Large-scale User Research Articles

MULTIPLE ACCESS TECHNIQUES FOR 5G NETWORKS

Fifth generation (5G) wireless networks face various challenges in order to support largescale heterogeneous traffic and users, therefore new modulation and multiple access (MA) schemes are being developed to meet the changing demands. As this research space is ever increasing, it becomes more important to analyze the various approaches, therefore, in this article we present a comprehensive overview of the most promising Multiple Access schemes for 5G networks. Our article focuses on various types of non-orthogonal multiple access (NOMA) techniques. Specifically, we first introduce different types of modulation schemes, potential for OMA. We then pay close attention to various types of NOMA candidates, including power-domain NOMA, code-domain NOMA, and NOMA multiplexing in multiple domains. From this exploration, we can identify the opportunities and challenges that will have the most significant impacts on modulation and MA designs for 5G networks.

Visualization Classification and Prediction Based on Data Mining

The paper aims to solve huge amounts and noise of current internet information. In order to better excavate the user’s interests and provide accurate recommendations, improve the Internet environment and services and accurately analyze and evaluate the behavior of large-scale user groups. This article compares web pages with sentences in natural language, and uses the natural language processing algorithm Word2vec to extract the feature vectors of massive web pages browsed by users. PCA has been reduced to two dimensions, and webpage visualization and logistic regression have been implemented to classify webpages. A series of user clicks on webpages can be viewed as a walk in n-dimensional space. Probabilistic language models and Monte Carlo simulations are used as the next step for users. Click behavior to make predictions. Experiments show that the machine learning processing natural language algorithm used in this paper has a good performance in classification and prediction accuracy.

Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

Profiling users via their reviews: an extended systematic mapping study

With the extensive development of big data and social networks, the user profile field has received much attention. User profiling is essential for understanding the characteristics of various users, contributing to better understanding of their requirements in specific scenarios. User-generated contents which directly reflect people’s thoughts and intention are a valuable source for profiling users, among which user reviews by nature are invaluable sources for acquiring user requirements and have drawn increasing attention from both academia and industry. However, review-based user profiling (RBUP), as an emerging research direction, has not been systematically reviewed, hindering researchers from further investigation. In this work, we carry out a systematic mapping study on review-based user profiling, with an emphasis on investigating the generic analysis process of RBUP and identifying potential research directions. Specifically, 51 out of 2478 papers were carefully selected for investigation under a standardized and systematic procedure. By carrying out in-depth analysis over such papers, we have identified a generic process that should be followed to perform review-based user profiling. In addition, we perform multi-dimensional analysis on each step of the process in order to review current research progress and identify challenges and potential research directions. The results show that although traditional methods have been continuously improved, they are not sufficient to unleash the full potential of large-scale user reviews, especially the use of heterogeneous data for multi-dimensional user profiling.

Appied Nuclear Physics-Advanced Ion Beam Materials Analysis at small Accelerators

The ossendorf Ion Beam Laboratory has been developed to an international large scale user facility in the field of ion beam physics and ion beam materials research. The laboratory operates a large number of modern experimental equipment at three MeV accelerators, three implanters, an ECR source and a FIB which together pro- vide almost all ions species in a wide energy range from a few hundred eV to a few ten MeV. Also IBAD and PHI devices were installed for various purposes. The re- search stations at the accelerators are supplemented by complementary techniques like TEM, SEM, AUGER, AFM etc., all contributing useful information to thin film investigations. In this paper a short overview of the laboratory is given and a few recent experiments and their results are shown. Rutherford Backscattering Spec- trometry ( RBS) and Elastic Recoil Detection Analysis (ERDA) are well established techniques for quantitative thin film. The advantage of these methods consists in the simple physics they are basing on, namely the stopping of energetic ions in matter and the binary scattering of at the Coulomb potential of atomic nuclei. The increasing importance of ultra-thin layers for novel technologies demands quantita- tive analysis techniques with a depth resolution of atomic monolayers, which can be obtained for RBS and ERDA by magnetic spectrometers only. The magnetic spec- trometers we have installed at the 3 MV Tandetron and at the 5 MV tandem are described , recent applications are shown and a few problems to achieve high depth resolution will be discussed. Heavy ion detectors as Bragg IC, dE-Erest-telescopes and ToF spectrometers, developed for nuclear physics experiments, are now applied for ERDA, providing an efficient analysis of thin films containing light elements. The lateral position resolution of such detectors enables kinematic corrections and allows large solid angles. Thus by ERDA in situ studies during surface modification processes are possible like in the case of the nitridation of aluminum and stainless steel. At the external beam mainly objects of fine arts or of historical value are analysed. It will be shown, how the complementary application of PIXE, RBS and PIGE can help to detect the beginning corrosion of mediaeval glass objects.

Sequential Recommendations on Board-Game Platforms

The game market is an increasingly large industry. The board-game market, which is the most traditional in the game market, continues to show a steady growth. It is very important for both publishers and players to predict the propensity of users in this huge market and to recommend new games. Despite its importance, no study has been performed on board-game recommendation systems. In this study, we propose a method to build a deep-learning-based recommendation system using large-scale user data of an online community related to board games. Our study showed that new games can be effectively recommended for board-game users based on user big data accumulated for a long time. This is the first study to propose a personalized recommendation system for users in the board-game market and to introduce a provision of new large datasets for board-game users. The proposed dataset shares symmetric characteristics with other datasets and has shown its ability to be applied to various recommendation systems through experiments. Therefore, the dataset and recommendation system proposed in this study are expected to be applied for various studies in the field.

Live streaming: Data mining and behavior analysis

With the rapid development of mobile communication and Internet technologies, online live streaming has gradually become popular for information communication and entertainment in the new media environment. Live streaming has been widely used in teaching, reality show, E-sports games and events, brand marketing and other aspects. With the active participation of millions of streamers and hundreds of millions of viewers, massive online crowd behavior activity data are generated, which offers rich experimental scenarios for large-scale crowd behavior dynamics research, live streaming channel recommendation and online community evolution. In this paper, we summarize the relevant research literature of live streaming, and review current studies from a comprehensive list of aspects: workload pattern, viewers and streamers behavior, community network discovery and analysis, etc. We summarize the temporal and spatial patterns of live streaming platform workload, heavy tailed effect of large-scale crowd behavior in live streaming platform, etc. We believe that the future work on live streaming can be directed in the examination of formation and evolution mechanism of various community networks formed by large-scale users, as well as the recommendation and detection of live streaming content.

Provisioning big data applications as services on containerised cloud: a microservices-based approach

We present the fundamental challenges for dynamic provisioning of big data applications. The findings are based on our previous experience in this domain, as well as a comprehensive study on a selected set of state-of-the-art tools in the big data ecosystem. We then incorporate these findings in a framework aiming at dynamically provisioning big data applications as services on containerised cloud. The innovations behind the framework are to optimise the whole lifecycle of big data applications in a holistic manner by the adoption of microservices(μServices) methodologies. The feasibility of our approach is verified through a case study of provisioning a large-scale user traffic data processing application in a private cloud environment backed by Kubernetes. We also show that while hosting big data applications in containerised cloud can significantly eliminate the presumed complexity of deployment and operation, it in the meantime also comes with a certain amount of cost in terms of learning curve and traceability. Our research helps technical decision makers to assess the adoption of microservices for big data applications more objectively.

Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

Aspect-based sentiment analysis (ABSA) has recently attracted increasing attention due to its extensive applications. Most of the existing ABSA methods been applied on small-sized labeled datasets. However, real datasets such as the Amazon and TripAdvisor contain a massive number of reviews. Thus, applying these methods on large-scale datasets may produce inefficient results. Furthermore, these existing methods extract huge number of aspects, most of which are not relevant to the domain of interest. But, on other hand, some of the infrequent relevant aspects are excluded during the extraction process. These limitations negatively affect the performance of the ABSA process. This article, therefore, aims to overcome such limitations by proposing an efficient approach that is suitable for real large-scale unlabeled datasets. The proposed approach is a combination of hybridizing a frequency-based approach (word level) and a syntactic-relation based approach (sentence level). It was enhanced further with a semantic similarity-based approach to extract aspects that are relevant to the domain, even terms (related to the aspects) are not frequently mentioned in the reviews. The extracted aspects according to the proposed approach are used to generate a total review sentiment score after estimating the weight and the rating of each extracted aspect mentioned in the review. The assignment of the weight of each extracted aspect is calculated based on a modified TF-IDF weighting scheme and the assignment of the aspect rating is calculated based on a domain-specific lexicon. Effectiveness of the extracted aspects is evaluated against two baselines available from existing literature: fixed aspect and extracted aspects. Evaluation was also performed by using a general lexicon and a domain-specific lexicon. Results in terms of F-measure and accuracy on Amazon and Yelp datasets show that the extracted aspects using the proposed approach with the domain-specific lexicon outperformed all the baselines.

Optimal Pricing for Peer-to-Peer Sharing With Network Externalities

In this paper, we analyse how a peer-to-peer sharing platform should price its service to maximize profit, when user participation increases the value of the service to others by causing positive externalities. Modelling the service as an excludable public good, we propose a bounded utility model to capture many infrastructure sharing applications with bounded network value, in which complete coverage generates finite user valuation (e.g., WiFi or hotspot). Unbounded utility models are used to capture the large-scale user interactions in social media, where the network value follows Metcalfe's or Zipf's law. For these utility models, we analyze the optimal pricing schemes in the case of heterogeneous users under complete and incomplete information of users' service valuations. We propose the concept of `price of information' (PoI) to characterize the profit loss due to lack of information, and present asymptotic PoI bounds for different utility models. We also show that the difficult-to-implement differentiated pricing scheme, which is optimal under incomplete user information, can be replaced by a simple uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of `pure' service users who do not contribute to the public good, and show that the platform may charge zero price to the original group of users in order to attract this pure user group.

Context-Aware Group Buying in Ultra-Dense Small Cell Networks: Unity Is Strength

The ultra-dense small cell networks (SCNs) have been regarded as a promising technology to solve the data traffic explosion in future. However, the complicated relationships among large scale users and cells practically demand a cooperative grouping mechanism to account for the shortage of resources in SCNs. Then, a group buying market mechanism provides a win-win situation and an effective resource allocation in the view of economics. With additional decision information being provided, the context awareness enhances and improves the group buying mechanism. In this article, we propose a Context-Aware Group Buying (CAGB) mechanism to allocate the resources in ultra-dense SCNs. The feasibility, necessity, and effectiveness of CAGB are analyzed first. Then, we introduce the group buying mechanism and some common context awareness. The relationship between context awareness and group buying is also analyzed. Some important technologies in SCNs are discussed in the view of CAGB, such as load balancing, spectrum management, and cooperative caching. Then, the graphical coalition formation games (CFGs) of CAGB are also presented to match the complicated network topologies in SCNs. Two CAGB-based use cases about spectrum market and cooperative caching are presented. Finally, future research issues about context awareness, grouping mechanism, and buying mechanism are discussed.

Understanding Municipal Water Use and Data Availability: a Case Study across North Dakota, USA

Tremendous variation exists in how cities, states, countries, and other jurisdictional areas classify water use and consumption. Additionally, water conservation measures have become more common in the past few decades, but little is known about how these conservation measures are implemented across the landscape. The goal of this study is to increase the breadth of knowledge regarding municipal water-use data: retention, storage, availability, and delving into water conservation measures, along with the impact of shale oil extraction on municipal water use in the Bakken region. Two questionnaires were developed and administered to all municipalities in the state of North Dakota, USA with populations greater than 1000 residents, and between 500 and 1000 residents in the Bakken region (total number (n) =59) willing to participate. Results indicate water conservation measures are inconsistent across the landscape, and data categorization at the municipal level is minimal which limits the analysis, interpretations, and projections that can be made from the data. Additionally, the Bakken region is different from the rest of the state in how they acquire water and sell water to other large scale users. Results from this study will aid water use professionals worldwide by providing valuable information on water-use data, access, storage, and impacts of large water users, such as shale oil extraction, on municipal water. This information will also help researchers to understand availability of data for making comparisons across different areas, assessing trends, and making water use projections.

Latent Question Interpretation Through Variational Adaptation

Most artificial neural network models for question-answering rely on complex attention mechanisms. These techniques demonstrate high performance on existing datasets; however, they are limited in their ability to capture natural language variability, and to generate diverse relevant answers. To address this limitation, we propose a model that learns multiple interpretations of a given question. This diversity is ensured by our interpretation policy module which automatically adapts the parameters of a question-answering model with respect to a discrete latent variable. This variable follows the distribution of interpretations learned by the interpretation policy through a semi-supervised variational inference framework. To boost the performance further, the resulting policy is fine-tuned using the rewards from the answer accuracy with a policy gradient. We demonstrate the relevance and efficiency of our model through a large panel of experiments. Qualitative results, in particular, underline the ability of the proposed architecture to discover multiple interpretations of a question. When tested using the Stanford Question Answering Dataset 1.1, our model outperforms the baseline methods in finding multiple and diverse answers. To assess our strategy from a human standpoint, we also conduct a large-scale user study. This study highlights the ability of our network to produce diverse and coherent answers compared to existing approaches. Our Pytorch implementation is available as open source. 1 1 github.com/parshakova/APIP .

User-opinion mining for mobile library apps in China: exploring user improvement needs

PurposeTo further provide some insight into mobile library (m-library) applications (apps) user needs and help libraries or app providers improve the service quality, the purpose of this paper is to explore all the types of user improvement needs and to discover which need is the most important based on user results.Design/methodology/approachData were collected from more than 27,000 m-library app users from 16 provinces and autonomous regions in China. Text analysis using latent Dirichlet allocation and Word2Vec was carried out by text preprocessing. Furthermore, a visual presentation was conducted through pyLDAvis and word cloud. Finally, combined with expert opinions, the results were summarized to find the different types of needs.FindingsThere are three different types of needs for improvement: needs of function, needs of technology and needs of experience. These types can be further divided into six subtypes: richness of function, feasibility of function, easiness of technology, stableness of technology, optimization of experience and customization of experience. Besides the richness of function, the feasibility of function has received the most attention from users.Originality/valueMost studies on m-library user needs have only focused on a method of quantitative research based on questionnaire surveys. This study, however, is the first to apply text mining methods for large-scale user opinion texts, which place more focus on user needs and inspire libraries and app providers to further improve their services.

Local low-rank Hawkes processes for modeling temporal user–item interactions

Hawkes processes have become very popular in modeling multiple recurrent user–item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user–item pair as an independent Hawkes process is ineffective since the prediction accuracy of future event occurrences for users and items with few observed interactions is low. On the other hand, multivariate Hawkes processes (MHPs) can be used to handle multi-dimensional random processes where different dimensions are correlated with each other. However, an MHP either fails to describe the correct mutual influence between dimensions or become computational inhibitive in most real-world events involving a large collection of users and items. To tackle this challenge, we propose local low-rank Hawkes processes to model large-scale user–item interactions, which efficiently captures the correlations of Hawkes processes in different dimensions. In addition, we design an efficient convex optimization algorithm to estimate model parameters and present a parallel algorithm to further increase the computation efficiency. Extensive experiments on real-world datasets demonstrate the performance improvements of our model in comparison with the state of the art.

Self-adaptive load-balancing strategy based on a time series pattern for concurrent user access on Web map service

Load-balancing strategies address challenges stemming from intensive access and heavy communication traffic on a Web map service platform (WMSP) by collecting workload-related information from cluster-based servers, and distributing tasks to minimize the consumption of computational and caching resources. However, intensive user access has time series patterns that create temporal periodic variations that can be exploited to improve the performance of the WMSP. In this paper, we propose a variable feedback strategy based on time series variations in the intensity of user access to increase the efficiency and reliability of workload feedback with little resource consumption. A task distribution strategy, based on the expected values of the arrival rate that match real-time conditions in workload feedback periods and the real-time processing capability of each cluster-based server, is devised simply and accurately by an association strategy for workload and service rate that supports services insensitive to massive numbers of concurrent access requests. The results of experiments show that the proposed strategy provides quick responses and high throughput for large-scale user access. It implements efficient load balancing for service resource utilization, and can thus improve the stability and capacity of the WMSP server using heterogeneous back-end cloud cluster-based servers.

Design, Large-Scale Usage Testing, and Important Metrics for Augmented Reality Gaming Applications

Augmented Reality (AR) offers the possibility to enrich the real world with digital mediated content, increasing in this way the quality of many everyday experiences. While in some research areas such as cultural heritage, tourism, or medicine there is a strong technological investment, AR for game purposes struggles to become a widespread commercial application. In this article, a novel framework for AR kid games is proposed, already developed by the authors for other AR applications such as Cultural Heritage and Arts. In particular, the framework includes different layers such as the development of a series of AR kid puzzle games in an intermediate structure which can be used as a standard for different applications development, the development of a smart configuration tool, together with general guidelines and long-life usage tests and metrics. The proposed application is designed for augmenting the puzzle experience, but can be easily extended to other AR gaming applications. Once the user has assembled the real puzzle, AR functionality within the mobile application can be unlocked, bringing to life puzzle characters, creating a seamless game that merges AR interactions with the puzzle reality. The main goals and benefits of this framework can be seen in the development of a novel set of AR tests and metrics in the pre-release phase (in order to help the commercial launch and developers), and in the release phase by introducing the measures for long-life app optimization, usage tests and hint on final users together with a measure to design policy, providing a method for automatic testing of quality and popularity improvements. Moreover, smart configuration tools, as part of the general framework, enabling multi-app and eventually also multi-user development, have been proposed, facilitating the serialization of the applications. Results were obtained from a large-scale user test with about 4 million users on a set of eight gaming applications, providing the scientific community a workflow for implicit quantitative analysis in AR gaming. Different data analytics developed on the data collected by the framework prove that the proposed approach is affordable and reliable for long-life testing and optimization.

SNUSE: A secure computation approach for large-scale user re-enrollment in biometric authentication systems

Abstract Recent years have witnessed an increasing demand for biometrics based identification, authentication and access control (BIA) systems, which offer convenience, ease of use, and (in some cases) improved security. In contrast to other methods, such as passwords or pins, BIA systems face new unique challenges; chiefly among them is ensuring long-term confidentiality of biometric data stored in backends, as such data has to be secured for the lifetime of an individual. Cryptographic approaches such as Fuzzy Extractors (FE) and Fuzzy Vaults (FV) have been developed to address this challenge. FE/FV do not require storing any biometric data in backends, and instead generate and store helper data that enables BIA when a new biometric reading is supplied. Security of FE/FV ensures that an adversary obtaining such helper data cannot (efficiently) learn the biometric. Relying on such cryptographic approaches raises the following question: what happens when helper data is lost or destroyed (e.g., due to a failure, or malicious activity), or when new helper data has to be generated (e.g., in response to a breach or to update the system)? Requiring a large number of users to physically re-enroll is impractical, and the literature falls short of addressing this problem. In this paper we develop SNUSE , a secure computation based approach for non-interactive re-enrollment of a large number of users in BIA systems. We prototype SNUSE to illustrate its feasibility, and evaluate its performance and accuracy on two biometric modalities, fingerprints and iris scans. Our results show that thousands of users can be securely re-enrolled in seconds without affecting the accuracy of the system.

An Ethic of Constraint: Citizens, Sea-Level Rise Viewers, and the Limits of Agency

The design of online interactive visualizations is an ongoing area of research within technical communication, with recent work focusing on visualizations in risk-based contexts. This article shares the results of a large-scale user experience study on a popular interactive sea-level rise viewer aimed at facilitating decision making for individual users in coastal communities. Using this viewer, participants performed three major tasks related to individual property, community impacts, and future projections and gave feedback on the design, use value, and functionality of the tool. The participants were assessed on their ability to complete the three major tasks. The author discusses the implications of these results on the continued design of interactive risk visualizations and argues for a vision of user agency that is more constrained within the larger ethical paradigms of environmental communication.

Whose posts to read: Finding social sensors for effective information acquisition

In the era of big data, it is extremely challenging to decide what information to receive and filter out in order to effectively acquire high-quality information, particularly in social media where large-scale User Generated Contents (UGC) is widely and quickly disseminated. Considering that each individual user in social network can take actions to drive the process of information diffusion, it is naturally appealing to aggregate spreading information effectively at the individual level by regarding each user as a social sensor. Along this line, in this paper, we propose a framework for effective information acquisition in social media. To be more specific, we introduce a novel measurement, the preference-based Detection Ability to evaluate the ability of social sensors to detect diffusing events, and the problem of effective information acquisition is then reduced to achieving social sensing maximization through discovering valid social sensors. In pursuit of social sensing maximization, we propose two algorithms to resolve the longstanding problems in traditional greedy methods from the perspectives of efficiency and performance. On the one hand, we propose an efficient algorithm termed LeCELF, which resolves the redundant re-evaluations in the traditional Cost-Effective Lazy Forward (CELF) algorithm. On the other hand, we observe the participation paradox phenomenon in the social sensing network, and proceed to propose a randomized selection-based algorithm called FRIENDOM to choose social sensors to improve the effectiveness of information acquisition. Experiments on a disease spreading network and real-world microblog datasets have validated that LeCELF greatly reduces the running time, whereas FRIENDOM achieves a better detection performance. The proposed framework and corresponding algorithms can be applicable in many other settings in resolving information overload problems.