Reviewerly/exHarmony huggingface dataset:
Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis

Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysis. Unfortunately, like traditional neural networks, GNNs also suffer from a lack of transparency, that is, it is challenging to interpret the model predictions. Prior works focused on specific factor explanations for a GNN model. In this work, we have designed and implemented Illuminati, a comprehensive and accurate explanation framework for cybersecurity applications using GNN models. Given a graph and a pre-trained GNN model, Illuminati is able to identify the important nodes, edges, and attributes that are contributing to the prediction while requiring no prior knowledge of GNN models. We evaluate Illuminati in two cybersecurity applications, i.e., code vulnerability detection and smart contract vulnerability detection. The experiments show that Illuminati achieves more accurate explanation results than state-of-the-art methods, specifically, 87.6% of subgraphs identified by Illuminati are able to retain their original prediction, an improvement of 10.3% over others at 77.3%. Furthermore, the explanation of Illuminati can be easily understood by the domain experts, suggesting the significant usefulness for the development of cybersecurity applications.
Removal of Anti-Vaccine Content Impacts Social Media Discourse

Over the past several years, a growing number of social media platforms have begun taking an active role in content moderation and online speech regulation. While enforcement actions have been shown to improve outcomes within moderating platforms, less is known about possible spillover effects across platforms. We study the impact of removing groups promoting anti-vaccine content on Facebook on engagement with similar content on Twitter. We followed 160 Facebook groups discussing COVID-19 vaccines and prospectively tracked their removal from the platform between April and September 2021. We then identified users who cited these groups on Twitter, and examined their online behavior over time. Our findings from a stacked difference-in-differences analysis shows that users citing removed Facebook groups promoted more anti-vaccine content on Twitter in the month after the removals. In particular, users citing the removed groups used 10-33% more anti-vaccine keywords on Twitter, when compared to accounts citing groups that were not removed. Our results suggest that taking down anti-vaccine content on one platform can result in increased production of similar content on other platforms, raising questions about the overall effectiveness of these measures.
A Proposed VR Platform for Supporting Blended Learning Post COVID-19

The COVID-19 pandemic caused a shift in teaching practice towards blended learning for many higher education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs. This paper is an attempt to summarise and extend pre-COVID-19 pedagogical research to leverage digital immersive technologies for blended teaching in the post-pandemic era. This paper forms both a review of these methodologies and a case study of the I-Ulysses Virtual Learning Environment as an example of a platform that leverages such immersive digital technologies and employs instrumental use of VR. To further clarify, the purpose of the paper is to describe and propose a distance learning solution with immersive VR qualities; this is what the I-Ulysses environment represents, as the main obstacle to learners of site-specific information during the pandemic has been lack of on-site accessibility. Furthermore, this is of key importance, because Joyce’s novel takes place in historical Dublin, where access to the physical location of the story is indispensable to a reader.
Supporting Critical Data Literacy in K-9 Education: Three Principles for Enriching Pupils’ Relationship to Data

Children grow up in a data economy but grasping how their data are part of this eco-system and how data become valuable to others can be difficult. This work explores how to design tools and activities which support children's critical data literacy for K-9 education. We bring together two strands of work; First, insights from a co-design process where teachers and researchers designed tools and activities for teaching critical data literacy which they deployed in a lower secondary education classroom. Second, insights from didactic theories from maths and computer science education about working with multiple and rich representations of complex and intangible phenomena. Based on this we contribute three principles for enriching pupils' relationship to data in order to inform future research into how pupils can be scaffolded in forming richer relationships to the data-driven technologies in their everyday lives in order to retain agency in a data-driven world.
Manifold discrimination partial adversarial domain adaptation

Most traditional domain adaptations promote knowledge transfer with the assumption that source domain and target domain have the same label space. In a big data environment, we usually obtain the source dataset with a larger label space than the target dataset. Under this background, partial domain adaptation (PDA) comes into being naturally. In PDA, when the target domain label space is a subset of the source domain label space, cross-domain adaptation can be achieved by matching shared class samples in cross-domain. Many researchers make good progress in this field and propose different kinds of PDA methods. But in the existing PDA methods, the samples of the unknown class are still used for distribution alignment, and this makes the model suffer from the risk of negative transfer. In order to solve these problems, we propose manifold discrimination partial adversarial domain adaptation (MDPDA) to align the relevant classes across two domains and obtain effective domain adaptation. MDPDA combines manifold learning and adversarial learning. In MDPDA, we design the manifold discrimination module to map high-dimensional data into low-dimensional space and learn low-dimensional data information of source domain and target domain. We construct discriminative source domain feature space for model discrimination in MDPDA. MDPDA distinguishes and confuses the two domains through manifold learning and similarity structure, and generates discriminant feature space to promote the auxiliary network to better recognize the source domain samples. The comprehensive experiments on several benchmark datasets evaluate the superior performance of the proposed method compared to the state-of-the-art methods. • MDPDA is proposed as a model framework. It designs a manifold discrimination sample weighting mechanism. • The manifold alignment and graph based source domain sample similarity discrimination are constructed. • We provide comprehensive experiments in three datasets to validate the good perfomance of our method.
Tackling multiple object tracking with complicated motions — Re-designing the integration of motion and appearance

Although numerous data association methods have been proposed for Multiple Object Tracking (MOT), how to integrate different features in the data association remains an open problem. For instance, over-relying on the motion feature may fail to do the necessary data association when object movements are complicated, while only depending on the appearance feature may lead to incorrect association results when intra-frame objects have similar appearances. To make an improved trade-off between the appearance feature and motion feature, we re-designed the integration of motion and appearance. In our online approach, the location and motion of each object are cast to adaptive searching windows, and within searching windows, matching is only related to the similarity of appearance features. In our offline approach, tracklets generated from our online approach are refined by forming the motion feature as spatiotemporal constraints and utilizing the appearance for clustering. We conduct experiments on multiple MOT datasets from diverse perspectives, including variant motion speed, illumination condition, object categories, etc., and verify that our method can reach robust performance. Moreover, this method also demonstrates its effectiveness by further improving our previous 1st place solutions in two CVPR 2020 MOT challenges.
Self-adaptive classification learning hybrid JAYA and Rao-1 algorithm for large-scale numerical and engineering problems

In this paper, a self-adaptive classification learning hybrid JAYA and Rao-1 algorithm, which is called EHRJAYA, is proposed for solving large-scale numerical problems and real-world complex engineering optimization problems. JAYA algorithm and Rao-1 algorithm are two kinds of algorithms with simple structure and superior performance, which have the characteristics of no particular controlling parameters. In EHRJAYA the evolution strategies of the two algorithms are selected through a random selection mechanism. Then, a novel self-adaptive classification learning strategy is proposed, which fully utilizes information from different individuals. On this basis, two different adaptive coefficients are introduced to guide the population towards the optimal individual and away from the worst individual. Finally, combining the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed and ability to jump out of local optimum of the algorithm are greatly improved. To verify the performance of the proposed EHRJAYA, 59 complex functions from the CEC2014 and CEC2017 competitions are solved by EHRJAYA. Then, EHRJAYA and more than 20 algorithms with superior performance jointly solve ten challenging real-world engineering optimization problems. Experimental results show that the proposed EHRJAYA can obtain optimal results with the least computational resources in most cases. Therefore, in the face of these problems, effective solutions can be provided by EHRJAYA.
Creating a cloud-based translator training environment using Memsource

The paper deals with the possibilities of using the Memsource system as the main component of a cloud-based learning environment for training translators. The main advantages of Memsource in the educational process are identified: accessibility through the offer of a demo and an academic programme, easy for mastering, the user-friendly interface, a wide functional range. Experimental training of students in groups for translation projects with mastery of the tasks of team members of different statuses was carried out. Students' evaluation of the functionality of the Memsource system was analysed in terms of learning effectiveness and application in their future professional life. It is proved that the cloud environment formed through the Memsource platform helps future translators to master the tools and resources needed to systematically carry out the full range of translation project operations. The feasibility of basing a cloud-based learning environment for translators on the Memsource system has been established. It has been experimentally proven that the creation of a cloud environment will optimise the structure and components of translation projects in the training process of higher education institutions that train translators.
Variational expectation maximization attention broad learning systems

Recently, broad learning system (BLS) has received much attention due to its concise network structure and strong incremental learning ability. However, as it belongs to a simple feedforward neural network , when encountering time series with sequential characteristic, it cannot effectively fit them and finish the related tasks such as wireless traffic prediction . In this paper, we propose a kind of attention mechanism-based BLS for dealing with time series while incremental learning can still be implemented. In order to learn more information from time series with relatively low computational complexity and infer the optimal number of bases in the attention mechanism, a variational form of expectation maximization attention mechanism for BLS is proposed. Experimental results show that the proposed algorithm can achieve good performance in both classification and regression tasks associated with time series.
A click-through rate model of e-commerce based on user interest and temporal behavior

In the advertising and marketing of e-commerce platform, click rate prediction is directly related to the revenue of e-commerce platform. In this paper, we propose an advertising click-through rate prediction model based on user interest and temporal behavior. First, in response to the phenomenon that traditional recurrent neural networks (RNNs) have the advantage of processing temporal data but ignore the time interval of input sequences, this paper designs a structure of time-weighted gated recurrent units (TW-GRU) to predict users’ short-term interest and long-term interest. In addition, we introduce auxiliary losses to supervise the extraction of user interest features and the learning of TW-GRU. Secondly, we introduce an attention mechanism-based gated recurrent unit A-GRU in the model to enhance the impact of interests associated with target advertisements. Finally, for the potential high-dimensional implicit information of non-temporal features and behaviorally hidden user interests, this paper proposes a click-through rate prediction model that fuses user interests with implicit generalization features. Experiments show that our model can effectively capture users’ interests and interest update process associated with target products from users’ historical behaviors, and then can effectively improve the click-through rate prediction accuracy of advertisements. • TW-GRU is proposed to model the user’s historical behavior in a temporal order. • An attention mechanism-based GRU network (A-GRU) is proposed. • An ad click-through rate prediction model that incorporates user interest and implicit generalization features is proposed.
Unifying Operational Weak Memory Verification: An Axiomatic Approach

In this article, we propose an approach to program verification using an abstract characterisation of weak memory models. Our approach is based on a hierarchical axiom scheme that captures the observational properties of a memory model. In particular, we show that it is possible to prove correctness of a program with respect to a particular axiom scheme, and we show this proof to suffice for any memory model that satisfies the axioms. Our axiom scheme is developed using a characterisation of weakest liberal preconditions for weak memory. This characterisation naturally extends to Hoare logic and Owicki-Gries reasoning by lifting weakest liberal preconditions (defined over read/write events) to the level of programs. We study three memory models (SC, TSO, and RC11-RAR) as example instantiations of the axioms, then we demonstrate the applicability of our reasoning technique on a number of litmus tests. The majority of the proofs in this article are supported by mechanisation within Isabelle/HOL.
A Revisiting Study of Appropriate Offline Evaluation for Top- <i>N</i> Recommendation Algorithms

In recommender systems, top- N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top- N recommendation, there are increasing concerns on appropriate evaluation of recommendation algorithms. It therefore is important to study how recommendation algorithms can be reliably evaluated and thoroughly verified. This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems. We carefully select 12 top- N recommendation algorithms and eight recommendation datasets. Our experiments are carefully designed and extensively conducted with these algorithms and datasets. In particular, all the experiments in our work are implemented based on an open sourced recommendation library, Recbole [ 139 ], which ensures the reproducibility and reliability of our results. Based on the large-scale experiments and detailed analysis, we derive several key findings on the experimental settings for evaluating recommender systems. Our findings show that some settings can lead to substantial or significant differences in performance ranking of the compared algorithms. In response to recent evaluation concerns, we also provide several suggested settings that are specially important for performance comparison.
Artificial agents’ explainability to support trust: considerations on timing and context

Abstract Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial agents’ reliability. Particularly, this paper focuses on non-expert users’ perspectives, since users with little technical knowledge are likely to benefit the most from “post-hoc”, everyday explanations. Drawing upon the explainable AI and social sciences literature, this paper investigates how artificial agent’s explainability and trust are interrelated at different stages of an interaction. Specifically, the possibility of implementing explainability as a trust building, trust maintenance and restoration strategy is investigated. To this extent, the paper identifies and discusses the intrinsic limits and fundamental features of explanations, such as structural qualities and communication strategies. Accordingly, this paper contributes to the debate by providing recommendations on how to maximize the effectiveness of explanations for supporting non-expert users’ understanding and trust.
A Hierarchical Approach for Traffic Sign Recognition Based on Shape Detection and Image Classification

In recent years, the development of self-driving cars and their inclusion in our daily life has rapidly transformed from an idea into a reality. One of the main issues that autonomous vehicles must face is the problem of traffic sign detection and recognition. Most works focusing on this problem utilize a two-phase approach. However, a fast-moving car has to quickly detect the sign as seen by humans and recognize the image it contains. In this paper, we chose to utilize two different solutions to solve tasks of detection and classification separately and compare the results of our method with a novel state-of-the-art detector, YOLOv5. Our approach utilizes the Mask R-CNN deep learning model in the first phase, which aims to detect traffic signs based on their shapes. The second phase uses the Xception model for the task of traffic sign classification. The dataset used in this work is a manually collected dataset of 11,074 Taiwanese traffic signs collected using mobile phone cameras and a GoPro camera mounted inside a car. It consists of 23 classes divided into 3 subclasses based on their shape. The conducted experiments utilized both versions of the dataset, class-based and shape-based. The experimental result shows that the precision, recall and mAP can be significantly improved for our proposed approach.
Dynamic GAN for high-quality sign language video generation from skeletal poses using generative adversarial networks

The recent advancements of unsupervised deep generative models have produced incredible results in image and video generation tasks. However, existing approaches still pose huge challenges in the high-quality video generation process. The generated videos consist of blurred effects and poor video quality. In this paper, we introduce a novel generative framework named dynamic generative adversarial networks (dynamic GAN) model for regulating the adversarial training and generating photo-realistic high-quality sign language videos. The proposed model uses skeletal poses information and person images as input and produces high-quality videos. In generator phase, the proposed model uses U-Net-like network to generate target frames from skeletal poses. Further, the generated samples are classified using the VGG-19 framework to identify its word class. The discriminator network classifies the real and fake samples as well as concatenates the resultant frames and generates the high-quality video output. Unlike, existing approaches the proposed novel framework produces photo-realistic video quality results without employing any animation or avatar approaches. To evaluate the model performance qualitatively and quantitatively, the proposed model has been evaluated using three benchmark datasets that yield plausible results. The datasets are RWTH-PHOENIX-Weather 2014T dataset, and our self-created dataset for Indian Sign Language (ISL-CSLTR), and the UCF-101 Action Recognition dataset. The proposed model achieves average 28.7167 PSNR score, 0.921 average SSIM score, 14 average FID score and 8.73 ± 0.23 average inception score which are relatively higher than existing approaches.
HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening

Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process. A reliable decision support system would assist healthcare systems that often suffer from a shortage of pathologists. Recent advances in digital pathology allow for high-resolution digitalization of pathological slides. Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times. In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes. The 200 digital slides, after pre-processing, resulted in 101,389 patches. A single patch is a 512 × 512 pixel image, covering 248 × 248 μm2 tissue area. Versions at higher resolution are available as well. Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research.