Abstract
Due to their universal accessibility, interactivity and scaling ease, Web applications relying on client-side code execution are currently the most common form of delivering applications and it is likely that they will continue to enter into less common realms such as IoT-based applications. We reason that modern Web applications should be able to exhibit advanced security protection mechanisms and review the research literature that points to useful partial solutions. Then, we propose a framework to support such characteristics and the features needed to implement them, providing a roadmap for a comprehensive solution to support Web application integrity.

Abstract

Abstract
Were you ever asked by a manager to ‘do what you want’, where you felt free to innovate? Did it feel like freedom? Maybe you felt encouraged since you could now experiment your idea, but did it mean that your performance was now on the radar? Could you then stay true to your vision or did you feel the need to compromise so that the ‘numbers lined up’? Either way, you should know that you are not alone. Arguably, we are in an age of paradox1 where simultaneous contradic- tions are all too common. Innovation paradox arises when “the aggressive pursuit of operational excellence and incremental innovation crowds out the possibility of creating ground-breaking innovations” (Davila & Epstein, 2014, p.2). Often these contradictions are meaningful on their own merit but when interdependent on each other, they create tensions in economic, social, environmental and ethical decision-making. In previous editorials, we have shared how digital innovations and societal disparity across the world are influencing strategic decision-making and shifting the innovation mindset. We now stretch the boundaries by suggesting that paradigms relying on economic trade-offs and shared-value that have shaped conventional organisational strategies are no longer sufficient to guide paradoxical tensions in decision-making. (...)

Abstract
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample data-sets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios.

Abstract
Cutting raw-material into smaller parts is a fundamental phase of many production processes. These operations originate raw-material waste that can be minimised. These problems have a strong economic and ecological impact and their proper solving is essential to many sectors of the economy, such as the textile, footwear, automotive and shipbuilding industries, to mention only a few. Two-dimensional (2D) nesting problems, in particular, deal with the cutting of irregularly shaped pieces from a set of larger containers, so that either the waste is minimised or the value of the pieces actually cut from the containers is maximised. Despite the real-world practical relevance of these problems, very few approaches have been proposed capable of dealing with concrete characteristics that arise in practice. In this paper, we propose a new general heuristic (H4NP) for all 2D nesting problems with limited-size containers: the Placement problem, the Knapsack problem, the Cutting Stock problem, and the Bin Packing problem. Extensive computational experiments were run on a total of 1100 instances. H4NP obtained equal or better solutions for 73% of the instances for which there were previous results against which to compare, and new benchmarks are proposed.

Abstract
Despite recent advances, always-on broadband Internet connectivity is still not available in Temporary Crowded Events (TCEs). To solve this problem, this paper envisions a novel concept named Traffic-Aware Multi-Tier Flying Network (TMFN). A TMFN consists of a mobile and physically reconfigurable network of Flying Mesh Access Points (FMAPs) and Gateways, which is able to dynamically reconfigure its topology according to the users' traffic demands - characterized by the users' positions and offered traffic. To implement this concept, a novel traffic-aware Network Planning (NetPlan) algorithm is proposed, which dynamically determines the FMAPs' coordinates and Wi-Fi cell ranges according to the users' traffic demands, in order to improve the TMFN's aggregate throughput, without compromising the overall coverage. Simulation results obtained in scenarios typically observed in TCEs demonstrate improved Quality of Service metrics, specifically the mean throughput, thus validating the proposed NetPlan algorithm.