bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Sequencing of RNA from Blood and Oral Swab Samples Reveals the Presence of Nucleic Acid from a Number of Pathogens in Patients with Acute Ebola Virus Disease and Is Consistent with Bacterial Translocation across the Gut
Authors: Carroll Miles W. , Haldenby Sam , Rickett Natasha Y. , Pályi Bernadett , Garcia-Dorival Isabel , Liu Xuan , Barker Gary , Bore Joseph Akoi , Koundouno Fara Raymond , Williamson E. Diane , Laws Thomas R. , Kerber Romy , Sissoko Daouda , Magyar Nóra , Di Caro Antonino , Biava Mirella , Fletcher Tom E. , Sprecher Armand , Ng Lisa F. P. , Rénia Laurent , Magassouba N’faly , Günther Stephan , Wölfel Roman , Stoecker Kilian , Matthews David A. , Hiscox Julian A.
Published: August 2017

Our results highlight the identification of an array of pathogens in the blood of patients with Ebola virus disease (EVD). This has not been done before, and the data have important implications for the treatment of patients with EVD, particularly considering antibiotic stewardship. We show that EVD patients who were also infected with
Plasmodium
, particularly at higher loads, had more adverse outcomes than patients with lower levels of
Plasmodium
. However, the presence of
Plasmodium
did not influence the innate immune response, and it is likely that the presence of EBOV dominated this response. Several viruses other than EBOV were identified, and bacteria associated with sepsis were also identified. These findings were indicative of bacterial translocation across the gut during the acute phase of EVD.
Compressed 3D and 2D digital images versus standard 3D slide film for the evaluation of glaucomatous optic nerve features
Authors: Sandhu Simrenjeet , Rudnisky Chris , Arora Sourabh , Kassam Faazil , Douglas Gordon , Edwards Marianne C , Verstraten Karin , Wong Beatrice , Damji Karim F
Published: August 2017

Synopsis

Clinicians can feel confident compressed three-dimensional digital (3DD) and two-dimensional digital (2DD) imaging evaluating important features of glaucomatous disc damage is comparable to the previous gold standard of stereoscopic slide film photography, supporting the use of digital imaging for teleglaucoma applications.

Background/aims

To compare the sensitivity and specificity of 3DD and 2DD photography with stereo slide film in detecting glaucomatous optic nerve head features.

Methods

This prospective, multireader validation study imaged and compressed glaucomatous, suspicious or normal optic nerves using a ratio of 16:1 into 3DD and 2DD (1024×1280 pixels) and compared both to stereo slide film. The primary outcome was vertical cup-to-disc ratio (VCDR) and secondary outcomes, including disc haemorrhage and notching, were also evaluated. Each format was graded randomly by four glaucoma specialists. A protocol was implemented for harmonising data including consensus-based interpretation as needed.

Results

There were 192 eyes imaged with each format. The mean VCDR for slide, 3DD and 2DD was 0.59±0.20, 0.60±0.18 and 0.62±0.17, respectively. The agreement of VCDR for 3DD versus film was κ=0.781 and for 2DD versus film was κ=0.69. Sensitivity (95.2%), specificity (95.2%) and area under the curve (AUC; 0.953) of 3DD imaging to detect notching were better (p=0.03) than for 2DD (90.5%; 88.6%; AUC=0.895). Similarly, sensitivity (77.8%), specificity (98.9%) and AUC (0.883) of 3DD to detect disc haemorrhage were better (p=0.049) than for 2DD (44.4%; 99.5%; AUC=0.72). There was no difference between 3DD and 2DD imaging in detecting disc tilt (p=0.7), peripapillary atrophy (p=0.16), grey crescent (p=0.1) or pallor (p=0.43), although 3D detected sloping better (p=0.013).

Conclusions

Both 3DD and 2DD imaging demonstrates excellent reproducibility in comparison to stereo slide film with experts evaluating VCDR, notching and disc haemorrhage. 3DD in this study was slightly more accurate than 2DD for evaluating disc haemorrhage, notching and sloping.
Observational study of sleep disturbances in advanced cancer
Authors: Davies Andrew Neil , Patel Shuchita D , Gregory Amanda , Lee Bernadette
Published: August 2017

Objectives

To determine the prevalence of nightmares, sleep terrors and vivid dreams in patients with advanced cancer (and the factors associated with them in this group of patients).

Methods

The study was a multicentre, prospective observational study. Participants were patients with locally advanced/metastatic cancer, who were under the care of a specialist palliative care team. Data were collected on demographics, cancer diagnosis, cancer treatment, current medication, performance status, sleep quality (Pittsburgh Sleep Quality Index), dreams and nightmares, and physical and psychological symptoms (Memorial Symptom Assessment Scale-Short Form).

Results

174 patients completed the study. Sleep quality was poor in 70.5% participants and was worse in younger patients and in inpatients (hospital, hospice). 18% of patients reported nightmares, 8% sleep terrors and 34% vivid dreams. Nightmares were associated with poor sleep quality and greater sleep disturbance; nightmares were also associated with greater physical and psychological burden. Nightmares (and vivid dreams) were not associated with the use of opioid analgesics.

Conclusions

Nightmares do not seem to be especially common in patients with advanced cancer, and when they do occur, there is often an association with sleep disturbance, and/or physical and psychological burden.
High exposure to endotoxin in farming is associated with less new-onset pollen sensitisation
Authors: Elholm Grethe , Schlünssen Vivi , Doekes Gert , Basinas Ioannis , Bolund Anneli Clea Skjelmose , Hjort Charlotte , Grønager Pernille Milvang , Omland Øyvind , Sigsgaard Torben
Published: August 2017

Objectives

Little is known about risk factors for new onset and loss of atopic sensitisation in adulthood. The aim is to examine the longitudinal effect of quantitatively assessed endotoxin exposures on changes in specific allergen sensitisation in young adults.

Methods

The cohort consisted of 1113 young Danish farmers and rural controls, with a mean age of 19 years at baseline. Sensitisation to birch pollen, grass pollen, cat dander and house dust mite was measured by specific IgE levels in serum samples from baseline and at 15 years’ follow-up. Changes in sensitisation were analysed in relation to cumulative endotoxin exposure during follow-up, considering early life farm exposure.

Results

Endotoxin exposure during follow-up was significantly associated with less new onset of specifically grass and birch pollen sensitisation. For the highest versus lowest quartile of cumulative endotoxin exposure, the OR for new-onset IgE sensitisation was 0.35 (0.13–0.91) for birch and 0.14 (0.05–0.50) for grass. On the other hand, loss of pollen sensitisation showed a positive, although mostly non-significant, association with increased levels of endotoxin exposure. Endotoxin exposure was not associated with significant changes in cat dander and house dust mite sensitisation.

Conclusions

High exposure to endotoxin during young adulthood appears to protect against new onset of pollen sensitisation, independent of childhood farm exposure.
Portal vein ligation alters coding and noncoding gene expression in rat livers
Authors: Li Bin , Zhu Yan , Xie Lei , Hu Shuyang , Liu Shupeng , Jiang Xiaoqing
Published: August 2017

Portal vein occlusion increases the resectability of initially unresectable liver cancer by inducing hypertrophy in non-occluded liver lobes. However, the mechanisms of how portal vein occlusion induces hepatic hypertrophy remain unclear. A cDNA microarray was used to identify the gene expression signatures of ligated (LLLs) and nonligated liver lobes (NLLLs) at different time points after portal vein ligation (PVL). The results of a bioinformatics analysis revealed that LLLs and NLLLs displayed different gene expression profiles. Moreover, the expression levels of both coding and noncoding RNA were different between LLLs and NLLLs at different time points after PVL. A series test of cluster analysis revealed that the No. 22 and No. 5 expression patterns, which showed altered expression at 24 h and maintained this altered expression over the following 14 days, had the lowest P values and the highest number of differentially expressed genes in both the LLLs and NLLLs. The results of a GO analysis showed the activation of hypoxia pathways in LLLs and the activation of cell proliferation and cell-cycle pathways in NLLLs, suggesting the involvement of these pathways in PVL-induced hepatic hypertrophy and regeneration. These results provide insight into the molecular mechanisms underlying hepatic hypertrophy and regeneration induced by portal vein occlusion, and they identify potential targeting pathways that can promote the clinical application of PVL in liver cancer therapy.
A calibration mechanism based on the principles of the Michelson interferometer micro-thrust test device
Authors: Yan Biao , Wang Hai , Yang Chunlai , Wen Li
Published: August 2017

A micro-thrust test system based on Michelson interferometer was proposed and tested. The relationship between thrust and output voltage of the calibration component in the system was calculated and verified with numerical modeling. The fitting function of the calibration component was obtained, which will be tested during future thrust test experiments.
First-principles study of newly synthesized nanolaminate Mo2Ga2C
Authors: Ling Wei Dong , Wei Pan , Duan Ji Zheng , Duan Wen Shan
Published: August 2017

Mo2Ga2C, which is a new member of MAX phases family discovered recently, has been regarded as the first compound of M2A2X family considering that no members of M2A2X phases have been found. Our work conducted first-principles calculations of new nanolaminate Mo2Ga2C followed by comparison with Mo-containing M2AX phases (Mo2GaC). Our results have shown that electric conductivity and ductile properties are greatly increased by adding Ga-layer, while theoretical hardness is retained. The Mo2Ga2C is found to possess more metallicity.
Critical Buckling of Prestress-Stable Tensegrity Structures Solved by Real-Coded Genetic Algorithm
Authors: Zhang Pei , Feng Jian
Published: August 2017

Tensegrity structures are classified as kinematically determinate ones with two subcases and kinematically indeterminate ones with three subcases in view of their respective stability properties. How the stiffness of a tensegrity structure changes as the level of prestress changes is explored for different scenarios using six carefully chosen samples. For a tensegrity structure merely satisfying the prestress-stability condition, a new optimization model is presented to determine its critical buckling state corresponding to zero stiffness. A real-coded genetic algorithm (RCGA) is then developed to solve this problem, featured by the fact that a special sign-control technique is embedded in the fundamental genetic operations, making the individuals generated in each step fall into the admissible region automatically. The stability of the neutral equilibrium state for tensegrity structures violating the prestress-stability condition is also discussed. Several numerical examples are tested to validate the efficiency of the present approach.
FUNCTION AND BIOMECHANICS OF UPPER LIMB IN POST-STROKE PATIENTS — A SYSTEMATIC REVIEW
Authors: RAMLEE MUHAMMAD HANIF , GAN KOK BENG
Published: August 2017

Current clinical services are struggling to provide the most favorable rehabilitation treatment for patients with stroke, which inspired researchers to investigate and explore the use of rehabilitation devices suitable for the patients and rehabilitation therapy. This review paper addresses the importance of biomechanical features in patients who experienced stroke to the upper limb. First and foremost, a review was done on general biomechanical description associated with motor control, shoulder, elbow, wrist and fingers joint. This included the ability of the patients to move their affected arm and the affect on peak joint torque, range of motion, joint forces, grip strength and muscle activities during the activities of daily living. In addition, we also reviewed the material properties and geometrical condition of tissue in stroke patient. The repercussions of post-stroke patient regarding the bone density, stiffness of muscle as well as the thickness of cartilage are described in this review. Based on the findings, the movement of affected stroke hand is associated with the motor control and material properties of tissue. To strengthen the motor control and maintaining tissue properties, early physical training on patients should be conducted in two to four weeks after stroke. In conclusion, this report suggests a new approach for future biomechanical studies in order to enhance the quality of physiotherapy rehabilitation peculiarly for post-stroke patients.
MANAGING INNOVATION PROCESSES THROUGH VALUE CO-CREATION: A PROCESS CASE FROM BUSINESS-TO-BUSINESS SERVICE PRACTISE
Authors: NARDELLI GIULIA , BROUMELS MARCEL
Published: August 2017

Value co-creation is a specific type of collaboration that is considered to be an innovative and interactive process between end users and organizations; it aims to increase the value of a product or service. This study investigates how a network of stakeholders collaborating to manage innovation openly co-creates value over time; it contributes to the existing literature on value co-creation by taking the perspective of the network as a whole. The study follows a case in which value co-creation unfolds over time across a network of stakeholders within the business-to-business facility service context. The in-depth longitudinal investigation of a network composed of a corporate customer and its external facility service providers revealed that a network of stakeholders co-creates value over time by (i) offering an adaptable structure for the network to organize innovation activities and establish support routines, (ii) facilitating interactions to support stakeholder relation development and (iii) allowing participants to achieve self-empowerment. Therefore, stakeholder value co-creation entails the combination of single value co-creation activities and overarching network progressions that allow for learning and inter-organizational trust among stakeholders.
Experimental Study of Normal Contact Force Between a Rolling Pneumatic Tyre and a Single Asperity
Authors: Zhang Yuan-Fang , Cesbron Julien , Yin Hai-Ping , Bérengier Michel
Published: August 2017

This paper proposes a novel experimental test apparatus that permits direct measurements of tyre/asperity normal contact forces under rolling conditions without interfacial layer. A reduced-sized pneumatic tyre is set rolling on the exterior surface of a cylindrical test rig simulating a smooth road surface except a single asperity of simple geometric shape connected to an embedded force transducer. Distinct asperity geometries lead to similar shapes of force signal but different magnitudes whose relationships with the indentation have exponents close to those in classical analytical solutions. By analyzing the time signals of the contact force and their frequency contents for different rolling speeds, the quasi-static nature of the contact, commonly assumed in numerical models, is verified.
STRUCTURAL RICARDIAN ANALYSIS OF SOUTH-EAST ASIAN AGRICULTURE
Authors: ABIDOYE BABATUNDE O. , KURUKULASURIYA PRADEEP , REED BRIAN , MENDELSOHN ROBERT
Published: August 2017

This paper examines the impact of climate change on the net revenue (NR) of farmers from Bangladesh, Indonesia, Sri Lanka, Thailand and Vietnam. Two Ricardian models are estimated: (1) a traditional Ricardian model of the impact of climate change on annual farm NR and (2) a structural Ricardian model that first estimates the number of growing seasons and then the net revenue per season. The traditional model reveals annual NR is sensitive to autumn and summer climate variables. The seasonal effects offset each other so that uniform marginal effects are insignificant. Future climate scenarios likely harm Sri Lanka but could either benefit or harm Indonesia depending on the climate scenario. The structural Ricardian model suggests climate change will reduce the net revenue of three-season farms and increase the revenue of one-season farms causing farmers to switch from three-season farming to one-season farming. Expected losses by 2100 for the region range from [Formula: see text]10% to [Formula: see text]18%. Impacts in Indonesia may be higher ranging from [Formula: see text]20% to [Formula: see text]28%.
Analysis of Mixed-Integer Linear Programming Formulations for a Fuel-Constrained Multiple Vehicle Routing Problem
Authors: Sundar Kaarthik , Venkatachalam Saravanan , Rathinam Sivakumar
Published: August 2017

This paper addresses a fuel-constrained, multiple vehicle routing problem (FCMVRP) in the presence of multiple refueling stations. We are given a set of targets, a set of refueling stations, and a depot where [Formula: see text] vehicles are stationed. The vehicles are allowed to refuel at any refueling station, and the objective of the problem is to determine a route for each vehicle starting and terminating at the depot, such that each target is visited by at least one vehicle, the vehicles never run out of fuel while traversing their routes, and the total travel cost of all the routes is a minimum. We present four new mixed-integer linear programming (MILP) formulations for the problem. These formulations are compared both analytically and empirically, and a branch-and-cut algorithm is developed to compute an optimal solution. Extensive computational results on a large class of test instances that corroborate the effectiveness of the algorithm are also presented.
A Cloud-Based Black-Box Solar Predictor for Smart Homes
Authors: Iyengar Srinivasan , Sharma Navin , Irwin David , Shenoy Prashant , Ramamritham Krithi
Published: August 2017

The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally generated solar energy. In this article, we present two machine-learning techniques to predict solar power from publicly available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service that automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a “black box” approach that requires only (1) a site’s geographic location and (2) a
minimal
amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site.

We evaluate the accuracy of SolarCast’s different algorithms on two publicly available datasets, each containing over 100 rooftop deployments with a variety of attributes (e.g., climate, tilt, orientation, etc.). We show that SolarCast learns a more accurate model using much less data (∼1 month) than prior SVM-based approaches, which require ∼3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a “sunny” load scheduler, which schedules a dryer’s energy usage to maximally align with a home’s solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of ∼5 hours) in the dryer’s start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.
Exploring Communication Behaviors of Users to Target Potential Users in Mobile Social Networks
Authors: Chen Chien-Cheng , Hsu Kuo-Wei , Peng Wen-Chih
Published: August 2017

In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users’ interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users.
A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories
Authors: Yang Cheng , Sun Maosong , Zhao Wayne Xin , Liu Zhiyuan , Chang Edward Y.
Published: August 2017

Two characteristics of location-based services are mobile trajectories and the ability to facilitate social networking. The recording of trajectory data contributes valuable resources towards understanding users’ geographical movement behaviors. Social networking is possible when users are able to quickly connect to anyone nearby. A social network with location based services is known as location-based social network (LBSN). As shown in Cho et al. [2013], locations that are frequently visited by socially related persons tend to be correlated, which indicates the close association between social connections and trajectory behaviors of users in LBSNs. To better analyze and mine LBSN data, we need to have a comprehensive view of each of these two aspects, i.e., the mobile trajectory data and the social network.

Specifically, we present a novel neural network model that can jointly model both social networks and mobile trajectories. Our model consists of two components: the construction of social networks and the generation of mobile trajectories. First we adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user. The key to our model lies in generating mobile trajectories. Second, we consider four factors that influence the generation process of mobile trajectories: user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. To characterize the last two contexts, we employ the RNN and GRU models to capture the sequential relatedness in mobile trajectories at the short or long term levels. Finally, the two components are tied by sharing the user network representations. Experimental results on two important applications demonstrate the effectiveness of our model. In particular, the improvement over baselines is more significant when either network structure or trajectory data is sparse.
Teaching Computational Thinking Using Agile Software Engineering Methods
Authors: Fronza Ilenia , Ioini Nabil El , Corral Luis
Published: August 2017

Computational Thinking (CT) has been recognized as one of the fundamental skills that all graduates should acquire. For this reason, motivational concerns need to be addressed at an early age of a child, and reaching students who do not consider themselves candidates for science, technology, engineering, and mathematics disciplines is important as well if the broadest audience possible is to be engaged. This article describes a framework for teaching and assessing CT in the context of K-12 education. The framework is based on Agile software engineering methods, which rely on a set of principles and practices that can be mapped to the activities of CT. The article presents as well the results of an experiment applying this framework in two sixth-grade classes, with 42 participants in total. The results show that Agile software engineering methods are effective at teaching CT in middle schools, after the addition of some tasks to allow students to explore, project, and experience the potential product before using the software tools at hand. Moreover, according to the teachers’ feedback, the students reached all the educational objectives of the topics involved in the multidisciplinary activities. This result can be taken as an indicator that it is possible to use computing as a medium for teaching other subjects, besides computer science.
Exploring Indoor White Spaces in Metropolises
Authors: Ying Xuhang , Zhang Jincheng , Yan Lichao , Chen Yu , Zhang Guanglin , Chen Minghua , Chandra Ranveer
Published: August 2017

It is a promising vision to exploit
white spaces
, that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, the indoor story is largely open for investigation. Motivated by this observation and discovering that 70% of the spectrum demand comes from indoor environment, we carry out a comprehensive study to explore
indoor
white spaces. We first conduct a large-scale measurement study and compare outdoor and indoor TV spectrum occupancy at 30+ diverse locations in a typical metropolis—Hong Kong. Our results show that abundant white spaces are available in different areas in Hong Kong, which account for more than 50% and 70% of the entire TV spectrum in outdoor and indoor scenarios, respectively. Although there are substantially more white spaces indoors than outdoors, there have been very few solutions for identifying indoor white space. To fill in this gap, we develop the first data-driven, low-cost indoor white space identification system for White-space Indoor Spectrum EnhanceR (WISER), to allow secondary users to identify white spaces for communication without sensing the spectrum themselves. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%--40% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.
PROV
2R
Authors: Stamatogiannakis Manolis , Athanasopoulos Elias , Bos Herbert , Groth Paul
Published: August 2017

Information produced by Internet applications is inherently a result of processes that are executed locally. Think of a web server that makes use of a CGI script, or a content management system where a post was first edited using a word processor. Given the impact of these processes to the content published online, a consumer of that information may want to understand what those impacts were. For example, understanding from where text was copied and pasted to make a post, or if the CGI script was updated with the latest security patches, may all influence the confidence on the published content. Capturing and exposing this information provenance is thus important to ascertaining trust to online content. Furthermore, providers of internet applications may wish to have access to the same information for debugging or audit purposes. For processes following a rigid structure (such as databases or workflows), disclosed provenance systems have been developed that efficiently and accurately capture the provenance of the produced data. However,
accurately
capturing provenance from
unstructured
processes, for example, user-interactive computing used to produce web content, remains a problem to be tackled.

In this article, we address the problem of capturing and exposing provenance from unstructured processes. Our approach, called
PROV
2R
(
PROV
enance
R
ecord and
R
eplay) is composed of two parts: (a) the decoupling of provenance analysis from its capture; and (b) the capture of high-fidelity provenance from unmodified programs. We use techniques originating in the security and reverse engineering communities, namely,
record and replay
and
taint tracking
. Taint tracking fundamentally addresses the data provenance problem but is impractical to apply at runtime due to extremely high overhead. With a number of case studies, we demonstrate that
PROV
2R
enables the use of taint analysis for high-fidelity provenance capture, while keeping the runtime overhead at manageable levels. In addition, we show how captured information can be represented using the W3C PROV provenance model for exposure on the Web.
DMAD
Authors: Shen Jiaxing , Cao Jiannong , Liu Xuefeng , Zhang Chisheng
Published: August 2017

Wireless networks offer many advantages over wired local area networks such as scalability and mobility. Strategically deployed wireless networks can achieve multiple objectives like traffic offloading, network coverage, and indoor localization. To this end, various mathematical models and optimization algorithms have been proposed to find optimal deployments of access points (APs).

However, wireless signals can be blocked by the human body, especially in crowded urban spaces. As a result, the real coverage of an on-site AP deployment may shrink to some degree and lead to unexpected dead spots (areas without wireless coverage). Dead spots are undesirable, since they degrade the user experience in network service continuity, on one hand, and, on the other hand paralyze some applications and services like tracking and monitoring when users are in these areas. Nevertheless, it is nontrivial for existing methods to analyze the impact of human beings on wireless coverage. Site surveys are too time consuming and labor intensive to conduct. It is also infeasible for simulation methods to predict the number of on-site people.

In this article, we propose DMAD, a Data-driven Measuring of Wi-Fi Access point Deployment, which not only estimates potential dead spots of an on-site AP deployment but also quantifies their severity, using simple Wi-Fi data collected from the on-site deployment and shop profiles from the Internet. DMAD first classifies static devices and mobile devices with a decision-tree classifier. Then it locates mobile devices to grid-level locations based on shop popularities, wireless signal, and visit duration. Last, DMAD estimates the probability of dead spots for each grid during different time slots and derives their severity considering the probability and the number of potential users.

The analysis of Wi-Fi data from static devices indicates that the Pearson Correlation Coefficient of wireless coverage status and the number of on-site people is over 0.7, which confirms that human beings may have a significant impact on wireless coverage. We also conduct extensive experiments in a large shopping mall in Shenzhen. The evaluation results demonstrate that DMAD can find around 70% of dead spots with a precision of over 70%.
Detecting Communities of Authority and Analyzing Their Influence in Dynamic Social Networks
Authors: Chikhaoui Belkacem , Chiazzaro Mauricio , Wang Shengrui , Sotir Martin
Published: August 2017

Users in real-world social networks are organized into communities that differ from each other in terms of influence, authority, interest, size, etc. This article addresses the problems of detecting communities of authority and of estimating the influence of such communities in dynamic social networks. These are new issues that have not yet been addressed in the literature, and they are important in applications such as marketing and recommender systems. To facilitate the identification of communities of authority, our approach first detects communities sharing common interests, which we call “meta-communities,” by incorporating topic modeling based on users’ community memberships. Then, communities of authority are extracted with respect to each meta-community, using a new measure based on the betweenness centrality. To assess the influence between communities over time, we propose a new model based on the Granger causality method. Through extensive experiments on a variety of social network datasets, we empirically demonstrate the suitability of our approach for community-of-authority detection and assessment of the influence between communities over time.
Mining Exploratory Behavior to Improve Mobile App Recommendations
Authors: He Jiangning , Liu Hongyan
Published: August 2017

With the widespread usage of smart phones, more and more mobile apps are developed every day, playing an increasingly important role in changing our lifestyles and business models. In this trend, it becomes a hot research topic for developing effective mobile app recommender systems in both industry and academia. Compared with existing studies about mobile app recommendations, our research aims to improve the recommendation effectiveness based on analyzing a psychological trait of human beings, exploratory behavior, which refers to a type of variety-seeking behavior in unfamiliar domains. To this end, we propose a novel probabilistic model named Goal-oriented Exploratory Model (GEM), integrating exploratory behavior identification with personalized item recommendation. An algorithm combining collapsed Gibbs sampling and Expectation Maximization is developed for model learning and inference. Through extensive experiments conducted on a real dataset, the proposed model demonstrates superior recommendation performances and good interpretability compared with state-of-art recommendation methods. Moreover, empirical analyses on exploratory behavior find that individuals with a strong exploratory tendency exhibit behavioral patterns of variety seeking, risk taking, and higher involvement. Besides, mobile apps that are less popular or in the long tail possess greater potential of arousing exploratory behavior in individuals.
The Tractability Frontier of Graph-Like First-Order Query Sets
Authors: Chen Hubie
Published: August 2017

The focus of this work is first-order model checking, by which we refer to the problem of deciding whether or not a given first-order sentence is satisfied by a given finite structure. In particular, we aim to understand on which sets of sentences this problem is tractable, in the sense of parameterized complexity theory. To this end, we define the notion of a graph-like sentence set; the definition is inspired by previous work on first-order model checking wherein the permitted connectives and quantifiers were restricted. Our main theorem is the complete tractability classification of such graph-like sentence sets, which is (to our knowledge) the first complexity classification theorem concerning a class of sentences that has no restriction on the connectives and quantifiers. To present and prove our classification, we introduce and develop a novel complexity-theoretic framework that is built on parameterized complexity and includes new notions of reduction.
The calculus of service availability
Authors: Treynor Ben , Dahlin Mike , Rau Vivek , Beyer Betsy
Published: August 2017

You're only as available as the sum of your dependencies.
Search, Mining, and Their Applications on Mobile Devices
Authors: Wang Hongning , Li Rui , Shokouhi Milad , Li Hang , Chang Yi
Published: August 2017

In recent years, mobile devices have become the most popular interface for users to retrieve and access information: recent reports show that users spend significantly more time and issue more search queries on mobile devices than on desktops in the United States.
1
The accelerated growth of mobile usage brings unique opportunities to the information retrieval and data mining research communities.

Mobile devices capture rich contextual and personal signals that can be leveraged to accurately predict users’ intent for serving more relevant content and can even proactively provide novel zero-query recommendations. Apple Siri, Google Now, and Microsoft Cortana are recent examples of such emerging systems. Furthermore, mobile devices constantly generate a huge amount of sensor footprints (e.g., GPS, motion sensors) and user activity data (e.g., used apps) that are often missing from their desktop counterparts. These new sources of implicit and explicit user feedback are valuable for discovering actionable knowledge, and designing better systems that serve each individual the right content at the right time and location. In addition, by aggregating mobile interactions across individuals, one can infer interesting conclusions beyond search and recommendation. Generating real-time traffic estimates is one example of such applications.

This special issue focuses on research problems of search, mining, and their applications in mobile devices. Topics of interest in this special issue include but are not limited to mobile data mining and management, mobile search, personalization and recommendation, mobile user interfaces and human-computer interaction, and new applications in the mobile environment. The aim of this special issue is to bring together top experts across multiple disciplines, including information retrieval, data mining, mobile computing, and cyberphysical systems, such that academic and industrial researchers can exchange ideas and share the latest developments on the state of the art and practice of mobile search and mobile data mining.
Tweet Can Be Fit
Authors: Farseev Aleksandr , Chua Tat-Seng
Published: August 2017

Wellness is a widely popular concept that is commonly applied to fitness and self-help products or services. Inference of personal wellness--related attributes, such as body mass index (BMI) category or disease tendency, as well as understanding of global dependencies between wellness attributes and users’ behavior, is of crucial importance to various applications in personal and public wellness domains. At the same time, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness profiling for users from multiple perspectives. However, research efforts on wellness profiling and integration of social media and sensor data are relatively sparse. This study represents one of the first attempts in this direction. Specifically, we infer personal wellness attributes by utilizing our proposed multisource multitask wellness profile learning framework—WellMTL—which can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. To gain insights into the data at a global level, we also examine correlations between first-order data representations and personal wellness attributes. Our experimental results show that the integration of sensor data and multiple social media sources can substantially boost the performance of individual wellness profiling.
Curious Cat--Mobile, Context-Aware Conversational Crowdsourcing Knowledge Acquisition
Authors: Bradeško Luka , Witbrock Michael , Starc Janez , Herga Zala , Grobelnik Marko , Mladenić Dunja
Published: August 2017

Scaled acquisition of high-quality structured knowledge has been a longstanding goal of Artificial Intelligence research. Recent advances in crowdsourcing, the sheer number of Internet and mobile users, and the commercial availability of supporting platforms offer new tools for knowledge acquisition. This article applies context-aware knowledge acquisition that simultaneously satisfies users’ immediate information needs while extending its own knowledge using crowdsourcing. The focus is on knowledge acquisition on a mobile device, which makes the approach practical and scalable; in this context, we propose and implement a new KA approach that exploits an existing knowledge base to drive the KA process, communicate with the right people, and check for consistency of the user-provided answers. We tested the viability of the approach in experiments using our platform with real users around the world, and an existing large source of common-sense background knowledge. These experiments show that the approach is promising: the knowledge is estimated to be true and useful for users 95% of the time. Using context to proactively drive knowledge acquisition increased engagement and effectiveness (the number of new assertions/day/user increased for 175%). Using pre-existing and newly acquired knowledge also proved beneficial.
Parallelization of Massive Textstream Compression Based on Compressed Sensing
Authors: Peng Min , Gao Wang , Wang Hua , Zhang Yanchun , Huang Jiajia , Xie Qianqian , Hu Gang , Tian Gang
Published: August 2017

Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we propose a textstream compression framework based on
compressed sensing theory
and design a series of matching parallel procedures. The new approach uses a linear projection technique in the textstream compression process, achieving fast compression speed and low compression ratio. Two processes are executed by designing elaborated parallel procedures for efficient compressing and decompressing of large-scale textstreams. The decompression process is implemented for approximate solutions of underdetermined linear systems. Experimental results show that the new method can efficiently achieve the compression and decompression tasks on a large amount of text generated by social networks.
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings
Authors: Li Chenliang , Duan Yu , Wang Haoran , Zhang Zhiqian , Sun Aixin , Ma Zongyang
Published: August 2017

Many applications require semantic understanding of short texts, and inferring discriminative and coherent latent topics is a critical and fundamental task in these applications. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short texts are much more sparse in terms of word co-occurrences. Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic. However, DMM has two main limitations. First, even though it seems reasonable to assume that each short text has only one topic because of its shortness, the definition of “shortness” is subjective and the length of the short texts is dataset dependent. That is, the single-topic assumption may be too strong for some datasets. To address this limitation, we propose to model the topic number as a Poisson distribution, allowing each short text to be associated with a small number of topics (e.g., one to three topics). This model is named PDMM. Second, DMM (and also PDMM) does not have access to background knowledge (e.g., semantic relations between words) when modeling short texts. When a human being interprets a piece of short text, the understanding is not solely based on its content words, but also their semantic relations. Recent advances in word embeddings offer effective learning of word semantic relations from a large corpus. Such auxiliary word embeddings enable us to address the second limitation. To this end, we propose to promote the semantically related words under the same topic during the sampling process, by using the generalized Pólya urn (GPU) model. Through the GPU model, background knowledge about word semantic relations learned from millions of external documents can be easily exploited to improve topic modeling for short texts. By directly extending the PDMM model with the GPU model, we propose two more effective topic models for short texts, named GPU-DMM and GPU-PDMM. Through extensive experiments on two real-world short text collections in two languages, we demonstrate that PDMM achieves better topic representations than state-of-the-art models, measured by topic coherence. The learned topic representation leads to better accuracy in a text classification task, as an indirect evaluation. Both GPU-DMM and GPU-PDMM further improve topic coherence and text classification accuracy. GPU-PDMM outperforms GPU-DMM at the price of higher computational costs.
Monadic Second-Order Logic with Arbitrary Monadic Predicates
Authors: Fijalkow Nathanaël , Paperman Charles
Published: August 2017

We study Monadic Second-Order Logic (
MSO
) over finite words, extended with (non-uniform arbitrary) monadic predicates. We show that it defines a class of languages that has algebraic, automata-theoretic, and machine-independent characterizations. We consider the
regularity question
: Given a language in this class, when is it regular? To answer this, we show a
substitution property
and the existence of a
syntactical predicate
.

We give three applications. The first two are to give very simple proofs that the Straubing Conjecture holds for all fragments of
MSO
with monadic predicates and that the Crane Beach Conjecture holds for
MSO
with monadic predicates. The third is to show that it is decidable whether a language defined by an
MSO
formula with morphic predicates is regular.
Finding Semantically Valid and Relevant Topics by Association-Based Topic Selection Model
Authors: Gao Yang , Li Yuefeng , Lau Raymond Y. K. , Xu Yue , Bashar Md Abul
Published: August 2017

Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users’ preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users’ specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users’ specific interests, and hence to improve the performance of IF and IR.
Security in high-performance computing environments
Authors: Peisert Sean
Published: August 2017

Exploring the many distinctive elements that make securing HPC systems much different than securing traditional systems.