1,721,126 research outputs found
A Request for Clarity over the End of Sequence Token in the Self-Critical Sequence Training
No full textThe Image Captioning research field is currently compromised by the lack of transparency and awareness over the End-of-Sequence token () in the Self-Critical Sequence Training. If the token is omitted, a model can boost its performance up to +4.1 CIDEr-D using trivial sentence fragments. While this phenomenon poses an obstacle to a fair evaluation and comparison of established works, people involved in new projects are given the arduous choice between lower scores and unsatisfactory descriptions due to the competitive nature of the research. This work proposes to solve the problem by spreading awareness of the issue itself. In particular, we invite future works to share a simple and informative signature with the help of a library called SacreEOS. Code available at: https://github.com/jchenghu/sacreeos
On Using Artificial Intelligence to Predict Music Playlist Success
No full textThe emergence of digital music platforms has fundamentally transformed the way we engage with and organize music. As playlist creation has gained widespread popularity, there is an increasing desire among music aficionados and industry experts to comprehend the factors that drive playlist success. This paper presents a machine learning-based approach designed to predict the success of music playlists. By analyzing various musical characteristics of songs, our model achieves an impressive accuracy of 89.6% in predicting playlist success. Notably, it exhibits a remarkable 92.0% accuracy in forecasting the success of popular playlists, while also effectively identifying unpopular playlists with an accuracy of 89.4%. These findings provide invaluable insights into playlist creation, ultimately enhancing the overall music-listening experience. By harnessing the power of machine learning, our proposed approach unlocks new prospects for optimizing playlist design strategies and delivering personalized music recommendations. This has significant ramifications for music enthusiasts and industry professionals seeking to elevate playlist creation and enrich the music consumption experience
Collaborative Misbehaviour Response System for Improving Road Safety
No full textWrong-way driving (WWD), Driver Monitoring System (DMS), and parking violations pose significant threats to road safety. To address these challenges, we propose a collaborative misbehavior response system (MBR) that generates real-time, context-aware navigation recommendations to the nearest available parking spot. The MBR integrates individual misbehavior detection systems(MBDs) for a holistic approach to road safety and leverages Kafka and Avro for efficient communication under the 5GMETA Platform
A Taxonomy of Modern GPGPU Programming Methods: On the Benefits of a Unified Specification
No full textSeveral Application Programming Interfaces (APIs) and frameworks have been proposed to simplify the development of General-Purpose GPU (GPGPU) applications. GPGPU application development typically involves specific customization for the target operating systems and hardware devices. The effort to port applications from one API to the other (or to develop multi-target applications) is complicated by the availability of a plethora of specifications, which in essence offers very similar underlying functionality. In this work we provide an in-depth study of six state-of-the-art GPGPU APIs. From these we derive a taxonomy of the common semantics and propose a unified specification. We describe a methodology to translate this unified specification into different target APIs. This simplifies cross-platform application development and provides a clean framework for benchmarking. Our proposed unified specification is called GUST (GPGPU Unified Specification and Translation) and it captures common functionality found in compute-only APIs (e.g., CUDA and OpenCL), in the compute pipeline of traditional graphic-oriented APIs (e.g., OpenGL and Direct3D11) and in last-generation bare-metal APIs (e.g., Vulkan and Direct3D12). The proposed translation methodology solves differences between specific APIs in a transparent manner, without hiding available tuning knobs for compute kernel optimizations and fostering best programming practices in a simple manner
Collaborative Misbehaviour Response System for Improving Road Safety
No full textThis paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system's limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms
Stochastic Floyd-Steinberg dithering on GPU: image quality and processing time improved
No full textError diffusion dithering is a technique that is used to represent a grey-scale image in a format usable by a printer. At every step, an algorithm converts the grey-scale value of a pixel to a new value within the allowed ones, generating a conversion error. To achieve the effect of continuous-tone illusion, the error is distributed to the neighboring pixels. Among the existent algorithms, the most commonly used is Floyd-Steinberg. However, this algorithm suffers two issues: artifacts and slowness. Regarding artifacts, those are textures that can appear after the image elaboration, making it visually different from the original one. In order to avoid this effect, we will use a stochastic version of Floyd-Steinberg algorithm. To evaluate the results, we will apply the Weighted Signal to Noise Ratio (WSNR), a visual-based model to account for perceptivity of dithered textures. This filter has a low-pass characteristic and, in particular, it uses a Contrast Sensitivity Function to evaluate the similarity between the original image and the final image. Our claim is that the new stochastic algorithm is better suited for both the WSNR measure and the visual analysis. Secondly, we will face slowness: we will describe a parallel version of Floyd-Steinberg algorithm that will exploit GPU (Graphics Processing Unit), drastically reducing the spent time. Specifically, we noticed that the serial version computational time increases quadratically with the input size, while the parallel version one increases linearly. Both the image quality and the computational performance of the parallel algorithm are evaluated on several large-scale images
Automatic stochastic dithering techniques on GPU: Image quality and processing time improved
No full textDithering or error diffusion is a technique used to obtain a binary image, suitable for printing, from a grayscale one. At each step, the algorithm computes an allowed value of a pixel from a grayscale one, applying a threshold and, therefore, causing a conversion error. To obtain the optical illusion of a continuous tone, the obtained error is distributed to adjacent pixels. In literature there are many algorithms of this type, to cite some Jarvis, Judice and Ninke (JJN), Stucki, Atkinson, Burkes, Sierra but the most known and used is the Floyd-Steinberg. We compared various types of dithering, which differ from each other for the weights and number of pixels involved in the error diffusion scheme. All these algorithms suffer from two problems: artifacts and slowness. First, we address the artifacts, which are undesired texture patterns generated by the dithering algorithm, leading to a less appealing visual results. To address this problem, we developed a stochastic version of Floyd-Steinberg's algorithm. The Weighted Signal to Noise Ratio (WSNR) is adopted to evaluate the outcome of the procedure, an error measure based on human visual perception that also takes into account artifacts. This measure behaves similarly to a low-pass filter and, in particular, exploits a contrast sensitivity function to compare the algorithm's result and the original image in terms of similarity. We will show that the new stochastic algorithm is better in terms of both WSNR measurement and visual analysis. Secondly, we address the method's inherent computational slowness: We implemented a parallel version of the Floyd-Steinberg algorithm that takes advantage of GPGPU (General Purtose Graphics Processing Unit) computing, drastically reducing the execution time. Specifically, we observed a quadratic time complexity with respect to the input size for the serial case, whereas the computational time required for our parallel implementation increased linearly. We then evaluated both image quality and the performance of the parallel algorithm on a exhaustive image database. Finally, to make the method fully automatic, an empirical technique is presented to choose the best degree of stochasticity
AI-Based Melody Generation
No full textIn the evolving realm of social media, music significantly enhances post appeal and viewer engagement. However, challenges such as copyright and royalties complicate its usage. Artificial intelligence (AI) might be used to generate royalty-free music that complements visual content. This paper presents an AI-based melody generator designed to create music suitable for various applications, particularly for enhancing social media posts. Unlike full songs, which involve complex AI models, our focus on melodies addresses a more specific and manageable aspect of music generation. We developed an algorithm to differentiate between main and background melodies, leveraging an LSTM and Transformer architecture to capture musical dependencies. Training on the Lakn MIDI dataset, which includes 178,000 files, our model achieved 64% accuracy in predicting main melodies and 78% in background melodies. Evaluation by 23 volunteers revealed that AI-generated melodies were as pleasant as human-composed ones and revealed that participants struggled to distinguish whether the melody they heard was human-composed or AI-generated. This indicates that our AI model might offer significant benefits in scenarios where melodies play an important role
vkpolybench: A crossplatform Vulkan Compute port of the PolyBench/GPU benchmark suite
No full textPolyBench is a well-known set of benchmarks characterized by embarrassingly parallel kernels able to run on Graphic Processing Units (GPUs). While Polybench GPU kernels leverage well-established GP-GPU APIs such as CUDA and OpenCL, in this paper we present vkpolybench, a crossplatform PolyBench/GPU port built on top of Vulkan. Vulkan is the recently released Khronos standard for heterogeneous CPU–GPU computing that is gaining significant traction lately. Compared to CUDA and OpenCL, the Vulkan API improves GPU utilization while reducing CPU overheads
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