A Framework for Feature Selection using Natural Language Processing for User Profile Learning for Recommendations of Healthcare Related Content

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.

Topic Difficulty: Collection and Query Formulation Effects

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.

LDPCD: A Novel Method for Locally Differentially Private Community Detection

As one of the cores of data analysis in large social networks, community detection has become a hot research topic in recent years. However, user’s real social relationship may be at risk of privacy leakage and threatened by inference attacks because of the semitrusted server. As a result, community detection in social graphs under local differential privacy has gradually aroused the interest of industry and academia. On the one hand, the distortion of user’s real data caused by existing privacy-preserving mechanisms can have a serious impact on the mining process of densely connected local graph structure, resulting in low utility of the final community division. On the other hand, private community detection requires to use the results of multiple user-server interactions to adjust user’s partition, which inevitably leads to excessive allocation of privacy budget and large error of perturbed data. For these reasons, a new community detection method based on the local differential privacy model (named LDPCD) is proposed in this paper. Due to the introduction of truncated Laplace mechanism, the accuracy of user perturbation data is improved. In addition, the community divisive algorithm based on extremal optimization (EO) is also refined to reduce the number of interactions between users and the server. Thus, the total privacy overhead is reduced and strong privacy protection is guaranteed. Finally, LDPCD is applied in two commonly used real-world datasets, and its advantage is experimentally validated compared with two state-of-the-art methods.

On Using Multiple Disabilities Profiles to Adapt Multimedia Documents

Currently, advanced technological hardware can offer mobile devices which fits in the hand with a capacity to consult documents at anytime and anywhere. Multiple user context constraints as well as mobile device capabilities may involve the adaptation of multimedia content. In this article, the authors propose a new graph-based method for adapting multimedia documents in complex situations. Each contextual situation could correspond to a physical handicap and therefore triggers an adaptation action using ontological reasoning. Consequently, when several contextual situations are identified, this leads to multiple disabilities and may give rise to inconsistency between triggered actions. Their method allows modeling relations between adaptation-actions to select the compatible triggerable ones. In order to evaluate the feasibility and the performance of their proposal, an experimental study has been made on some real scenarios. When tested and compared with some existing approaches, their proposal showed improvements according to various criteria.

reString: an open-source Python software to perform automatic functional enrichment retrieval, results aggregation and data visualization

Functional enrichment analysis is an analytical method to extract biological insights from gene expression data, popularized by the ever-growing application of high-throughput techniques. Typically, expression profiles are generated for hundreds to thousands of genes/proteins from samples belonging to two experimental groups, and after ad-hoc statistical tests, researchers are left with lists of statistically significant entities, possibly lacking any unifying biological theme. Functional enrichment tackles the problem of putting overall gene expression changes into a broader biological context, based on pre-existing knowledge bases of reference: database collections of known expression regulation, relationships and molecular interactions. STRING is among the most popular tools, providing both protein–protein interaction networks and functional enrichment analysis for any given set of identifiers. For complex experimental designs, manually retrieving, interpreting, analyzing and abridging functional enrichment results is a daunting task, usually performed by hand by the average wet-biology researcher. We have developed reString, a cross-platform software that seamlessly retrieves from STRING functional enrichments from multiple user-supplied gene sets, with just a few clicks, without any need for specific bioinformatics skills. Further, it aggregates all findings into human-readable table summaries, with built-in features to easily produce user-customizable publication-grade clustermaps and bubble plots. Herein, we outline a complete reString protocol, showcasing its features on a real use-case.

Joint power control and user grouping for uplink power domain non-orthogonal multiple access

Orthogonal multiple access schemes based on assignment of communication resource blocks among multiple contenders, although widely available, still necessitate an upper limit on the number of concurrent users for minimization of multiple-user interference. The feature thwarts efforts to cater for pressing connectivity demands posed by modern-day cellular communication networks. Non-orthogonal multiple access, regarded as a key advancement towards realization of high-speed 5G wireless communication networks, enables multiple users to access the same set of resource blocks non-orthogonally in terms of power with controllable interference, thereby allowing for overall performance enhancement. Owing to the combinatorial nature of the underlying optimization problem involving user pairing/grouping scheme, power control and decoding order, the computational complexity in determining optimal and sub-optimal solutions remains considerably high. This work proposes three novel alternative approaches (Randomly, 2-Opt and Hybrid) for arriving at a near-optimal solution for the problem of user pairing/grouping. The algorithms not only offer reduced computational complexity but also outperform orthogonal multiple access and existing schemes reported in the literature for uplink non-orthogonal multiple access systems.

Enhancing 3D Models with Urban Information: A Case Study Involving Local Authorities and Property Professionals in New Zealand: Quantifying the Benefit of 3D over Alternative 2D systems

<p>This thesis aimed to reach two principal outcomes: To develop a robust testing methodology that allowed a detailed and fair comparative analysis of the benefit, or otherwise, of 3D methods of information interrogation over alternative 2D methods; and to test the ability for a single model to have multiple user-group functionality. The research used the examples of two user-groups within the urban planning industry and their typical decision making processes. A robust testing methodology was developed to investigate the usefulness of 3D in a detailed and focused manner involving individual end-users as participants in a case study. The development of this efficient process assisted the study in moving past the initial visual impact of the models. The method employed a combination of three research instruments: A focus group formed the base from which an urban planning task, questionnaire and guided discussion investigated evidence for the benefit or otherwise of 3D using both quantitative and subjective measures. Two widely disparate user-groups were selected to further test the functionality of a resource to meet the needs of multiple users: city council urban designers and property developers. The research revealed that 3D methods of information visualisation allow users to develop a greater spatial awareness, increasing their understanding of information, when compared to alternative 2D methods. While evidence for this benefit was established using both quantitative and subjective methods, the research proved that this increased understanding does not necessarily lead to quicker decisions as the 2D group completed the task faster and more accurately than the 3D group. The ability for a single model to have multiple user-group functionality was confirmed as each of two disparate user-groups noted that the availability of the other user-group's information was of positive benefit to their understanding of the proposed development within the urban planning task.</p>

Enhancing 3D Models with Urban Information: A Case Study Involving Local Authorities and Property Professionals in New Zealand: Quantifying the Benefit of 3D over Alternative 2D systems

<p>This thesis aimed to reach two principal outcomes: To develop a robust testing methodology that allowed a detailed and fair comparative analysis of the benefit, or otherwise, of 3D methods of information interrogation over alternative 2D methods; and to test the ability for a single model to have multiple user-group functionality. The research used the examples of two user-groups within the urban planning industry and their typical decision making processes. A robust testing methodology was developed to investigate the usefulness of 3D in a detailed and focused manner involving individual end-users as participants in a case study. The development of this efficient process assisted the study in moving past the initial visual impact of the models. The method employed a combination of three research instruments: A focus group formed the base from which an urban planning task, questionnaire and guided discussion investigated evidence for the benefit or otherwise of 3D using both quantitative and subjective measures. Two widely disparate user-groups were selected to further test the functionality of a resource to meet the needs of multiple users: city council urban designers and property developers. The research revealed that 3D methods of information visualisation allow users to develop a greater spatial awareness, increasing their understanding of information, when compared to alternative 2D methods. While evidence for this benefit was established using both quantitative and subjective methods, the research proved that this increased understanding does not necessarily lead to quicker decisions as the 2D group completed the task faster and more accurately than the 3D group. The ability for a single model to have multiple user-group functionality was confirmed as each of two disparate user-groups noted that the availability of the other user-group's information was of positive benefit to their understanding of the proposed development within the urban planning task.</p>

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC is aimed at providing connectivity to tens of billions of sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resources. Nonorthogonal multiple access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing-based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article is aimed at providing an overview of the current state-of-art research work in various compressive sensing-based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection