Query: Supporting Complex Search Tasks

Relevant passages:
Normalized ( Pointwise ) Mutual Information in Collocation Extraction <=SEP=> In this paper, we discuss the related information theoretical association measures of mutual information and pointwise mutual information, in the context of collocation extraction. We introduce normalized variants of these measures in order to make them more easily interpretable and at the same time less sensitive to occurrence frequency. We also provide a small empirical study to give more insight into the behaviour of these new measures in a collocation extraction setup.

Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs <=SEP=> Most analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users query search engines in order to accomplish tasks at a variety of granularities, issuing multiple queries as they attempt to accomplish tasks. In this work we study real sessions manually labeled into hierarchical tasks, and show that timeouts, whatever their length, are of limited utility in identifying task boundaries, achieving a maximum precision of only 70%. We report on properties of this search task hierarchy, as seen in a random sample of user interactions from a major web search engine's log, annotated by human editors, learning that 17% of tasks are interleaved, and 20% are hierarchically organized. No previous work has analyzed or addressed automatic identification of interleaved and hierarchically organized search tasks. We propose and evaluate a method for the automated segmentation of users' query streams into hierarchical units. Our classifiers can improve on timeout segmentation, as well as other previously published approaches, bringing the accuracy up to 92% for identifying fine-grained task boundaries, and 89-97% for identifying pairs of queries from the same task when tasks are interleaved hierarchically. This is the first work to identify, measure and automatically segment sequences of user queries into their hierarchical structure. The ability to perform this kind of segmentation paves the way for evaluating search engines in terms of user task completion.

Query segmentation revisited <=SEP=> We address the problem of query segmentation: given a keyword query, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve reasonable segmentation performance but are tested only against a small corpus of manually segmented queries. In addition, many of the previous approaches are fairly intricate as they use expensive features and are difficult to be reimplemented.
 The main contribution of this paper is a new method for query segmentation that is easy to implement, fast, and that comes with a segmentation accuracy comparable to current state-of-the-art techniques. Our method uses only raw web n-gram frequencies and Wikipedia titles that are stored in a hash table. At the same time, we introduce a new evaluation corpus for query segmentation. With about 50,000 human-annotated queries, it is two orders of magnitude larger than the corpus being used up to now.

Exploratory search: from finding to understanding <=SEP=> Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval.

A Statistical Approach to Mechanized Encoding and Searching of Literary Information <=SEP=> Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of "notions" and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.

Factors Influencing Quality of Mobile Apps: Role of Mobile App Development Life Cycle <=SEP=> In this paper, The mobile application field has been receiving astronomical attention from the past few years due to the growing number of mobile app downloads and withal due to the revenues being engendered .With the surge in the number of apps, the number of lamentable apps/failing apps has withal been growing.Interesting mobile app statistics are included in this paper which might avail the developers understand the concerns and merits of mobile apps.The authors have made an effort to integrate all the crucial factors that cause apps to fail which include negligence by the developers, technical issues, inadequate marketing efforts, and high prospects of the users/consumers.The paper provides suggestions to eschew failure of apps. As per the various surveys, the number of lamentable/failing apps is growing enormously, primarily because mobile app developers are not adopting a standard development life cycle for the development of apps. In this paper, we have developed a mobile application with the aid of traditional software development life cycle phases (Requirements, Design, Develop, Test, and, Maintenance) and we have used UML, M-UML, and mobile application development technologies.

Delphi method: techniques and applications <=SEP=> Cross-impact analysis is a method for revising estimated probabilities of future events in terms of estimated interactions among those events. This Report presents an elementary cross-impact model where the cross-impacts are formulated as relative probabilities. Conditions are derived for the consistency of the matrix of relative probabilities of n events. An extension also provides a necessary condition for the vector of absolute probabilities to be consistent with the relative probability matrix. An averaging technique is formulated for resolving inconsistencies in the matrix, and a nearest-point computation derived for resolving inconsistencies between the set of absolute probabilities and the matrix. Although elementary, the present model clarifies some of the conceptual problems associated with cross-impact analysis, and supplies a relatively sound basis for revising probability estimates in the limited case where interactions can be approximated by relative probabilities.

Phishing e-mail detection by using deep learning algorithms <=SEP=> Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.

Bundle Adjustment in the Large <=SEP=> We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.

REFLECTIONS ON FREQUENCY EFFECTS IN LANGUAGE PROCESSING <=SEP=> This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of “noticing”; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna-contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners’ language is a product of their history of usage in communicative interaction.

Flux-Weakening Control of Electric Starter–Generator Based on Permanent-Magnet Machine <=SEP=> This paper presents the control analysis and design for a permanent-magnet machine (PMM) operated in the flux-weakening (FW) mode for an aircraft electric starter–generator (SG) application. Previous literature has focused on FW control of PMMs in the motoring (starting) mode; however, the system stability and control in the generating mode have been inadequately studied. This paper reports detailed, rigorous control analysis and design for a PMM-based aircraft electric SG operated in the FW mode. It is shown that an unstable area of operation exists. A novel control scheme which eliminates this instability is proposed. The key analytical findings of the paper are verified by experimental investigation. This paper therefore concludes that the presented technique is able to ensure system stability under all modes of operation. Furthermore, it is noted that the findings of this work are also valuable for any two-quadrant PMM drive with frequent change between starting and generating regimes under current-limiting operation.

Building Caring Healthcare Systems in the Internet of Things <=SEP=> The nature of healthcare and the computational and physical technologies and constraints present a number of challenges to systems designers and implementers. In spite of the challenges, there is a significant market for systems and products to support caregivers in their tasks as the number of people needing assistance grows substantially. In this paper, we present a structured approach for describing Internet of Things (IoT) for healthcare systems. We illustrate the approach for three use cases and discuss relevant quality issues that arise, in particular, the need to consider caring as a requirement.

Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain <=SEP=> Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward the products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic values to vendors and other interested parties. In this paper, we conduct a case study in the movie domain, and tackle the problem of mining reviews for predicting product sales performance. Our analysis shows that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question. For the sentiment factor, we propose Sentiment PLSA (S-PLSA), in which a review is considered as a document generated by a number of hidden sentiment factors, in order to capture the complex nature of sentiments. Training an S-PLSA model enables us to obtain a succinct summary of the sentiment information embedded in the reviews. Based on S-PLSFA, we propose ARSA, an Autoregressive Sentiment-Aware model for sales prediction. We then seek to further improve the accuracy of prediction by considering the quality factor, with a focus on predicting the quality of a review in the absence of user-supplied indicators, and present ARSQA, an Autoregressive Sentiment and Quality Aware model, to utilize sentiments and quality for predicting product sales performance. Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach.

Entity Retrieval via Entity Factoid Hierarchy <=SEP=> We propose that entity queries are generated via a two-step process: users first select entity facts that can distinguish target entities from the others; and then choose words to describe each selected fact. Based on this query generation paradigm, we propose a new entity representation model named as entity factoid hierarchy. An entity factoid hierarchy is a tree structure composed of factoid nodes. A factoid node describes one or more facts about the entity in different information granularities. The entity factoid hierarchy is constructed via a factor graph model, and the inference on the factor graph is achieved by a modified variant of Multiple-try Metropolis algorithm. Entity retrieval is performed by decomposing entity queries and computing the query likelihood on the entity factoid hierarchy. Using an array of benchmark datasets, we demonstrate that our proposed framework significantly improves the retrieval performance over existing models.

A Foray into Conficker's Logic and Rendezvous Points <=SEP=> We present an in depth static analysis of the Conficker worm, primarily through the exploration of the client-side binary logic. In this paper, we summarize various aspects of the inner workings of binary variants A and B,1 which were the first in a chain of recent revisions aimed to keep this epidemic resistant to ongoing eradication attempts. These first two variants have combined to produce a multi-million node population of infected hosts, whose true main purpose has yet to be fully understood. We further validate aspects of our analysis through in-situ network analyses, and discuss some attribution links about its origins.

Information Security Threats Classification Pyramid <=SEP=> Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.

Collaborative privacy management: mobile privacy beyond your own devices <=SEP=> As the development of mobile devices and applications, mobile privacy has become a very important issue. Current researches on mobile privacy mainly focus on potential leakages on a particular device. However, leakage of sensitive data on a mobile device not only violates the privacy of the phone (or data) owner, but also violates the privacy of many other people whose information are contained in the data directly or indirectly (they are called data involvers). To address such problems, we introduce a collaborative privacy management framework, which aims to provide fine-grained data privacy protection for both data owners and data involvers in a distributed manner. Based on individual privacy policies specified by each user, a collaborative privacy policy is generated and enforced on different devices automatically. As a proof-of-concept prototype, we implement the proposed framework on Android and demonstrate its applicability with two case studies.

Adding nesting structure to words <=SEP=> We propose the model of nested words for representation of data with both a linear ordering and a hierarchically nested matching of items. Examples of data with such dual linear-hierarchical structure include executions of structured programs, annotated linguistic data, and HTML/XML documents. Nested words generalize both words and ordered trees, and allow both word and tree operations. We define nested word automata—finite-state acceptors for nested words, and show that the resulting class of regular languages of nested words has all the appealing theoretical properties that the classical regular word languages enjoys: deterministic nested word automata are as expressive as their nondeterministic counterparts; the class is closed under union, intersection, complementation, concatenation, Kleene-*, prefixes, and language homomorphisms; membership, emptiness, language inclusion, and language equivalence are all decidable; and definability in monadic second order logic corresponds exactly to finite-state recognizability. We also consider regular languages of infinite nested words and show that the closure properties, MSO-characterization, and decidability of decision problems carry over.
 The linear encodings of nested words give the class of visibly pushdown languages of words, and this class lies between balanced languages and deterministic context-free languages. We argue that for algorithmic verification of structured programs, instead of viewing the program as a context-free language over words, one should view it as a regular language of nested words (or equivalently, a visibly pushdown language), and this would allow model checking of many properties (such as stack inspection, pre-post conditions) that are not expressible in existing specification logics.
 We also study the relationship between ordered trees and nested words, and the corresponding automata: while the analysis complexity of nested word automata is the same as that of classical tree automata, they combine both bottom-up and top-down traversals, and enjoy expressiveness and succinctness benefits over tree automata.

Smart Refrigerator Based on Internet of Things (IoT): An Approach to Efficient Food Management <=SEP=> This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.

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.

1.3 μm, 56-Gbit/s EML module target to 400GbE <=SEP=> We developed 1.3 μm, EML module built in EML driver IC for both 50 and 56 Gbit/s. 40-km SMF transmission with clear eye openings under 56 Gbit/s operation were demonstrated for the first time.

Hybrid digital and analog beamforming design for large-scale MIMO systems <=SEP=> Large-scale multiple-input multiple-output (MIMO) systems enable high spectral efficiency by employing large antenna arrays at both the transmitter and the receiver of a wireless communication link. In traditional MIMO systems, full digital beamforming is done at the baseband; one distinct radio-frequency (RF) chain is required for each antenna, which for large-scale MIMO systems can be prohibitive from either cost or power consumption point of view. This paper considers a two-stage hybrid beamforming structure to reduce the number of RF chains for large-scale MIMO systems. The overall beamforming matrix consists of analog RF beamforming implemented using phase shifters and baseband digital beamforming of much smaller dimension. This paper considers precoder and receiver design for maximizing the spectral efficiency when the hybrid structure is used at both the transmitter and the receiver. On the theoretical front, bounds on the minimum number of transmit and receive RF chains that are required to realize the theoretical capacity of the large-scale MIMO system are presented. It is shown that the hybrid structure can achieve the same performance as the fully-digital beamforming scheme if the number of RF chains at each end is greater than or equal to twice the number of data streams. On the practical design front, this paper proposes a heuristic hybrid beamforming design strategy for the critical case where the number of RF chains is equal to the number of data streams, and shows that the performance of the proposed hybrid beamforming design can achieve spectral efficiency close to that of the fully-digital solution.

Design and implementation of embedded Web server based on arm and Linux <=SEP=> This paper achieves the design of an embedded Web server, which takes ARM920T-S3c2410s chip as its core and Linux as its operating system. This is because Linux can be reduced and transplanted. The method used to transplant Web server Boa on the embedded Linux platform is also discussed in detail, and through CGI technology functions of dynamic Web page is successfully realized. Relevant experiments show that after the Web server is embedded into the network video monitoring system, dynamic page interaction can be achieved between the Web server and the embedded system via the browser in the Windows environment.

Machine Learning Strategies for Time Series Forecasting <=SEP=> The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting.

A qualitative study on factors that influence women’s choice of delivery in health facilities in Addis Ababa, Ethiopia <=SEP=> BACKGROUND
Facility based delivery for mothers is one of the proven interventions to reduce maternal and neonatal morbidity and mortality. This study identified women's reasons for seeking to give birth in a health facility and captured their perceptions of the quality of care they received during their most recent birth, in a population with high utilization of facility based deliveries.


METHODS
This qualitative study was conducted in eight health centers in Addis Ababa. Women bringing their index child for first vaccinations were invited to participate in an in-depth interview about their last delivery. Sixteen in-depth interviews were conducted. Interviews were conducted by trained researchers using a semi-structured interview guide. The data were transcribed verbatim in Amharic and translated into English. A thematic analysis was conducted to answer specific study questions.


RESULTS
All research participants expressed a preference for facility based delivery because of their awareness of obstetric complications, and related perceptions that facility-birth is safer for the mother and child. Dimensions of quality of care and the cost of services were identified as influencing decisions about whether to seek care in the public or private sector. Media campaigns, information from social networks and women's experiences with healthcare providers and facilities influenced care-seeking decisions.


CONCLUSIONS
The universal preference for facility-based birth by women in this study indicates that, in Addis Ababa, facility based delivery has become a preferred norm. Sources of information for decision-making and the dimensions of quality prioritized by women should be taken into account to develop interventions to promote facility-based births in other settings.

Compressive Sensing Image Sensors-Hardware Implementation <=SEP=> The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.

Partitioning Well-Clustered Graphs with k-Means and Heat Kernel <=SEP=> We study a suitable class of well-clustered graphs that admit good k-way partitions and present the first almost-linear time algorithm for with almost-optimal approximation guarantees partitioning such graphs. A good k-way partition is a partition of the vertices of a graph into disjoint clusters (subsets) {Si}i=1, such that each cluster is better connected on the inside than towards the outside. This problem is a key building block in algorithm design, and has wide applications in community detection and network analysis. Key to our result is a theorem on the multi-cut and eigenvector structure of the graph Laplacians of these well-clustered graphs. Based on this theorem, we give the first rigorous guarantees on the approximation ratios of the widely used k-means clustering algorithms. We also give an almost-linear time algorithm based on heat kernel embeddings and approximate nearest neighbor data structures.

Approximate Computing for Low Power and Security in the Internet of Things <=SEP=> To save resources for Internet of Things (IoT) devices, a proposed approach segments operands and corresponding basic arithmetic operations that can be carried out by approximate function units for almost all applications. The approach also increases the security of IoT devices by hiding information for IP watermarking, digital fingerprinting, and lightweight encryption.

Speaker identification on the SCOTUS corpus <=SEP=> This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near-100% textindependent identification accuracy on utterances that are longer than one second; 2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; 3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved “perfect” forced alignment on very long speech segments (40 minutes). These findings and their significance are discussed.

Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D Model Retrieval <=SEP=> Aggregating a set of local features has become one of the most common approaches for representing a multi-media data such as 2D image and 3D model. The success of Bag-of-Features (BF) aggregation [2] prompted several extensions to BF, that are, VLAD [12], Fisher Vector (FV) coding [22] and Super Vector (SV) coding [34]. They all learn small number of codewords, or representative local features, by clustering a set of large number of local features. The set of local features extracted from a media data (e.g., an image) is encoded by considering distribution of features around the codewords; BF uses frequency, VLAD and FV uses displacement vector, and SV uses a combination of both. In doing so, these encoding algorithms assume linearity of feature space about a codeword. Consequently, even if the set of features form a non-linear manifold, its non-linearity would be ignored, potentially degrading quality of aggregated features. In this paper, we propose a novel feature aggregation algorithm called Diffusion-on-Manifold (DM) that tries to take into account, via diffusion distance, structure of non-linear manifold formed by the set of local features. In view of 3D shape retrieval, we also propose a local 3D shape feature defined for oriented point set. Experiments using shape-based 3D model retrieval scenario show that the DM aggregation results in better retrieval accuracy than the existing aggregation algorithms we've compared against, that are, VLAD, FV, and SV, etc..