consisting of limping and sideways bend in the posture. This often goes unnoticed for a long time. We propose a system using smartphones that can sense and detect abnormal walking patterns. The sensing of limb movement is performed by an embedded accelerometer in a smartphone and detection of abnormal walk pattern is performed by classifying different features such as stride length, walk velocity etc. By incorporating Naive Bayes and Decision trees classifiers, we obtained close to 89% accuracies in classifying different levels of abnormalaties. Further validation was done by implementing a decision tree based gait variation detector smartphone application across five users which resulted in 90% accuracy.
Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics
Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an “equivalent” POMDP in which all state transitions (given the current state and action) are deterministic. This reduces the general problem of policy search to one in which we need only consider POMDPs with deterministic transitions. We give a natural way of estimating the value of all policies in these transformed POMDPs. Policy search is then simply performed by searching for a policy with high estimated value. We also establish conditions under which our value estimates will be good, recovering theoretical results similar to those of Kearns, Mansour and Ng [7], but with “sample complexity” bounds that have only a polynomial rather than exponential dependence on the horizon time. Our method applies to arbitrary POMDPs, including ones with infinite state and action spaces. We also present empirical results for our approach on a small discrete problem, and on a complex continuous state/continuous action problem involving learning to ride a bicycle.
The main contribution of our work is in combining the methods for parametric urban design of highly specialized software such as CityEngine and general-purpose parametric modeling platform such as Grasshopper. Our work facilitates and prompts the use of parametric tools by architects and planners for urban design. In this paper we present a custom grasshopper component for street network generation and block subdivision. The component was developed in C# using the RhinoCommon SDK. We used Grasshopper for the development of an urban design proposal at a teaching exercise. To meet the requirements of the urban design project, additional functionalities had to be added to the range of existing Grasshopper components. In particular, we needed components for street network generation and block subdivision. To develop the component we implemented the street expansion strategies described in (Weber et al., 2009) and the methods for block subdivision described in (Vanegas et al., 2009). Additionally, we adapted and enhanced the strategies to meet the NURBS modeling capabilities of Rhinoceros.
Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable.
The authors report results from 2 studies assessing the extent to which narcissism is related to self- and other ratings of leadership, workplace deviance, and task and contextual performance. Study 1 results revealed that narcissism was related to enhanced self-ratings of leadership, even when controlling for the Big Five traits. Study 2 results also revealed that narcissism was related to enhanced leadership self-perceptions; indeed, whereas narcissism was significantly positively correlated with self-ratings of leadership, it was significantly negatively related to other ratings of leadership. Study 2 also revealed that narcissism was related to more favorable self-ratings of workplace deviance and contextual performance compared to other (supervisor) ratings. Finally, as hypothesized, narcissism was more strongly negatively related to contextual performance than to task performance.