Efficient and effective information retrieval (IR) systems are needed to fetch a large number of relevant documents and present them based on their relevance to the input queries. Previous work reported the use of sparse and dense retrievers. Sparse retrievers offer low latency but suffer from term mismatch issues, while dense retrievers improve performance at the cost of higher processing times. The literature proposed Fast-Forward Indexes, an interpolation-based re-ranking framework that leverages the benefits of both sparse and dense retrievers.

Although a lot of work was done in the field, most studies evaluate the performance of the proposed models only on the MS Marco dataset, neglecting other datasets. This study extends previous work by exploring how different sparse retrievers, employing no-encoder, uni-encoder, and bi-encoder architectures, perform in an interpolation-based re-ranking setting on datasets originating from various domains. Results show that bi-encoder-based retrievers outperform the other sparse retrievers in terms of recall but with a substantial increase in latency compared to simpler retrievers, which generally showed good performance. Further, when the retrievers were used in an interpolation-based re-ranking setting, they performed similarly in terms of ranking quality.

The research explores the impact of rank fusion functions within the retrieve-and-rerank framework with Fast-Forward Indexes. Using the BM25 sparse model for retrieval and TCT-ColBERT dense model for semantic score computation, various rank fusion functions are experimented for the interpolation stage. The interpolated rank in relation to the semantic and lexical ranks is explored. Parametric approach allows to easily adjust the influence of sparse and dense models on the final rank. On the other hand, non-parametric approach lacks flexibility and maintains an equal weight of sparse and dense scores by default. Moreover, the ranking effectiveness and latency are measured to further evaluate each function. Due to flexibility of parametric functions, convex rank fusion function and its normalized variants yield the best trade-off in latency and ranking effectiveness followed by reciprocal rank fusion. On the contrary, non-parametric functions, namely Inverse Square Rank Reciprocal, combMNZ, and Condorcet Fuse, generally performs worse.

Self-tracking has expanded exponentially in an era defined by the ubiquitous presence of wearable technologies and smart devices. From health and fitness to finances and productivity, these devices empower users to delve into their quantified self (QS) through an almost infinite amount of visualizations. However, a user has limited time to engage with the data, and the device with which they interact has limited screen space — this calls for the need to suggest the user proactively with valuable and informative plots. An algorithm can suggest and select plots of user activities and adapts to a user’s changing requirements while offering maximum usefulness in the information. We leverage combinatorial optimization to handle the multi-objective task of extracting the most informative 𝑘 plots from a much larger pool of 𝑁 plots. The novel optimization formulation encapsulates plot features into four unique objective functions designed to capture diverse aspects of data usefulness. We evaluate the efficacy of our selection method in silico for various diverse usage scenarios. The simulation results show that the proposed methodology is efficacious in realizing three objectives (‘relevance’, ‘freshness’, and ‘likability’) while identifying the need for refinement in the fourth objective (‘noteworthiness’). Our work demonstrates the design, development, and evaluation of a selection algorithm that delivers a relevant yet fresh selection of visualizations for a quantified-self user interested in keeping track of information related to several activities.

Artists that create mirror anamorphosis usually relies on the principle of trial-and-error and manual mathematical calculations. they have to draw a deformed image on the surface to get the desired reflected image on the mirror object. A drawing interface to make this kind of art does not exist currently. In this paper, we make a suitable drawing interface that satisfies the needs of artists. We analyzed the common needs of artists and propose methods to address them efficiently. Anyone can easily produce mirror anamorphosis with our tool as long they provide the mapping between surface image and reflection image.

This paper accelerates the rendering of diffusion curves using the ray tracing cores found on modern NVIDIA graphics cards using the method described by Bowers et al. [Bowers, Leahey and Wang 2011]. This method approximates the final result of the Poisson equation and in this paper is accelerated using Optix and dedicated ray tracing hardware. Using this method yielded a render time decrease of around 8 times between the Quadro P1000 and RTX 2060, while retaining the complex colour gradients and ease of use of traditional diffusion curves solved using the Poisson equation.

Minimalistic, low-poly images have gotten more popular in recent years. But making these low-poly images by hand can be time intensive. For this reason, a lot of research has gone into how to triangulate an image (semi-)automatically. In previous works, only constant color and bilinear interpolation have been used to color the triangles in the triangulation. In this paper, different coloring methods have been investigated and showcased on a fixed mesh. They show some promising first results, but these methods can still be improved upon. When that happens, these coloring methods, combined with previous works on how to optimally triangulate, may result in more accurate or visually pleasing low-poly images.

This research builds upon a previous method of vectorisation of pixel art by pixel neighbour connecting and boundary energy minimisation. The downside of the previous method is the lack of user input throughout the process and possible divergence between the results and the artist’s vision. The proposed method uses the first part of previous method to connect neighbour pixels and continues with a spring simulation of the boundaries. A process that can be heavily user-guided with adjusting the stiffness of the springs. Results show the proposed method to achieve higher variance in the results, possibly resulting in output images closer to the artist’s vision. However, introduce lengthy process for the user that can be removed by providing higher level GUI.

Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in methods incorporating such temporal dynamics through sequential processing of user consumption histories. Some works have also included additional temporal features such as time stamps and intervals between a given user’s interactions with the platform. The latter, in particular, may be a strong signal providing additional context with respect to the current user preferences. However, in previous works this source of information has only been used passingly, without any significant analysis of its impact on recommendation. In this thesis we examine the effects of such intervals, termed time gaps, on recommendation accuracy. In order to do so, we propose a family of novel DeepTimeDelta models, extending a state-of-the-art sequential Recurrent Neural Network based recommender. Through the comparison of our time-dependent models to the sequential baseline we demonstrate that the use of time gaps leads to improvements in recommendation performance, in particular for cases following longer user inactivity. Furthermore, we examine the mechanisms regulating the model recommendation behaviour. Our results suggest that the above performance improvements may be achieved through increased reliance on user long term preferences as well as strong regulation of the importance of the recently consumed items. Finally, we examine the performance differences for users groups with distinct consumption behaviours, demonstrating some improvement for groups featuring less active users as well as users consuming more popular content.

This report describes the implementation of a custom streaming solution from an IP camera to a web browser. The system aims to both provide live video and Video on Demand. This will be used to monitor premature babies in incubators.