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Modern distributed systems can benefit from the availability of large-scale and heterogeneous computing infrastructures. However, the complexity and dynamic nature of these environments also call for self-adaptation abilities, as guaranteeing efficient ...
Optimizing the performance of complex systems has always been a central issue for the control theory community. However, ideas and tools from this field often require very precise assumptions and extensive tuning to perform well, making them unsuited ...
In this paper, we explore the use of Graph Neural Networks (GNNs) for anomaly anticipation in high performance computing (HPC) systems. We propose a GNN-based approach that leverages the structure of the HPC system (particularly, the physical proximity ...
This paper presents a novel methodology based on first principles of statistics and statistical learning for anomaly detection in industrial processes and IoT environments. We present a 5-level analytical pipeline that cleans, smooths, and eliminates ...
The performance of distributed applications implemented using microservice architecture depends heavily on the configuration of various parameters, which are hard to tune due to large configuration search space and inter-dependence of parameters. While ...
We propose an incremental change detection method for data center (DC) energy efficiency metrics and consider its application to the power usage efficiency (PUE) metric. In recent years, there is an increasing focus on the sustainability of DCs and PUE ...
This paper proposes an auto-profiling tool for OSCAR, an open-source platform able to support serverless computing in cloud and edge environments. The tool, named OSCAR-P, is designed to automatically test a specified application workflow on different ...
We are pleased to welcome you to the 2023 ACM Workshop on Artificial Intelligence for Performance Modeling, Prediction, and Control - AIPerf'23.
In its first edition, AIPerf intends to foster the usage of AI (such as probabilistic methods, machine ...
When one hears the word Metaverse, it is automatically associated with millions of users, immersive experiences and its potential to change our lives. But, what enables the Metaverse to function at such a scale? This talk will present the different ...
Due to the proliferation of inference tasks on mobile devices, state-of-the-art neural architectures are typically designed using Neural Architecture Search (NAS) to achieve good tradeoffs between machine learning accuracy and inference latency. While ...
Cyber-Physical Systems (CPS) rely on sensing to control and optimize their operation. Nevertheless, sensing itself is prone to errors that can originate at several stages, from sampling to communication. In this context, several systems adopt ...
Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performance ...
Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. Typically, developers create their transformations based on small input models which ...
The Blockchain 2.0 era integrated with smart contract along with its platforms and applications have experienced explosive growth in recent years. However, many smart contracts deployed in practice are prone to errors and cannot be modified due to the ...
Sentiment analysis is an important natural language processing task that seeks to extract contextual subjective information and perform classification. CNN and Bi-LSTM are two of the most popular models in text-based sentiment analysis. However, with ...
Point-of-Interest (POI) recommendation recommends different personalized services to interested users, which are widely used in people's daily life. However, with the massive increase in users and POIs, the POI recommendation system faces the following ...
According to the "national surface water monitoring and evaluation scheme for the 14th Five-Year plan" issued by the Ministry of ecological environment of the people's Republic of China, this study constructs a back propagation (BP) neural network model ...
This study presented the development of a web-based system that visualizes real-time traffic by deploying lightweight and mobile monitoring devices at roadside intersections in the vicinity of Butuan City to assist commuters and drivers in making ...
Abstract: Fruit counting is an integral part of achieving precision orchard management. Accurate counting of the number of fruits on a tree can provide critical information for yield estimation, thus promoting precision agriculture. However, today fruit ...
Abstract: Haze rendering aims to generate realistic nighttime haze images from clear images. The results can be applied to various practical applications, such as nighttime image dehazing algorithms, game scene rendering, shooting filters, etc. We ...
Abstract: Most low-light enhancement methods based on curve estimation do not have the ability of multi-illumination processing. They cannot use one model to process images with different exposure levels. This paper proposes to process multi-...
Due to the complexity of taking photos on foggy days, images captured in the real world are prone to problems of detail loss, color dimness, and low saturation. Considering image dehazing as a problem of detail recovery and color enhancement, a two-...
The training of fully supervised semantic segmentation (FSSS) networks relies on a large amount of data with pixel-level class labels, which limits semantic segmentation's application in practical scenarios. Weakly supervised semantic segmentation (WSSS)...
Aiming at the problems of unclear boundaries and low segmentation accuracy in general real-time semantic segmentation networks, a real-time semantic segmentation network based on improved BiSeNet V1 was proposed. Based on the BiSeNetV1 network, a ...
Brain tumor is one of the common diseases of the central nervous system, and the incidence and death of brain tumors are among the highest in the world. Although the incidence of brain tumors is lower than that of other systemic tumors, due to the wide ...
With the advent of the era of big data, although the relationship between samples can be found from individual clustering of each view, data in the real world generally has multiple representations, and different data representations can complement each ...
Aiming at the limitations of the prior art in the sorting of frequency hopping signals under the condition of low signal-to-noise ratio, this paper proposes a method to reduce the noise of the image through image preprocessing, and then improve ...
Existing video summarization methods are classified into either shot-level or frame-level methods, which are individually used in a general way. This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a ...
Prior self-supervised video object segmentation models directly find pixel correspondences between pairs of frames. Instead, we propose a novel approach of employing superpixel features for learning visual correspondences between frames of a video ...
Keypoint feature extraction algorithms based on manual feature rely on the professional domain knowledge of the designer, and usually perform well in depicting the detail of an image. However, these manual feature-based feature extraction algorithms ...
Few studies present how to implement super resolution (SR) on abdominal Computed Tomography (CT) images. However, in the field of medical imaging, the development of an advanced SR system will potentially improve the clinical diagnosis and prognosis of ...
Ship detection in complex weathers is an important application of object detection approaches in real life. In recent years, ship detection approaches for single weather have gradually matured. However, there are few ship detection approaches for ...
Change detection is a challenging problem in remote sensing applications. In recent years, many Convolutional Neural Network (CNN)-based change detection methods have been proposed due to the rapid development of deep learning techniques. First, the ...
In recent years, elevator safety accidents occur frequently, and relevant news has aroused people's strong attention to elevator safety. Among them, electric vehicle explosion accident hot spot is the highest. In this paper, according to the elevator ...
The oracle bone script is the oldest writing system in China, and as the origin of Chinese characters, it is also the earliest cultural heritage in China. With the development of computer technology, deep learning has become an important technology of ...
In this paper, we improve the natural scene text detection and recognition technology based on 2d attention and encoder-decoder framework. Firstly, the related work of text detection and recognition in different natural view is discussed. Secondly, we ...
Team formation is concerned with the identification of a group of experts who have a high likelihood of effectively collaborating with each other in order to satisfy a collection of input skills. Solutions to this task have mainly adopted graph operations ...
Sentiment Analysis (SA) is one of the most active research areas in the Natural Language Processing (NLP) field due to its potential for business and society. With the development of language representation models, numerous methods have shown promising ...
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. ...
With the resurgent interest in building open-domain dialogue systems, the dialogue generation task has attracted increasing attention over the past few years. This task is usually formulated as a conditional generation problem, which aims to generate a ...
Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCN) can effectively learn node representations via aggregating neighbors based on the relation graph. However, despite a few exceptions, most of the previous work in this line does not ...
Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on ...
Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, which ...
Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. However, such ...
This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a ...
To autonomously detect the penetration point in the working area of trench excavation, a feature detection method of penetration point based on binocular cameras was proposed. First, the homogeneous coordinate transformation is established, which can ...
Semi-supervised learning methods have recently gained considerable attention for training deep learning networks with limited labeled samples and additional large label-free samples. Consistency regularization and pseudo-labeling methods are among the ...
There has been a lot of research on the use of deep neural networks in forecasting time series and chaotic time series data. However, there exist very few works on multi-step ahead forecasting in chaotic time series using deep neural networks. Several ...
Real-time objection detection is becoming more important and critical in all application areas, including Smart Transport and Smart City. From safety/security to resource efficiency, real-time image processing approaches are used more than ever. On the ...