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This paper describes, from motivations and main objectives to actual implementation, a novel agent-oriented programming language called JADEL. JADEL is designed to support the effective implementation of JADE agents and multi-agent systems in the scope of real-world model-driven development because it concretely helps developers by natively supporting agent-oriented abstractions, and because it is based on mature industrial-strength technologies. The four core abstractions that JADEL supports, namely agents, behaviours, communication ontologies, and interaction protocols, are presented by gradually introducing the specific syntaxes that the language provides for them. In the last part of the paper, a complete example of a simple JADEL multi-agent system is presented and discussed. Such an example is a well-known example from the official distribution of JADE and many programmers learned the basics of JADE from it. The choice of this example allows comparing JADEL source code with reference Java source code using JADE, and it clearly emphasizes the practical advantages of agent-oriented programming. Section 1 introduces and motivates the presented work. Section 2 surveys major AOP languages and, even if it cannot be considered exhaustive, it highlights the specific features of selected languages that have been relevant for the design of JADEL. Section 3 provides an overview of the features of JADE that are supported by JADEL. Section 4 presents the language in details by discussing main supported abstractions, and by specifying its formal syntax. Section 5 presents the most interesting parts of a complete example of the use of JADEL to develop a simple multi-agent system. Finally, Section 6 concludes the paper with a discussion of the current state of the presented work, and a preliminary assessment of the proposed language.

Visual classification of commercial products is a branch of the wider fields of object detection and feature extraction in computer vision, and, in particular, it is an important step in the creative workflow in fashion industries. Automatically classifying garment features makes both designers and data experts aware of their overall production, which is fundamental in order to organize marketing campaigns, avoid duplicates, categorize apparel products for e-commerce purposes, and so on. There are many different techniques for visual classification, ranging from standard image processing to machine learning approaches: this work, made by using and testing the aforementioned approaches in collaboration with Adidas AG™, describes a real-world study aimed at automatically recognizing and classifying logos, stripes, colors, and other features of clothing, solely from final rendering images of their products. Specifically, both deep learning and image processing techniques, such as template matching, were used. The result is a novel system for image recognition and feature extraction that has a high classification accuracy and which is reliable and robust enough to be used by a company like Adidas. This paper shows the main problems and proposed solutions in the development of this system, and the experimental results on the Adidas AG™ dataset.

In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available. What about when the data at disposal are scarce? The main objective of this work is to prove that a network can be trained and can provide excellent colorization results even without a large quantity of data. The adopted approach is a mixed one, which uses an adversarial method for the actual colorization, and a meta-learning technique to enhance the generator model. Also, a clusterization a-priori of the training dataset ensures a task-oriented division useful for meta-learning, and at the same time reduces the per-step number of images. This paper describes in detail the method and its main motivations, and a discussion of results and future developments is provided.

This paper reports a quantitative assessment of JADEL, an agent-oriented programming language designed to implement JADE agents and multi-agent systems. The assessment is structured in two parts. The first part is intended to evaluate the effectiveness of JADEL for the concrete implementation of agent-based algorithms expressed using a pseudocode. The second part examines the functionality of the language regarding concurrency and message passing by comparing the implementation in JADEL of a set of benchmark algorithms with the corresponding implementations in Scala. The metrics introduced for the two parts of the assessment are meant to evaluate the expressiveness and ease of use of JADEL, and reported results are encouraging.

The most important technological trend of the last years has been the rise of social networking systems to social phenomena involving hundreds of millions of people, attracting users from several social groups. Social networking systems blur the distinction between the private and working spheres, and users use such systems both at home and in the workplace, both professionally and with recreational goals. Social networking systems can be equally used to organize a work meeting, a dinner with colleagues or a birthday party with friends. For example, the chat systems that are embedded in social networking platforms are often the most practical way to contact a colleague to ask an urgent question, especially in technologically oriented companies. Moreover, several traditional information systems have been modified in order to include social aspects. Currently, social networking platforms are mostly used without corporate blessing, maintaining their status as feral systems.

This paper presents the support for interaction protocols that has been recently added to the JADEL programming language. JADEL is an agent-oriented programming language designed to ease the development of JADE agents and multi-agent systems by offering general-purpose agent-oriented abstractions to the programmer. The first part of the paper presents JADEL and motivates the need for a new agent-oriented programming language. Then, the agent-oriented abstractions that JADEL has been providing since its first version-namely agents, behaviours and communication ontologies-are described. The new support for interaction protocols is finally presented, and a simple example of a JADEL multi-agent system that uses interaction protocols is reported. The paper is concluded with an overview of the current state of JADEL and related tools.

This paper presents the core features of JADEL, an agent-oriented domain-specific programming language for the construction of JADE agents, behaviours and ontologies. The work on JADEL originates from the need to assist programmers by means of tools that reduce the complexity and speed up the construction of a JADE agents and multi-agent systems. The features of JADEL discussed in this paper include abstractions for the main entities of JADE—agents, behaviours and ontologies—and they also encompass the features needed for the construction of domain-specific tasks, thus enriching JADE APIs with novel and simple notations.