Large-scale User Research Articles

The Automated Crystallography Pipelines at the EMBL HTX Facility in Grenoble

EMBL Grenoble operates the High Throughput Crystallization Laboratory (HTX Lab), a large-scale user facility offering high throughput crystallography services to users worldwide. The HTX lab has a strong focus in the development of new methods in macromolecular crystallography. Through the combination of a high throughput crystallization platform, the CrystalDirect technology for fully automated crystal mounting and cryocooling and the CRIMS software we have developed fully automated pipelines for macromolecular crystallography that can be remotely operated over the internet. These include a protein-to-structure pipeline for the determination of new structures, a pipeline for the rapid characterization of protein-ligand complexes in support of medicinal chemistry, and a large-scale, automated fragment screening pipeline enabling evaluation of libraries of over 1000 fragments. Here we describe how to access and use these resources.

Inviting and/or inclusive: A study of the Flemish public library sector

Comparing the results of two large-scale user surveys conducted in Flanders in 2004 and 2018 shows that the Flemish public library sector has successfully adapted to challenging circumstances, such as increasing budgetary stringency and the pervasive digitalization of society. However, it is also clear that attendance numbers have decreased in Flemish public libraries in the last two decades, especially among visitors with lower education levels, which is cause for concern. In this article, the authors present an overview of exploratory analyses that try to ascertain how many of the public libraries that took part in their study were considered to be significantly more (or less) ‘inviting’ by specific subgroups (age, gender, education level, etc.) of the visiting public. Moreover, regression analyses are presented, showing which library characteristics have a positive effect on the attendance of two under-represented groups among Flemish library users: men and the lower educated.

RECAST

With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments. However, most automated systems, when detecting and moderating toxic language, do not provide feedback to their users, let alone provide an avenue of recourse for these users to make actionable changes. We present our work, RECAST, an interactive, open-sourced web tool for visualizing these models' toxic predictions, while providing alternative suggestions for flagged toxic language. Our work also provides users with a new path of recourse when using these automated moderation tools. RECAST highlights text responsible for classifying toxicity, and allows users to interactively substitute potentially toxic phrases with neutral alternatives. We examined the effect of RECAST via two large-scale user evaluations, and found that RECAST was highly effective at helping users reduce toxicity as detected through the model. Users also gained a stronger understanding of the underlying toxicity criterion used by black-box models, enabling transparency and recourse. In addition, we found that when users focus on optimizing language for these models instead of their own judgement (which is the implied incentive and goal of deploying automated models), these models cease to be effective classifiers of toxicity compared to human annotations. This opens a discussion for how toxicity detection models work and should work, and their effect on the future of online discourse.

Social media data crowdsourcing as a new stream for environmental planning & monitoring: A review

This research focused on social media applications that had been used by large-scale users. Data might be in the form of text, image, video, each with its own data processing complexity. In this study, the researchers had performed a systematic review of literature on the development and usage of crowdsourcing data collection in recent days, with a range of techniques/methods/approaches to present meaningful data results/conclusions for environmental management. From the collected references, it is found that most papers discuss the monitoring and planning the environment through the use of social media data. An in-depth review of the literature was performed on the state-of-the-art environmental monitoring and planning and the general architecture of the data crowdsourcing system. Finally, based on the literature review, the challenge of data crowdsourcing for environmental monitoring and planning is identified and addressed.

Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities

Beamline experiments at central facilities are increasingly demanding of remote, high-throughput, and adaptive operation conditions. To accommodate such needs, new approaches must be developed that enable on-the-fly decision making for data intensive challenges. Reinforcement learning (RL) is a domain of AI that holds the potential to enable autonomous operations in a feedback loop between beamline experiments and trained agents. Here, we outline the advanced data acquisition and control software of the Bluesky suite, and demonstrate its functionality with a canonical RL problem: cartpole. We then extend these methods to efficient use of beamline resources by using RL to develop an optimal measurement strategy for samples with different scattering characteristics. The RL agents converge on the empirically optimal policy when under-constrained with time. When resource limited, the agents outperform a naive or sequential measurement strategy, often by a factor of 100%. We interface these methods directly with the data storage and provenance technologies at the National Synchrotron Light Source II, thus demonstrating the potential for RL to increase the scientific output of beamlines, and layout the framework for how to achieve this impact.

Access Control Policy Maintenance in IoT Based on Machine Learning

The rapid growth of communication networking, ubiquitous sensing, and signal processing, has promoted the development of the Internet of Things (IoT). However, the IoT is essentially dynamic and has no clearly defined network boundary, unauthorized access and data leakage may be much easier. Attribute-based access control (ABAC) can solve the problem of fine-grained access control and large-scale user dynamic expansion in complex information systems, and provides an ideal access control solution for an open network environment, which is more suitable for the dynamic access environment of IoT. However, the dynamic nature of IoT brings new challenges to access control. On the one hand, as new devices and services are continuously deployed, administrators need to manually formulate new rules, which is time-consuming and error-prone. On the other hand, as the IoT environment is continuously changing, the access policy easily becomes unsuitable for the current environment. In order to solve the above two problems, we propose a new scheme named Policy Maintenance-based machine learning (PMML), which includes two modules named Policy Generalization (PG) and Policy Evaluation (PE). After the access control model is deployed, automated PG and PE are carried out to maintain the rule set. In the PG module, we define a novel measure, resource similarity, and integrate it into policy mining so that policies could generalize among related resources. In the PE module, we introduce a quantitative method to assess rules and prune rules of low-quality. We conduct our experiments on real-world enterprise access logs from Amazon, and thoroughly analyzed the effects of different hyper-parameters on the experimental results. The experimental results have qualitatively and quantitatively shown the effectiveness of our proposed scheme.

Double Auction-Based Two-Level Resource Allocation Mechanism for Computation Offloading in Mobile Blockchain Application

It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the problem of computation offloading that requires sufficient computing resources with high utilization in large-scale users and multiprovider MEC system was investigated. A mechanism based on the combinatorial double auction, G-TRAP, is proposed in this paper to solve the above problem. In the mechanism, resources are provided both in the cloud and at the edge of the network. Mobile users compete for these resources to offload computing tasks by the rule that the edge-level resources will be allocated at first while cloud-level resources could be the supplement for the edge level. Given that the proof-of-work (PoW), the core issue of blockchain application, is resource-expensive to implement in mobile devices, we provide resource allocation service to users of blockchain application as experimental subjects. Simulation results show that the proposed mechanism for serving large-scale users in a short execution time outperforms two existing algorithms in terms of social utility and resource utilization. Consequently, our proposed system can effectively solve the intensive computation offloading problem of mobile blockchain applications.

A Novel Collective User Web Behavior Simulation Method

A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range. Existing studies almost focus on individual web behavior analysis and prediction, which cannot simulate human dynamics that widely exist in large-scale users’ behaviors. To address these issues, we propose a novel collective user web behavior simulation method, in which an algorithm for constructing a connected virtual social network is proposed, and then a collective user web behavior simulation algorithm is designed on the virtual social network. In the simulation method, a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social network. We specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the method. The experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’ web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.

Application of robust information game decision theory for risk-assessment problem in large scale users

This work suggested a novel method in order to solve uncertain problem with uncertainty on pool market tariff with respect to wind and photovoltaic generations as well as storage units. Aiming this regard, information gap decision theory method is applied for solving the considered problem. This way, 3 major models are considered into account for big industrial consumers including: risk-taker, risk-averse, and risk-neutral models. Efficiency of suggested methods are studied by exertion of proposed method on big consumer risk via various pool yield tariffs. In addition, appraised obtained results prove the efficiency of demand response program for decreasing the over expenditure of big consumer. Therefore, good impacts of demand response program caused decrease in big user expenditure.

Research on the Influence of New Media Technology on Internet Short Video Content Production under Artificial Intelligence Background

With the rapid development of the Internet and smart phone technology, a large number of short videos are shared through social platforms. Therefore, video content analysis is a very important and popular work in machine learning and artificial intelligence currently. However, it is very difficult to analyze all aspects of video content originally produced by large‐scale users. How to screen out bad and illegal content from short videos published by a large number of users, select high‐quality videos to share with other users, and improve the quality of video on the distribution platform of the entire user is a top priority. Based on this background, this paper focuses on optimizing video auditing to provide basic features for algorithm judgment, supporting original content and increasing the distribution of new content, and strengthening manual intervention combining algorithm recommendation with manual recommendation. Four major aspects of the artificial training algorithm model discuss the optimization effect of artificial intelligence on the algorithm in order to provide some guidance for the sustainable and healthy development of mobile short video.

Using Information and Communication Technologies to Facilitate Mobility Behaviour Change and Enable Mobility as a Service

Our mobility is responsible for substantial global greenhouse gas emissions and urban problems such as air pollution, usage of public spaces for infrastructure and parking, and congestion. Therefore, the transformation of our mobility towards sustainability is essential to achieve the sustainable development goals #11 (sustainable cities and communities) and #13 (climate action). Mobility as a Service (MaaS) is a core concept for this transformation; however, there are still many open questions and challenges due to its novelty and complexity. The Empirical use and Impact Analysis of MaaS (EIM) project conducts a large-scale user study during the roll-out of a MaaS offer in Switzerland to gather empirical data that help to address and answer challenges and open questions.

Sparse Trust Data Mining

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

Incentive Mechanism for Mobile Devices in Dynamic Crowd Sensing System

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.

User Identity Linkage Across Social Media via Attentive Time-Aware User Modeling

In this paper, we work towards linking users’ identities on different social media platforms by exploring the user-generated contents (UGCs). This task is non-trivial due to the following challenges. 1) As UGCs involve multiple modalities (e.g., text and image), how to accurately characterize the user account based on their heterogeneous multi-modal UGCs poses the main challenge. 2) As people tend to post similar UGCs on different social media platforms during the same period, how to effectively model the temporal post correlation is a crucial challenge. And 3) no public benchmark dataset is available to support our user identity linkage based on heterogeneous UGCs with timestamps. Towards this end, we present an attentive time-aware user identity linkage scheme, which seamlessly integrates the temporal post correlation modeling and attentive user similarity modeling. To facilitate the evaluation, we create a comprehensive large-scale user identity linkage dataset from two popular social media platforms: Instagram and Twitter. Extensive experiments have been conducted on our dataset and the results verify the effectiveness of the proposed scheme. As a residual product, we have released the dataset, codes, and parameters to facilitate other researchers.

Analysis of Online Teaching Mode and Effect of Computer Network under Large-Scale Users

Under the influence of the epidemic situation, many teachers and students choose the online teaching of computer network. We should study how to use the online teaching of computer network on a large scale. Based on the online teaching data of the school, this paper makes a comparative analysis of the functions and teaching methods of the computer network teaching platform. Through the feedback survey of teachers and students, most of them are satisfied with the online teaching of computer network, but there are also some problems. These problems are: teachers and students do not adapt to the new online teaching method; they are not familiar with the software of network platform, which makes it difficult to use; there are technologies in the teaching process, such as not fluent, unable to log in, unable to open web pages, etc Question. Through the continuous improvement of computer network teaching problems, teachers and students experience better and better, and their satisfaction is higher and higher. Generally speaking, online teaching of computer network has become an important teaching method, which will continue to be valued and applied in the post epidemic era.

Angle Aware User Cooperation for Secure Massive MIMO in Rician Fading Channel

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g., disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5,060 annotated POIs, which achieves the area of under curve (AUC) of 0.9202. As of June 18, 2020, α-Satellite has had 56,980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicate the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website.

Nanotechnology based Polymerized Solar Cells and Screen-Printing Technique for Power Generation

Energy is central to improved social and economic well-being, and is indispensable to most industrial and commercial wealth generation. It is key for improving human welfare and raising living standards. Industrialization of developing countries like India needs more and more power to meet the demand of life amenities. The global demand for energy is increasing drastically. The previous primary sources of energy like fossil fuel, coal, diesel etc which are decreasing day by day due to energy demand and there is a global warming problem also. Even hydro, thermal, biomass, solar, wind energy are not permanent solutions for the rise in demand. So there is a need for non-conventional energy sources to fill the gap in an improved version of power conversion efficiencies. This is fulfilled by organic polymerized solar cells, which have brought and gained the attention of practical large-scale users' minds to meet the energy required for work life. This paper explains nanotechnology application for energy storing and implementation of polymer technique to the plastic solar cells and also screen-printing technology in the fabrication of organic based bulk heterojunction solar cells.

The sustainability of shared mobility: Can a platform for shared rides reduce motorized traffic in cities?

Studies in several cities indicate that ridesourcing (ride-hailing) may increase traffic and congestion, given the substitution of more sustainable modes and the addition of empty kilometers. On the other hand, there is little evidence if smartphone apps that target shared rides have any influence on reducing traffic levels. We study the effects of a shared-mobility service offered by a start-up in Mexico City, Jetty, which is used by travelers to book a shared ride in a car, van or bus. A large-scale user survey was conducted to study trip characteristics, reasons for using the platform and the general travel choices of Jetty users. We calculate travel distance per trip leg, for the current choices and for the modes that riders would have chosen if the platform was not available. We find that the effect of the platform on vehicle kilometers traveled (VKT) depends on the rate of empty kilometers introduced by the fleet of vehicles, the substitution of public versus private transport modes, the occupancy rate of Jetty vehicles and assumptions on the occupancy rate of substituted modes. Following a sensitivity analysis approach for variables with unavailable data, we estimate that shared rides in cars increase VKT (in the range of 7 to 10 km/passenger), shared vans are able to decrease VKT (around −0.2 to −1.1 km/passenger), whereas buses are estimated to increase VKT (0.4 to 1.1 km/passenger), in our preferred scenarios. These results stem from the tradeoff between the effects of the occupancy rates per vehicle (larger vehicles are shared by more people) and the attractiveness of the service for car users (shared vans attract more car drivers than buses booked through Jetty). Our findings point to the relevance of shared rides in bigger vehicles such as vans as competitors to low occupancy car services for the future of mobility in cities, and to the improvement of public transportation services through the inclusion of quality attributes as provided by new shared-mobility services.

COMP: Online Control Mechanism for Profit Maximization in Privacy- Preserving Crowdsensing

As a novel sensing paradigm, crowdsensing has gained great attention due to large-scale user participation, low cost and wide data source, replacing traditional sensor based sensing in intelligent transportation, environmental monitoring, urban public management, etc. In crowdsensing, however, user privacy leakage is a common but fatal problem, where the participants in crowdsensing might not provide their data if their sensing data expose their personal private information or even lead to malicious attacks. Additionally, it is still challenging for the platform to consider the randomness of sensing task arrival, the dynamic participation of participants and the complexity of task allocation. To this end, an online control mechanism is presented to maximize the profit of platform while guaranteeing system stability and providing personalized location privacy protection. By exploiting Lyapunov optimization theory, we transform the optimization problem into a queue stability problem, decomposing it into three subproblems further. Through rigorous theoretical analysis, we prove that our time-averaged profit is approximately optimal. We also carry out extensive simulations to verify the superiority of our proposed mechanism.