1,720,957 research outputs found
A Relevance-Based CNN Trimming Method for Low-Resources Embedded Vision
No full textA significant amount of Deep Learning research deals with the reduction of network complexity. In most scenarios the preservation of very high performance has priority over size reduction. However, when dealing with embedded systems, the limited amount of resources forces a switch in perspective. In fact, being able to dramatically reduce complexity could be a stronger requisite for overall feasibility than excellent performance. In this paper we propose a simple to implement yet effective method to largely reduce the size of Convolutional Neural Networks with minimal impact on their performance. The key idea is to assess the relevance of each kernel with respect to a representative dataset by computing the output of its activation function and to trim them accordingly. The resulting network becomes small enough to be adopted on embedded hardware, such as smart cameras or lightweight edge processing units. In order to assess the capability of our method with respect to real-world scenarios, we adopted it to shrink two different pre-trained networks to be hosted on general purpose low-end FPGA hardware to be found in embedded cameras. Our experiments demonstrated both the overall feasibility of the method and its superior performance when compared with similar size-reducing techniques introduced in recent literature
A Comparison of Machine Learning Techniques for Ethereum Smart Contract Vulnerability Detection
No full textVulnerability detection is particularly relevant in smart contracts, where modifying the code after deployment is impossible. Machine learning solutions provide greater efficiency than static analyzers in speed and detection. This study evaluates various classic machine-learning techniques and state-of-the-art neural networks for training a vulnerability detector. We analyze the largest and most reliably labelled dataset of smart contracts currently available, experimenting with six data representations of smart contracts and a multimodal approach. Our experiments show that both deep and traditional machine learning methods excel in different scenarios. Notably, eXtreme Gradient Boosting achieved an F1-score of 0.91 with the multimodal approach, which suggests its potential for more robust classification. At the same time, the results underscore the need for larger datasets to showcase the full potential of the evaluated methods
AI-enhanced blockchain technology: A review of advancements and opportunities
No full textBlockchain technology has rapidly gained popularity, permeating various fields due to its inherent features of security, transparency, and decentralization. Blockchain-based applications, spanning from financial transactions to supply chain management, have revolutionized numerous industries. Concurrently, Artificial Intelligence (AI) techniques have emerged as a powerful tool for efficiently solving complex problems. The integration of AI into blockchain applications has shown promise in addressing key challenges such as security, consensus, scalability, and interoperability. While existing literature offers several surveys on the intersection of AI and blockchain, our work takes a distinct perspective by focusing on how AI solutions can enhance and optimize blockchain technology and its applications. Our goal is to provide a comprehensive literature overview of the methods that have been employed to improve blockchain technology through AI, encompassing machine learning, deep learning, natural language processing and reinforcement learning. Our contribution highlights AI's potential to enhance blockchain, improving efficiency, security, and reliability of blockchain-based applications. By exploring AI's role in consensus, smart contracts, and data privacy, it advances theory and practical applications, fostering innovation across sectors for a more secure and efficient digital future
Neural Networks Reduction via Lumping
Full text linkThe increasing size of recently proposed Neural Networks makes it hard to implement them on embedded devices, where memory, battery and computational power are a non-trivial bottleneck. For this reason during the last years network compression literature has been thriving and a large number of solutions has been published to reduce both the number of operations and the parameters involved with the models. Unfortunately, most of these reducing techniques are actually heuristic methods and usually require at least one re-training step to recover the accuracy. The need of procedures for model reduction is well-known also in the fields of Verification and Performances Evaluation, where large efforts have been devoted to the definition of quotients that preserve the observable underlying behaviour. In this paper we try to bridge the gap between the most popular and very effective network reduction strategies and formal notions, such as lumpability, introduced for verification and evaluation of Markov Chains. Elaborating on lumpability we propose a pruning approach that reduces the number of neurons in a network without using any data or fine-tuning, while completely preserving the exact behaviour. Relaxing the constraints on the exact definition of the quotienting method we can give a formal explanation of some of the most common reduction techniques
Compressing neural networks via formal methods
Full text linkAdvancements in Neural Networks have led to larger models, challenging implementation on embedded devices with memory, battery, and computational constraints. Consequently, network compression has flourished, offering solutions to reduce operations and parameters. However, many methods rely on heuristics, often requiring re-training for accuracy. Model reduction techniques extend beyond Neural Networks, relevant in Verification and Performance Evaluation fields. This paper bridges widely-used reduction strategies with formal concepts like lumpability, designed for analyzing Markov Chains. We propose a pruning approach based on lumpability, preserving exact behavioral outcomes without data dependence or fine-tuning. Relaxing strict quotienting method definitions enables a formal understanding of common reduction techniques
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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