Reviewerly/exHarmony huggingface dataset:
Deep Adaptive Interest Network for CTR Prediction

Click-Through Rate (CTR) prediction is important in many industrial applications, such as E-commerce, news and information. Understanding sophisticated feature interactions behind users’ behaviors is essential for CTR prediction. Although existing methods have made significant improvements, there exist some problems: (1) only concentrate on modeling implicit information from the user side, while ignoring the interest hidden in the historical interactive behaviors; (2) insufficient feature extraction, only focus on high-order feature interactions or low-order feature interactions. To overcome these limitations, we propose a Deep Adaptive Interest Network (DAIN) for CTR Prediction in the local and global views, respectively. Specifically, to extract user’s interest, we first develop a local attention mechanism applied to the user behaviors and candidate ads, which can adaptively calculate users’ interest representation given a candidate ad in the local views. To capture feature interactions, we propose a feature interaction extractor containing Multi-layer Perceptrons (MLP) and Factorization Machines (FM) components to capture high-order and low-order feature interactions. To adaptively learn the influences of high-order and low-order feature interactions on the target item, we finally employ a linear-based global attention mechanism in the feature interaction extractor. The effectiveness of DAIN is verified by comprehensive experiments on three datasets.
Postdigital Bildung as a Guiding Principle to Foster Inclusion in Educational Media

Abstract Pieper, Neuhaus, and Vogt propose that the German concept of Bildung enriches existing debates around education and learning in the digital realm. This argument is based on the implications of (post)digitality for the handling of teaching and learning materials, with a special focus on inclusion sensitivity in the context of Open Educational Resources (OER). OER have inclusive potential which must be actively shaped in order to avoid exclusionary effects. Against this background, the concept of postdigital Bildung is developed, recognizing the entanglements of the subject with an equally entangled digitality. It is suggested that the critical approach of postdigital Bildung fosters an active, inclusion-oriented, and participatory discourse around the ideals, potentials, and limitations of education in general and educational media and resources in particular.
Let’s Figure It Out: Participatory Methods for Reflecting on Educational Media in a Postdigital World

Abstract The “postdigital condition” is characterized by an entanglement of digital and analogue media technologies and practices, with two of today’s primary societal challenges being a loss of privacy in data-driven media practices and the implicit power relations within media constellations that determine who is able to participate how. Education plays a crucial role in this context, being both the sphere in which reflection on these challenges can take place and also being itself based on digital media constellations that must in turn be reflected upon. This chapter considers two workshop concepts that facilitate such reflection: one based on Data Protection Impact Assessments (DPIA), based on risk management and the tradition of technology impact assessments, and an original approach for reflection on media constellations that has its roots in media and practice theory.
Social Participation in a Postdigital–Biodigital Age

Abstract This chapter introduces the concept of social participation and analyzes its recent transformations in a postdigital–biodigital reality. It explores the main sources of biodigital (in)equality and links them to the nature of biodigital communication. Drawing firstly on the work of Kate O’Riordan, the chapter argues that traditional communication based on an exchange of information is significantly different from emerging forms of biodigital communication and identifies the regulation of biodigital communication as the key question of social participation in a postdigital–biodigital age. The discussion then addresses in detail the complex power relationships that emerge in this age, the varying levels of participation that different individuals enjoy, and associated political rationalities. The chapter then explores aspects of postdigital–biodigital policymaking using the case of education and drawing on Human Data Interaction (HDI) theory.
Learning Academic Practices: Enabling Students to Participate in a Postdigital Society

Abstract By understanding participation as a critical and reflexive mindset and attitude, the authors assume that learning about and undergoing research practices supports students’ participation in a postdigital society. Focusing on teaching and learning in higher education, the chapter draws attention to the conceptional work of research-based learning and shows how research practices such as networking, reflecting, researching and organizing introduce students not only to academic research but also, to a greater extent, foster their abilities to participate in a postdigital society.
From Official Document Utopias to a Collective Utopian Imagination

Abstract In their chapter, Teräs, Teräs, and Suoranta critique the current dominant narrative of the digitalization of higher education. They argue that reports and vision papers by powerful international and national organizations such as the OECD not only predict but also build a certain kind of digital future. They call these reports “official document utopias,” and reflect on their discursive truth with past utopian literature. The authors argue that we should use collective utopian methods to imagine genuinely alternative “postdigital” futures, and to this end, they introduce the Method of Empathy-Based Stories. While discussing its history, they also reflect on its potential and limitations through a study with Finnish teacher students, imagining the digitalization of higher education in 2050.
Distance Learning and the Question of Educational Justice: A Dialogic Approach to Digital Diversity in Schools

Abstract This chapter introduces a theory of postdigital recognition that highlights the vulnerable, empowered, critical, and resistant learner in transformative education via co-creative learning settings. It problematizes digital learning in online scenarios fostered by the COVID-19 pandemic, particularly drill-and-practice routines, and shows how, while e-portfolios can overcome the schematized self-guidance demanded by drill-and-practice scenarios, they can also exercise a more harmful form of control as a result of postdigital practices that norm creativity. This can be particularly harmful to disadvantaged students. Similarly, while video conferences provide a digital space in which processes of transformative education can be inspired, these are also prone to the postdigital pressures of control and contingency. The theory of postdigital recognition proposed by the author empowers students to interact with digitality in a meaningful and responsible manner.
Participation as a Key Principle of Education for Sustainable Development in the Postdigital Era

Abstract This chapter explores the significance of Education for Sustainable Development (ESD) and media education for active participation and co-creation within postdigital structures. While democracy, co-determination, and active design were the hopes of the early Internet, these are only rarely compatible with the actual structures of postdigitality. Our ubiquitous digital environment and corresponding media structures are powerfully shaped by the monopolistic providers of a digital capitalism, especially in the education sector, and students are rarely introduced to alternatives. Yet a guiding didactic principle of ESD and media education is not to provide clear-cut answers but to enter into an exploratory and a co-creative process in the postdigital era. By jointly exploring and critically considering the implications of digital technologies for sustainable development, actors can better understand the madeness and feasibility of postdigitality and their role in it.
An ensemble framework for patent classification

An important task when a patent application arrives at a patent office is to assign one or more classification codes. This manual, intellectually demanding task needs to be supported or even fully automated by classification systems that will classify patent applications, hopefully with an accuracy close to patent professionals. Like in many other text analysis problems, in the last years, this task has been studied using deep learning techniques. However, these techniques did not manage to reach a classification accuracy high enough to totally depend on. An ensemble system that combines multiple classifiers obtaining better results could address this patent classification problem. However, this technique has not been explored in the domain of patent classification, and even in general, there are few studies focusing on the design of such systems. Our study investigates the design aspects of ensemble systems for patent classification and introduces an ensemble framework, which although is targeting the patent classification problem can be transferred to any other research domain.
Photovoltaic generation forecasting using convolutional and recurrent neural networks

Due to climate change consequences, it is very important to replace fossil energy resources with renewable energy resources. Solar energy is one of the main types of renewable energy resources which is harnessed by Photovoltaic (PV) Cells. It is important to accurately forecast how much electricity these energy resources generate to help operate and maintain the electricity grid. But the generation of electricity by PV is often associated with large uncertainty due to varying features like radiation, wind, humidity, and temperature. Deep learning methods have proved useful for this forecasting problem but the spatial information of features for this type of method has not received the due attention for PV generation forecasting. This study aimed to explore how both spatial and temporal information can be considered via a deep learning approach. In this paper, we propose a PV generation forecaster that considers both spatial and temporal information. A convolutional neural network is used as a pre-processing step to capture spatial information. The convolutional neural network is followed by a gated recurrent unit neural network to model temporal characteristics. The proposed model enriches the forecaster model by feeding more meaningful features into the recurrent neural network rather than raw data. The proposed model can predict a horizon for which there is no available information on irradiance, humidity, or wind. We show experimentally that our method is competitive with the state-of-the-art in terms of time and memory requirement while resulting in better prediction performance. The proposed model is applied to real data collected by the research team, and its performance is compared with some state-of-the-art methods. The results show the advantage of the proposed method.
Maximizing model generalization for machine condition monitoring with Self-Supervised Learning and Federated Learning

Deep Learning (DL) can diagnose faults and assess machine health from raw condition monitoring data without manually designed statistical features. However, practical manufacturing applications require robust and repeatable solutions that can be trusted in dynamic environments. Machine data is often unlabeled and from very few health conditions (e.g., only normal operating data). Furthermore, models often encounter shifts in domain as process parameters change and new categories of faults emerge. Traditional supervised learning may struggle to learn compact, discriminative representations that generalize to these unseen target domains since it depends on having plentiful classes to partition the feature space with decision boundaries. Transfer Learning (TL) with domain adaptation attempts to adapt these models to unlabeled target domains but assumes similar underlying structure that may not be present if new faults emerge. This study proposes focusing on maximizing the feature generality on the source domain and applying TL via weight transfer to copy the model to the target domain. Specifically, Self-Supervised Learning (SSL) with Barlow Twins may produce more discriminative features for monitoring health condition than supervised learning by focusing on semantic properties of the data. Furthermore, Federated Learning (FL) for distributed training may also improve generalization by efficiently expanding the effective size and diversity of training data by sharing information across multiple client machines. Results show that Barlow Twins outperforms supervised learning in an unlabeled target domain with emerging motor faults when the source training data contains very few distinct categories. Incorporating FL may also provide a slight advantage by diffusing knowledge of health conditions between machines. Future work should continue investigating SSL and FL performance in these realistic manufacturing scenarios.
Simulating quantum transport via collisional models on a digital quantum computer

Digital quantum computers have the potential to study the dynamics of many-body quantum systems. Nonequilibrium open quantum systems are, however, less straightforward to be implemented. Here we explore the feasibility of studying steady-state transport in strongly interacting many-body quantum systems on a digital quantum computer. To do so, we consider a collisional model representation of the nonequilibrium open dynamics for a boundary-driven $XXZ$ spin chain. More specifically, we investigate how the depth of the quantum circuit is affected by how close we want the steady state to be to the one expected from the underlying master equation. We study the simulation of a boundary-driven spin chain in regimes of weak and strong interactions, which would lead in large systems to diffusive and ballistic dynamics, considering also possible errors in the implementation of the protocol. Last, we analyze the effectiveness of digital simulation via the collisional model of current rectification when the $XXZ$ spin chains are subject to nonuniform magnetic fields and show that, although the circuit depths required to reach steady states are still prohibitive for today's hardware, few collisions are enough to suggest a strong rectifying power.
Is There a Difference between Paper and Electronic Chinese Signatures?

The purpose of this study is to investigate whether there are differences in handwritten Chinese signatures on different media including paper and electronic devices. Participants were asked to sign specified names on various types of media and the signatures were scanned or saved digitally for subsequent analysis. In this study, using convolutional neural networks and Siamese neural networks as classifiers and comparators, the performance plunge is revealed and thus considerable dissimilarity between the signatures on different media is implied. To further explore this, cubic Bézier curves are fitted to the signatures using the least square method for quantitative statistical analysis. By analyzing the visual changes in the morphology of strokes, several features of signatures are selected and computed, and the paired t ‐test and the Wilcoxon signed‐rank test are implemented, which provides a deeper substantiation and explanation of the findings.
IDRM: Brain tumor image segmentation with boosted RIME optimization

Timely diagnosis of medical conditions can significantly mitigate the risks they pose to human life. Consequently, there is an urgent demand for an effective auxiliary model that assists physicians in accurately diagnosing medical conditions based on imaging data. While multi-threshold image segmentation models have garnered considerable attention due to their simplicity and ease of implementation, the selection of threshold combinations greatly influences the segmentation performance. Traditional optimization algorithms often require substantial time to address multi-threshold image segmentation problems, and their segmentation accuracy is frequently unsatisfactory. As a result, metaheuristic algorithms have been employed in this domain. However, several algorithms suffer from drawbacks such as premature convergence and inadequate exploration of the solution space when it comes to threshold selection. For instance, the recently proposed optimization algorithm RIME, inspired by the physical phenomenon of rime-ice, falls short in terms of avoiding local optima and fully exploring the solution space. Therefore, this study introduces an enhanced version of RIME, called IDRM, which incorporates an interactive mechanism and Gaussian diffusion strategy. The interactive mechanism facilitates information exchange among agents, enabling them to evolve towards more promising directions and increasing the likelihood of discovering the optimal solution. Additionally, the Gaussian diffusion strategy enhances the agents' local exploration capabilities and expands their search within the solution space, effectively preventing them from becoming trapped in local optima. Experimental results on 30 benchmark test functions demonstrate that IDRM exhibits favorable optimization performance across various optimization functions, showcasing its robustness and convergence properties. Furthermore, the algorithm is applied to select threshold combinations for brain tumor image segmentation, and the results are evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The overall findings consistently highlight the exceptional performance of this approach, further validating the effectiveness of IDRM in addressing image segmentation problems.
Traffic Congestion Detection from Surveillance Videos using Deep Learning

Countless cameras, both public and private, have been installed in recent years for the objectives of surveillance, the monitoring of anomalous human activities, and traffic surveillance. Numerous worrisome and aberrant actions, such as theft, aggression, and accidents, make it difficult to notice and recognise such behaviour in a real-world setting. The topic of this study is car wrecks as depicted in online videos of traffic. Modern traffic monitoring and surveillance rely heavily on video traffic surveillance cameras (VTSS). Consequences of a rapidly expanding human population include a higher frequency of accidental injuries. The VTSS is employed to identify unusual occurrences on various roads and highways, such as traffic congestion and car accidents. When accidents happen on lengthy roadways or in remote areas, victims are often powerless and some don't make it. The purpose of this study is to provide a method for automatically identifying incidents in surveillance footage. Convolutional-neural-networks (CNNs), a specific deep learning approach developed to cope with grid-like data, have been shown to be useful in image and video processing, according to a study of the relevant literature. This study use a rolling prediction method and convolutional neural networks (CNNs) to detect accidents in VTSS footage. A dataset of anomalous photographs, called the Vehicle Accident Image Dataset (VAID), was created and used in the training of the CNN model. The proposed method was put through its paces by analysing data gathered from running the trained CNN model on a number of different films. This study's findings demonstrate a 93% success rate in identifying traffic accident incidents in films from traffic surveillance systems.
A Deep Learning Approach to Detect Plagiarism in Bengali Textual Content using Similarity Algorithms

Plagiarism occurs when someone uses another person’s words or ideas without properly citing the original writers. It can be considered with or without the original author’s consent. This includes all manuscripts, print works, electronic works, and published and unpublished works. Detecting Bengali plagiarism has been the topic of several previous research. Numerous scholars have attempted to detect and prevent plagiarism using various methodologies and models, each with limits and drawbacks. Plagiarism detection in Bangla documents is uncommon and unavailable. As a result, this article developed a more efficient way that uses deep learning techniques to encode Bengali words and phrases as vectors using the word2vec and fasttext method. The proposed method was implemented to a Bengali Wikipedia dataset as well as original datasets collected by authors totaling 112,183 entries. In this study, the proposed model works effectively and can find both plagiarized and actual documents with a high accuracy of 100% based on data similarity and the extrinsic evaluation of the embeddings on a downstream NLP task, which is sentiment analysis, with a high accuracy of 91.4% using word embeddings generated by fasttext. Cosine similarity outperforms Jaccard similarity techniques in this suggested model.
Application of Steady State Data Compressed Sensing Based on LSTM and RNN in Rural Power Grid

With the advancement of intelligent power systems and the upgrading of rural power grids, steady-state signal compression technology for rural power grids has been widely applied. The use of neural network models for encoding and compressing steady-state signals in rural power grids is currently a commonly used data compression method. This article analyzes data compression methods based on LSTM and RNN models, and compares their compression effects. The results show that RNN based models can better achieve data compression while maintaining high data accuracy.
Proactive Horizontal Pod Autoscaling in Kubernetes using Bi-LSTM

The management and implementation of distributed applications has been completely transformed by software containerization technologies. Before providing the containers to the clients, they must be correctly scaled. It is crucial that cloud service providers efficiently scale and distribute their resources and avoid under and over-provisioning of resources. Particularly, additional care should be taken for under provisioning of resources to avoid crashing of distributed applications. It is hard to manually assign resources to customers based on their continuously fluctuating workloads. The resource provisioning must be quick and automatic. Kubernetes provides a feature called autoscaler which allocates resources dynamically. However, the default autoscaler in Kubernetes is reactive as it will scale resources when load comes and allocation takes some time to get ready. This reactive nature may decrease the overall performance of application deployed. Hence, in this work, we present a proactive autoscaler mechanism that employs Bi-LSTM model to anticipate future demands and scale the containers automatically. The results using 3 node Kubernetes setup reveals that Bi-LSTM performs better than stacked LSTM and proactive autoscaler performs better than default Kubernetes autoscaler.
Research on Intelligent Monitoring Technology of Traffic Flow Based on Computer Vision

Due to the limitations of traditional video image processing methods, such as the inability to accurately obtain the location information of moving targets, and the inability to meet practical application requirements, this paper proposes a new computer vision-based intelligent traffic flow monitoring system solution. In our experiments, we use a large amount of data collected in real scenarios to verify the effectiveness and feasibility of the proposed algorithm. The results show that the proposed intelligent traffic flow monitoring system can meet the practical application requirements and provide strong support for urban road traffic management.