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In today's world, closed circuit television, cellphone photographs and videos, open-source intelligence (i.e., social media/web data mining), and other sources of photographic evidence are commonly used by police forces to identify suspects and victims of both online and offline crimes. Human characteristics, such as age, height, weight, gender, hair color, etc., are often used by police officers and witnesses in their description of unidentified suspects. In certain circumstances, the age of the victim can result in the determination of the crime's categorization, e.g., child abuse investigations. Various automated machine learning-based techniques have been implemented for the analysis of digital images to detect soft biometric traits, such as age and gender, and thus aid detectives and investigators in progressing their cases. This paper documents an evaluation of existing cognitive age prediction services. The evaluative and comparative analysis of the various services was conducted to identify trends and issues inherent to their performance. One significant contributing factor impeding the accurate development of the services investigated is the notable lack of sufficient sample images in specific age ranges, i.e., underage and elderly. To overcome this issue, a dataset generator was developed, which harnesses collections of several unbalanced datasets and forms a balanced, curated dataset of digital images annotated with their corresponding age and gender.

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Recent developments in the field of data fusion have seen a focus on techniques that use training queries to estimate the probability that various documents are relevant to a given query and use that information to assign scores to those documents on which they are subsequently ranked. This paper introduces SlideFuse, which builds on these techniques, introducing a sliding window in order to compensate for situations where little relevance information is available to aid in the estimation of probabilities. SlideFuse is shown to perform favourably in comparison with CombMNZ, ProbFuse and SegFuse. CombMNZ is the standard baseline technique against which data fusion algorithms are compared whereas ProbFuse and SegFuse represent the state-of-the-art for probabilistic data fusion methods

Abstract. Agent-Oriented Programming (AOP) researchers have successfully developed a range of agent programming languages that bridge the gap between theory and practice. Unfortunately, despite the incommunity success of these languages, they have proven less compelling to the wider software engineering community. One of the main problems facing AOP language developers is the need to bridge the cognitive gap that exists between the concepts underpinning mainstream languages and those underpinning AOP. In this paper, we attempt to build such a bridge through a conceptual mapping that we subsequently use to drive the design of a new programming language entitled ASTRA, which has been evaluated by a group of experienced software engineers attending an Agent-Oriented Software Engineering Masters course.

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Maintaining traceability links of software systems is a crucial task for software management and development. Unfortunately, dealing with traceability links are typically taken as afterthought due to time pressure. Some studies attempt to use information retrieval-based methods to automate this task, but they only concentrate on calculating the textual similarity between various software artifacts and do not take into account the properties of such artifacts. In this paper, we propose a novel traceability link recovery approach, which comprehensively measures the similarity between use cases and source code by exploring their particular properties. To this end, we leverage and combine machine learning and logical reasoning techniques. On the one hand, our method extracts features by considering the semantics of the use cases and source code, and uses a classification algorithm to train the classifier. On the other hand, we utilize the relationships between artifacts and define a series of rules to recover traceability links. In particular, we not only leverage source code’s structural information, but also take into account the interrelationships between use cases. We have conducted a series of experiments on multiple datasets to evaluate our approach against existing approaches, the results of which show that our approach is substantially better than other methods.

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