Query: Learning Semantic Similarity

Relevant passages:
Probabilistic Latent Semantic Indexing <=SEP=> Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.

Distributional Clustering of English Words <=SEP=> We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical “soft” clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.

Probabilistic Latent Semantic Analysis <=SEP=> Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, ma­ chine learning from text, and in related ar­ eas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results. in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of ex­ periments.

Latent Semantic Indexing (LSI): TREC-3 Report <=SEP=> We used LSI for both TREC-3 routing and adhoc tasks. For the routing tasks an LSI space was constructed using the training documents. We compared profiles constructed using just the topic words (no training) with profiles constructed using the average of relevant documents (no use of the topic words). Not surprisingly, the centroid of the relevant documents was 30% better than the topic words. This simple feedback method was quite good compared to the routing performance of other systems. Various combinations of information from the topic words and relevant documents provide small additional improvements in performance. For the adhoc task we compared LSI to keyword vector matching (i.e. using no dimension reduction). Small advantages were obtained for LSI even with the long TREC topic statements.

Introduction to Modern Information Retrieval <=SEP=> Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.

Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks <=SEP=> In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness measures, LPOCSIN utilizes a simple yet effective one. Using this measure, we transform the cluster assignment problem into a series of alternating linear programs, and further propose a highly efficient procedure for solving those alternating problems. We evaluate LPOCSIN and other state-of-the-art methods by extensive experiments covering a wide range of synthetic and real networks. The experiments show that LPOCSIN significantly outperforms the other methods in recovering ground-truth clusters while being an order of magnitude faster than the most efficient state-of-the-art method.

A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification <=SEP=> A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.

The Software Concordance: A User Interface for Advanced Software Documents <=SEP=> The Software Concordance is a hypermedia software development environment exploring how document technology and versioned hypermedia can improve software document management. The Software Concordance’s central tool is a document editor that integrates program analysis and hypermedia services for both source code and multimedia documentation in XML. The editor allows developers to embed inline multimedia documentation, including images and audio clips, into their program sources and to bind them to any program fragment. Web style hyperlinks are also supported. The developers are able to move seamlessly between source code and the documentation that describes its motivation, design, correctness and use without disrupting the integrated program analysis services, which include lexing, parsing and type checking. This paper motivates the need for environments like the Software Concordance, describes the key features and architecture of its document editor and discusses future work to improve its functionality.

Cloud-enabled privacy-preserving collaborative learning for mobile sensing <=SEP=> In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.

Malware Detection Using Deep Transferred Generative Adversarial Networks <=SEP=> Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic. In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack. Since the GAN is unstable in training process, often resulting in generator that produces nonsensical outputs, a method to pre-train GAN with autoencoder structure is proposed. We analyze the detector, and the performance of the detector is visualized by observing the clustering pattern of malicious software using t-SNE algorithm. The proposed model gets the best performance compared with the conventional machine learning algorithms.

Example-based hinting of true type fonts <=SEP=> Hinting in TrueType is a time-consuming manual process in which a typographer creates a sequence of instructions for better fitting the characters of a font to a grid of pixels. In this paper, we propose a new method for automatically hinting TrueType fonts by transferring hints of one font to another. Given a hinted source font and a target font without hints, our method matches the outlines of corresponding glyphs in each font, and then translates all of the individual hints for each glyph from the source to the target font. It also translates the control value table (CVT) entries, which are used to unify feature sizes across a font. The resulting hinted font already provides a great improvement over the unhinted version. More importantly, the translated hints, which preserve the sound, hand-designed hinting structure of the original font, provide a very good starting point for a professional typographer to complete and fine-tune, saving time and increasing productivity. We demonstrate our approach with examples of automatically hinted fonts at typical display sizes and screen resolutions. We also provide estimates of the time saved by a professional typographer in hinting new fonts using this semi-automatic approach.

Learning Progressions: Supporting Instruction and Formative Assessment Margaret Heritage the Council of Chief State School Officers T He Fast Scass  Formative Assessment for Teachers and Learners Learning Progressions: Supporting Instruction and Formative Assessment <=SEP=> By its very nature, learning involves progression. To assist in its emergence, teachers need to understand the pathways along which students are expected to progress. These pathways or progressions ground both instruction and assessment. Yet, despite a plethora of standards and curricula, many teachers are unclear about how learning progresses in specific domains. This is an undesirable situation for teaching and learning, and one that particularly affects teachers’ ability to engage in formative assessment.

Scaling Textual Inference to the Web <=SEP=> Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, HOLMES utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus—they are “approximately” functional in a well-defined sense.

Corporate Social Responsibility and Innovation in Management Accounting <=SEP=> • This study develops a conceptual framework to illustrate how sustainability issues are embedded in the management control system to operationalise firms' corporate social responsibility (CSR) objectives and strategies. • Categorising firms' CSR activities into Responsive and Strategic CSR agendas (Porter and Kramer 2006), this framework emphasises that different uses of MCS must be adopted to effectively operationalise different CSR agendas. • Boundary systems and diagnostic uses of budgets and performance management systems are more pertinent to operationalising Responsive CSR agendas, whereas the belief system and interactive uses of MCS are more effective in facilitating the selection and implementation of Strategic CSR programmes. • This study provides empirical evidence on the different types of Responsive and Strategic CSR programmes in two Chinese State-owned Enterprises (SOEs). Our empirical data shed light on the role of MCS in implementing CSR strategies at these firms.

A High Gain Input-Parallel Output-Series DC/DC Converter With Dual Coupled Inductors <=SEP=> High voltage gain dc-dc converters are required in many industrial applications such as photovoltaic and fuel cell energy systems, high-intensity discharge lamp (HID), dc back-up energy systems, and electric vehicles. This paper presents a novel input-parallel output-series boost converter with dual coupled inductors and a voltage multiplier module. On the one hand, the primary windings of two coupled inductors are connected in parallel to share the input current and reduce the current ripple at the input. On the other hand, the proposed converter inherits the merits of interleaved series-connected output capacitors for high voltage gain, low output voltage ripple, and low switch voltage stress. Moreover, the secondary sides of two coupled inductors are connected in series to a regenerative capacitor by a diode for extending the voltage gain and balancing the primary-parallel currents. In addition, the active switches are turned on at zero current and the reverse recovery problem of diodes is alleviated by reasonable leakage inductances of the coupled inductors. Besides, the energy of leakage inductances can be recycled. A prototype circuit rated 500-W output power is implemented in the laboratory, and the experimental results shows satisfactory agreement with the theoretical analysis.

Development of DC to Single-Phase AC Voltage Source Inverter With Active Power Decoupling Based on Flying Capacitor DC/DC Converter <=SEP=> In the present, a power decoupling method without additional component is proposed for a dc to single-phase ac converter, which consists of a flying capacitor dc/dc converter (FCC) and the voltage source inverter (VSI). In particular, a small flying capacitor in the FCC is used for both a boost operation and a double-line-frequency power ripple reduction. Thus, the dc-link capacitor value can be minimized in order to avoid the use of a large electrolytic capacitor. In addition, component design, of, e.g., the boost inductor and the flying capacitor, is clarified when the proposed control is applied. Experiments were carried out using a 1.5-kW prototype in order to verify the validity of the proposed control. The experimental results revealed that the use of the proposed control reduced the dc-link voltage ripple by 74.5%, and the total harmonic distortion (THD) of the inverter output current was less than 5%. Moreover, a maximum system efficiency of 95.4% was achieved at a load of 1.1 kW. Finally, the high power density design is evaluated by the Pareto front optimization. The power densities of three power decoupling topologies, such as a boost topology, a buck topology, and the proposed topology are compared. As a result, the proposed topology achieves the highest power density (5.3 kW/dm3) among the topologies considered herein.

On the minimum node degree and connectivity of a wireless multihop network <=SEP=> This paper investigates two fundamental characteristics of a wireless multi -hop network: its minimum node degree and its k--connectivity. Both topology attributes depend on the spatial distribution of the nodes and their transmission range. Using typical modeling assumptions :--- :a random uniform distribution of the nodes and a simple link model :--- :we derive an analytical expression that enables the determination of the required range r0 that creates, for a given node density ρ, an almost surely k--connected network. Equivalently, if the maximum r0 of the nodes is given, we can find out how many nodes are needed to cover a certain area with a k--connected network. We also investigate these questions by various simulations and thereby verify our analytical expressions. Finally, the impact of mobility is discussed.The results of this paper are of practical value for researchers in this area, e.g., if they set the parameters in a network--level simulation of a mobile ad hoc network or if they design a wireless sensor network.

Cryptography on FPGAs: State of the Art Implementations and Attacks <=SEP=> In the last decade, it has become aparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as FPGAs become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.

Vehicle Number Plates Detection and Recognition using improved Algorithms : A Review with Tanzanian Case study <=SEP=> — Invented in 1976, Number Plates Recognition (NPR) has since found wide commercial applications, making its research prospects challenging and scientifically interesting. A complete NPR system functions by vz steps, license plate; localization, sizing and orientation, normalization, character recognitions and geometric analysis. This paper is a review of NPR preliminary stages; it explains number plate localization, sizing and orientations as well as normalizations sections of the Number Plates Detection and Recognition-Tanzania Case study. MATLAB R2012b is employed in these processes. The input incorporated includes front and rear photographic images of vehicles, for proximity and simulation purposes the ample angle of image is 90 degree +-15. The captured image is converted to gray scale, binarized and edge detection algorithms are used to enhance edges. The output of this stage provides the input feature extraction, segmentation and

The mass appraisal of the real estate by computational intelligence <=SEP=> Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches – support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center. © 2009 Elsevier B.V. All rights reserved.

Computer analysis of computed tomography scans of the lung: a survey <=SEP=> Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

A 0.5-V bulk-input fully differential operational transconductance amplifier <=SEP=> We present a fully differential two-stage Miller op-amp operating from a 0.5 V power supply. The input signal is applied to the bulk nodes of the input devices. A prototype was designed in a standard 0.18 /spl mu/m CMOS process using standard 0.5 V V/sub T/ devices. It has a measured 52 dB DC gain, a 2.5 MHz gain-bandwidth and consumes 110 /spl mu/W.

A 40-50-GHz SiGe 1 : 8 differential power divider using shielded broadside-coupled striplines <=SEP=> This paper presents a 1 : 8 differential power divider implemented in a commercial SiGe BiCMOS process using fully shielded broadside-coupled striplines integrated vertically in the silicon interconnect stackup. The 1 : 8 power divider is only 1.12 x1.5 mm2 including pads, and shows 0.4-dB rms gain imbalance and <3deg rms phase imbalance from 40 to 50 GHz over all eight channels, a measured power gain of 14.9 plusmn0.6 dB versus a passive divider at 45 GHz, and a 3-dB bandwidth from 37 to 52 GHz. A detailed characterization of the shielded broadside-coupled striplines is presented and agrees well with simulations. These compact lines can be used for a variety of applications in SiGe/CMOS millimeter-wave circuits, including differential signal distribution, miniature power dividers, matching networks, filters, couplers, and baluns.

AutoRec: Autoencoders Meet Collaborative Filtering <=SEP=> This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.

Neither Quick Nor Proper - Evaluation of QuickProp for Learning Deep Neural Networks <=SEP=> Neural networks and especially convolutional neural networks are of great interest in current computer vision research. However, many techniques, extensions, and modifications have been published in the past, which are not yet used by current approaches. In this paper, we study the application of a method called QuickProp for training of deep neural networks. In particular, we apply QuickProp during learning and testing of fully convolutional networks for the task of semantic segmentation. We compare QuickProp empirically with gradient descent, which is the current standard method. Experiments suggest that QuickProp can not compete with standard gradient descent techniques for complex computer vision tasks like semantic segmentation.

A –$21.2$ -dBm Dual-Channel UHF Passive CMOS RFID Tag Design <=SEP=> Previous research results showed that UHF passive CMOS RFID tags had difficulty to achieve sensitivity less than -20 dBm. This paper presents a dual-channel 15-bit UHF passive CMOS RFID tag prototype that can work at sensitivity lower than -20 dBm. The proposed tag chip harvests energy and backscatters uplink data at 866.4-MHz (for ETSI) or 925-MHz (for FCC) channel and receives downlink data at 433-MHz channel. Consequently, the downlink data transmission does not interrupt our tag from harvesting RF energy. To use the harvested energy efficiently, we design a tag chip that includes neither a regulator nor a VCO such that the harvested energy is completely used in receiving, processing, and backscattering data. Without a regulator, our tag uses as few active analog circuits as possible in the receiver front-end. Instead, our tag uses a novel digital circuit to decode the received data. Without a VCO, the design of our tag can extract the required clock signal from the downlink data. Measurement result shows that the sensitivity of the proposed passive tag chip can reach down to -21.2 dBm. Such result corresponds to a 19.6-m reader-to-tag distance under 36-dBm EIRP and 0.4-dBi tag antenna gain. The chip was fabricated in TSMC 0.18- μm CMOS process. The die area is 0.958 mm ×0.931mm.

Quantifying and Visualizing Attribute Interactions <=SEP=> Interactions are the prototypical examples of unexpected and irreducible patterns in data that encompass several attributes. We attempt to unify the divergent definitions and conceptions of interactions present in the literature, and propose two separate conceptions of what interactions are: if a group of attributes interacts observationally, we cannot understand their relationship without looking at all the attributes simultaneously; if a group of attributes interacts representationally, we cannot create a model that would not include conditions involving all the attributes. Rather than test for interactions, we attempt to quantify them, and visually present the most important interactions of the data. Our quantification is based on how well we can approximate a joint probability distribution without admitting that there are interactions. Judging from these illustrations, most machine learning procedures are not treating interactions appropriately.

Development of a coax-to-microstrip transition over the range of 1–15GHz <=SEP=> In this paper, a coax-to-microstrip transition incorporating a miniature coaxial connector with glass bead is developed. The transition is firstly simulated with the aid of HFSS and then measured in the frequency range of 1-15 GHz. The simulated and measured results are found in good agreement.

18 kW three phase inverter system using hermetically sealed SiC phase-leg power modules <=SEP=> Power electronics play an important role in electricity utilization from generation to end customers. Thus, high-efficiency power electronics help to save energy and conserve energy resources. Research on silicon carbide (SiC) power electronics has shown their better efficiency compared to Si power electronics due to the significant reduction in both conduction and switching losses. Combined with their high-temperature capability, SiC power electronics are more reliable and compact. This paper focuses on the development of such a high efficiency, high temperature inverter based on SiC JFET and diode modules. It involves the work on high temperature packaging (>200 °C), inverter design and prototype development, device characterization, and inverter testing. A SiC inverter prototype with a power rating of 18 kW is developed and demonstrated. When tested at moderate load levels compared to the inverter rating, an efficiency of 98.2% is achieved by the initial prototype without optimization, which is higher than most Si inverters.

Top-down facilitation of visual recognition. <=SEP=> Cortical analysis related to visual object recognition is traditionally thought to propagate serially along a bottom-up hierarchy of ventral areas. Recent proposals gradually promote the role of top-down processing in recognition, but how such facilitation is triggered remains a puzzle. We tested a specific model, proposing that low spatial frequencies facilitate visual object recognition by initiating top-down processes projected from orbitofrontal to visual cortex. The present study combined magnetoencephalography, which has superior temporal resolution, functional magnetic resonance imaging, and a behavioral task that yields successful recognition with stimulus repetitions. Object recognition elicited differential activity that developed in the left orbitofrontal cortex 50 ms earlier than it did in recognition-related areas in the temporal cortex. This early orbitofrontal activity was directly modulated by the presence of low spatial frequencies in the image. Taken together, the dynamics we revealed provide strong support for the proposal of how top-down facilitation of object recognition is initiated, and our observations are used to derive predictions for future research.