Reviewerly/exHarmony huggingface dataset:
Sparse-to-Local-Dense Matching for Geometry-Guided Correspondence Estimation

Establishing reliable correspondences between two views is one of the most important components of various vision tasks. This paper proposes a novel sparse-to-local-dense (S2LD) matching method to conduct fully differentiable correspondence estimation with the prior from epipolar geometry. The sparse-to-local-dense matching asymmetrically establishes correspondences with consistent sub-pixel coordinates while reducing the computation of matching. The salient features are explicitly located, and the description is conditioned on both views with the global receptive field provided by the attention mechanism. The correspondences are progressively established in multiple levels to reduce the underlying re-projection error. We further propose a 3D noise-aware regularizer with differentiable triangulation. Additional guidance from 3D space is encoded by the regularizer in training to handle the supervision noise caused by the errors in camera poses and depth maps. The proposed method demonstrates outstanding matching accuracy and geometric estimation capability on multiple datasets and tasks.
Recognizing Social Relationships in Long Videos via Multimodal Character Interaction

Social relationships between characters in multiple clips of a long video may necessitate multi-step reasoning. However, most existing long video understanding approaches fail to capture the relational dependencies between characters in different clips. To solve this problem, we propose a Multimodal and Multi-granularity Relation Recognition (MMRR) framework to extract social relationships from long videos. First, we design a novel Multimodal Heterogeneous Graph (MHG) that learns the relational interactions between characters by propagating information between multimodal character nodes and multimodal clip nodes. Second, to better incorporate the contextual information from multiple character feature representations, we build a Multi-granularity Character Representation Module (MCRM) that learns global character representations over the entire long video, as well as person-specific local character representations via attention mechanism. Experimental results on two real-world datasets demonstrate the superiority of our method.
Graph-Based Progressive Fusion Network for Multi-Modality Vehicle Re-Identification

Vehicle re-identification (Re-ID) is a critical task in intelligent transportation, aiming to match vehicle images of the same identity captured by non-overlapping cameras. However, it is difficult to achieve satisfactory results based on RGB images alone in darkness. Therefore, it is of great importance to consider multi-modality vehicle re-identification. Currently, the proposed works deal with different modality features through direct summation and fusion based on heat map, which however ignores the relationship between them. Meanwhile, there is a huge gap between the different modalities, which needs to be reduced. In this paper, to solve the above two problems, we propose a Graph-based Progressive Fusion Network (GPFNet) using a graph convolutional network to adaptively fuse multi-modality features in an end-to-end learning framework. GPFNet consists of a CNN feature extraction module (FEM), a GCN feature fusion module (FFM), and a loss function module (LFM). Firstly, in FEM, we employ a multi-stream network architecture to extract single-modality features and common-modality features and employ a random modality substitution module to extract mixed-modality features. Secondly, in FFM, we design an efficient graph structure to associate the features of different modalities and adopt a progressive two-stage strategy to fuse them. Finally, in LFM, we use GCN-aware multi-modality loss to constrain the features. For reducing modality differences and contributing better initial mixed-modality features to FFM, we propose random modality substitution as a data enhancement method for multi-modality datasets. Extensive experiments on multi-modality vehicle Re-ID datasets RGBN300 and RGBNT100 show that our model achieves state-of-the-art performance.
How useful are native language tests for research with advanced second language users?

Abstract We investigated the extent to which language tests developed for native speakers (L1) can be used with advanced speakers of a second language (L2). We compared the performance of Dutch–English bilinguals with that of native English speakers on a series of English language tests, looking at vocabulary knowledge, crystallized intelligence, reading comprehension, and reading speed. It was found that advanced L2 speakers know fewer L2 words than native speakers and take longer to read texts but perform equally well on text comprehension. Tests optimized for native English-speakers predicted text comprehension less well than tests better adapted to the skill level of the bilinguals (which include the Lextale test). An exploratory graphical analysis suggested that L2 users’ performance on challenging vocabulary tests, along with performance on an English author recognition test, forms a distinct cluster – arguably also measuring interest in English language and culture besides knowledge in general (also called crystallized intelligence).
A Robust Oriented Filter-Based Matching Method for Multisource, Multitemporal Remote Sensing Images

The accurate matching of multisource, multi-temporal remote sensing images is challenging because of significant nonlinear intensity differences (NIDs) and severe geometric distortions. To address these problems, we developed a robust image matching method: oriented filter-based matching (OFM). OFM is insensitive to NIDs, while exhibiting scale and rotational invariance. First, salient feature points with multiscale attributes were detected in the Gaussian-scale space of the input images. Then, the images were convoluted using multi-oriented filters, and unified feature maps were constructed by the extraction of orientation indices using effective data pooling operations. The constructed feature maps were highly resistant to NIDs. Five filters were integrated into the OFM framework to investigate their applicabilities in different application scenarios. Next, a novel rotation-invariant feature descriptor was constructed, using a dominant direction determination approach and a descriptor-grouping strategy. The dominant direction determination approach enables accurate dominant direction estimation, whereas the descriptor-grouping strategy improves the stability of the method under different rotational angles. Finally, brute-force matching was implemented to obtain initial matches; an improved mismatch elimination method was used to identify reliable putative matches. To evaluate the performance of OFM, we created a large dataset comprising 4,427 pairs of multitemporal optical–optical, optical–synthetic aperture radar (SAR), optical–infrared, and optical–depth images. OFM outperformed state-of-the-art methods in terms of number of correct matches, recall, inlier ratio, root mean square error and success rate. Our implement is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Comparative Study of Consensus Algorithms for Blockchain

Blockchain (BC) technology has recently gained significant popularity due to its ability to provide a secure and decentralized platform for data storage and transactions. There are tens of thousands of BC-based cryptocurrencies and apps. However, the proof of work consensus mechanism used by most BC networks requires significant energy consumption, leading to concerns about its environmental impact. According to the Cambridge Bitcoin Electricity Consumption Index, the estimated annual electricity consumption of Bitcoin mining in 2022 was approximately 107.65 TWh. This paper provides a recent comparative study of BC consensus mechanisms (CMs). We overview BC technology, consensus mechanisms, and mining algorithms. We investigate the energy and time consumption of the PoW consensus mechanism by executing the SHA-256 mining algorithm at different difficulty levels. Finally, we present new mining approaches to reduce or recycle energy consumption.
CDFed: Contribution-based Dynamic Federated Learning for Managing System and Statistical Heterogeneity

Federated learning (FL) allows local clients to train a global model by cooperating with a server while ensuring that their raw data is not revealed. However, most existing works usually choose clients randomly, regardless of their capabilities and contributions to training. Additionally, FL client selection mechanisms concentrate on a significant challenge associated with system or statistical heterogeneity. This paper tries to manage both the system and statistical heterogeneity of distributed clients in the networks. First, to manage the system heterogeneity, an optimization objective is first proposed to maximize the number of clients with similar capabilities such as storage, computational, and communication capabilities. Then, a network framework with a logical layer is proposed to logically group similar clients by checking their capabilities. Finally, to manage the statistical heterogeneity among clients, a novel Contribution-based Dynamic Federated training strategy, called CDFed, is designed to dynamically adjust the probability of clients being chosen based on Shapley values in each global round. Experimental results on two baseline datasets: MNIST and FMNIST, demonstrate that our proposal has a faster convergence rate, about 50%, and a higher average test accuracy, at least 1%, than baselines in most cases.
An extension of the ADVISE Meta modeling framework and its application for an early-stage security analysis of a public transport supervision system

Abstract Early-stage security analysis can be used for a preliminary assessment of the security level of a system, thus providing useful insights to guide the whole system’s development. In this paper, we focus on a specific meta-level modeling framework for security analysis, ADVISE Meta, which allows representing a system using generic built-in blocks and relationships constituting the ontology of the framework, and to automatically derive complex low-level stochastic models representing attack steps and adversaries. In this paper, we extend the ADVISE Meta ontology to enlarge the variety of the possible attack paths and adversaries that can be represented in the framework, by modeling (i) attack patterns available in the CAPEC database, a comprehensive dictionary of known patterns of attack, and (ii) the adversaries’ profiles defined in the Threat Agent Library (TAL), a reference library which describes the characteristics of threat agents. The paper provides a detailed description of the whole process for extending the ADVISE Meta ontology, and the application of the extended modeling framework for an early-stage security analysis of a public transport supervision system. The framework enables a variety of security-oriented analyses, in particular to assess the probability that a given adversary can successfully reach a specific goal, to analyze the most probable attack path that adversaries can follow to reach a goal, to perform sensitivity analysis at varying of attack patterns and adversaries’ profiles, to compare different architectural solutions, and to identify the system’s components that can be more probably attacked by adversaries.
Human attention during goal-directed reading comprehension relies on task optimization

The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.
Future developments in sociology in the age of the metaverse

The intent of this article is to shed light on the future challenges that sociology, in concert with other disciplines, will have to face from now on, starting with one of the possible hypotheses of research methodology. Indeed, as much as some of these issues in the last two decades have become the preserve of neuroscience, their origins, namely the conceptualizations of the classics of sociology, should not be forgotten. In this regard, it will be the task of researchers and sociology to investigate, through applied research attempts, phenomena such as empathy and emotions from innovative methodologies and research different from the current ones, capable of highlighting how emotions are modified by cultural context and spaces of interaction, moving away from a depersonalizing structuralism we were used to, refuting, for example, what neuroscientists have pointed out, according to which empathy and emotions belong to biological universalisms. Therefore, within this brief and informative article we intend to present a possibility of investigation, without any claim to truth or the only way to be able to do research in this field, moved only by the desire to open a fruitful discourse that can lead to the methodological approach toward applied sociology or laboratory research. The idea is to move beyond online netnography, not because the latter cannot provide satisfactory results, but because it will be necessary to broaden the range of choices, such as analysis in the metaverse, generating a viable alternative where this type of analysis cannot be pursued.
An Efficient Few-Shot Object Detection Method for Railway Intrusion via Fine-Tune Approach and Contrastive Learning

Railway intrusion detection plays an important role in the railway intelligent transportation system, assisting the safe operation of trains. The existing deep learning-based object detection methods excessively rely on a large number of supervision samples, and build up such a railway intrusion dataset is usually costly. The emerging few-shot object detection (FSOD) can realize object detection task by learning a small number of samples, which is a suitable candidate for railway intrusion detection. However, small databases lead to poor classification performances, and most existing FSOD methods have the drawbacks of high computational complexity and slow adaptation speed. To deal with these issues, an efficient few-shot object detection method for railway intrusion (FSRD) is proposed in this paper, extending FSOD to railway intrusion detection with few training samples to achieve good performance. A simple yet effective fine-tuning method is also developed to improve overall performance. There is a classifier head initializer to perform an appropriate initialization of the model weights and speed up the adaptation of the detector. The contrastive learning method is further introduced to cope with the misclassification. The proposed novel method is tested on a real railway intrusion dataset. Experimental results show that it achieves an average detection accuracy of 80.51 mAP50 under the setting of 30 shots. Compared with the state-of-the-art (SOTA) FSOD methods, it is capable of yielding the optimal and suboptimal performance in all settings. Furthermore, conducting extensive experiments demonstrates its good detection accuracy, fast adaptability, and satisfactory generalization ability in railway intrusion detection.
The epistemologies of the South and the future of the university

Abstract Even though the university has the potential to help humanity in what amounts to a paradigmatic transition, it has been very restrictive and very selective in the kinds of knowledges it validates. In fact, the kinds of knowledges in which it has excelled are those most responsible for the paradigmatic crisis in which humanity finds itself. In a nutshell, the paradigmatic change calls for cognitive justice, justice for the different ways of knowing that circulate in society. Cognitive justice is the polar opposite of ‘anything goes’. The assumption is that there is no global social justice without global cognitive justice, justice among knowledges. Looking back, while privileging one specific kind of knowledge and, indeed, granting it a cognitive monopoly, the university has been the privileged site for producing and legitimating cognitive injustice. As a result, the immense epistemic diversity of the world has been ignored or suppressed. In the following, I identify the problem and the promise for overcoming it.
Progressive Rapidly-exploring Random Tree for Global Path Planning of Robots

Global path planning is an essential task for autonomous robots operating on the moon. To increase the efficiency of global path planning, we propose a Progressive Rapidly-exploring Random Tree (P-RRT) algorithm, which improves RRT in three aspects. First, to speed up the convergence rate, we proposed the Feasible Region-aware Ellipse Sampling strategy, which incrementally draws biased samples from an elliptical subspace with the current point and the target point as the focus. Second, we introduce dynamic forward step length to accelerate random tree growth. Third, given the wide range of applications for multi-core processors, we introduce parallel path planning, segmenting global path planning into several processing units, in an effort to further enhance the application efficiency of the algorithm. Finally, the proposed P-RRT is verified by extensive simulation experiments. The experimental results demonstrate the efficiency and practicability of the P-RRT algorithm.
Assessment of the Acceptance of Internet of Things Technology in the Health Field Using TAM2: A Survey Study of Medical Staff in Nineveh Governorate

This study aims to identify the acceptance of Internet of Things (IoT) technologies, by medical care providers, in healthcare institutions in Nineveh Governorate. The study uses the deductive approach through the Technology Acceptance Model (TAM2). The survey was conducted using an electronic questionnaire prepared based on several studies in this field. The data population included physicians, dentists, and pharmacists with the sample size being 341. Structural equation modeling was used to test the hypotheses of the study. The results revealed that job relevance and result demonstrability have a significant impact on the perceived usefulness of using IoT technology in the medical field, whereas, job relevance, output quality, perceived ease of use, and subjective norms have no direct impact on the perceived usefulness of this technology. Furthermore, the participant-perceived benefit does not significantly influence behavioral intent.
Siber Güvenlik İhbarcılarının Korunması Açısından ABD ve AB Yaklaşımlarının Karşılaştırılması

This study compares the laws in the United States and the European Union protecting cybersecurity whistleblowers from employer retaliation. Similarities and differences exist regarding the scope of laws, the definition of “retaliation,” and required reporting procedures to be eligible for legal protection. In the US, no anti-retaliation federal statute directly addresses cybersecurity whistleblowing, but whistleblowers may still be protected when they disclose cybersecurity-related violations of laws falling within the scope of protected activity under the current laws. In the EU, the Directive (EU) 2019/1937 directly protects employees who report breaches falling within the scope of the EU acts, including the protection of privacy and personal data and the security of network and information systems. The two approaches also differ concerning the confidentiality of the reporting person’s identity. This study provides a brief foundation for understanding how the US and EU’s approaches differ in providing legal protection against retaliation for whistleblowers.
RSS: Effective and Efficient Training for Sequential Recommendation using Recency Sampling

Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items, but require a lot of time to train. Slow training increases the costs of training, hinders product development timescales and prevents the model from being regularly updated to adapt to changing user preferences. The training of such sequential models involves appropriately sampling past user interactions to create a realistic training objective. The existing training objectives have limitations. For instance, next item prediction never uses the beginning of the sequence as a learning target, thereby potentially discarding valuable data. On the other hand, the item masking used by the state-of-the-art BERT4Rec recommender model is only weakly related to the goal of the sequential recommendation; therefore, it requires much more time to obtain an effective model. Hence, we propose a novel Recency-based Sampling of Sequences (RSS) training objective (which is parameterized by a choice of recency importance function) that addresses both limitations. We apply our method to various recent and state-of-the-art model architectures – such as GRU4Rec, Caser, and SASRec. We show that the models enhanced with our method can achieve performances exceeding or very close to the effective BERT4Rec, but with much less training time. For example, on the MovieLens-20M dataset, RSS applied to the SASRec model can result in a 60% improvement in NDCG over a vanilla SASRec, and a 16% improvement over a fully-trained BERT4Rec model, despite taking 93% less training time than BERT4Rec. We also experiment with two families of recency importance functions and show that they perform similarly. We further empirically demonstrate that RSS-enhanced SASRec successfully learns to distinguish differences between recent and older interactions – a property that the original SASRec model does not exhibit. Overall, we show that RSS is a viable (and frequently better) alternative to the existing training objectives, which is both effective and efficient for training sequential recommender model when the computational resources for training are limited.
It Wasn’t All Zoom and Gloom: Teacher and Student Experiences of Online Learning during the COVID-19 Pandemic in an Irish Post-Primary School

Research into the impact of the COVID-19 pandemic on educational institutions in Ireland has primarily focused on third-level courses and initial teacher education programmes. Little research has captured the experiences of practising post-primary teachers and their students as they made the transition to online learning. This study begins by examining the digital context of post-primary education in Ireland prior to the COVID-19 pandemic, paying particular attention to ICT policies and their recommendations. Following this, the importance of schools as more than a centre for curriculum delivery is examined, as well as the potential challenges adolescents face in non-face-to-face contexts. In total, 49 teachers and 130 students participated in the study by completing online surveys about their online learning experiences. The findings indicate that although teachers felt underprepared, many benefits to online learning were identified, especially around assessment and the provision of asynchronous content. Students enjoyed elements of online learning but felt overwhelmed and isolated.