This paper presents a resolution of a space management optimisation problem such as 0-1 knapsack problems (KP) by discrete cuckoo search algorithm (DCS). The proposed approach includes an adaptation process of three main components: the objective function, the solution representation, and the step move operator. A simplified conception of these three components is designed without introducing an additional technique, especially in the search process for the optimal solution. Three sets of benchmark instances have been taken from the literature to test the performance of DCS. Experimental results prove that DCS is effective in solving different types of 0-1 KP instances. The result comparisons with other state-of-the-art algorithms show that DCS is a competitive approach that outperforms most of them.

Address parsing, decomposing address strings to semantically meaningful components, is a measure to convert unstructured or semi-structured address data to structured one. Flexibility and variability in real-world address formats make parser development a non-trivial task. Even after all the time and effort dedicated to obtaining a capable parser, updating or even re-training is required for out-of-domain data and extra costs will be incurred. To minimise the cost of model building and updating, this study experiments with active learning for model training and adaptation. Models composed of character-level embedding and recurrent neural networks are trained to parse address in Taiwan. Results show that by active learning, 420 additional instances to the training data are sufficient for a model to adapt itself to unfamiliar data while its competence in the original domain is retained. This suggests that active learning is helpful for model adaptation when data labelling is expensive and restricted.

In the so-called massively multiplayer online role-playing games (MMORPGs), malicious players have the possibility of obtaining some kind of gains from competitions, via easy victories achieved thanks to the introduction of game bots in the games. In order to maintain fairness among players, it is important to detect the presence of game bots during video games so that they can be expelled from the games. This paper describes an approach to distinguish human players from game bots based on behavioural analysis. This implemented via supervised machine learning (ML) and deep learning (DL) algorithms. In order to detect game bots, considered algorithms are first trained with labelled features and then used to classify unseen-before features. In this paper, the performance of our game bots detection approach is experimentally obtained. The dataset we use for training and classification is extracted from logs generated during online video games matches of a real-life MMORPG.

Query expansions are being used in many search applications for retrieving relevant documents. Although retrieving the relevant documents are important for search users, the complexity of Arabic morphology remains a challenge. As such, many irrelevant documents were still retrieved from the ranked results. To address this challenge, this paper proposes a new searching method for Arabic text semantic-based query expansion. The proposed method combines Arabic word synonyms and ontology to expand the query with additional terms. Specifically, the proposed method combined lexical words within the ranking algorithm and then improved with ontology links to expand query. The performance of Arabic text semantic-based query expansion was evaluated in terms of average precision, means average precision and means reciprocal rank. Experiments on Quran datasets show that the proposed method using Arabic text semantic-based query expansion approach outperforms the previous methods using other dataset which is called Tafsir dataset. The proposed method achieved 15.44% mean average precision.