Effective information management has long been a problem in organisations that are not of a scale that they can afford their own department dedicated to this task. Growing information overload has made this problem even more pronounced. On the other hand we have recently witnessed the emergence of intelligent tools, packages and resources that made it possible to rapidly transfer knowledge from the academic community to industry, government and other potential beneficiaries. Here we demonstrate how adopting state-of-the-art natural language processing (NLP) and crowdsourcing methods has resulted in measurable benefits for a human rights organisation by transforming their information and knowledge management using a novel approach that supports human rights monitoring in conflict zones. More specifically, we report on mining and classifying Arabic Twitter in order to identify potential human rights abuse incidents in a continuous stream of social media data within a specified geographical region . Results show deep learning approaches such as LSTM allow us to push the precision close to 85% for this task with an F1-score of 75%. Apart from the scientific insights we also demonstrate the viability of the framework which has been deployed as the Ceasefire Iraq portal for more than three years which has already collected thousands of witness reports from within Iraq. This work is a case study of how progress in artificial intelligence has disrupted even the operation of relatively small-scale organisations.

From their impacts to potential threats, privacy and misinformation are a recurring top news story. Social media platforms (e.g. Facebook) and information retrieval (IR) systems (e.g. Google), are now in the public spotlight to address these issues. Our research investigates an approach, known as Nudging, applied to the domain of IR, as a potential means to minimize impacts and threats surrounding both matters. We perform our study in the space of health search for two reasons. First, encounters with misinformation in this space have potentially grave outcomes. Second, there are many potential threats to personal privacy as a result of the data collected during a search task. Adopting methods and a corpus from previous work as the foundation, our study asked users to determine the effectiveness of a treatment for 10 medical conditions. Users performed the tasks on 4 variants of a search engine results page (SERP) and a control, with 3 of the SERP's being a Nudge (re-ranking, filtering and a visual cue) intended to reduce impacts to privacy with minimal impact to search result quality. The aim of our work is to determine the Nudge that is least impactful to good decision making while simultaneously increasing privacy protection. We find privacy impacts are significantly reduced for the re-ranking and filtering strategies, with no significant impacts on quality of decision making.

Privacy concerns are becoming a dominant focus in search applications, thus there is a growing need to understand implications of efforts to address these concerns. Our research investigates a search system with privacy warning labels, an approach inspired by decision making research on food nutrition labels. This approach is designed to alert users to potential privacy threats in their search for information as one possible avenue to address privacy concerns. Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy. In the present study, participants were given a set of fact-based decision tasks from the domain of health search. Participants were rotated through variations of search engine results pages (SERPs) including a SERP with a privacy warning light system. Lastly, participants completed a survey to capture attitudes towards privacy, behaviors to protect privacy, and other demographic information. In addition to the comparison of interactive search behaviors of a privacy warning SERP with a control SERP, we compared self-report privacy measures with interactive search behaviors. Participants reported strong concerns around privacy of health information while simultaneously placing high importance on the correctness of this information. Analysis of our interactive experiment and self-report privacy measures indicate that 1) choice of privacy-protective browsers has a significant link to privacy attitudes and privacy-protective behaviors in a SERP and 2) there are no significant links between reported concerns towards privacy and recorded behavior in an information retrieval system with warnings that enable users to protect their privacy.

Information systems that utilise contextual information have the potential of helping a user identify relevant information more quickly and more accurately than systems that work the same for all users and contexts. Contextual information comes in a variety of types, often derived from records of past interactions between a user and the information system. It can be individual or group based. We are focusing on the latter, harnessing the search behaviour of cohorts of users, turning it into a domain model that can then be used to assist other users of the same cohort. More specifically, we aim to explore how such a domain model is best utilised for profile-biased summarisation of documents in a navigation scenario in which such summaries can be displayed as hover text as a user moves the mouse over a link. The main motivation is to help a user find relevant documents more quickly. Given the fact that the Web in general has been studied extensively already, we focus our attention on Web sites and similar document collections. Such collections can be notoriously difficult to search or explore. The process of acquiring the domain model is not a research interest here; we simply adopt a biologically inspired method that resembles the idea of ant colony optimisation. This has been shown to work well in a variety of application areas. The model can be built in a continuous learning cycle that exploits search patterns as recorded in typical query log files. Our research explores different summarisation techniques, some of which use the domain model and some that do not. We perform task-based evaluations of these different techniques—thus of the impact of the domain model and profile-biased summarisation—in the context of Web site navigation.

Chris Fox, Maribel Fernandez, Shalom Lappin; Lambda Calculus, Type Theory, and Natural Language II, Journal of Logic and Computation, Volume 18, Issue 2, 1

Program slicing is an automated source code extraction technique that has been applied to a number of problems including testing, debugging, maintenance, reverse engineering, program comprehension, reuse and program integration. In all these applications the size of the slice is crucial; the smaller the better. It is known that statement minimal slices are not computable, but the question of dataflow minimal slicing has remained open since Weiser posed it in 1979. This paper proves that static slicing algorithms produce dataflow minimal end slices for programs which can be represented as schemas which are free and liberal.

Test data generation by hand is a tedious, expensive and error-prone activity, yet testing is a vital part of the development process. Several techniques have been proposed to automate the generation of test data, but all of these are hindered by the presence of unstructured control flow. This paper addresses the problem using testability transformation. Testability transformation does not preserve the traditional meaning of the program, rather it deals with preserving test-adequate sets of input data. This requires new equivalence relations which, in turn, entail novel proof obligations. The paper illustrates this using the branch coverage adequacy criterion and develops a branch adequacy equivalence relation and a testability transformation for restructuring. It then presents a proof that the transformation preserves branch adequacy.

In previous work we have developed Property Theory with Curry Typing (PTCT, an intensional first-order logic for natural language semantics. PTCT permits fine-grained specifications of meaning. It also supports polymorphic types and separation types.1 We develop an intensional number theory within PTCT order to represent proportional generalized quantifiers like ‘most’, and we suggest a dynamic type-theoretic approach to anaphora and ellipsis resolution. Here we extend the type system to include product types, and use these to define a permutation function that generates underspecified scope representations within PTCT. We indicate how filters can be added to encode constraints on possible scope readings. Our account offers several important advantages over other current theories of underspecification.

Abstract Conditioned slicing is a source code extraction technique. The extraction is performed with respect to a slicing criterion which contains a set of variables and conditions of interest. Conditioned slicing removes the parts of the original program which cannot affect the variables at the point of interest, when the conditions are satisfied. This produces a conditioned slice, which preserves the behaviour of the original with respect to the slicing criterion. Conditioned slicing has applications in source code comprehension, reuse, restructuring and testing. Unfortunately, implementation is not straightforward because the full exploitation of conditions requires the combination of symbolic execution, theorem proving and traditional static slicing. Hitherto, this difficultly has hindered development of fully automated conditioning slicing tools. This paper describes the first fully automated conditioned slicing system, CONSIT, detailing the theory that underlies it, its architecture and the way it combines symbolic execution, theorem proving and slicing technologies. The use of CONSIT is illustrated with respect to the applications of testing and comprehension. Copyright © 2003 John Wiley & Sons, Ltd.

Evolutionary testing is a search based approach to the automated generation of systematic test data, in which the search is guided by the test data adequacy criterion. Two problems for evolutionary testing are the large size of the search space and structural impediments in the implementation of the program which inhibit the formulation of a suitable fitness function to guide the search. In this paper we claim that slicing can be used to narrow the search space and transformation can be applied to the problem of structural impediments. The paper presents examples of how these two techniques have been successfully employed to make evolutionary testing both more efficient and more effective.

Abstract This paper describes the use of conditioned slicing to assist partition testing, illustrating this with a case study. The paper shows how a conditioned slicing tool can be used to provide confidence in the uniformity hypothesis for correct programs, to aid fault detection in incorrect programs and to highlight special cases. Copyright © 2001 John Wiley & Sons, Ltd

Th paper shows how analysis of programs in terms of pre- and postconditions can be improved using a generalisation of conditioned program slicing called pre/post conditioned slicing. Such conditions play an important role in program comprehension, reuse, verification and reengineering. Fully automated analysis is impossible because of the inherent undecidability of pre- and post- conditions. The method presented reformulates the problem to circumvent this. The reformulation is constructed so that programs which respect the pre- and post-conditions applied to them have empty slices. For those which do not respect the conditions, the slice contains statements which could potentially break the conditions. This separates the automatable part of the analysis from the human analysis.

Conditioned slicing is a powerful generalisation of static and dynamic slicing which has applications to many problems in software maintenance and evolution, including reuse, reengineering and program comprehension. However there has been relatively little work on the implementation of conditioned slicing. Algorithms for implementing conditioned slicing necessarily involve reasoning about the values of program predicates in certain sets of states derived from the conditioned slicing criterion, making implementation particularly demanding. The paper introduces ConSIT, a conditioned slicing system which is based upon conventional static slicing, symbolic execution and theorem proving. ConSIT is the first fully automated implementation of conditioned slicing. An implementation of ConSIT is available for experimentation at http://www.mcs.gold.ac.uk//spl tilde/mas01sd/consit.html.

The relationships between the work products of a security engineering process can be hard to understand, even for persons with a strong technical background but little knowledge of security engineering. Market forces are driving software practitioners who are not security specialists to develop software that requires security features. When these practitioners develop software solutions without appropriate security-specific processes and models, they sometimes fail to produce effective solutions. We have adapted a proven object oriented modeling technique, use cases, to capture and analyze security requirements in a simple way. We call the adaptation an abuse case model. Its relationship to other security engineering work products is relatively simple, from a user perspective.