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The prevalence of smartphones has promoted the popularity of mobile apps in recent years. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a Pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. The inter-diversity of app-category and individual app usage exhibits opposing trends: app-category usage assimilates while individual app usage diversifies. Nevertheless, the intra-diversity of both app-category and app usage declines over time. Also, we demonstrate the country barriers of app category usage. We further investigate how different demographics affect the evolutionary processes of app usage. Our study provides useful implications for app developers, market intermediaries, and service providers

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed., Comment: Accepted by IEEE Transactions on Big Data

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals' cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app usage. We first segment app usage traces into small time windows and then design a probabilistic topic model to infer users' cyber activities of each window. By exploring the coherence of users' activity sequences, the daily patterns of individuals are identified. Next, we recognize the common patterns across diverse groups of individuals using a hierarchical clustering algorithm. We then apply our framework on a large-scale and real-world dataset, consisting of 653,092 users with 971,818,946 usage records of 2,000 popular mobile apps. Our analysis shows that people usually obey yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. We also discover five common daily cyber activity patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. Our findings have profound implications on identifying the demographics of users and their lifestyles, habits, service requirements, and further detecting other disrupting trends such as working overtime and addiction to the game and social media.

Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However, some challenges remain, especially regarding graph construction and the time complexity of models. In this paper, we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model, we construct a multi-stream feature fusion block (MFFB) module, which includes a three-channel network and the soft-attention mechanism. The three-channel networks are graph convolutional neural network (GCN), gated recurrent unit (GRU) and fully connected neural network (FNN), which are used to extract spatial, temporal and other features, respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked, and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity.

Interaction design for Augmented Reality (AR) is gaining attention from both academia and industry. This survey discusses 260 articles (68.8% of articles published between 2015–2019) to review the field of human interaction in connected cities with emphasis on augmented reality-driven interaction. We provide an overview of Human-City Interaction and related technological approaches, followed by reviewing the latest trends of information visualization, constrained interfaces, and embodied interaction for AR headsets. We highlight under-explored issues in interface design and input techniques that warrant further research and conjecture that AR with complementary Conversational User Interfaces (CUIs) is a crucial enabler for ubiquitous interaction with immersive systems in smart cities. Our work helps researchers understand the current potential and future needs of AR in Human-City Interaction.

Publisher Copyright: © 2021 ACM. Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm∗14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.

Internet of Thing (IoT) devices are rapidly becoming an indispensable part of our life with their increasing deployment in many promising areas, including tele-health, smart city, intelligent agriculture. Understanding the mobility of IoT devices is essential to improve quality of service in IoT applications, such as route planning in logistic management, infrastructure deployment, cellular network update and congestion detection in intelligent traffic. Despite its importance, there are not many results pertaining to the mobility of IoT devices. In this article, we aim to answer three research questions: (i) what are the mobility patterns of IoT device? (ii) what are the differences between IoT device and smartphone mobility patterns? (iii) how the IoT device mobility patterns differ among device types and usage scenarios? We present a comprehensive characterization of IoT device mobility patterns from the perspective of cellular data networks, using a 36-days long signal trace, including 1.5 million IoT devices and 0.425 million smartphones, collected from a nation-wide cellular network in China. We first investigate the basic patterns of IoT devices from two perspectives: temporal and spatial characteristics. Our study finds that IoT device mobility exhibits significantly different patterns compared with smartphones in multiple aspects. For instance, IoT devices move more frequently and have larger radius of gyration. Then we explore the essential mobility of IoT devices by utilizing two models that reveal the nature of human mobility, i.e., exploration and preferential return (EPR) model and entropy based predictability model. We find that IoT devices, with few exceptions, behave totally different from human, and we further derive a new formulation to describe their movement. We also find the gap mobility predictability and predictability limit between IoT and human is not as big as people expected.

Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.

User authentication is key in user authorization on smart and personal devices. Over the years, several authentication mechanisms have been proposed: these also include behavioral-based biometrics. However, behavioral-based biometrics suffer from two issues: they are prone to degradation in performance (accuracy) over time (e.g., due to data distribution changes arising from user behavior) and the need to learn the machine learning model from scratch, when adding new users. In this paper, we propose ContAuth, a system that can enhance the robustness of behavioral-based authentication. ContAuth continuously adapts to new incoming data (data incremental learning) and is able to add new users without retraining (class incremental learning). Specifically, ContAuth combines deep learning models with online learning models to achieve learning on the fly, thereby preventing a severe drop in the accuracy between sessions (over time). To add new users, ContAuth employs class incremental learning methods. We evaluate ContAuth on multiple behavior-based user authentication modalities: breathing, gait. and EMG. Our results show that our framework can help True Positive Rate (TPR) to remain high (>85 %) compared to other methods for all the modalities except EMG (>70%) across the sessions while keeping False Positive Rates (FPR) at a minimum (0-10%). It can achieve up to 35% improvement in TPR over a traditional deep learning model. Additionally, iCaRL (an incremental learning method) enables ContAuth to allow the addition of new users by alleviating catastrophic forgetting, to a large extent. Finally, we also show that ContAuth can be deployed efficiently and effectively on device, further providing data privacy.

Steady State Motion Visual Evoked Potential (SSMVEP)-based Brain Computer Interface (BCI) is widely studied and has been used to varies of occasions on account of its good performance, mild stimulation, and free of additional training. We design a trolley control system based on SSMVEP signals and observe a phenomenon named “BCI Illiterate,” in which case some subjects present unsatisfactory performance with low classification accuracies. In order to cope with this challenging problem in real-world contexts, we introduce a deep learning (DL) method. The method allows improving the accuracies for both EEG literate and EEG illiterate. In particular, we firstly conduct SSMVEP experiments to obtain EEG signals from 10 subjects, including 5 EEG literates and 5 EEG illiterates. Then we construct a convolutional neural network with long short-term memory (CNN-LSTM) framework, which allows extracting the spectral, spatial, and temporal features of EEG signals, to realize the high classification accuracies of SSMVEP signals. The results show that DL method can reach 96.83% and 91.86% for EEG literate and EEG illiterate respectively, which are 12.68% and 31.08% higher than the results of traditional methods. These results indicate that DL method is not only suitable for EEG literate, but more importantly, can greatly improve the performance for EEG illiterate, which finally can enhance the robustness and universality of the SSMVEP-based BCI.

Influence map (IM) is often used as a decision supporting technology in game artificial intelligence (AI). However, the traditional influence map does not describe dynamic information. Some improved IM models can describe dynamic information, but not accurately enough. When an object moves, it would produce large influence in its moving direction than other directions. Therefore, the influence produce by the object to a location depends on the relation between the location and the object’s moving direction. This paper proposed a dynamic influence map model based on distance adjustment, DADIM. This model produces different influence values in different direction by adjusting the “distance” between two locations. This method can encode dynamic information into the influence map easily. Experiments show this model avoids the weakness of dynamic influence map with location prediction. Compared with other influence maps, this model could improve the performance of the game AI with time complexity being unchanged.

Many schemes have been recently advanced for storing data on multiple clouds. Distributing data over different cloud storage providers (CSPs) automatically provides users with a certain degree of information leakage control, for no single point of attack can leak all the information. However, unplanned distribution of data chunks can lead to high information disclosure even while using multiple clouds. In this paper, we study an important information leakage problem caused by unplanned data distribution in multicloud storage services. Then, we present StoreSim , an information leakage aware storage system in multicloud. StoreSim aims to store syntactically similar data on the same cloud, thus minimizing the user's information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity-preserving signatures for data chunks based on MinHash and Bloom filter, and also design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluate our scheme using two real datasets from Wikipedia and GitHub . We show that our scheme can reduce the information leakage by up to 60 percent compared to unplanned placement. Furthermore, our analysis on system attackability demonstrates that our scheme makes attacks on information more complex.

Vehicular communication applications require an efficient communication architecture for timely information delivery. Centralized, cloud-based infrastructures present latencies too high to satisfy the requirements of emergency information processing and transmission, while Vehicle-to-Vehicle communication is too variable for reliable in-time information transmission. In this paper, we present EAVVE, a novel Vehicle-to-Everything system, consisting of vehicles with and without comprehensive data processing capabilities, facilitated by edge servers co-located with roadside units. Adding computation capabilities at the edge of the network allows reducing the overall latency compared to vehicle-to-cloud and makes up for scenarios in which in-vehicle computational power is not sufficient to satisfy the service demand. To improve the offloading efficiency, we propose a decentralized algorithm for real-time task scheduling and a client/server algorithm for information filtering. We demonstrate the practical applications of EAVVE with a bandwidth-hungry, latency constrained real-life prototype system that connects vehicular vision through Augmented Reality vision. We evaluate this prototype system with real-life road tests. We complement this practical evaluation with extensive simulations based on real-world base station and vehicular traffic data to demonstrate the scalability of EAVVE and its performance in citywide scenarios. EAVVE decreases the latency by 42.6% and 78.7% compared to local and remote cloud solutions while relaxing congestion at the bottleneck by 99% with reasonable infrastructure expenditure.

Service liability interconnections among globally networked IT- and IoT-driven service organizations create potential channels for cascading service disruptions worth billions of dollars, due to modern cyber-crimes such as DDoS, APT, and ransomware attacks. A natural question that arises in this context is: What is the likelihood of a cyber-blackout? , where the latter term is defined as the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this article, we investigate this question in general as a function of service chain networks and different cyber-loss distribution types. We show somewhat surprisingly (and discuss the potential practical implications) that, following a cyber-attack, the effect of (a) a network interconnection topology and (b) a wide range of loss distributions on the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations are mostly very small. The primary rationale behind these results are attributed to degrees of heterogeneity in the revenue base among organizations and the Increasing Failure Rate property of popular (i.i.d/non-i.i.d) loss distributions, i.e., log-concave cyber-loss distributions. The result will enable risk-averse cyber-risk managers to safely infer the impact of cyber-attacks in a worst-case network and distribution oblivious setting.

Correction IEEE ACCESS Volume9 Pages14179-14180 Article Numbere abe4038 DOI10.1109/ACCESS.2021.3051825 Published 2021 In the modern era of the mobile apps (the era of surveillance capitalism - as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising - a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are 'compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.

In the traditional cloud architecture, data needs to be uploaded to the cloud for processing, bringing delays in transmission and response. Edge network emerges as the times require. Data processing on the edge nodes can reduce the delay of data transmission and improve the response speed. In recent years, the need for artificial intelligence of edge network has been proposed. However, the data of a single, individual edge node is limited and does not satisfy the conditions of machine learning. Therefore, performing edge network machine learning under the premise of data confidentiality became a research hotspot. This paper proposes a Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing (PAFLM), which can allow multiple edge nodes to achieve more efficient federated learning without sharing their private data. Compared with the traditional distributed learning, the proposed method compresses the communications between nodes and parameter server during the training process without affecting the accuracy. Moreover, it allows the node to join or quit in any process of learning, which can be suitable to the scene with highly mobile edge devices.

Topic detection technology can automatically discover new topics on the Internet. This paper investigates domain-oriented feature extraction methods, and proposes a keyword feature extraction method ITFIDF-LP, a subject word feature extraction method LDA-SLP and a topic clustering model based on vector product similarity. A novel Domain-oriented Topic Discovery based on Features Extraction and Topic Clustering (DTD-FETC) model is proposed to analyze open source web of a domain and identify emerging topics in the domain in real time. This article describes a DTD-FETC system built for cyber security domain. It filters and aggregates web for specical security threat topics such as vulnerability and malware, and helps security staff respond quickly and defends against the emerging cyber threats as early as possible. The recall rate, accuracy and F1 value results of the DTD-FETC method applied to the cyber security dataset are all above 0.99.

Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

Emotion is a complex mental state that guides people's thoughts and behaviors. The ability to recognize one's own emotions and to understand others' emotions helps people manage their personal lives and social relations more successfully. Meanwhile, mobile and wearable devices have become indispensable tools for most of the population. They provide great opportunities for building emotional intelligent applications. In this article, we introduce how mobile and wearable devices work as "emotion sensors" by leveraging their sensing, computing, and communication capabilities, which help monitor people's mental health, facilitate social interactions, and improve user experience. We first describe a general emotion sensing framework that consists of sensing, inferring, and responding steps. After reviewing the state-of-the-art systems on this topic, we summarize and discuss various sensing modalities enabled by mobile and wearable devices. We then describe the widely used inferring procedures and methods. Additionally, we present three solutions of facial expression recognition on smartphones as a case of emotion inference. Finally, we introduce emerging emotion sensing applications, and discuss challenges and opportunities.

Mobile cloud gaming requires a very low end-to-end latency. Edge computing significantly reduces network latency. However, in mobility scenarios, the user will frequently move out of the edge server's coverage area, requiring frequent migration of the game instance. This paper presents Talaria, an in-engine content synchronisation solution for unnoticeable game instance migration between edge servers. Talaria creates a minimal instance with content immediately relevant to the game experience, allowing the client to switch servers in a minimal amount of time. The remaining content is then synchronised according to priority until the game's state is coherent between both instances. Our implementation of Talaria as a Unity engine plugin reduces the game's downtime by 61% compared to one-off server migration, with an average latency below 25 ms for the server migration, and 87 ms for the entire game synchronisation.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

The ubiquity of smart devices, combined with a lack of information about data garnered by them, make privacy a significant challenge for adopting smart devices. Ensuring users can safeguard their privacy without compromising the devices’ functionality requires effective yet intuitive ways to manage personal privacy preferences. Current solutions for privacy management are severely lacking as they are ineffective in making users aware of potential privacy risks or how to mitigate them and as they offer limited support for interaction. As our first contribution, we develop a novel AR privacy management interface (PARA) that uses AR visualization to contextualize data disclosure and improve user’s perceptions of privacy threats. Besides offering support for enhancing user’s privacy perceptions, our interface supports privacy control on compatible devices through privacy-enhancing technologies. As our second contribution, we systematically study factors affecting privacy perceptions and privacy control for two device classes (smart camera and smart speaker) through a user study with N = 32 participants. Our results show that PARA’s contextualization and visualization of privacy disclosure strongly affect the participants’ privacy perceptions. For privacy control, we demonstrate that our prototype improves the participant’s capability to identify risks and provides an effective and easy-to-use mechanism for controlling privacy disclosure, in contrast to existing state-of-the-art privacy management interfaces.

Research on smart device privacy has consistently highlighted how privacy is an important concern for users, but they fail to act on their concerns. While this discrepancy between user perceptions and actions has been consistently reported, currently there is a limited understanding of why this is the case or how the situation can be ameliorated. This paper systematically studies how visualizations in privacy assistants can improve the situation, reporting on two studies that explore the users' privacy perceptions in smart device ecosystems. The first study shows that displaying device location and data type reduces the users' privacy perceptions. Participants also weigh the use of media such as online news as a source to inform users about the possible inferences. The second study analyzes participants' preferences to visualize smart device information and privacy policies using augmented reality. Through these two studies, we derive insights and guidelines on how to design effective privacy assistants and to improve user's knowledge of risks associated with data disclosure in smart home scenarios.

Augmented Reality (AR) offers new capabilities for blurring the boundaries between physical reality and digital media. However, the capabilities of integrating web contents and AR remain underexplored. This paper presents an AR web browser with an integrated context-aware AR-to-Web content recommendation service named as A2W browser, to provide continuously user-centric web browsing experiences driven by AR headsets. We implement the A2W browser on an AR headset as our demonstration application, demonstrating the features and performance of A2W framework. The A2W browser visualizes the AR-driven web contents to the user, which is suggested by the content-based filtering model in our recommendation system. In our experiments, 20 participants with the adaptive UIs and recommendation system in A2W browser achieve up to 30.69% time saving compared to smartphone conditions. Accordingly, A2W-supported web browsing on workstations facilitates the recommended information leading to 41.67% faster reaches to the target information than typical web browsing.

Many factors affect speech intelligibility in face-to-face conversations. These factors lead conversation participants to speak louder and more distinctively, exposing the content to potential eavesdroppers. To address these issues, we introduce Theophany, a privacy-preserving framework for augmenting speech. Theophany establishes ad-hoc social networks between conversation participants to exchange contextual information, improving speech intelligibility in real-time. At the core of Theophany, we develop the first privacy perception model that assesses the privacy risk of a face-to-face conversation based on its topic, location, and participants. This framework allows to develop any privacy-preserving application for face-to-face conversation. We implement the framework within a prototype system that augments the speaker's speech with real-life subtitles to overcome the loss of contextual cues brought by mask-wearing and social distancing during the COVID-19 pandemic. We evaluate Theophany through a user survey and a user study on 53 and 17 participants, respectively. Theophany's privacy predictions match the participants' privacy preferences with an accuracy of 71.26%. Users considered Theophany to be useful to protect their privacy (3.88/5), easy to use (4.71/5), and enjoyable to use (4.24/5). We also raise the question of demographic and individual differences in the design of privacy-preserving solutions.

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely RLoc, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work RLoc can greatly improve Telco location accuracy. For example, RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4 percent better than state-of-the-art.

The current explosion of video traffic compels service providers to deploy caches at edge networks. Nowadays, most caching systems store data with a high programming voltage corresponding to the largest possible ‘expiry date’, typically on the order of years, which maximizes the cache damage. However, popular videos rarely exhibit lifecycles longer than a couple of months. Consequently, the programming voltage can instead be adapted to fit the lifecycle and mitigate the cache damage accordingly. In this paper, we propose LiA-cache, a Lifecycle-Aware caching policy for online videos. LiA-cache finds both near-optimal caching retention times and cache eviction policies by optimizing traffic delivery cost and cache damage cost conjointly. We first investigate temporal patterns of video access from a real-world dataset covering 10 million online videos collected by one of the largest mobile network operators in the world. We next cluster the videos based on their access lifecycles and integrate the clustering into a general model of the caching system. Specifically, LiA-cache analyzes videos and caches them depending on their cluster label. Compared to other popular policies in real-world scenarios, LiA-cache can reduce cache damage up to 90%, while keeping a cache hit ratio close to a policy purely relying on video popularity.

Digital tools for collocated synchronous communication, like backchannels and question management systems, are increasingly used in events and education to support interaction between performer(s) and audience. Discussions in the communication tool are prone to various unexpected interruptions which violate against the communication tools' original purpose, i.e. violating performers' expected norms. We study such norm violations and related performers' coping approaches in two 15-week-long field experiments in two elementary schools. Our results demonstrate how the collocated synchronous tools form dual-channel communication environment where both physical and digital channel are in use at the same time. We show how norm violations 'spill over' from their onset in one channel to continue in the other. However, we observed performers coping approaches were primarily focused in the physical channel. This resulted in an asymmetry of presence and delayed the performers' ability to act on violations. Our findings show that the channels in dual-channel communication are tightly interwoven and must be considered as an integrated communication space instead of two co-occurring but separate channels. We provide a framework to study norm violations and discuss implications of this framework on the design of these systems.

Incremental and quantitative improvements of two-way interactions with e x tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR.

Attention-based techniques have been successfully used for rating image quality, and have been widely employed for set-based face recognition. Nevertheless, for video face recognition, where the base convolutional neural network (CNN) trained on large-scale data already provides discriminative features, fusing features with only predicted quality scores to generate representation are likely to cause duplicate sample dominant problem, and degrade performance correspondingly. To resolve the problem mentioned above, we propose a redundancy removing aggregation network (RRAN) for video face recognition. Compared with other quality-aware aggregation schemes, RRAN can take advantage of similarity information to tackle the noise introduced by redundant video frames. By leveraging metric learning, RRAN introduces a distance calibration scheme to align distance distributions of negative pairs of different video representations, which improves the accuracy under a uniform threshold. A series of experiments is conductedon multiple realistic data sets to evaluate the performance of RRAN, including YouTube Faces, IJB-A, and IJB-C. In comprehensive experiments, we demonstrate that our method can diminish the overall influence of poor quality components with large proportion in the video and further improve the overall recognition performance with individual difference. Specifically, RRAN achieves a 96.84% accuracy on YouTube Face, outperforming all existing aggregation schemes.

BACKGROUND: The present study aimed to investigate the determinant factors of survival in patients with pretreated advanced stage non-small cell lung cancer (NSCLC) who received anti-PD-1/PD-L1 therapy. METHODS: In this observational retrospective study, the clinical profiles and laboratory parameters of patients with NSCLC treated with anti-PD-1/PD-L1 therapy were consecutively collected. Lung Immune Prognostic Index (LIPI) was calculated based on the derived neutrophil-to-lymphocyte ratio (dNLR) and lactate dehydrogenase level (LDH). Modified Glasgow Prognostic Score (mGPS) was calculated based on serum C reactive protein and albumin, and tumor mutation burden (TMB) was calculated using a targeted next-generation sequencing panel based on 422 cancer-relevant genes. The primary and secondary end points were overall survival (OS) and progression-free survival (PFS), respectively. The Cox regression model was used to identify the potential determinant factors of survival benefit. Trained oncologists at Sun Yat-sen University Cancer Center followed all of the participants through visits to doctors’ offices or via telephone calls to determine their clinical status. RESULTS: Seventy-three patients were included in our study. With a median follow up time of 637 days, there was a significant difference in PFS between patients with high TMB compared to those with low TMB (3.7 vs. 2.1 months; P=0.004), while no significant difference was found in OS (14.0 vs. 16.4 months; P=0.972). Patients with a good LIPI score had a significantly longer OS compared to patients with a poor LIPI score (19.2 vs. 12.6 months; P=0.010). The median OS in patients with a good and a poor mGPS was 16.8 and 4.3 months, respectively (P=0.029). In multivariate analysis, TMB was found to be significantly associated with PFS (HR, 0.38; 95% CI: 0.21–0.69; P=0.002), while LIPI score was found to be significantly associated with OS (HR, 0.50; 95% CI: 0.28–0.89; P=0.012). CONCLUSIONS: In the present study, LIPI score was a significant determinant of OS in patients with advanced NSCLC who received ICIs; however, TMB was only associated with PFS and not associated with OS.

The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate the collection of traffic data and its interpretation towards alleviating congestion and providing better traffic light control. DRLE operates within the coverage of the edge servers and uses aggregated data from neighboring edge servers to provide city-scale traffic light control. DRLE decomposes the highly complex problem of large area control. into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. The proposed decentralized reinforcement learning algorithm running at each edge node adapts the traffic lights in real time. We conduct extensive evaluations and demonstrate the superiority of this approach over several state-of-the-art algorithms., Accepted by IEEE Transactions on Intelligent Transportation Systems

Although many children are born with congenital limb malformation, contemporary functional artificial hands are costly and are not meant to be adapted to growing hand. In this work, we develop a low cost, adaptable and personalizable system of an artificial prosthetic hand accompanied with hardware and software modules. Our solution consists of (i) a consumer grade electromyography (EMG) recording hardware, (ii) a mobile companion device empowered by deep learning classification algorithms, (iii) an cloud component for offloading computations, and (iv) mechanical 3D printed arm operated by the embedded hardware. We focus on the flexibility of the designed system making it more affordable than the alternatives. We use 3D printed materials and open-source software thus enabling the community to contribute and improve the system. In this paper, we describe the proposed system and its components and present the experiments we conducted in order to show the feasibility and applicability of our approach. Extended experimentation shows that our proposal is energy efficient and has high accuracy.

We previously demonstrated that ortho-topolin riboside (oTR) as a naturally occurring cytokinin secreted fromAfter the incubation of HL-60 cells with oTR, its effect was analyzed with cell viability assay, Wright-Giemsa staining, CD11b protein expression analysis, western blot analysis, and polymerase chain reaction.We found that oTR arrested the cell cycle at the S phase, upregulated the expression of myeloid surface marker CD11b, reduced the nuclear cytoplasmic ratio, and altered the horseshoe shape of nuclei, as evidenced by Wright-Giemsa staining. Furthermore, we found that the protein level of phosphorylated STAT3 was decreased when cells were treated with oTR, while phosphorylated STAT1 was activated. Moreover, the protein level of phosphorylated STAT3 and its upstream kinase, Janus kinase 2, were also inhibited when cells were treated with oTR after increased time. Additionally, the levels of phosphorylated SHP-1 were increased while phosphorylated SHP-2 was decreased.Collectively, our data indicate a differentiation-induced mechanism underlying the inhibition of STAT3 signaling upon treatment with oTR. Therefore, oTR may constitute a novel differentiation-induced therapeutic for use in clinical treatment of AML.Daha önceki çalışmalarımızdaHL-60 hücrelerinin oTR ile inkübasyonunu takiben hücre üzerideki etkileri hücre canlılık testleri, Wright-Giemsa boyaması, CD11b protein ekspresyon analizi, western blot analizi ve polimeraz zincir reaksiyonu ile araştırıldı.oTR’nin hücre siklusunun S fazında duraklattığını, myeloid hücre yüzey belirteçlerinden CD11b ekspresyonunun arttığını, çekirdek sitoplazma oranının azaldığını ve çekirdeğin atnalı şeklinin değiştiğini Wright-Giemsa boyası ile destekleyerek gördük. oTR ile muamele edilmiş hücrelerde fosforile STAT3 protein düzeyinin azaldığını, fosforile STAT1’in ise aktive olduğunu bulduk. Ayrıca fosforile STAT3 ve yukarı yöndeki kinaz olan Janus kinaz 2’nin, hücrelerin oTR ile inkübasyonunda artmış zamanla inhibe olduğu görüldü. Ek olarak fosforile SHP-1 düzeyleri artarken fosforile SHP-2 düzeyi azaldı.Birlikte değerlendirildiğinde, sonuçlarımız oTR ile STAT3 inhibisyonu üzerinden bir diferansiyasyon indükleme mekanizmasını işaret etmektedir. Bu nedenle, oTR AML tedavisinde yeni bir diferansiyasyon-indükleyici terapötik olarak yer alabilir.

The ever-growing popularity of space exploration has attracted many organizations and private companies to attempt increasingly challenging missions. Each of these missions bring us one step closer to the colonization of the solar system. Many initiatives have been taken to aim at providing an architecture in the so-called interplanetary (IPN) Internet to serve these upcoming missions. In this paper, we propose an evolutionary, scalable, and extensible architecture that adapted the current IPN communication challenges and the milestones of future space exploration missions. This architecture provides the foundations for the communication and navigation services to run the missions. We introduce the key elements of our architecture, from the communication spectrum and technologies to the physical elements to place in deep space, going through the communication protocols with support of delay tolerant network (DTN), autonomous operation, and software defined networks (SDN). We then analyze the implications of such architecture on data delivery over the (Jupiter → Mars → Earth) path. We finally perform a preliminary performance evaluation of the architecture to support our proposal and display the evolution of the end-to-end latency at each milestone.

Execution speed seriously bothers application developers and users for wearable devices such as Google Glass. Intensive applications like 3D games suffer from significant delays when CPU is busy. Energy is another concern when the devices are in low battery level, but users need them for urgency use. To ease such pains, one approach is to expand the computational power by cloud offloading. This paradigm works well when the available Internet access has enough bandwidth. Another way is to leverage nearby devices for computation-offloading, which is known as device-to-device (D2D) offloading. In this paper, we present Dandelion, a unified code offloading system for wearable computing. Such applications can leverage both the nearby devices and cloud for performance acceleration and energy efficiency. Dandelion is a novel generic code offloading system for wearable computing with a reference implementation on Google Glass. Dandelion includes a programmer-friendly framework based on Java annotation, a lightweight offloading service, and a runtime task scheduler to make offloading decisions. We design some wearable applications and several parallel execution benchmark methods for Dandelion performance evaluation. Extensive experiments on a testbed of Google Glass and Android phones demonstrate that Dandelion generally achieves over 5X execution speedup for local execution and can quickly recover from errors caused by network disruption.

D2D interactions are promoted both in the context of traffic and computation offloading. Applications with functionalities that are executed in parallel in more than one mobile device have been proposed while the idea of using another mobile device as a relay is going to be part of 5G. In this work, we first present the main types of D2D applications, then we discuss the characteristics of task offloading and the quality of experience in D2D ecosystems. After that, we list five popular questions regarding the ability of other users to be helpful in terms of resources, connectivity, availability, incentives, and security. To answer the listed questions, we use two mobile big data datasets, one experimental study, and arguments from our experience and the literature.

Publisher Copyright: © The author(s) 2021. Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user's booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (Pals) recommendation are mainly focused on users' historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users' historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of pals nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users' next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro Fl-score.

The sixth generation of mobile networks (6G) is expected to be deployed in the early 2030s. By this time, the density of autonomous Internet-connected machines is expected to explode up to hundreds of devices per cubic meter. These devices (1) generate voluminous multisensory data, (2) access sophisticated artificial intelligence-based services with high frequency, and (3) have widely diverse constraints in terms of latency, bandwidth, energy, and computation power. Such devices are not operated by humans, and communicate with each other and with remote servers located on the network core or edge. The wireless communications between these machines are called machine-type communications (MTC) and can either be between multiple machines that collectively collect and process multidimensional information or between machines that interact with services located on servers. Representative examples include autonomous driving, piloting crewless aerial vehicles, smart grid energy trading, and others. In this chapter, we define the following requirements for 6G, following the predicted density and heterogeneity of the autonomous connected device landscape: (1) ultra-dense wireless communication networks, (2) massive multi-access edge computing, (3) large-scale autonomous operation of devices with heterogeneous requirements and constraints. To address these requirements, 6G will enable a convergence of computing, energy, and communication for device- and application-aware communications. We discuss how 6G can achieve such convergence and highlight the future trends for MTC to ubiquitously integrate the computing landscape.

https://doi.org/10.1049/blc2.12001 Abstract Peer-to-peer (P2P) networks utilize centralized entities (trackers) to assist peers in finding and exchanging information. Although modern P2P protocols are now trackerless and their function relies on distributed hash tables (DHTs), centralized entities are still needed to build file indices (indexing) and assist users in joining DHT swarms (bootstrapping). Although the functionality of these centralized entities are limited, every peer in the network is expected to trust them to function as expected (e.g. to correctly index new files). In this work, a new approach for designing and building decentralized online applications is proposed by introducing DIBDApp. The approach combines blockchain, smart contracts and BitTorrent for building up a combined technology that permits to create decentralized applications that do not require any assistance from centralized entities. DIBDApp is a software library composed of Ethereum smart contracts and an API to the BitTorrent protocol that fully decentralizes indexing, bootstrapping and file storing. DIBDApp enables any peer to seamlessly connect to the designed smart contracts via the Web3J protocol. Extensive experimentation on the Rinkeby Ethereum testnet shows that applications built using the DIBDApp library can perform the same operations as in traditional back-end architectures with a gas cost of a few USD cents.

This work has been partially supported by the UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”, and by Academy of Finland projects, grant number 325774, 3196669, 319670, 326305, and 325570. The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices. Postprint

This paper constructs the index system and risk assessment model of information security risk in smart cities. According to the principles of integrity, pertinence, operability and completeness of index selection, and according to the key factors specified in the information security risk assessment standard, 20 risk factors are selected as evaluation indicators from five levels: smart city infrastructure, data service, information content, information management and public literacy, forming the final evaluation index system. By analyzing the requirements of smart city information security risk assessment, the common risk assessment methods are compared, and finally the decision tree algorithm is selected to build the information security risk assessment model of smart city.

Question-and-answer (Q&A) websites are one of the latest evolutions in crowdsourced knowledge aggregation. Q&A websites provide more diverse opinions, as they involve the entire community. Quora made its reputation out of enhancing the traditional Q&A model with popular aspects of social media and incites its users to provide their names, locations, and references. This model allows higher quality control � including anonymous content, but more importantly, it leads users to form communities based on other criteria (e.g. profession, city) than similar interests. In this paper, we study the interactions among Quorans to unveil how such communities emerge. We perform both quantitative and qualitative analysis on the user-generated content and relate this content to social and demographic features. We show that being anonymous significantly affects the answers' length and subjectivity. On the other hand, most of the user interactions relate to their geographic locations.

Smartphones are nowadays the dominant end-user device. As a result, they have become gateways to all users' communications, including sensitive personal data. In this paper, we present Aquilis, a privacy-preserving system for mobile platforms following the principles of contextual integrity to define the appropriateness of an information flow. Aquilis takes the form of a keyboard that reminds users of potential privacy leakages through a simple three-colour code. Aquilis considers the instantaneous privacy risk related to posting information (Local Sensitivity), the risk induced by repeating information over time (Longitudinal Sensitivity) and on different platforms (Cross-platform Sensitivity). Considering 50% of Aquilis warnings decreases the proportion of inappropriate information by up to 30%. Repeating information over time or in a broader exposure context increases the risk by 340% in a one-to-one context. We develop our own labeled privacy dataset of over 1000 input texts to evaluate Aquilis' accuracy. Aquilis significantly outperforms other state-of-the-art methods (F-1-0.76). Finally, we perform a user study with 35 highly privacy-aware participants. Aquilis privacy metric is close to users' privacy preferences (average divergence of 1.28/5). Users found Aquilis useful (4.41/5), easy to use (4.4/5), and agreed that Aquilis improves their online privacy awareness (4.04/5).

Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within 24 h using commonly available bedside and laboratory parameters taken at critical care admission. We extracted data from 2 large critical care databases (MIMIC-III and eICU-CRD). Missing variables were imputed using autoencoder. Machine learning classifiers using logistic regression and random forest were trained using 60% of the data and tested using the remaining 40% of the data. We compared the performance of logistic regression and random forest models to predict intubation in critically ill patients. After excluding patients with limitations of therapy and missing data, we included 17,616 critically ill patients in this retrospective cohort. Within 24 h of admission, 2,292 patients required intubation, whilst 15,324 patients were not intubated. Blood gas parameters (PaO2, PaCO2, HCO3−), Glasgow Coma Score, respiratory variables (respiratory rate, SpO2), temperature, age, and oxygen therapy were used to predict intubation. Random forest had AUC 0.86 (95% CI 0.85–0.87) and logistic regression had AUC 0.77 (95% CI 0.76–0.78) for intubation prediction performance. Random forest model had sensitivity of 0.88 (95% CI 0.86–0.90) and specificity of 0.66 (95% CI 0.63–0.69), with good calibration throughout the range of intubation risks. The results showed that machine learning could predict the need for intubation in critically ill patients using commonly collected bedside clinical parameters and laboratory results. It may be used in real-time to help clinicians predict the need for intubation within 24 h of intensive care unit admission.

Wearable devices collect user information about their activities and provide insights to improve their daily lifestyles. Smart health applications have achieved great success by training Machine Learning (ML) models on a large quantity of user data from wearables. However, user privacy and scalability are becoming critical challenges for training ML models in a centralized way. Federated learning (FL) is a novel ML paradigm with the goal of training high quality models while distributing training data over a large number of devices. In this demo, we present FL4W, a FL system with wearable devices enabling training a human activity recognition classifier. We also perform preliminary analytics to investigate the model performance with increasing computation of clients.

BACKGROUND Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24 h of intensive care unit (ICU) admission using only vital signs. METHODS We used data from the Medical Information Mart for Intensive Care III database and the eICU Collaborative Research Database to develop a vasopressor prediction model. We performed systematic data preprocessing using matching of cohorts, oversampling, and imputation to control for bias, class imbalance, and missing data. Bidirectional long short-term memory (Bi-LSTM), a multivariate time series model, was used to predict the need for vasopressor therapy using serial physiological data collected 21 h prior to prediction time. RESULTS Using data from 10,941 critically ill patients from 209 ICUs, our model achieved an initial area under the curve of 0.96 (95% CI 0.96-0.96) to predict the need for vasopressor therapy in 2 h within the first day of ICU admission. After matching to control class imbalance, the Bi-LSTM model had area under the curve of 0.83 (95% CI 0.82-0.83). Heart rate, respiratory rate, and mean arterial pressure contributed most to the model. CONCLUSIONS We used Bi-LSTM to develop a model to predict the need for vasopressor for critically ill patients for the first 24 h of ICU admission. With attention mechanism, respiratory rate, mean arterial pressure, and heart rate were identified as key sequential determinants of vasopressor requirements.

We propose Agora, the first blockchain-based data marketplace that enables multiple privacy-concerned parties to get compensated for contributing and exchanging data, without relying on a trusted third party during the exchange. Agora achieves data privacy, output verifiability, and atomicity of payments by leveraging cryptographic techniques, and is designed as a decentralized application via smart contracts. Particularly, data generators provide encrypted data to data brokers who use a functional secret key to learn nothing but the output of a specific, agreed upon, function over the raw data. Data consumers can purchase decrypted outputs from the brokers, accompanied by corresponding proofs of correctness. We implement a working prototype of Agora on Ethereum and experimentally evaluate its performance and deployment costs. As a core building block of Agora, we propose a new functional encryption scheme with additional public parameters that operate as a trust anchor for verifying decrypted results.