Jon Crowcroft

Pranvera Kortoçi, Yilei Liang, Pengyuan Zhou et al.

Mobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications. Traditional ML techniques have shifted toward new paradigms such as federated (FL) and split learning (SL) to improve the protection of user's data privacy. However, these paradigms often rely on server(s) located in the edge or cloud to train computationally-heavy parts of a ML model to avoid draining the limited resource on client devices, resulting in exposing device data to such third parties. This work proposes an alternative approach to train computationally-heavy ML models in user's devices themselves, where corresponding device data resides. Specifically, we focus on GANs (generative adversarial networks) and leverage their inherent privacy-preserving attribute. We train the discriminative part of a GAN with raw data on user's devices, whereas the generative model is trained remotely (e.g., server) for which there is no need to access sensor true data. Moreover, our approach ensures that the computational load of training the discriminative model is shared among user's devices-proportional to their computation capabilities-by means of SL. We implement our proposed collaborative training scheme of a computationally-heavy GAN model in real resource-constrained devices. The results show that our system preserves data privacy, keeps a short training time, and yields same accuracy of model training in unconstrained devices (e.g., cloud). Our code can be found on https://github.com/YukariSonz/FSL-GAN

Muhammad Bilal, Jon Crowcroft, Xiaolong Xu et al.

Packet networks are controlled dynamical systems with discontinuities, delayed observations, and partial state information. Adaptive or learning-driven proposers can improve performance, but an unsafe proposal may still cause starvation, tail-delay spikes, or unstable queue behaviour. This paper treats packet-network control as an executed-action certification problem. A certified operator sits between any proposer and the dataplane. At each control tick, the proposer emits an arbitrary candidate action $\tilde u(t)$. The operator either projects it to an executable action $u(t)$ that satisfies a configuration-compiled certificate, or reports INFEASIBLE and executes an always-defined fallback with quantified slack. The certificate also exports an auditable envelope $\bar z(t)$ for downstream composition. The guarantees are conditional and explicit. They apply on ticks where the operator reports CERTIFIED, the declared arrival envelope and backlog bound are valid, and the platform realises the assumed service lower bound. Under these conditions, one mechanism covers backlog caps, service floors, mitigation caps, Foster--Lyapunov drift constraints, and compositional envelope contracts. We prove operator-level safety, feed-forward compositional safety and stability using exported envelopes, and a cyclic closure result under a small-gain condition. We also define breach and infeasibility semantics, discuss calibration of the service-tracking factor that links certified targets to realised scheduler behaviour, and evaluate the design under delayed telemetry, delayed actuation, weak proposers, envelope mismatch, overload, and millisecond-scale certification. The present evaluation validates the certified execution boundary in a byte-level closed-loop backend; deployment-level scheduler tracking is left to future Linux or hardware experiments.

Muhammad Bilal, Jon Crowcroft, Ruizhi Wang et al.

Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing. In both NetOps and AIOps, this shift is changing how tasks are managed. Agent-based operations work as workflows, from gathering evidence to taking action, following permissions, policies, and checks, and providing rollback options when necessary. This is crucial because operational decisions can have instant impacts. To make the argument concrete, we organise the relevant literature around the hierarchy of autonomy, tool scope, evidence traces, and assurance contracts. These contracts define what an agent may observe, propose, and execute. They also define the checks that must pass before any action is allowed. A consistent pattern appears across work on telemetry query recommendation, diagnosis, root-cause analysis, configuration synthesis, change planning, and limited self-healing. Operational reliability does not come chiefly from the model itself. It depends on the machinery around the model. We also argue that evaluation should go beyond static question answering. Agentic NetOps and AIOps systems require workflow-centred evaluation, including trace quality, bounded tool use, safe proposal generation, replay in sandboxed environments, and canary trials with rollback-aware scoring. Without these measures, a system may appear robust yet remain too fragile. Finally, we examine security, privacy, and governance risks that become acute when agents sit close to operational control surfaces. Taken together, the survey concludes that progress in intelligent NetOps and AIOps will depend on treating autonomy as a constrained operational control problem, whose outputs must be reliable, auditable, and securely deployable.

Stefan Kambiz Behfar, Jon Crowcroft

This study addresses the security challenges associated with the current internet transformations, specifically focusing on emerging technologies such as blockchain and decentralized storage. It also investigates the role of Web3 applications in shaping the future of the internet. The primary objective is to propose a novel design for 'smart certificates,' which are digital certificates that can be programmatically enforced. Utilizing such certificates, an enterprise can better protect itself from cyberattacks and ensure the security of its data and systems. Web3 recent security solutions by companies and projects like Certik, Forta, Slither, and Securify are the equivalent of code scanning tool that were originally developed for Web1 and Web2 applications, and definitely not like certificates to help enterprises feel safe against cyberthreats. We aim to improve the resilience of enterprises' digital infrastructure by building on top of Web3 application and put methodologies in place for vulnerability analysis and attack correlation, focusing on architecture of different layers, Wallet/Client, Application and Smart Contract, where specific components are provided to identify and predict threats and risks. Furthermore, Certificate Transparency is used for enhancing the security, trustworthiness and decentralized management of the certificates, and detecting misuses, compromises, and malfeasances.

Stefan Kambiz Behfar, Jon Crowcroft

The rapid evolution of the Ethereum network necessitates sophisticated techniques to ensure its robustness against potential threats and to maintain transparency. While Graph Neural Networks (GNNs) have pioneered anomaly detection in such platforms, capturing the intricacies of both spatial and temporal transactional patterns has remained a challenge. This study presents a fusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) enhanced by probabilistic sampling to bridge this gap. Our approach, unlike traditional GCNs, leverages the strengths of TRW to discern complex temporal sequences in Ethereum transactions, thereby providing a more nuanced transaction anomaly detection mechanism. Preliminary evaluations demonstrate that our TRW-GCN framework substantially advances the performance metrics over conventional GCNs in detecting anomalies and transaction bursts. This research not only underscores the potential of temporal cues in Ethereum transactional data but also offers a scalable and effective methodology for ensuring the security and transparency of decentralized platforms. By harnessing both spatial relationships and time-based transactional sequences as node features, our model introduces an additional layer of granularity, making the detection process more robust and less prone to false positives. This work lays the foundation for future research aimed at optimizing and enhancing the transparency of blockchain technologies, and serves as a testament to the significance of considering both time and space dimensions in the ever-evolving landscape of the decentralized platforms.

Jingjing Zheng, Xin Yuan, Kai Li et al.

Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in federated learning. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual format. The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models. To address the risk of misclassifications with LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several rounds of communication. Extensive tests of LayerCAM-AE on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with existing ResNet-50 and REGNETY-800MF defense models. Experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and test accuracy in FL, surpassing the performance of both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE

Vasilios Mavroudis, Chris Hicks, Jon Crowcroft

In developing regions a substantial number of users rely on legacy and ultra-low-cost mobile devices. Unfortunately, many of these devices are not equipped to run the standard authentication or identity apps that are available for smartphones. Increasingly, apps that display Quick Response (QR) codes are being used to communicate personal credentials (e.g., Covid-19 vaccination certificates). This paper describes a novel interface for QR code credentials that is compatible with legacy mobile devices. Our solution, which we have released under open source licensing, allows Web Application Enabled legacy mobile devices to load and display standard QR codes. This technique makes modern identity platforms available to previously excluded and economically disadvantaged populations.

David Butler, Chris Hicks, James Bell et al.

In the fight against Covid-19, many governments and businesses are in the process of evaluating, trialling and even implementing so-called immunity passports. Also known as antibody or health certificates, there is a clear demand for any technology that could allow people to return to work and other crowded places without placing others at risk. One of the major criticisms of such systems is that they could be misused to unfairly discriminate against those without immunity, allowing the formation of an `immuno-privileged' class of people. In this work we are motivated to explore an alternative technical solution that is non-discriminatory by design. In particular we propose health tokens -- randomised health certificates which, using methods from differential privacy, allow individual test results to be randomised whilst still allowing useful aggregate risk estimates to be calculated. We show that health tokens could mitigate immunity-based discrimination whilst still presenting a viable mechanism for estimating the collective transmission risk posed by small groups of users. We evaluate the viability of our approach in the context of identity-free and identity-binding use cases and then consider a number of possible attacks. Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0.03 on average and thus the aggregated results can be useful in a number of identity-free contexts. Finally, we present the results of our open-source prototype which demonstrates the practicality of our solution.

Stefan Kambiz Behfar, Jon Crowcroft

Blockchain technology has revolutionized the way information is propagated in decentralized networks. Ethereum plays a pivotal role in facilitating smart contracts and decentralized applications. Understanding information propagation dynamics in Ethereum is crucial for ensuring network efficiency, security, and scalability. In this study, we propose an innovative approach that utilizes Graph Convolutional Networks (GCNs) to analyze the information propagation patterns in the Ethereum network. The first phase of our research involves data collection from the Ethereum blockchain, consisting of blocks, transactions, and node degrees. We construct a transaction graph representation using adjacency matrices to capture the node embeddings; while our major contribution is to develop a combined Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability. It learns the best actions to take in various network states, ultimately leading to improved network efficiency, throughput, and optimize gas limits for block processing. In the experimental evaluation, we analyze the performance of our model on a large-scale Ethereum dataset. We investigate effectively aggregating information from neighboring nodes capturing graph structure and updating node embeddings using GCN with the objective of transaction pattern prediction, accounting for varying network loads and number of blocks. Not only we design a gas limit optimization model and provide the algorithm, but also to address scalability, we demonstrate the use and implementation of sparse matrices in GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance. It effectively propagates information across the network, optimizing gas limits for block processing and improving network efficiency.

Quanxi Zhou, Wencan Mao, Yilei Liang et al.

The widespread application of wireless communication technology has promoted the development of smart agriculture, where unmanned aerial vehicles (UAVs) play a multifunctional role. We target a multi-UAV smart agriculture system where UAVs cooperatively perform data collection, image acquisition, and communication tasks. In this context, we model a Markov decision process to solve the multi-UAV trajectory planning problem. Moreover, we propose a novel Elite Imitation Actor-Shared Ensemble Critic (EIA-SEC) framework, where agents adaptively learn from the elite agent to reduce trial-and-error costs, and a shared ensemble critic collaborates with each agent's local critic to ensure unbiased objective value estimates and prevent overestimation. Experimental results demonstrate that EIA-SEC outperforms state-of-the-art baselines in terms of reward performance, training stability, and convergence speed.

Zeynep Engin, Jon Crowcroft, David Hand et al.

As artificial intelligence transforms public sector operations, governments struggle to integrate technological innovations into coherent systems for effective service delivery. This paper introduces the Algorithmic State Architecture (ASA), a novel four-layer framework conceptualising how Digital Public Infrastructure, Data-for-Policy, Algorithmic Government/Governance, and GovTech interact as an integrated system in AI-enabled states. Unlike approaches that treat these as parallel developments, ASA positions them as interdependent layers with specific enabling relationships and feedback mechanisms. Through comparative analysis of implementations in Estonia, Singapore, India, and the UK, we demonstrate how foundational digital infrastructure enables systematic data collection, which powers algorithmic decision-making processes, ultimately manifesting in user-facing services. Our analysis reveals that successful implementations require balanced development across all layers, with particular attention to integration mechanisms between them. The framework contributes to both theory and practice by bridging previously disconnected domains of digital government research, identifying critical dependencies that influence implementation success, and providing a structured approach for analysing the maturity and development pathways of AI-enabled government systems.

Omer Tariq, Muhammad Bilal, Muneeb Ul Hassan et al.

Data-driven inertial sequence learning has revolutionized navigation in GPS-denied environments, offering superior odometric resolution compared to traditional Bayesian methods. However, deep learning-based inertial tracking systems remain vulnerable to privacy breaches that can expose sensitive training data. \hl{Existing differential privacy solutions often compromise model performance by introducing excessive noise, particularly in high-frequency inertial measurements.} In this article, we propose ConvXformer, a hybrid architecture that fuses ConvNeXt blocks with Transformer encoders in a hierarchical structure for robust inertial navigation. We propose an efficient differential privacy mechanism incorporating adaptive gradient clipping and gradient-aligned noise injection (GANI) to protect sensitive information while ensuring model performance. Our framework leverages truncated singular value decomposition for gradient processing, enabling precise control over the privacy-utility trade-off. Comprehensive performance evaluations on benchmark datasets (OxIOD, RIDI, RoNIN) demonstrate that ConvXformer surpasses state-of-the-art methods, achieving more than 40% improvement in positioning accuracy while ensuring $(ε,δ)$-differential privacy guarantees. To validate real-world performance, we introduce the Mech-IO dataset, collected from the mechanical engineering building at KAIST, where intense magnetic fields from industrial equipment induce significant sensor perturbations. This demonstrated robustness under severe environmental distortions makes our framework well-suited for secure and intelligent navigation in cyber-physical systems.

Jon Crowcroft, Hamed Haddadi, Arthur Gervais et al.

We introduce Proof-of-Steak (PoS) as a fundamental net-zero block generation technique, often accompanied by Non-Frangipane Tokens. Genesis cut is gradually heated and minted (using the appropriate sauce), enabling the miners to redirect the extracted gold and the dissipated heat into the furnace, hence enabling the first fully-circular economy ever built using blockchain technology, utilising tamper-evident steak haché. In this paper we present the basic ingredients for building Proof-of-Steak, assessing its global impact, and opportunities to save the world and beyond!

Carlos Molina-Jimenez, Ioannis Sfyrakis, Linmao Song et al.

The hype about Bitcoin has overrated the potential of smart contracts deployed on-blockchains (on-chains) and underrated the potential of smart contracts deployed on-Trusted Third Parties (on-TTPs). As a result, current research and development in this field is focused mainly on smart contract applications that use on-chain smart contracts. We argue that there is a large class of smart contract applications where on-TTP smart contracts are a better alternative. The problem with on-chain smart contracts is that the fully decentralised model and indelible append-only data model followed by blockchains introduces several engineering problems that are hard to solve. In these situations, the inclusion of a TTP (assuming that the application can tolerate its inconveniences) instead of a blockchain to host the smart contract simplifies the problems and offers pragmatic solutions. The intention and contribution of this paper is to shed some light on this issue. We use a hypothetical use case of a car insurance application to illustrate technical problems that are easier to solve with on-TTP smart contracts than with on-chain smart contracts.

Carlos Molina-Jimenez, Hazem Danny Al Nakib, Linmao Song et al.

We suggest the re-introduction of bartering to create a cryptocurrencyless, currencyless, and moneyless economy segment. We contend that a barter economy would benefit enterprises, individuals, governments and societies. For instance, the availability of an online peer-to-peer barter marketplace would convert ordinary individuals into potential traders of both tangible and digital items and services. For example, they will be able to barter files and data that they collect. Equally motivating, they will be able to barter and re-introduce to the economy items that they no longer need such as, books, garden tools, and bikes which are normally kept and wasted in garages and sheds. We argue that most of the pieces of technology needed for building a barter system are now available, including blockchains, smart contracts, cryptography, secure multiparty computations and fair exchange protocols. However, additional research is needed to refine and integrate the pieces together. We discuss potential research directions.

Chris Hicks, David Butler, Carsten Maple et al.

COVID-19 has resulted in unprecedented social distancing policies being enforced worldwide. As governments seek to restore their economies, open workplaces and permit travel there is a demand for technologies that may alleviate the requirement for social distancing whilst also protecting healthcare services. In this work we explore the controversial technique of so-called immunity passports and present SecureABC: a decentralised, privacy-preserving protocol for issuing and verifying antibody certificates. We consider the implications of antibody certificate systems, develop a set of risk-minimising principles and a security framework for their evaluation, and show that these may be satisfied in practice. Finally, we also develop two additional protocols that minimise individual discrimination but which still allow for collective transmission risk to be estimated. We use these two protocols to illustrate the utility-privacy trade-offs of antibody certificates and their alternatives.

James Bell, David Butler, Chris Hicks et al.

Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns, however, regarding the privacy and security of users data when using these apps. These concerns are paramount for users who contract the virus, as they are generally required to release all their data. Motivated by the need to protect users privacy we propose two solutions to this problem. Our first solution builds on current "message based" methods and our second leverages ideas from secret sharing and additively homomorphic encryption.

Dianlei Xu, Tong Li, Yong Li et al.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. 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 paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely 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 analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft et al.

We present a federated, asynchronous, and $(\varepsilon, δ)$-differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its $r$ leading principal components when only $\mathcal{O}(dr)$ memory is available with $d$ being the dimensionality of the data. We guarantee differential privacy via an input-perturbation scheme in which the covariance matrix of a dataset $\mathbf{X} \in \mathbb{R}^{d \times n}$ is perturbed with a non-symmetric random Gaussian matrix with variance in $\mathcal{O}\left(\left(\frac{d}{n}\right)^2 \log d \right)$, thus improving upon the state-of-the-art. Furthermore, contrary to previous federated or distributed algorithms for PCA, our algorithm is also invariant to permutations in the incoming data, which provides robustness against straggler or failed nodes. Numerical simulations show that, while using limited-memory, our algorithm exhibits performance that closely matches or outperforms traditional non-federated algorithms, and in the absence of communication latency, it exhibits attractive horizontal scalability.

Fakhar ul Hassan, Anwaar Ali, Mohamed Rahouti et al.

Blockchain is challenging the status quo of the central trust infrastructure currently prevalent in the Internet towards a design principle that is underscored by decentralization, transparency, and trusted auditability. In ideal terms, blockchain advocates a decentralized, transparent, and more democratic version of the Internet. Essentially being a trusted and decentralized database, blockchain finds its applications in fields as varied as the energy sector, forestry, fisheries, mining, material recycling, air pollution monitoring, supply chain management, and their associated operations. In this paper, we present a survey of blockchain-based network applications. Our goal is to cover the evolution of blockchain-based systems that are trying to bring in a renaissance in the existing, mostly centralized, space of network applications. While re-imagining the space with blockchain, we highlight various common challenges, pitfalls, and shortcomings that can occur. Our aim is to make this work as a guiding reference manual for someone interested in shifting towards a blockchain-based solution for one's existing use case or automating one from the ground up.

Aniruddh Rao Kabbinale, Emmanouil Dimogerontakis, Mennan Selimi et al.

Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low cost Internet access with infrastructures built from routers contributed by diverse owners, while blockchain enables transparency and accountability for investments, revenue or other forms of economic compensations from sharing of network traffic, content and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means every participant can invest in resources, and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self-organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city-wide production mesh network and also in laboratory network. We quantify the performance, bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains.

Carlos Molina-Jimenez, Ioannis Sfyrakis, Ellis Solaiman et al.

Recently, decentralised (on-blockchain) platforms have emerged to complement centralised (off-blockchain) platforms for the implementation of automated, digital (smart) contracts. However, neither alternative can individually satisfy the requirements of a large class of applications. On-blockchain platforms suffer from scalability, performance, transaction costs and other limitations. Off-blockchain platforms are afflicted by drawbacks due to their dependence on single trusted third parties. We argue that in several application areas, hybrid platforms composed from the integration of on- and off-blockchain platforms are more able to support smart contracts that deliver the desired quality of service (QoS). Hybrid architectures are largely unexplored. To help cover the gap, in this paper we discuss the implementation of smart contracts on hybrid architectures. As a proof of concept, we show how a smart contract can be split and executed partially on an off-blockchain contract compliance checker and partially on the Rinkeby Ethereum network. To test the solution, we expose it to sequences of contractual operations generated mechanically by a contract validator tool.

Carlos Molina-Jimenez, Ellis Solaiman, Ioannis Sfyrakis et al.

In this paper we discuss how conventional business contracts can be converted into smart contracts---their electronic equivalents that can be used to systematically monitor and enforce contractual rights, obligations and prohibitions at run time. We explain that emerging blockchain technology is certainly a promising platform for implementing smart contracts but argue that there is a large class of applications, where blockchain is inadequate due to performance, scalability, and consistency requirements, and also due to language expressiveness and cost issues that are hard to solve. We explain that in some situations a centralised approach that does not rely on blockchain is a better alternative due to its simplicity, scalability, and performance. We suggest that in applications where decentralisation and transparency are essential, developers can advantageously combine the two approaches into hybrid solutions where some operations are enforced by enforcers deployed on--blockchains and the rest by enforcers deployed on trusted third parties.

Jianxin Zhao, Tudor Tiplea, Richard Mortier et al.

Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.

Jianxin Zhao, Richard Mortier, Jon Crowcroft et al.

Machine Learning (ML) techniques, such as Neural Network, are widely used in today's applications. However, there is still a big gap between the current ML systems and users' requirements. ML systems focus on improving the performance of models in training, while individual users cares more about response time and expressiveness of the tool. Many existing research and product begin to move computation towards edge devices. Based on the numerical computing system Owl, we propose to build the Zoo system to support construction, compose, and deployment of ML models on edge and local devices.

Richard Mortier, Hamed Haddadi, Tristan Henderson et al.

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.

Jisun An, Daniele Quercia, Jon Crowcroft

To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.