Hasil untuk "General Works"

Stuart Hall

The aim of this collection of essaysl is to facilitate &dquo;a more sophisticated examination of the hitherto poorly elucidated phenomena of racism and to examine the adequacy of the theoretical formulations, paradigms and interpretive schemes in the social and human sciences...with respect to introlerance and racism and in relation to the complexity of problems they pose.&dquo; This general rubric enables me to situate more precisely the kind of contribution which a study of Gramsci’s work can make to the larger enterprise. In my view, Gramsci’s work does not offer a general social science which can be applied to the analysis of social phenomena across a wide comparative range of historical societies. His potential contribution is more limited. It remains, for all that, of seminal importance. His work is, precisely, of a &dquo;sophisticating&dquo; kind. He works, broadly, within the marxist paradigm. However, he has extensively revised, renovated and sophisticated many aspects of that theoretical framework to make it

B. Doshi

G. Pagani, Marco Aiello

The statistical tools of Complex Network Analysis are of useful to understand salient properties of complex systems, may these be natural or pertaining human engineered infrastructures. One of these that is receiving growing attention for its societal relevance is that of electricity distribution. In this paper, we present a survey of the most relevant scientific studies investigating the properties of different Power Grids infrastructures using Complex Network Analysis techniques and methodologies. We categorize and explore the most relevant literature works considering general topological properties, physical properties, and differences between the various graph-related indicators and reliability aspects. We also trace the evolution in such field of the approach of study during the years to see the improvement achieved in the analysis.

S. K. Dwivedy, P. Eberhard

Bhawna Goyal, Ayush Dogra, S. Agrawal et al.

Abstract At the crossing of the statistical and functional analysis, there exists a relentless quest for an efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising algorithms have been documented in the literature, in spite of which the level of functionality of these algorithms still holds margin to acquire desired level of applicability. Quite often noise affecting the pixels in image is Gaussian in nature and uniformly deters information pixels in image. Based on some specific set of assumptions all methods work optimally, however they tend to create artefacts and remove fine structural details under general conditions. This article focuses on classifying and comparing some of the significant works in the field of denoising.

D. Ruppert

A. Chan, David Steven Scott, Ada P.L. Chan

Alexandre Heuillet, Fabien Couthouis, Natalia Díaz Rodríguez

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data. However, assessing how XAI techniques can help understand models beyond classification tasks, e.g. for reinforcement learning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable Reinforcement Learning (XRL), a relatively new subfield of Explainable Artificial Intelligence, intended to be used in general public applications, with diverse audiences, requiring ethical, responsible and trustable algorithms. In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box. We evaluate mainly studies directly linking explainability to RL, and split these into two categories according to the way the explanations are generated: transparent algorithms and post-hoc explainaility. We also review the most prominent XAI works from the lenses of how they could potentially enlighten the further deployment of the latest advances in RL, in the demanding present and future of everyday problems.

Hanme Kim, Stefan Leutenegger, A. Davison

Amine Drira, H. Pierreval, Sonia Hajri-Gabouj

O. Sauter, C. Angioni, Y. Lin-Liu

Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, P. Frossard

Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to random noise. In this paper, we propose to study a \textit{semi-random} noise regime that generalizes both the random and worst-case noise regimes. We propose the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime. We establish precise theoretical bounds on the robustness of classifiers in this general regime, which depend on the curvature of the classifier's decision boundary. Our bounds confirm and quantify the empirical observations that classifiers satisfying curvature constraints are robust to random noise. Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes. We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. This result suggests bounds on the curvature of the classifiers' decision boundaries that we support experimentally, and more generally offers important insights onto the geometry of high dimensional classification problems.

Jie Mei, W. Ren, Jie Chen

Yongcheng Liu, Bin Fan, Gaofeng Meng et al.

Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.

Lukás Neumann, Jiri Matas

Fan Zhou, Chengtai Cao, Kunpeng Zhang et al.

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e., acquiring new knowledge and skills with little or even no demonstration. Most of the existing meta-learning methods are proposed to tackle few-shot learning problems such as image and text, in rather Euclidean domain. However, there are very few works applying meta-learning to non-Euclidean domains, and the recently proposed graph neural networks (GNNs) models do not perform effectively on graph few-shot learning problems. Towards this, we propose a novel graph meta-learning framework -- Meta-GNN -- to tackle the few-shot node classification problem in graph meta-learning settings. It obtains the prior knowledge of classifiers by training on many similar few-shot learning tasks and then classifies the nodes from new classes with only few labeled samples. Additionally, Meta-GNN is a general model that can be straightforwardly incorporated into any existing state-of-the-art GNN. Our experiments conducted on three benchmark datasets demonstrate that our proposed approach not only improves the node classification performance by a large margin on few-shot learning problems in meta-learning paradigm, but also learns a more general and flexible model for task adaption.

USM ADMIN

Articolul abordează aspectele stilistice ale textelor de pe site-uri turistice, cu scopul de a analiza funcția persuasivă a elementelor lexicale precum metafora, epitetul și comparația. Scopul cercetării constă în identificarea strategiilor de traducere care permit păstrarea expresivității și a impactului emoțional al textului-sursă în limba-țintă. Cercetarea de monstrează că utilizarea frecventă a adjectivelor evaluative, a superlativelor și a figurilor de stil intensifică impactul emoțional asupra cititorului, contribuind la construirea unei imagini pozitive și memorabile a destinației promovate. Ipoteza studiului pornește de la ideea că traducerea comunicativă, conform abordării lui Peter Newmark, facilitează obținerea unui efect pragmatic și estetic echivalent. Analiza corpusului de texte turistice demonstrează că utilizarea strategiilor precum reproducerea imaginii metaforice, echivalența culturală și compensarea contribuie la menținerea funcției persuasive a textelor localizate. Rezultatele obținute validează ipoteza formulată și subliniază importanța păstrării expresivității în procesul de traducere a textului turistic, esențială pentru menținerea atractivității ofertei turistice. Cuvinte-cheie: turism, stilistică, metaforă, epitet, comparație, traducere comunicativă, echivalență culturală DOI: https://doi.org/10.59295/sum10(220)2025_13

USM ADMIN

Studiul investighează tranziția de la alfabetul chirilic la alfabetul latin în documentația oficială a instituțiilor de învățământ superior din Republica Moldova, în contextul aplicării Legii privind funcționarea limbilor (1989). Analiza proceselor-verbale universitare, a ordinelor rectorale și a dosarelor studențești evidențiază caracterul complex al transformării, cu dimensiuni tehnico-administrative și identitare. Ritmul implementării a variat în funcție de profilul instituțiilor: facultățile umaniste (Institutul Pedagogic „A. Russo”, Universitatea de Stat din Chișinău) au adoptat rapid grafia latină și glotonimul „limba română”, în timp ce unitățile tehnice și reale (Institutul Politehnic „S. Lazo”, unele catedre științifice) au menținut o perioadă bilingvismul. La Institutul Agricol „M. V. Frunze”, schimbarea s-a produs gradual. Concluziile subliniază rolul universităților ca spații de legitimare a alfabetului latin și a redefinirii glotonimice de la „limba moldovenească” la „limba română”. Cuvinte-cheie: alfabet latin, procese-verbale universitare, legislație lingvistică, identitate națională, învățământ superior, limba română, glotonim, tranziție lingvistică DOI: https://doi.org/10.59295/sum10(220)2025_18

Felix Olajide Talabi, Christiana Shade Ade-johnson, Joseph Moyinoluwa Talabi et al.

Abstract The wave of artificial intelligence (AI) is transforming all spheres of human life. AI is continuously expanding, shaping the future of humanity and raising important ethical and societal implications. Hence, this study explored the bandwagon effect of AI and its use among media houses in Oyo State, Nigeria. The study adopted the ethnographic qualitative design, chiefly utilising focus group discussion (FGD to gain rich empirical insight into the phenomenon. Twelve media professionals were purposively sampled for the FGD. The study found that AI is becoming more prevalent in Oyo State, Nigerian media houses for tasks like generating content, analysing data, verifying facts, and managing social media. The study concluded that AI is revolutionising the media industry and can serve as a competitive edge for media houses that embrace it, bearing in mind that responsible use, ethical considerations, and technical challenges are crucial for harnessing AI’s potential.

Lu Yang, Min Tianwei, Tony Fang

As digital transformation (Digital) accelerates globally, conventional enterprise production models are proving increasingly insufficient to meet the demands of today’s dynamic market landscape. China has innovated the concept of New Quality Productivity (NQPF), and exploring its functioning is critical to promoting high-quality enterprise development. This study examines the impact mechanism of Digital on NQPF in manufacturing firms by applying spatial econometric models—including the spatial Durbin model, spatial mediation model, and spatial threshold model—to panel data from A-share listed manufacturers (2013–2022). The results indicate that digital transformation significantly influences the level of NQPF, exhibiting spatial spillover effects and spatial attenuation boundaries. This influence initially promotes and subsequently inhibits productivity. The analysis of the spatial mediation effect reveals that Digital affects enterprise productivity levels by influencing total factor productivity. Furthermore, the spatial threshold effect analysis indicates that higher total enterprise assets enhance the positive impact of Digital on NQPF. These results provide robust micro-level empirical evidence to inform manufacturing enterprise development strategies.