Search

Query-based searching and browsing-based navigation are the two main components of exploratory search. Search lets users dig in deep by controlling their actions to focus on and find just the information they need, whereas navigation helps them to get an overview to decide which content is most important. In this paper, we introduce the concept of "search powered navigation" and investigate the effect of empowering navigation with search functionality on information seeking behavior of users and their experience by conducting a user study on exploratory search tasks, differentiated by different types of information needs. Our main findings are as follows: First, we observe radically different search tactics. Using search, users are able to control and augment their search focus, hence they explore the data in a depth-first, bottom-up manner. Conversely, using pure navigation they tend to check different options to be able to decide on their path into the data, which corresponds to a breadth-first, top-down exploration. Second, we observe a general natural tendency to combine aspects of search and navigation, however, our experiments show that the search functionality is essential to solve exploratory search tasks that require finding documents related to a narrow domain. Third, we observe a natural need for search powered navigation: users using a system without search functionality find creative ways to mimic searching using navigation., Accepted for publication in ACM SIGIR International Conference on the Theory of Information Retrieval

Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models., Comment: In proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2017)

Molecular dynamics (MD) and Monte Carlo (MC) simulations have been used to study the PEBA 1074-PEG 400 blended membranes. By blending the membranes and TiO2 nanomaterials, separation of CO2 gas from CH4 (which represents the natural gas) has been investigated. Structural analysis including WAXD, XRD, FFV and glass transition temperature has been studied to check thermal and structural properties of total simulated membranes. In addition, the adsorption and MSD isotherms have been used to measure solubility and diffusivity through these mixed matrix membranes (MMMs), respectively. Finally, based on the solubility and diffusivity coefficients, permselectivity and permeability of these gas molecules were measured. By increasing the PEG 400 and nanomaterials content, remarkable advance was observed in the transport properties of membranes, i.e. membranes permeability after addition of 40 wt% of PEG 400 and 8 wt% of TiO2 became 47 barrer and 143 barrer, respectively. Also, the PEBA 1074/40 wt% PEG/8 wt% TiO2 composite was selected as the optimum membrane in this study.

An email conversation thread is defined as a topic-centric discussion unit that is composed of exchanged emails among the same group of people by reply or forwarding. Detecting conversation threads contained in email corpora can be beneficial for both humans to digest the content of discussions and automatic methods to extract useful information from the conversations. This research explores two new feature-enriched learning approaches, LExLinC and LExTreC, to reconstruct linear structure and tree structure of conversation threads in email data. In this work, some simplifying assumptions considered in previous methods for extracting conversation threads are relaxed, which makes the proposed methods more powerful in detecting real conversations. Additionally, the supervised nature of the proposed methods makes them adaptable to new environments by automatically adjusting the features and their weights. Experimental results show that the proposed methods are highly effective in detecting conversation threads and outperform the existing methods.

Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion., Comment: Accepted to be published at The 26th ACM International Conference on Information and Knowledge Management (CIKM2017)

Supporting exploratory search tasks with the help of structured data is an effective way to go beyond keyword search, as it provides an overview of the data, enables users to zoom in on their intent, and provides assistance during their navigation trails. However, finding a good starting point for a search episode in the given structure can still pose a considerable challenge, as users tend to be unfamiliar with exact, complex hierarchical structure. Thus, providing lookahead clues can be of great help and allow users to make better decisions on their search trajectory.In this paper, we investigate the behaviour of users when a recommendation engine is employed along with the browsing tool in an exploratory search system. We make use of an exploratory search system that facilitates browsing by mapping the data on a hierarchical structure. We designed and developed a path recommendation engine as a feature for this system, which given a text query, ranks different browsing paths in the hierarchy based on their likelihood of covering relevant documents. We conduct a user study comparing the baseline system with the featured system.Our main findings are as follows: We observe that, using the baseline system the users tend to explore the data in a breadth-first-like approach by visiting different data points at the same level of abstraction to choose one of them to expand and go deeper. Conversely, with browsing path recommendation (BPR) as a feature, the users tend to drive their search in a more depth-first-like approach by quickly going deep into the data hierarchy. While the users still incline to explore different parts of the search space by using BPR, they are able to restrain or augment their search focus more quickly and access smaller but more promising regions of the data. Therefore, they can complete their tasks with less time and effort

Recently, researchers started to pay attention to the detection of temporal shifts in the meaning of words. However, most (if not all) of these approaches restricted their efforts to uncovering change over time, thus neglecting other valuable dimensions such as social or political variability. We propose an approach for detecting semantic shifts between different viewpoints--broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party. For each viewpoint, we learn a semantic space in which each word is represented as a low dimensional neural embedded vector. The challenge is to compare the meaning of a word in one space to its meaning in another space and measure the size of the semantic shifts. We compare the effectiveness of a measure based on optimal transformations between the two spaces with a measure based on the similarity of the neighbors of the word in the respective spaces. Our experiments demonstrate that the combination of these two performs best. We show that the semantic shifts not only occur over time, but also along different viewpoints in a short period of time. For evaluation, we demonstrate how this approach captures meaningful semantic shifts and can help improve other tasks such as the contrastive viewpoint summarization and ideology detection (measured as classification accuracy) in political texts. We also show that the two laws of semantic change which were empirically shown to hold for temporal shifts also hold for shifts across viewpoints. These laws state that frequent words are less likely to shift meaning while words with many senses are more likely to do so., Comment: In Proceedings of the 26th ACM International on Conference on Information and Knowledge Management (CIKM2017)

In this paper, we present an inverse analysis to estimate the thermal boundary conditions over a two-dimensional radiant enclosure from the knowledge of the measured temperatures for some points on a solid object within the enclosure. The conduction heat transfer in the solid object and the radiative heat transfer between the surface elements of the enclosure are formulated by the finite volume method and the net radiative method, respectively. The resultant set of nonlinear equations is solved by the Newton's method. The inverse problem for estimation of boundary conditions over the radiant enclosure is solved by the conjugate gradient method.

A palladium membrane has been prepared by electroless plating on the surface of a Porous Stainless Steel disk. The disk surface was modified with WO3 to prevent metal penetration into palladium layer and hence a membrane with nano-sized pores could be obtained. The mean thickness of the resulting Pd membrane on the modified disk was 12 μm. The permeability and selectivity of membrane was investigated. The permeability tests at 773 K showed that the membrane prepared was highly selective towards hydrogen. Additionally aging tests showed hydrogen permeation through the membrane was independent of time.

Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance - in particular when the initial query retrieves only little relevant information - when overfitting to accidental features of the particular observed feedback documents. Inspired by the early work of Hans Peter Luhn, we propose significant words language models of feedback documents that capture all, and only, the significant shared terms from feedback documents. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of rare terms that are only explained by specific feedback documents, which eventually results in having only the significant terms left in the feedback model. Establishing a set of 'Significant Words' Our main contributions are the following. First, we present significant words language models as the effective models capturing the essential terms and their probabilities. Second, we apply the resulting models to the relevance feedback task, and see a better performance over the state-of-the-art methods. Third, we see that the estimation method is remarkably robust making the models insensitive to noisy non-relevant terms in feedback documents. Our general observation is that the significant words language models more accurately capture relevance by excluding general terms and feedback document specific terms.

Transforming the data into a suitable representation is the first key step of data analysis, and the performance of any data-oriented method is heavily depending on it. We study questions on how we can best learn representations for textual entities that are: 1) precise, 2) robust against noisy terms, 3) transferable over time, and 4) interpretable by human inspection. Inspired by the early work of Luhn[1], we propose significant words language models of a set of documents that capture all, and only, the significant shared terms from them. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of incidental rare terms that are only explained by specific documents, which eventually results in having only the significant terms left in the model.

There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile sharing one or more characteristics with the user at hand. Personal profiles are often sparse, due to cold start problems and the fact that users typically search for new items or information, necessitating to back-off to customization, but group profiles often suffer from accidental features brought in by the unique individual contributing to the group. In this paper we propose a generalized group profiling approach that teases apart the exact contribution of the individual user level and the "abstract" group level by extracting a latent model that captures all, and only, the essential features of the whole group. Our main findings are the followings. First, we propose an efficient way of group profiling which implicitly eliminates the general and specific features from users' models in a group and takes out the abstract model representing the whole group. Second, we employ the resulting models in the task of contextual suggestion. We analyse different grouping criteria and we find that group-based suggestions improve the customization. Third, we see that the granularity of groups affects the quality of group profiling. We observe that grouping approach should compromise between the level of customization and groups' size., Comment: Short paper (4 pages) published in proceedings of ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR'16)

The use of feedback information is an effective approach to address the vocabulary gap between a user's query and the relevant documents. It has been shown that some relevant documents act like "poison pills," i.e. they hurt the performance of feedback systems despite the fact that they are relevant. In this paper, we study the positive counterpart of this by investigating the helpfulness of nonrelevant documents in feedback. In general, we find that although documents that are explicitly judged as non-relevant are normally assumed to be poisonous for feedback systems, sometimes considering high-scored non-relevant documents as a positive feedback helps to improve the performance of retrieval. In our experimental data, we observe a considerable fraction of non-relevant documents in higher ranked positions of the initial retrieval run, for most of the topics. Hence, by ignoring the potential value of non-relevant documents, we may loose a lot of useful information. We investigate the potential contribution of non-relevant documents using existing state-of-the-art feedback methods. Our main findings are the following. First, we find that some of the nonrelevant documents are exclusively helpful, they improve retrieval on their own, and others are complementary helpful, they lead to further improvement when added to a set of relevant documents. Second, we discover that, on average, exclusively helpful non-relevant documents have a higher contribution to the performance improvement, compared to the complementary ones. Third, we show that non-relevant documents in topics with poor average precision in the initial retrieval are more likely to help in the feedback.

Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause difficulties in the estimation of "separable" models that can distinguish between the entities in the hierarchy. Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy. In this paper, we present an investigation of the effect of separability in text-based entity classification and argue that in hierarchical classification, a separation property should be established between entities not only in the same layer, but also in different layers. Our main findings are the followings. First, we analyse the importance of separability on the data representation in the task of classification and based on that, we introduce a "Strong Separation Principle" for optimizing expected effectiveness of classifiers decision based on separation property. Second, we present Hierarchical Significant Words Language Models (HSWLM) which capture all, and only, the essential features of hierarchical entities according to their relative position in the hierarchy resulting in horizontally and vertically separable models. Third, we validate our claims on real-world data and demonstrate that how HSWLM improves the accuracy of classification and how it provides transferable models over time. Although discussions in this paper focus on the classification problem, the models are applicable to any information access tasks on data that has, or can be mapped to, a hierarchical structure., Comment: Full paper (10 pages) accepted for publication in proceedings of ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR'16)

Rank aggregation, that is merging multiple ranked lists, is a pivotal challenge in many information retrieval (IR) systems, especially in distributed IR and multilingual IR. From the evaluation point of view, being able to calculate the upper-bound of performance of the final aggregated list lays the ground for evaluating different aggregation strategies, independently. In this paper, we propose an algorithm based on dynamic programming which, using relevancy information, obtains the aggregated list with the maximum performance that could be possibly achieved by any aggregation strategy. We also provide a detailed proof for the optimality of the result of the algorithm. Furthermore, we demonstrate that the previous proposed algorithm fails to reach the optimal result in many circumstances, due to its greedy essence.

Identifying authors of short texts on Internet or social media based communication systems is an important tool against fraud and cybercrimes. Besides the challenges raised by the limited length of these short messages, evolving language and writing styles of authors of these texts makes authorship attribution difficult. Most current short text authorship attribution approaches only address the challenge of limited text length. However, neglecting the second challenge may lead to poor performance of authorship attribution for authors who change their writing styles.In this paper, we analyse the temporal changes of word usage by authors of tweets and emails and based on this analysis we propose an approach to estimate the dynamicity of authors' word usage. The proposed approach is inspired by time-aware language models and can be employed in any time-unaware authorship attribution method. Our experiments on Tweets and the Enron email dataset show that the proposed time-aware authorship attribution approach significantly outperforms baselines that neglect the dynamicity of authors.

Text alignment is one of the main steps of plagiarism detection in textual environments. Considering the pattern in distribution of the common semantic elements of the two given documents, different strategies may be suitable for this task. In this paper we assume that the obfuscation level, i.e the plagiarism type, is a function of the distribution of the common elements in the two documents. Based on this assumption, we propose Meta Text Aligner which predicts plagiarism relation of two given documents and employs the prediction results to select the best text alignment strategy. Thus, it will potentially perform better than the existing methods which use a same strategy for all cases. As indicated by the experiments, we have been able to classify document pairs based on plagiarism type with the precision of $$89\%$$. Furthermore exploiting the predictions of the classifier for choosing the proper method or the optimal configuration for each type we have been able to improve the Plagdet score of the existing methods.

Background Drug dependence is one of the major problems in today’s world which has turned into a serious, concerning threat due to its destructive and preventive effects on the development and prosperity of the society. Objectives The study aimed to investigate the effectiveness of social skills training on tendency to addiction among high school female students of Ahvaz city. Patients and Methods The population of this study consisted of all female students in Ahvaz city high schools, who scored one standard deviation higher than the mean in the zargar addiction potential questionnaire. Out of them, 40 students were selected by random sampling method and classified into two experimental and control groups (20 persons in each group). Eight treatment sessions were scheduled for the experimental group. In these sessions, social skills such as assertiveness, anger management, and communication with others were instructed to the group whereas, the control group received no training. Evaluation device wasscattere zargar addiction potential questionnaire. Results The results of the analysis of covariance (ANCOVA) test showed that there was a significant difference between the tendency to addiction reduction scores in control and experimental groups (P < 0.01). The results indicated that training the social skills were effective to reduce the tendency to addiction reduction in high school female students of Ahvaz. Conclusions Teaching the appropriate social skills is effective in reducing the tendency to addiction among the students.

Recently, Entity Linking and Retrieval turned out to be one of the most interesting tasks in Information Extraction due to its various applications. Entity Linking (EL) is the task of detecting mentioned entities in a text and linking them to the corresponding entries of a Knowledge Base. EL is traditionally composed of three major parts: i)spotting, ii)candidate generation, and iii)candidate disambiguation. The performance of an EL system is highly dependent on the accuracy of each individual part. In this paper, we focus on these three main building blocks of EL systems and try to improve on the results of one of the open source EL systems, namely DBpedia Spotlight. We propose to use text pre-processing and parameter tuning to "focus" a general-purpose EL system to perform better on different kinds of input text. Also, one of the main drawbacks of EL systems is identifying where a name does not refer to any known entity. To improve this so-called NIL-detection, we define different features using a set of texts and their known entities and design a classifier to automatically classify DBpedia Spotlight's output entities as "NIL" or "Not NIL". The proposed system has participated in the SIGIR ERD Challenge 2014 and the performance analysis of this system on the challenge's datasets shows that the proposed approaches successfully improve the accuracy of the baseline system.

Authorship identification was introduced as one of the important problems in the law and journalism fields and it is one of the major techniques in plagiarism detection. In this paper, to tackle the authorship verification problem, we propose a probabilistic distribution model to represent each document as a feature set to increase the interpretability of the results and features. We also introduce a distance measure to compute the distance between two feature sets. Finally, we exploit a KNN-based approach and a dynamic feature selection method to detect the features which discriminate the author’s writing style.

Change and the growth of information technology, all aspects of human society have taken in the light of self. Human need, interest in using new technologies emerged as a dominant factor in their organizations and they will force to keep pace with the condition. Since the major factor in advances in information technology in the present world, is the need and necessity. This important, changed the methods and practices and have been transformed of paper-based systems to electronic information systems and software. Accounting and auditing profession, perhaps more than in other areas of financial knowledge have been disposable of Enjoyment and use of the facility is subject to the new environment. New environment recording and reporting of information, has created much effects in the efficiency and effectiveness of the auditing profession And the need to use information technology audit to be felt more than ever. Therefore, in this study, are followed to find the factors affecting on information technology audit quality. In order to, Standard questionnaire was used that test its validity before and also reliability 89% was measured. And results show that from the view of CPAs working in the audit institute and the audit organization, properly accountability of audit team and existence audit framework and process, business criteria and audit scope, auditability, planning and operations, access to resources, relationship with the entity and the business environment, affects IT audit quality

Domestic sheep and their wild relatives harbor substantial genetic variants that can form the backbone of molecular breeding, but their genome landscapes remain understudied. Here, we present a comprehensive genome resource for wild ovine species, landraces and improved breeds of domestic sheep, comprising high-coverage (∼16.10×) whole genomes of 810 samples from 7 wild species and 158 diverse domestic populations. We detected, in total, ∼121.2 million single nucleotide polymorphisms, ∼61 million of which are novel. Some display significant (P < 0.001) differences in frequency between wild and domestic species, or are private to continent-wide or individual sheep populations. Retained or introgressed wild gene variants in domestic populations have contributed to local adaptation, such as the variation in the HBB associated with plateau adaptation. We identified novel and previously reported targets of selection on morphological and agronomic traits such as stature, horn, tail configuration, and wool fineness. We explored the genetic basis of wool fineness and unveiled a novel mutation (chr25: T7,068,586C) in the 3′-UTR of IRF2BP2 as plausible causal variant for fleece fiber diameter. We reconstructed prehistorical migrations from the Near Eastern domestication center to South-and-Southeast Asia and found two main waves of migrations across the Eurasian Steppe and the Iranian Plateau in the Early and Late Bronze Ages. Our findings refine our understanding of genome variation as shaped by continental migrations, introgression, adaptation, and selection of sheep., This study was financially supported by grants from the National Natural Science Foundation of China (Nos. 32061133010, 31825024, 31661143014, and 31972527), the National Key Research and Development Program-Key Projects of International Innovation Cooperation between Governments (2017YFE0117900), the External Cooperation Program of Chinese Academy of Sciences (152111KYSB20150010), and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (No. 2019QZKK0501), and the Special Funds of the State Key Laboratory of Sheep Genetic Improvement and Healthy Production (Nos. 2018CA001, and 2019CA009, 2020CA001).