This paper presents a co-creative system, designed to assist users in the ideation and generation of visual and textual humour using generative models. Our system employs a 2D-spatial interface, where the user interacts with blocks in an infinite canvas, allowing for the exploration and merge of multiple ideas at the same time. We also present some of the outputs generated by user during preliminary testing.

Digital images are inherently fragile and vulnerable to malicious tampering, significantly compromising their authenticity and integrity. Image recovery is crucial for restoring altered content and preserving the reliability of digital images. Traditional fragile watermarking methods achieve high-quality recovery but fail under post-processing attacks, while existing deep learning-based approaches offer some robustness, yet often produce lower-quality recovered images, typically with a PSNR of around 28 dB. To address these challenges, we propose a novel Symmetric Self-embedding Mechanism for High-Fidelity Image Recovery against tampering (SSEM-HIR), which is capable of restoring tampered images with high quality while maintaining some robustness against common attacks. Unlike existing methods that use the fragility of watermarking solely for tampering localization, SSEM-HIR is the first work to integrate fragility with spatial symmetry, enabling high-quality tampering recovery. Specifically, our SSEM-HIR employs a hierarchical watermark embedding module to embed an inverted version of the original image, utilizing spatial symmetry to retrieve lost information from the extracted watermark. To further improve recovery quality, we design a Dual-branch Region-based Self-Recovery module, where a Spatial-based Watermark Extraction block restores tampered regions using embedded watermark information, while a Frequency-assisted Image Repair block compensates for quality degradation in the untampered area. Extensive experiments show that our method achieves an average PSNR of 34.14 dB under common attack scenarios, including noise addition, image scaling, Gaussian blurring, and no post-processing. This represents an improvement of over 5 dB and 18% in recovered image quality compared to state-of-the-art approaches.

There has been growing development of co-creative systems for game design, where both humans and computers work as colleagues, proactively contributing with creative input. However, the collaborative process is still not as seamless as in human-human co-creativity. A key element still underdeveloped in these approaches is the communication between the human and the machine, which can be facilitated by providing the computational agent with explanatory capabilities. Based on principles of explainability for co-creative systems from previous literature, we propose a framework of explainability specifically applied to mixed-initiative scenarios in game design. We illustrate the applications of the framework by suggesting possible solutions adapted to different use cases of existing approaches and, additionally, of our own proposed approach.

The field of Information Visualization has undergone major changes in the last decades due to the growing computational power and easier access to various technologies by a greater number of people. However, Information Visualization and its techniques literacy continue to be a knowledge associated to a reduced audience. In order to surpass this condition of Information Visualization, new practitioners have applied techniques from other areas, such as the arts, to develop visualizations that could transmit information in a more casual and accessible way to a larger number of people, weighing heavily on the artistic component. In this paper, we present a visualization that portrays information about public contracts held in Portugal, that despite being public data, is not analyzed or much less visualized by the majority. To do so we taken a casual representation approach with a strong aesthetics component in mind with the objective of promoting awareness about the dimension and distribution of the money applied daily throughout Portugal. We perform a phenomenological experiment to assess the effectiveness of our work in transmitting the information and evoking the desired insights. The experiment allowed us to collect distinct interpretations that could lead to further approaches and improvements in future iterations.

Despite some authors stating that data-relatedness helps interpretation, glyphs are often used unrelated to the represented data. In order to automatically produce data-related glyphs, a large visual repository is required, as well as, image structure suitable for data representation. In this paper, we propose a strategy that fulfills the two requirements and allows the production of glyphs related to the data thematic (literal and metaphorical). We compare used approach with current glyph techniques and discuss the results.

This project is about the creation of a music festival’s dynamic brand, which reacts to music and customises itself to any person participating in the event.

Through Data Visualisation and Sonification models, we present a study of multimodal representations to characterise the Portuguese consumption patterns, which were gathered from Portuguese hypermarkets and supermarkets over the course of two years. We focus on the rhythmic nature of the data to create and discuss audio and visual representations that highlight disruptions and sudden changes in the normal consumption patterns. For this study, we present two distinct visual and audio representations and discuss their strengths and limitations.

Traditional data warehouses integrate new data during lengthy offline periods, with indexes being dropped and rebuilt for efficiency reasons. There is the idea that these and other factors make them unfit for realtime warehousing. We analyze how a set of factors influence near-realtime and frequent loading capabilities, and what can be done to improve near-realtime capacity using a traditional architecture. We analyze how the query workload affects and is affected by the ETL process and the influence of factors such as the type of load strategy, the size of the load data, indexing, integrity constraints, refresh activity over summary data, and fact table partitioning. We evaluate the factors experimentally and show that partitioning is an important factor to deliver near-realtime capacity.

Parallel share-nothing architectures are currently used to handle large amounts of data arriving in real-time for processing. The continuous increase on data volume and organization, introduce several limitations to scalability and quality of service (QoS) due to processing requirements and joins. Parallelism may improve query performance, however some business require timely results (results not faster or slower than specified) which, even with additional parallelism and significant upgrade costs (both monetary and due to disturbance of normal operations), cannot be guaranteed. We propose a timely-aware execution architecture, Cloudy, which balances data and queries processing among an elastic set of non-dedicated and heterogeneous nodes in order to provide scale-out performance and timely results, nor faster or slower, using both Complex Event Processing (CEP) and database (DB). Data is distributed by nodes accordingly with their hardware characteristics, then a set of layered mechanisms rearrange queries in order to provide in timely results. We present experimental evaluation of Cloudy and demonstrate its ability to provide timely results.

We introduce a context-aware keypoint extractor, coined as CAKE, aimed at capturing the most informative image content. We find this algorithm particularly useful in tasks such as image retrieval, scene classification, and object (class) recognition, in which local features are mainly used to provide a robust and efficient image representation. We are motivated by the fact that the majority of local feature extractors are designed to respond to a reduced number of structures. Furthermore, we observe that the existent complementarity among feature sets is often neglected. Our context-aware algorithm is designed to respond to complementary features as long as they are informative. In the particular case of images with different types of structures, one can expect a high complementarity among the features retrieved by a context-aware extractor. By contrast, images with repetitive patterns will inhibit our method from retrieving a clear summarised description of the image content. Nonetheless, the extracted set of features can be complemented with a counterpart that retrieves the repetitive elements in the image. These two cases are depicted in Figure 1. The upper image shows a context-aware keypoint extraction on a well-structured scene, which retrieves the 100 most informative keypoints. This small number of features is sufficient to provide a good coverage of the content, which includes different types of structures. The lower image illustrates the advantages of combining context-aware keypoints with strictly local ones (SFOP keypoints [2]) to obtain a better coverage of images with repetitive patterns. An information theoretic framework is used to formulate our contextaware keypoint extraction. A keypoint will correspond to a certain image location within a structure with a low probability of occurrence (high information content). For each image location x, we consider w(x) ∈RD, any viable local representation (e.g, the Hessian matrix or the structure tensor matrix) as a “codeword” that represents the neighbourhood of x. To define the saliency measure, we regard the image codewords as samples of a multivariate probability density function. We compute the probability of a codeword w(y) using a Kernel Density Estimator [4] in which the kernel is a multidimensional Gaussian function with zero mean and standard deviation σk:

Parallel Shared-Nothing architectures are frequently used to handle large star-schema Data Warehouses (DW). The continuous increase in data volume and the star-schema storage organization introduce severe limitations to scalability due to the well-known parallel join issues and the resulting need to use solutions such as on-the fly repartitioning of data or intermediate results, or massive replication of large data sets that still need to be joined locally, constraining their ability to deliver fast results. Parallelism may improve query performance, however some business decisions may require that query results be timely available which, even with additional parallelism and significant upgrade costs (both monetary and due to disturbance of normal operations), cannot be guaranteed. We propose a Timely-aware Execution Parallel Architecture (TEEPA) which balances data load and query processing among an elastic set of non-dedicated heterogeneous nodes in order to provide scale-out performance and timely query results. Data is allocated using adaptable storage models to minimize join costs (the major uncertainty factor) which best fit the nodes' capabilities, while preserving a consistent logical view of the star-schema. We present experimental evaluation of TEEPA and demonstrate its ability to provide timely results.

The advancements in communications and embedded systems have led to the proliferation of wireless sensor and actuator networks (WSANs) in a wide variety of application domains. One important key of many such WSAN applications is how to configure/program functionalities after deployment. Some application domains even require that sensor nodes be deployed in harsh environments (e.g., refineries), where they need to be configured over-the-air. Over-the-air programming takes more than 4 second to transmit an image code to a node where, typically, a single image has 40kB. With the proposed model, small code blocks (states and transitions) are generated (about 4kB) to upload to nodes. This methodology allows reducing the programming time to 5 times less, release bandwidth and keep battery on nodes. To support our approach we propose an architecture based on state machines model which provides an easy user-readable high-level representation of states and transitions. The communication time is reduced through the reduction of the code image size that it is needed to send to each node. We develop an architecture that allows users to define and program applications based on Markov techniques, to simultaneously facilitate the application design and take into account important requirements such as reliability. The experimental section shows a working deployment of this concept in an industrial refinery setting.

Star schema model, has been widely used as the facto DW storage organization on RDBMS. Business measures are stored in a central fact table along with a set of foreign keys referencing dimension tables. While this storage organization offers a good trade-off between storage size and performance for a single node, it doesn't scale in a predictable manner in shared-nothing parallel architectures. Although fact tables can be linearly partitioned among nodes, the same doesn't apply to dimensions, which unbalances (increases) the dimensions/fact_table size ratio, and consequently introduces limits to the number of parallel nodes. In this paper we propose and evaluate a parallel DW storage model, that overcomes these limitations and deliver optimal speed-up and scale-up capabilities with top efficiency. We use the TPC-H benchmark to evaluate the scalability and efficiency of the proposed model.

Wireless sensor networks (WSNs) are deployed to sense, monitor and act on the environment. Deployments in scenarios such as industrial sense and react environments require a set of functionality, and for both ease and reconfiguration capabilities it is important to offer an appropriate framework. We propose a framework for interaction with real-world devices by abstracting proprietary protocols, allowing the interaction between client applications and heterogeneous sensor network platforms and protocols. The framework allows the (re)configuration of alarms, actions or closed-loop techniques, offering flexibility and the possibility to modify for providing performance control guarantees. It allows users to configure and apply various operations, including complex closed-loop techniques that monitor and act over any actuator in the WSN, independently of the underlying WSN infrastructure. The framework is being deployed in a real scenario in the context of European FP7 GINSENG project (wireless networks with performance control guarantees).

A comparison and a performance evaluation of 3D interest point detectors based on the structure tensor matrix under a medical image registration context is presented. The study regards the distinctiveness and the repeatability rate exhibited by the detectors on medical images as the fundamental criteria for selecting a detector to be the support of a registration task. We empirically assess those requirements under the specificities of medical image analysis and registration.

Spectral data estimation from image data is an ill-posed problem since (i) due to the integral nature of imaging sensors, the same output can be obtained from an infinity of input signals and (ii) color signals are spectrally smooth in nature and, therefore, limit the number of linear independent data than can be collected. To enable the solution of these problems the solution's search space has to be constrained. The question that arises is how to select/parameterize these constraints? In this paper several model selection criteria are extended for spectral data estimation and evaluated in the context of spectral sensitivity function estimation of CCD sensors.

A robust image watermarking scheme relying on an affine invariant embedding domain is presented. The invariant space is obtained by triangulating the image using affine invariant interest points as vertices and performing an invariant triangle representation with respect to affine transformations based on the barycentric coordinates system. The watermark is encoded via quantization index modulation with an adaptive quantization step

Recovering spectral sensitivities of imaging devices with indirect methods, as well as spectral stimuli estimation from device responses are ill-posed problems. All known methods have to rely on a priori information to constrain the solution space, which, in most situations, is difficult or even impossible to obtain. In this paper we introduce a simple and fully data-driven approach for indirect spectral sensitivity estimation, which does not rely on explicit a priori information. The method is built upon an extension of our previous work on generalized cross-validation for constraint Tikhonov problems and utilizes a linear combination of band-limited basis functions.