Data privacy is an increasingly important aspect of many real-world analytics tasks. Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output. Furthermore, finding the right balance between privacy and utility is often a tricky trade-off. In this work, we propose a novel approach for data privatization, which involves two steps: in the first step, it removes the sensitive information, and in the second step, it replaces this information with an independent random sample. Our method builds on adversarial representation learning which ensures strong privacy by training the model to fool an increasingly strong adversary. While previous methods only aim at obfuscating the sensitive information, we find that adding new random information in its place strengthens the provided privacy and provides better utility at any given level of privacy. The result is an approach that can provide stronger privatization on image data, and yet be preserving both the domain and the utility of the inputs, entirely independent of the downstream task. 

There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.

There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.

A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real-time, client-side service metrics for video streaming and key-value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service-specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning. 

Managing and balancing load in distributed systems remains a challenging problem in resource management, especially in networked systems where scalability concerns favour distributed and dynamic approaches. Distributed methods can also integrate well with centralised control paradigms if they provide high-level usage statistics and control interfaces for supporting and deploying centralised policy decisions. We present a general method to compute target values for an arbitrary metric on the local system state and show that autonomous rebalancing actions based on the target values can be used to reliably and robustly improve the balance for metrics based on probabilistic risk estimates. To balance the trade-off between balancing efficiency and cost, we introduce 2 methods of deriving rebalancing actuations from the computed targets that depend on parameters that directly affects the trade-off. This enables policy level control of the distributed mechanism based on collected metric statistics from network elements. Evaluation results based on cellular radio access network simulations indicate that load balancing based on probabilistic overload risk metrics provides more robust balancing solutions with fewer handovers compared to a baseline setting based on average load.

Accurately and efficiently estimating word similarities from text is fundamental in natural language processing. In this paper, we propose a fast and lightweight method for estimating similarities from streams by explicitly counting second-order co-occurrences. The method rests on the observation that words that are highly correlated with respect to such counts are also highly similar with respect to first-order co-occurrences. Using buffers of co-occurred words per word to count second-order co-occurrences, we can then estimate similarities in a single pass over data without having to do prohibitively expensive similarity calculations. We demonstrate that this approach is scalable, converges rapidly, behaves robustly under parameter changes, and that it captures word similarities on par with those given by state-of-the-art word embeddings.

Appropriately defining and efficiently calculating similarities from large data sets are often essential in data mining, both for gaining understanding of data and generating processes, and for building tractable representations. Given a set of objects and their correlations, we here rely on the premise that each object is characterized by its context, i.e. its correlations to the other objects. The similarity between two objects can then be expressed in terms of the similarity between their contexts. In this way, similarity pertains to the general notion that objects are similar if they are exchangeable in the data. We propose a scalable approach for calculating all relevant similarities among objects by relating them in a correlation graph that is transformed to a similarity graph. These graphs can express rich structural properties among objects. Specifically, we show that concepts - abstractions of objects - are constituted by groups of similar objects that can be discovered by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of fields, and will here be demonstrated in three domains: computational linguistics, music and molecular biology, where the numbers of objects and correlations range from small to very large.

The IoT paradigm proposes to connect entities intelligently with massive heterogeneous nature, which forms an ocean of devices and data whose complexity and volume are incremental with time. Different from the general big data or IoT, the data-intensive feature of IoT introduces several specific challenges, such as circumstance dynamicity and uncertainties. Hence, intelligence techniques are needed in solving the problems brought by the data intensity. Until recent, there are many different views to handle IoT data and different intelligence enablers for IoT, with different contributions and different targets. However, there are still some issues have not been considered. This paper will provide a fresh survey study on the data-intensive IoT issue. Besides that, we conclude some shadow issues that have not been emphasized, which are interesting for the future. We propose also an extended big data model for intelligent data-intensive IoT to tackle the challenges.

This paper presents a method for load balancing heterogeneous networks by dynamically assigning values to the LTE cell range expansion (CRE) parameter. The method records hand-over events online and adapts flexibly to changes in terminal traffic and mobility by maintaining statistical estimators that are used to support autonomous assignment decisions. The proposed approach has low overhead and is highly scalable due to a modularised and completely distributed design that exploits self- organisation based on local inter-cell interactions. An advanced simulator that incorporates terminal traffic patterns and mobility models with a radio access network simulator has been developed to validate and evaluate the method.