Reviewerly/exHarmony huggingface dataset:
The Split Matters: Flat Minima Methods for Improving the Performance of GNNs

When training a Neural Network, it is optimized using the available training data with the hope that it generalizes well to new or unseen testing data. At the same absolute value, a flat minimum in the loss landscape is presumed to generalize better than a sharp minimum. Methods for determining flat minima have been mostly researched for independent and identically distributed (i.i.d.) data such as images. Graphs are inherently non-i.i.d. since the vertices are edge-connected. We investigate flat minima methods and combinations of those methods for training graph neural networks (GNNs). We use GCN and GAT as well as extend Graph-MLP to work with more layers and larger graphs. We conduct experiments on small and large citation, co-purchase, and protein datasets with different train-test splits in both the transductive and inductive training procedure. Results show that flat minima methods can improve the performance of GNN models by over 2 points, if the train-test split is randomized. Following Shchur et al., randomized splits are essential for a fair evaluation of GNNs, as other (fixed) splits like "Planetoid" are biased. Overall, we provide important insights for improving and fairly evaluating flat minima methods on GNNs. We recommend practitioners to always use weight averaging techniques, in particular EWA when using early stopping. While weight averaging techniques are only sometimes the best performing method, they are less sensitive to hyperparameters, need no additional training, and keep the original model unchanged. All source code is available under https://github.com/Foisunt/FMMs-in-GNNs .
Students’ Generated Text Quality in a Narrative-Centered Learning Environment: Effects of Pre-Collaboration, Individual, and Chat-Interface Submissions

Narrative-centered Learning Environments (NcLE) offer a powerful tool for enhancing students’ learning through interactive experiences. The integration of open-ended questions in NcLEs encourages students to express their thoughts, opinions, and ideas without any restrictions. While different submission formats are implemented in NcLE, little research has argued the importance of assessing their potential effects. This study, therefore, investigates how the text quality in writing varies based on different submission formats for open-ended submissions in a NcLE. We proceed to analyze three types of submission formats within a narrative scripts platform: a) formal individual, b) pre-collaboration, and c) a chatbot interface. For this study, data was collected in a randomized controlled trial that involved 311 secondary school students participating in a social media literacy workshop. The quality of the submissions was assessed using an automated text cohesion and coherence analysis tool (Coh-Metrix). Our results show that having formal submission pages for open-ended questions has an effect in students’ efforts to create submissions with higher writing quality, while submissions via chatbot interfaces tend to be brief and conversational in nature. The results presented in this study have practical implications for learning technology developers and educators, as they can guide decisions related to the creation, and integration of elements to promote writing quality in online learning environments.
Standing Still Is Not an Option: Alternative Baselines for Attainable Utility Preservation

Specifying reward functions without causing side effects is still a challenge to be solved in Reinforcement Learning. Attainable Utility Preservation (AUP) seems promising to preserve the ability to optimize for a correct reward function in order to minimize negative side-effects. Current approaches however assume the existence of a no-op action in the environment's action space, which limits AUP to solve tasks where doing nothing for a single time-step is a valuable option. Depending on the environment, this cannot always be guaranteed. We introduce four different baselines that do not build on such actions and therefore extend the concept of AUP to a broader class of environments. We evaluate all introduced variants on different AI safety gridworlds and show that this approach generalizes AUP to a broader range of tasks, with only little performance losses.
Citation Recommendation Chatbot for Professional Communities

In recent years, the proliferation of academic literature has made it increasingly challenging for researchers and professionals to discover relevant citations for their work. To address this issue, this paper presents CitBot, a novel Citation Recommendation Chatbot designed specifically for professional communities. We describe the design, development, and evaluation of CitBot focusing on its performance and usefulness. CitBot combines the citation context with document-level embeddings utilizing SPECTER to generate personalized citation recommendations based on the community’s research interests. The system is designed to seamlessly integrate with online professional platforms, providing users with citation suggestions in response to their queries. A user study was conducted to assess the chatbot’s performance, comparing it to other citation recommendation tools. The findings of the study, along with a discussion of CitBot’s benefits and limitations, are presented. By enhancing the citation discovery process, CitBot has the potential to improve the productivity of professional communities and transform the way researchers and practitioners access and engage with scientific knowledge.
Support How-To Instruction Following via Conversational Agent

People indispensably use instructions shared by one another to work on unfamiliar tasks in daily or professional life. However, personally shared tutorials are often based on personal experiences and represent a collective overview of past encounters, which can be misaligned with the specific work context during the re-enacting time. Drawing inspiration from the effective dynamics observed in conversational instruction-giving and -following between experts and novices, we propose a chatbot system that delivers archived how-to tutorials by providing necessary information in a just-in-time manner, tailored to the needs of the instruction-follower. Our aim is to transform unaided instruction reading activities into conversational instruction-following experiences. We implemented a chatbot system and evaluated it through a between subject study. The results demonstrate a promise of leveraging human-chatbot interaction to support actionable instruction-following.
Memorization of Named Entities in Fine-Tuned BERT Models

Privacy preserving deep learning is an emerging field in machine learning that aims to mitigate the privacy risks in the use of deep neural networks. One such risk is training data extraction from language models that have been trained on datasets, which contain personal and privacy sensitive information. In our study, we investigate the extent of named entity memorization in fine-tuned BERT models. We use single-label text classification as representative downstream task and employ three different fine-tuning setups in our experiments, including one with Differentially Privacy (DP). We create a large number of text samples from the fine-tuned BERT models utilizing a custom sequential sampling strategy with two prompting strategies. We search in these samples for named entities and check if they are also present in the fine-tuning datasets. We experiment with two benchmark datasets in the domains of emails and blogs. We show that the application of DP has a detrimental effect on the text generation capabilities of BERT. Furthermore, we show that a fine-tuned BERT does not generate more named entities specific to the fine-tuning dataset than a BERT model that is pre-trained only. This suggests that BERT is unlikely to emit personal or privacy sensitive named entities. Overall, our results are important to understand to what extent BERT-based services are prone to training data extraction attacks (Source code and datasets are available at: https://github.com/drndr/bert_ent_attack . An extended version of this paper can be also found on arXiv [12]).
An improved single-stage convolutional neural network for rail transit obstacle detection

Abstract Obstacles that intrude into the rail area can lead to serious rail accidents, so obstacle detection technology is an essential guarantee for the safe operation of fully automatic trains. To meet the high-performance requirements of onboard obstacle detection, an efficient feature-aware convolutional neural network (EFA-Net) is proposed in this paper. The multi-scale aware feature pyramid network (MA-FPN) is designed as feature fusion network to extract multi-scale context information. In the detection head, the dynamic awareness block is used to refine the features. A joint representation branch and the generalized focal loss function are introduced to optimize the training effect. The experiments are based on the dataset of real-world rail transit environment. The results show that EFA-Net can achieve a detection accuracy of 90.4% mAP at a detection speed of 20.4 frames per second, and the lightweight design significantly reduces the computational complexity of the proposed model. Compared with other classical detectors, EFA-Net has the best comprehensive performance.
Technocentrism

Technocentrism is the tendency to view technology as a central component for addressing complex social issues and driving transformative changes. In the context of education, technocentrism manifests as an excessive focus on the use of technology in teaching, learning, and assessment processes through prioritising the adoption of technological tools, platforms, and digital resources to enhance educational outcomes without adequately considering the broader educational context. According to Papert (1987), technocentric thinking leads to questions that investigate the impact of technology on human interaction and development, such as learning, without considering the complexity of the context in which the technology is situated. Technocentric thinking separates digital technologies from their social and cultural context and suggests a one-way influence of technology on educational policies and practices, including pedagogy, teacher roles, and education objectives. Researchers in the digital education field have proposed several approaches to address technocentrism in education by acknowledging the role of technology and the complexity of the relationships between different social and material components in the educational setting (cf., Brennan, 2015; O’Donoghue et.al, 2001; Papert, 1988).
Multiscale and multidirection depth map super resolution with semantic inference

Abstract Depth map super resolution has been paid much attention in 3D applications due to the limitation of depth sensors. Few textures in objects with clear contours along them is the most important characteristic of depth map. An efficient image representation should be directional, multiscale and anisotropic. From this we propose a novel multiscale and multidirection depth map super resolution framework with semantic inference to improve the quality of depth maps. In this framework, a multiscale and multidirection depth map contour fusion scheme captures and assembles intrinsic geometrical structures through a multiview non‐subsampled contourlet transform manner. This scheme not only isolates the discontinuities of contours but retains the smoothness along the contours. The semantic inference is also utilized to segment and label the depth map into objects/backgrounds‐level which are coplanar. Furthermore, a semantic‐aware label refinement strategy is introduced to correct the rarely inaccurate labels of the label map for upscaling the target pixel with pixels in the same object or background. Experimental results on benchmark depth map dataset demonstrate that the proposed multiscale and multidirection depth map super resolution framework with semantic inference has a significant improvement than the state‐of‐the‐art algorithms both visually and quantitatively.
Technology integration in higher education and student privacy <i>beyond</i> learning environments—A comparison of the <scp>UK</scp> and <scp>US</scp> perspective

Abstract Technology integration in higher education (HE) has brought immense innovation. While research is investigating the benefits of leveraging, through learning analytics, the data created by the greater presence of technology in HE, it is also analysing the privacy implications of vast universes of data now at the fingertips of HE administrators. This paper argues that student privacy challenges linked to technology integration occur not only within but also beyond learning environments, namely at the enterprise level. By analysing the UK and US legal frameworks surrounding how HE institutions respond to parents demanding disclosure of their adult children's personal data in the event of mental health crises, this paper offers an example of real and complex privacy issues, often overlooked by interdisciplinary inquiry, that exist in the ‘interstitial space’ between HE technology and privacy law. The purpose of conducting a comparative analysis was to demonstrate that countries with different privacy regimes are similarly ill‐equipped to address certain student privacy issues at the HE enterprise level, leaving HEIs exposed to potential litigation/regulatory risks. The contribution of this work is to invite greater interdisciplinary awareness of, and inquiry into, student privacy beyond learning environments.
Beyond federated data: a data commoning proposition for the EU’s citizen-centric digital strategy

Abstract In various official documents, the European Union has declared its goal to pursue a citizen-centric governance of digital transformation. Through a critical review of several of these documents, here we show how “citizen-centric” is more a glamouring than a driving concept. De facto, the EU is enabling a federated data system that is corporate-driven, economic-oriented, and GDPR-compliant; in other words, a Digital Single Market (DSM). This leaves out societal and collective-level dimensions of digital transformation—such as social inclusion, digital sovereignty, and environmental sustainability—which are acknowledged, but not operationalized, by the EU as pillars of a citizen-centric governance. Hence, the door is open to a complementary approach to the governance of digital transformation. We argue that, while a federated data model can constitute the tech-legal backbone of the emerging DSM, a commoning of data, as an ecosystemic approach that maintains a societal and collective outlook by default, can represent a complement to enact a truly citizen-centric governance.
Financial inclusion and healthcare in Africa: Examining the moderating role of education

Abstract The 2023 Sustainable Development Goal Report reveals that Africa is still struggling in its pursuit to achieve universal healthcare coverage. However, financial risk protection and human capital development could come to the rescue by facilitating the attainment of quality healthcare services. This study examines the effect of financial inclusion on healthcare in Africa, spanning from 2000 to 2021. Healthcare is proxied by life expectancy at birth, the immunisation rate, and the lifetime risk of maternal death. Financial inclusion is approached through access and usage of financial services. After controlling for the problem of potential endogeneity through the system generalised method of moment (GMM), the findings reveal that financial inclusion enhances healthcare in Africa, signifying that financially included individuals have a higher conditional probability of spending more on improving their health relative to the financially excluded individuals. Moreover, the findings indicate that education and technology diffusion are imperative in the quest for enhancing healthcare in Africa. In addition, after testing for sensitivity analysis by adopting different indicators of healthcare, the results remain consistent throughout the study, confirming the role of financial inclusion in enhancing healthcare in Africa. After computing the marginal effects, the findings depict that education and financial inclusion interact to produce positive synergy effects, signifying that the positive role of financial inclusion and education in enhancing healthcare outweighs the negative conditional effect. The results recommend policymakers establish a framework that promotes financial literacy for the enhancement of healthcare in Africa.
Scale-preserving automatic concept extraction (SPACE)

Abstract Convolutional Neural Networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the Industry 4.0. When these CNNs behave in ways unexpected to human users or developers, severe consequences can arise, such as economic losses or an increased risk to human life. Concept extraction techniques can be applied to increase the reliability and transparency of CNNs through generating global explanations for trained neural network models. The decisive features of image datasets in quality control often depend on the feature’s scale; for example, the size of a hole or an edge. However, existing concept extraction methods do not correctly represent scale, which leads to problems interpreting these models as we show herein. To address this issue, we introduce the Scale-Preserving Automatic Concept Extraction (SPACE) algorithm, as a state-of-the-art alternative concept extraction technique for CNNs, focused on industrial applications. SPACE is specifically designed to overcome the aforementioned problems by avoiding scale changes throughout the concept extraction process. SPACE proposes an approach based on square slices of input images, which are selected and then tiled before being clustered into concepts. Our method provides explanations of the models’ decision-making process in the form of human-understandable concepts. We evaluate SPACE on three image classification datasets in the context of industrial quality control. Through experimental results, we illustrate how SPACE outperforms other methods and provides actionable insights on the decision mechanisms of CNNs. Finally, code for the implementation of SPACE is provided.
Global temporal pyramid for human abnormal action recognition

With the development of monitoring technology and the improvement of people's security awareness, intelligent human abnormal action recognition technology in the field of action recognition is increasingly high. In most cases, abnormal human action may have little difference in appearance compared with normal behavior, so the control of visual rhythm information becomes an important factor affecting action recognition, but people often focus on the appearance information of the action and ignore the rhythm information. In this paper, we introduce the temporal pyramid module to process the visual tempos information, meanwhile, the traditional LSTM local history information transfer method is very easy to lose the context information, which is not conducive to the grasp of global information and thus will greatly affect the processing effect of the temporal pyramid. This paper introduces a non-local neural network module to enhance the network's ability to grasp global information and the model's long-range modeling capability, which is used to supplement the temporal pyramid module. Finally, this paper uses the mainstream anomaly dataset UCF-Crime to test the network performance, and the improved network model recognition accuracy AUC reaches 0.82, which is better than other stateof-the-art methods.
Face mask recognition during KYC generation from a live photo detection methodology

Post Pandemic world of Covid-19 has set human race to a transitioned frequency. Through this transitional period the world needs to serve itself with much needed technology and services. The need of time is now to build the system compatible to the crisis we face together that ensures a risk-free safe environment. New regulations and measures have been established in order to provide safety that includes regular wear of face mask. It is necessary to strictly execute this new rule in public places that helps reducing the spread of virus. Along the mask, a mask detector system comes hand in hand playing a vital role. On official image recording sectors, the mask detection is a requirement to ensure the identity of a personnel. This surveillance method enables the alert system to remove the mask while taking any photograph for documentation that demands clear photographic identity. In this paper, we have briefly discussed this approach of face mask detection system while capturing a photo using deep learning algorithms.
A novel feature ranking algorithm for text classification: Brilliant probabilistic feature selector <scp>(BPFS)</scp>

Abstract Text classification (TC) is a very crucial task in this century of high‐volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter‐based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based on its algorithm to order the features. The classification process is then carried out by choosing the top‐N features. However, each method's feature order is distinct from the others. Each method selects by giving the qualities that are critical to its algorithm a high score, but it does not select by giving the features that are unimportant a low value. In this paper, we proposed a novel filter‐based FS method namely, brilliant probabilistic feature selector (BPFS), to assign a fair score and select informative features. While the BPFS method selects unique features, it also aims to select sparse features by assigning higher scores than common features. Extensive experimental studies using three effective classifiers decision tree (DT), support vector machines (SVM), and multinomial naive bayes (MNB) on four widely used datasets named Reuters‐21,578, 20Newsgroup, Enron1, and Polarity with different characteristics demonstrate the success of the BPFS method. For feature dimensions, 20, 50, 100, 200, 500, and 1000 dimensions were used. The experimental results on different benchmark datasets show that the BPFS method is more successful than the well‐known and recent FS methods according to Micro‐F1 and Macro‐F1 scores.
Multi-sensor fusion for the security surveillance of public areas

Increasing security awareness in the public sector are leading to a more and more widespread use of surveillance applications. Although the available technologies like video processing are already well advanced, they still suffer from high false alarm rates when used under realistic conditions. We present a method for sensor fusion based on probability density maps and a rule engine. The system was tested in a public area using the combination of audio localization, audio classification and video detection using 79 simulated scenarios and 44 hours of sample data recorded over a period of several weeks. The false positive rate decreased by 60% and the event localization rate increased by 25% with the fusion approach compared to the detection performance of individual techniques