zeta-alpha-ai/NanoSCIDOCS huggingface dataset:
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. The algorithm often outperforms the well-known ReliefF attribute estimator when used as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees. It performs more feature selection than ReliefF does—reducing the data dimensionality by fifty percent in most cases. Also, decision and model trees built from the preprocessed data are often significantly smaller.
We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients hat are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.
With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.
This paper presents a novel visual representation, called orderlets, for real-time human action recognition with depth sensors. An orderlet is a middle level feature that captures the ordinal pattern among a group of low level features. For skeletons, an orderlet captures specific spatial relationship among a group of joints. For a depth map, an orderlet characterizes a comparative relationship of the shape information among a group of subregions. The orderlet representation has two nice properties. First, it is insensitive to small noise since an orderlet only depends on the comparative relationship among individual features. Second, it is a frame-level representation thus suitable for real-time online action recognition. Experimental results demonstrate its superior performance on online action recognition and cross-environment action recognition.
Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedyand only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging “equivalent” stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster.
Some queries cannot be answered by machines only. Processing such queries requires human input for providing information that is missing from the database, for performing computationally difficult functions, and for matching, ranking, or aggregating results based on fuzzy criteria. CrowdDB uses human input via crowdsourcing to process queries that neither database systems nor search engines can adequately answer. It uses SQL both as a language for posing complex queries and as a way to model data. While CrowdDB leverages many aspects of traditional database systems, there are also important differences. Conceptually, a major change is that the traditional closed-world assumption for query processing does not hold for human input. From an implementation perspective, human-oriented query operators are needed to solicit, integrate and cleanse crowdsourced data. Furthermore, performance and cost depend on a number of new factors including worker affinity, training, fatigue, motivation and location. We describe the design of CrowdDB, report on an initial set of experiments using Amazon Mechanical Turk, and outline important avenues for future work in the development of crowdsourced query processing systems.
In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE algorithm [20] that allows any local algorithm to return an approximate solution to a local subproblem. We show that such a strategy does not affect the theoretical guarantees of DANE significantly. In fact, our approach can be viewed as a robustification strategy since the method is substantially better behaved than DANE on data partition arising in practice. It is well known that DANE algorithm does not match the communication complexity lower bounds. To bridge this gap, we propose an accelerated variant of the first method, called AIDE, that not only matches the communication lower bounds but can also be implemented using a purely first-order oracle. Our empirical results show that AIDE is superior to other communication efficient algorithms in settings that naturally arise in machine learning applications.
The paper reviews the past and present results in the area of decentralized control of large-scale complex systems. An emphasis is laid on decentralization, decomposition, and robustness. These methodologies serve as effective tools to overcome specific difficulties arising in largescale complex systems such as high dimensionality, information structure constraints, uncertainty, and delays. Several prospective topics for future research are introduced in this contents. The overview is focused on recent decomposition approaches in interconnected dynamic systems due to their potential in providing the extension of decentralized control into networked control systems. # 2008 Elsevier Ltd. All rights reserved.
Feature selection can improve classification accuracy and decrease the computational complexity of classification. Data features in intrusion detection systems (IDS) always present the problem of imbalanced classification in which some classifications only have a few instances while others have many instances. This imbalance can obviously limit classification efficiency, but few effort s have been made to address it. In this paper, a scheme for the many-objective problem is proposed for feature selection in IDS, which uses two strategies, namely, a special domination method and a predefined multiple targeted search, for population evolution. It can differentiate traffic not only between normal and abnormal but also by abnormality type. Based on our scheme, NSGA-III is used to obtain an adequate feature subset with good performance. An improved many-objective optimization algorithm (I-NSGA-III) is further proposed using a novel niche preservation procedure. It consists of a bias-selection process that selects the individual with the fewest selected features and a fit-selection process that selects the individual with the maximum sum weight of its objectives. Experimental results show that I-NSGA-III can alleviate the imbalance problem with higher classification accuracy for classes having fewer instances. Moreover, it can achieve both higher classification accuracy and lower computational complexity. © 2016 Published by Elsevier B.V.
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation, often by introducing suitable modifications of the objective function. We synthesize this growing body of literature by formulating a generalization of the evidence lower bound that explicitly represents the trade-offs between sparsity of the latent code, bijectivity of representations, and coverage of the support of the empirical data distribution. Our objective is also suitable to learning hierarchical representations that disentangle blocks of variables whilst allowing for some degree of correlations within blocks. Experiments on a range of datasets demonstrate that learned representations contain interpretable features, are able to learn discrete attributes, and generalize to unseen combinations of factors.
Electromyogram or EMG signal is a very small signal; it requires a system to enhance for display purpose or for further analysis process. This paper presents the development of low cost physiotherapy EMG signal acquisition system with two channel input. In the acquisition system, both input signals are amplified with a differential amplifier and undergo signal pre-processing to obtain the linear envelope of EMG signal. Obtained EMG signal is then digitalized and sent to the computer to be plotted.
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.
Emerging applications for networked and cooperative robots motivate the study of motion coordination for groups of agents. For example, it is envisioned that groups of agents will perform a variety of useful tasks including surveillance, exploration, and environmental monitoring. This paper deals with basic interactions among mobile agents such as “move away from the closest other agent” or “move toward the furthest vertex of your own Voronoi polygon.” These simple interactions amount to distributed dynamical systems because their implementation requires only minimal information about neighboring agents. We characterize the close relationship between these distributed dynamical systems and the disk-covering and sphere-packing cost functions from geometric optimization. Our main results are: (i) we characterize the smoothness properties of these geometric cost functions, (ii) we show that the interaction laws are variations of the nonsmooth gradient of the cost functions, and (iii) we establish various asymptotic convergence properties of the laws. The technical approach relies on concepts from computational geometry, nonsmooth analysis, and nonsmooth stability theory.
Recent developments have clari ed the process of generating partially ordered, partially speci ed sequences of actions whose execution will achive an agent's goal. This paper summarizes a progression of least commitment planners, starting with one that handles the simple strips representation, and ending with one that manages actions with disjunctive precondition, conditional e ects and universal quanti cation over dynamic universes. Along the way we explain how Chapman's formulation of the Modal Truth Criterion is misleading and why his NP-completeness result for reasoning about plans with conditional e ects does not apply to our planner. 1 I thank Franz Amador, Tony Barrett, Darren Cronquist, Denise Draper, Ernie Davis, Oren Etzioni, Nort Fowler, Rao Kambhampati, Craig Knoblock, Nick Kushmerick, Neal Lesh, Karen Lochbaum, Drew McDermott, Ramesh Patil, Kari Pulli, Ying Sun, Austin Tate and Mike Williamson for helpful comments, but retain sole responsibility for errors. This research was funded in part by O ce of Naval Research Grant 90-J-1904 and by National Science Foundation Grant IRI-8957302
The whole design cycle, including design, fabrication and characterization of a broadband double-ridged horn antenna equipped with a parabolic lens and a waveguide adapter is presented in this paper. A major goal of the presented work was to obtain high directivity with a flat phase characteristic within the main radiation lobe at an 18–40 GHz frequency range, so that the antenna can be applicable in a freespace material characterization setup.
In the next generation heterogeneous wireless networks, a user with a multi-interface terminal may have a network access from different service providers using various technologies. It is believed that handover decision is based on multiple criteria as well as user preference. Various approaches have been proposed to solve the handover decision problem, but the choice of decision method appears to be arbitrary and some of the methods even give disputable results. In this paper, a new handover criteria is introduced along with a new handover decision strategy. In addition, handover decision is identified us a fuzzy multiple attribute decision making (MADM) problem, and fuzzy logic is applied to deal with the imprecise information of some criteria and user preference. After a systematic analysis of various fuzzy MADM methods, a feasible approach is presented. In the end, examples are provided illustrating the proposed methods and the sensitivity of the methods is also analysed.
The performance and accuracy of classifier are affected by the result of feature selection directly. Based on the one-class F-Score feature selection and the improved F-Score feature selection and genetic algorithm, combined with machine learning methods like the K nearest neighbor, support vector machine, random forest, naive Bayes, a hybrid feature selection algorithm is proposed to process the two classification unbalanced data problem and multi classification problem. Compared with the traditional machine learning algorithm, it can search in wider feature space and promote classifier to deal with the characteristics of unbalanced data sets according to heuristic rules, which can handle the problem of unbalanced classification better. The experiment results show that the area under receiver operating characteristic curve for two classifications and the accuracy rate for multi classification problem have been improved compared with other models
Machine learning for text classification is the cor nerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and m ore accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g . Information Gain) evaluated on a benchmark of 229 text classification problem instances that w ere gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal p ers ectives—accuracy, F-measure, precision, and recall—since each is appropriate in different si tuat ons. The results reveal that a new feature selection me tric we call ‘Bi-Normal Separation’ (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text clas sification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focus es on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspect iv , BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Informati on Gain and Chi-Squared have correlated failures, and so they work poorly together. When c hoosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a membe r of the pair—e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin.
Dynamic response of boost power factor correction (PFC) converter operating in continuous-conduction mode (CCM) is heavily influenced by low-bandwidth of voltage control loop. A novel tri-state boost PFC converter operating in pseudo-continuous-conduction mode (PCCM) is proposed in this paper. An additional degree of control-freedom introduced by a freewheeling switching control interval helps to achieve PFC control. A simple and fast voltage control loop can be used to maintain a constant output voltage. Furthermore, compared with boost PFC converter operating in conventional discontinuous-conduction mode (DCM), boost PFC converter operating in PCCM demonstrates greatly improved current handling capability with reduced current and voltage ripples. Analytical and simulation results of the tri-state boost PFC converter have been presented and compared with those of the boost PFC converter operating in conventional CCM and DCM. Simulation results show excellent dynamic performance of the tri-state boost PFC converter.
This article summarizes the major developments in the history of efforts to use fingerprint patterns to identify individuals, from the earliest fingerprint classification systems of Vucetich and Henry in the 1890s through the advent of automated fingerprint identification. By chronicling the history of “manual” systems for recording storing, matching, and retrieving fingerprints, the article puts advances in automatic fingerprint recognition in historical context and highlights their historical and social significance.
Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterwards, semantics of sentences and their relations are adaptively encoded in document representation with gated recurrent neural network. We conduct document level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimental results show that: (1) our neural model shows superior performances over several state-of-the-art algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling for sentiment classification.1
We present a fast method for re-purposing existing semantic word vectors to improve performance in a supervised task. Recently, with an increase in computing resources, it became possible to learn rich word embeddings from massive amounts of unlabeled data. However, some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train. We propose a method that takes as input an existing embedding, some labeled data, and produces an embedding in the same space, but with a better predictive performance in the supervised task. We show improvement on the task of sentiment classification with respect to several baselines, and observe that the approach is most useful when the training set is sufficiently small.
We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers. Preprint submitted to Elsevier 19 February 2014
Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene or event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape or appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.
Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint `2,1-norm minimization on both loss function and regularization. The `2,1-norm based loss function is robust to outliers in data points and the `2,1norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.
We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.
We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5 neurons ("mirror neurons', n = 92) all of which became active both when the monkey performed a given action and when it observed a similar action performed by the experimenter. Mirror neurons, in order to be visually triggered, required an interaction between the agent of the action and the object of it. The sight of the agent alone or of the object alone (three-dimensional objects, food) were ineffective. Hand and the mouth were by far the most effective agents. The actions most represented among those activating mirror neurons were grasping, manipulating and placing. In most mirror neurons (92%) there was a clear relation between the visual action they responded to and the motor response they coded. In approximately 30% of mirror neurons the congruence was very strict and the effective observed and executed actions corresponded both in terms of general action (e.g. grasping) and in terms of the way in which that action was executed (e.g. precision grip). We conclude by proposing that mirror neurons form a system for matching observation and execution of motor actions. We discuss the possible role of this system in action recognition and, given the proposed homology between F5 and human Brocca's region, we posit that a matching system, similar to that of mirror neurons exists in humans and could be involved in recognition of actions as well as phonetic gestures.
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.
Available online 18 July 2010 Information overload is a severe problem for human operators of large-scale control systems as, for example, encountered in the domain of road traffic management. Operators of such systems are at risk to lack situation awareness, because existing systems focus on themere presentation of the available information on graphical user interfaces—thus endangering the timely and correct identification, resolution, and prevention of critical situations. In recent years, ontology-based approaches to situation awareness featuring a semantically richer knowledge model have emerged. However, current approaches are either highly domain-specific or have, in case they are domain-independent, shortcomings regarding their reusability. In this paper, we present our experience gained from the development of BeAware!, a framework for ontology-driven information systems aiming at increasing anoperator's situation awareness. In contrast to existing domain-independent approaches, BeAware!'s ontology introduces the concept of spatio-temporal primitive relations betweenobserved real-world objects thereby improving the reusability of the framework. To show its applicability, a prototype of BeAware! has been implemented in thedomain of road trafficmanagement. An overviewof this prototype and lessons learned for the development of ontology-driven information systems complete our contribution. © 2010 Elsevier B.V. All rights reserved.
User satisfaction with general information system (IS) and certain types of information technology (IT) applications has been thoroughly studied in IS research. With the widespread and increasing use of portal technology, however, there is a need to conduct a user satisfaction study on portal use -in particular, the business-to-employee (b2e) portal. In this paper, we propose a conceptual model for determining b2e portal user satisfaction, which has been derived from an extensive literature review of user satisfaction scales and the b2e portal. Nine dimensions of b2e portal user satisfaction are identified and modeled: information content, ease of use, convenience of access, timeliness, efficiency, security, confidentiality, communication, and layout.
The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.
For most researchers in the ever growing fields of probabilistic graphical models, belief networks, causal influence and probabilistic inference, ACM Turing award winner Dr. Pearl and his seminary papers on causality are well-known and acknowledged. Representation and determination of Causality, the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first, is a challenging problem. Over the years, Dr. pearl has written significantly on both Art and Science of Cause and Effect. In this book on "Causality: Models, Reasoning and Inference", the inventor of Bayesian belief networks discusses and elaborates on his earlier workings including but not limited to Reasoning with Cause and Effect, Causal inference in statistics, Simpson's paradox, Causal Diagrams for Empirical Research, Robustness of Causal Claims, Causes and explanations, and Probabilities of causation Bounds and identification.
A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies—I/S success —has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success—SYSTEM OUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed (a total of 180 articles are cited) and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.
Representation learning is a fundamental problem in natural language processing. This paper studies how to learn a structured representation for text classification. Unlike most existing representation models that either use no structure or rely on pre-specified structures, we propose a reinforcement learning (RL) method to learn sentence representation by discovering optimized structures automatically. We demonstrate two attempts to build structured representation: Information Distilled LSTM (ID-LSTM) and Hierarchically Structured LSTM (HS-LSTM). ID-LSTM selects only important, taskrelevant words, and HS-LSTM discovers phrase structures in a sentence. Structure discovery in the two representation models is formulated as a sequential decision problem: current decision of structure discovery affects following decisions, which can be addressed by policy gradient RL. Results show that our method can learn task-friendly representations by identifying important words or task-relevant structures without explicit structure annotations, and thus yields competitive performance.
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions [7] or robot hand design and their control [1]. Robot grasp synthesis algorithms have been reviewed in [63], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.
Silicon offers a new set of possibilities and challenges for RF, microwave, and millimeter-wave applications. While the high cutoff frequencies of the SiGe heterojunction bipolar transistors and the ever-shrinking feature sizes of MOSFETs hold a lot of promise, new design techniques need to be devised to deal with the realities of these technologies, such as low breakdown voltages, lossy substrates, low-Q passives, long interconnect parasitics, and high-frequency coupling issues. As an example of complete system integration in silicon, this paper presents the first fully integrated 24-GHz eight-element phased array receiver in 0.18-/spl mu/m silicon-germanium and the first fully integrated 24-GHz four-element phased array transmitter with integrated power amplifiers in 0.18-/spl mu/m CMOS. The transmitter and receiver are capable of beam forming and can be used for communication, ranging, positioning, and sensing applications.
CubeSat platforms grow increasingly popular in commercial ventures as alternative solutions for global Internet networks deep space exploration, and aerospace research endeavors. Many technology companies and system engineers plan to implement small satellite systems as part of global Low Earth Orbit (LEO) inter-satellite constellations. High performing low cost hardware is of key importance in driving these efforts. This paper presents the heterodyne architecture and performance of Ka-Band Integrated Transmitter Assembly (ITA) Module, which could be implemented in nano/microsatellite or other satellite systems as a low-cost solution for high data rate space communication systems. The module converts a 0.9 to 1.1 GHz IF input signal to deliver linear transmission of +29 dBm at 26.7 to 26.9 GHz frequency range with a built-in phase locked oscillator, integrated transmitter, polarizer, and lens corrected antenna.
In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389
We present a novel technique to fabricate conformal and pliable substrates for microwave applications including systems-on-package. The produced materials are fabricated by combining ceramic powders with polymers to generate a high-contrast substrate that is concurrently pliable (bendable). Several such polymer-ceramic substrates are fabricated and used to examine the performance of a patch antenna and a coupled line filter. This paper presents the substrate mixing method while measurements are given to evaluate the loss performance of the substrates. Overall, the fabricated composites lead to flexible substrates with a permittivity of up to epsivr=20 and sufficiently low loss
Network forensics is the science that deals with capture, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper makes an exhaustive survey of various network forensic frameworks proposed till date. A generic process model for network forensics is proposed which is built on various existing models of digital forensics. Definition, categorization and motivation for network forensics are clearly stated. The functionality of various Network Forensic Analysis Tools (NFATs) and network security monitoring tools, available for forensics examiners is discussed. The specific research gaps existing in implementation frameworks, process models and analysis tools are identified and major challenges are highlighted. The significance of this work is that it presents an overview on network forensics covering tools, process models and framework implementations, which will be very much useful for security practitioners and researchers in exploring this upcoming and young discipline. a 2010 Elsevier Ltd. All rights reserved.
This study extends Davis' (1989) TAM model and Straub's (1994) SPIR addendum by adding gender to an IT diffusion model. The Technology Acceptance Model (TAM) has been widely studied in IS research as an explanation of the use of information systems across IS-types and nationalities. While this line of research has found significant cross-cultural differences, it has ignored the effects of gender, even though in socio-linguistic research, gender is a fundamental aspect of culture. Indeed, sociolinguistic research that has shown that men tend to focus discourse on hierarchy and independence while women focus on intimacy and solidarity. This literature provides a solid grounding for conceptual extensions to the IT diffusion research and the Technology Acceptance Model. Testing gender differences that might relate to beliefs and use of computer-based media, the present study sampled 392 female and male responses via a cross-sectional survey instrument. The sample drew from comparable groups of knowledge workers using E-mail systems in the airline industry in North America, Asia, and Europe. Study findings indicate that women and men differ in their perceptions but not use of E-mail. These findings suggest that researchers should include gender in IT diffusion models along with other cultural effects. Managers and co-workers, moreover, need to realize that the same mode of communication may be perceived differently by the sexes, suggesting that more favorable communications environments might be created, environments that take into account not only organizational contextual factors, but also the gender of users. The creation of these environments involves not only the actual deployment of communication media, but also organizational training on communications media.
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
The modern automobile is controlled by networked computers. The security of these networks was historically of little concern, but researchers have in recent years demonstrated their many vulnerabilities to attack. As part of a defence against these attacks, we evaluate an anomaly detector for the automotive controller area network (CAN) bus. The majority of attacks are based on inserting extra packets onto the network. But most normal packets arrive at a strict frequency. This motivates an anomaly detector that compares current and historical packet timing. We present an algorithm that measures inter-packet timing over a sliding window. The average times are compared to historical averages to yield an anomaly signal. We evaluate this approach over a range of insertion frequencies and demonstrate the limits of its effectiveness. We also show how a similar measure of the data contents of packets is not effective for identifying anomalies. Finally we show how a one-class support vector machine can use the same information to detect anomalies with high confidence.
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The lowresolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.
BACKGROUND
The scoping review has become an increasingly popular approach for synthesizing research evidence. It is a relatively new approach for which a universal study definition or definitive procedure has not been established. The purpose of this scoping review was to provide an overview of scoping reviews in the literature.


METHODS
A scoping review was conducted using the Arksey and O'Malley framework. A search was conducted in four bibliographic databases and the gray literature to identify scoping review studies. Review selection and characterization were performed by two independent reviewers using pretested forms.


RESULTS
The search identified 344 scoping reviews published from 1999 to October 2012. The reviews varied in terms of purpose, methodology, and detail of reporting. Nearly three-quarter of reviews (74.1%) addressed a health topic. Study completion times varied from 2 weeks to 20 months, and 51% utilized a published methodological framework. Quality assessment of included studies was infrequently performed (22.38%).


CONCLUSIONS
Scoping reviews are a relatively new but increasingly common approach for mapping broad topics. Because of variability in their conduct, there is a need for their methodological standardization to ensure the utility and strength of evidence.
Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.