Reviewerly/exHarmony huggingface dataset:
Don’t Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance

This paper does not describe a novel method. Instead, it studies an incremental, yet must-know baseline given the recent progress in sparse neural network training and Generative Adversarial Networks (GANs). GANs have received an upsurging interest since being proposed due to the high quality of the generated data. While achieving increasingly impressive results, the resource demands associated with the large model size hinders the usage of GANs in resource-limited scenarios. For inference, the existing model compression techniques can reduce the model complexity with comparable performance. However, the training efficiency of GANs has less been explored due to the fragile training process of GANs. In this paper, we, for the first time, explore the possibility of directly training sparse GAN from scratch without involving any dense or pre-training steps. Even more unconventionally, our proposed method enables directly training sparse unbalanced GANs with an extremely sparse generator from scratch. Instead of training full GANs, we start with sparse GANs and dynamically explore the parameter space spanned over the generator throughout training. Such a sparse-to-sparse training procedure enhances the capacity of the highly sparse generator progressively while sticking to a fixed small parameter budget with appealing training and inference efficiency gains. Extensive experiments with modern GAN architectures validate the effectiveness of our method. Our sparsified GANs, trained from scratch in one single run, are able to outperform the ones learned by expensive iterative pruning and re-training. Perhaps most importantly, we find instead of inheriting parameters from expensive pre-trained GANs, directly training sparse GANs from scratch can be a much more efficient solution. For example, only training with a 80% sparse generator and a 70% sparse discriminator, our method can achieve even better performance than the dense BigGAN.
Constructing smarter and more sustainable urban ecosystems: a dynamic analysis of challenges and initiatives

Abstract Cities’ weaknesses have been revealed by their struggle to respond to current challenges ( e.g. , pandemic crises and population and urbanization exponential growth). Urban planners are thus increasingly concerned about implementing initiatives in their metropolises that favor a better present and future quality of life. In this context, cities need to become enablers of economic and social development and increased prosperity through the integration of technologies into projects promoting smarter and more sustainable urban ecosystems. These ecosystems are, however, highly complex due to their specificities and multifaceted nature, which makes analyzing them a difficult endeavor. Based on a constructivist and complementarity logic, this study sought to develop a multicriteria analysis model to support relevant decision-making processes in this study context. A panel of experts was recruited to create the analysis system using a combination of cognitive mapping and the decision-making trial and evaluation laboratory technique in an neutrosophic context. This approach overcomes various flaws identified in previous related research. The entire procedure focused on enhancing the experts’ learning through participation not only to structure the problem under analysis but also to identify and prioritize the factors and/or determinants of smartness and urban sustainability. The model provides a holistic, solid, and clear vision of the decision problem that fosters appropriate choices when creating and evaluating smart, sustainable urban ecosystems.
An Empirical Study of Web API Versioning Practices

As Web APIs evolve, developers assign them version identifiers to reflect the amount and the nature of changes that the API clients should expect. In this work we focus on identifying versioning practices adopted by Web API developers by extracting and classifying version identifiers found in a large collection of OpenAPI descriptions. In particular, we observe how frequently different versioning schemes have been adopted for identifying both stable and preview releases (e.g., simple version counters, semantic versioning, or release timestamps). We further study the stability of versioning schemes during APIs evolution. We also detect APIs which offer dynamic access to versioning metadata through dedicated endpoints as well as APIs which support clients expecting to reach up to 14 different versions of the same API at the same time. Overall the results offer a detailed view over current Web API versioning practices and can serve as the basis for future discussions on how to standardize critical API versioning metadata.
Uncovering Hidden Vulnerabilities in Convolutional Neural Networks through Graph-based Adversarial Robustness Evaluation

Convolutional neural networks (CNNs) are widely used for image classification, but their vulnerability to adversarial attacks poses challenges to their reliability and security. However, current adversarial robustness (AR) measures lack a theoretical foundation, limiting the insight into the decision process. To address this issue, we propose a new AR evaluation framework based on Graph of Patterns (GoPs) models and graph distance algorithms. Our approach provides a fine-grained analysis of AR from three perspectives, providing targeted insight into the vulnerability of CNNs. Compared to current standards, our approach is theoretically grounded and allows fine-tuning of model components without repeated attempts and validation. Our experimental results demonstrate its effectiveness in uncovering hidden vulnerabilities in CNNs and providing actionable approaches to improve their AR. Our GoPs modeling approach and graph distance algorithms can be extended to apply to other graph machine learning tasks such as Metric Learning on multi-relational graphs. Overall, our framework represents significant progress in AR evaluation, providing a more interpretable, targeted, and efficient approach to assess CNN robustness in complex graph-based systems.
Enhancing image steganography via adversarial optimization of the stego distribution

Adversarial steganography aims to fool steganalysis models and enhance embedding security. Its application in the sequential min-max steganographic game has recently shown state-of-the-art embedding security. However, current methods suffer from high computational costs and inferior convergence. These issues could be caused by unnecessary optimization on distortions and the monotonous criterion for adversaries. This paper introduces a novel and concise scheme for conducting adversarial steganography and refines the criterion for adversaries. The suggested adversarial optimizations leave out distortions and focus on optimizing the stego distribution, whose difference with the intrinsic cover distribution is the primary factor affecting embedding security. These optimization techniques are accomplished by utilizing gradient descent on embedding modification probabilities and introducing a novel protocol for cover enhancement. Moreover, the proposed criteria for adversaries prioritize enhancing the similarity between each cover-stego pair, rather than evading steganalysis models, to break monotony and enhance convergence. The discussion and refinement of the loss function and termination setting of the optimization process are also provided. The proposed method demonstrates its benefits in increased security and computation efficiency in experiments. After collaborating with the steganographic min-max game, the state-of-the-art embedding security and stronger resistance to various and unexpected steganalysis models are seen.
Macaroni: Crawling and Enriching Metadata from Public Model Zoos

Machine learning (ML) researchers and practitioners are building repositories of pre-trained models, called model zoos. These model zoos contain metadata that detail various properties of the ML models and datasets, which are useful for reporting, auditing, reproducibility, and interpretability. Unfortunately, the existing metadata representations come with limited expressivity and lack of standardization. Meanwhile, an interoperable method to store and query model zoo metadata is missing. These two gaps hinder model search, reuse, comparison, and composition. In this demo paper, we advocate for standardized ML model metadata representation, proposing Macaroni, a metadata search engine with toolkits that support practitioners to obtain and enrich that metadata.
DCM: Dynamic Client-Server Code Migration

The underlying Client/Server architecture of the Web inherently raises the question of the distribution of application logic between client and server. Currently, this distribution is static and fixed at design time, inhibiting dynamic and individual load distribution between clients and server at runtime. The benefits of dynamic migration allow balancing the needs of users, through increased responsiveness, and software providers, through better resource usage and cost reductions. Recent additions to the Web environment like WebAssembly provide a technological basis to move units of code at runtime. However, making use of them to extend a web application with dynamic code migration capabilities is challenging for web engineers. To that end, we devise a novel distributed Client/Server software architecture for web applications that supports dynamic migration of code at runtime, addressing the technical challenges of dependency management, distribution of control and data flow, and the required communication and interfaces. Our novel software architecture aims at providing a point of reference to web engineers seeking to extend their web applications with dynamic code migration capabilities and to contribute to the current re-consideration of the Web environment in the light of the standardization and wide-spread support of WebAssembly in all major browsers. Our experiments with 3 scenarios show that implementing such architecture is not only feasible but also that the impact on performance is negligible.
Quantum Web Services Orchestration and Management Using DevOps Techniques

Quantum computing is a new approach to computing based on quantum mechanics. It holds great promise for solving problems that classical computers cannot reach in a reasonable time, up to the point that it is already attracting the interest of the scientific and industrial communities. As quantum technology continues to advance and quantum computers become more prevalent, the need for hybrid systems that can integrate quantum software with classical information systems is becoming increasingly pressing. One of the most promising solutions for this integration is web engineering, which provides artifacts that are already well consolidated in classical computing and that can be integrated into quantum systems. Therefore, web engineering can provide a leap forward in the advancement of quantum computing. This thesis aims to design an orchestration method to coordinate and integrate quantum services into classic software systems and propose DevOps techniques for managing the life cycle of these systems.
Waiter and AUTRATAC: Don’t Throw It Away, Just Delay!

The modern web is built with a mixture of HTML, CSS, and an increasing amount of JavaScript. Since JavaScript determines $$\approx $$ 70% of the overall data of websites on average, JavaScript code efficiency significantly influences their loading performance. A recent study revealed that the average JavaScript code usage until render is $$\approx $$ 40%. For economical and convenience reasons, a significant amount of research already focuses on optimizing the delivered data to reduce loading times. For example, a large area of research focuses on eliminating dead code, where unused functions are deleted. Code in this context is classified as “dead” when the results of the code or the whole code itself are never executed or used. Since code elimination is based on heuristics/code classification, there is always a trade-off between code elimination and missing JavaScript code that harms the correct functioning of the website. As a result, some pages do not load correctly after elimination. Even the most advanced attempts with user input emulation do not achieve 100% accuracy. In this paper, we introduce two new open-source frameworks called Waiter, which waits until a resource is available, and AUTRATAC, an automatic transpiler to awaitable code. Both can be used separately or in combination to robustly delay pieces of JavaScript code without breaking it. For example, it allows code to be executed without loading all called functions beforehand. Therefore, when eliminating dead code optimized with AUTRATAC, the loading of unused functions can be delayed in a non-render-blocking way. This also opens up multiple new opportunities, as future code-splitting techniques might be significantly stricter without breaking a page. Our results show that delaying render-blocking JavaScript reduces loading times until First Contentful Paint (FCP) significantly, especially at slower network speeds. In one instance, an 85% drop in loading time was measured. Furthermore, it was visible that deferring the code with the developed frameworks still matches the total code execution time of the original render-blocking JavaScript.
Video anomaly detection with NTCN-ML: A novel TCN for multi-instance learning

A key challenge in video anomaly detection is the identification of rare abnormal patterns in the positive instances as they exhibit only a small variation compared to normal patterns, and they are largely biased by the dominant negative instances. To address this issue, we propose a weakly supervised video anomaly detection model called NTCN-ML - Novel Temporal Convolutional Network Multi-Instance Learning Model. The NTCN-ML model extracts temporal representations of video data to construct a time-series pattern to optimize the multi-instance learning process. The model examines the correlation between positive and negative samples in the multi-instance learning process to balance the feature association between rare positive and negative instances. The video anomaly detection with the NTCN-ML model achieved 95.3% and 85.1% accuracy for UCF-Crime and ShanghaiTech datasets, respectively, and outperformed the baseline models.
A Reliable Energy Trading Strategy in Intelligent Microgrids Using Deep Reinforcement Learning

A microgrid is a system that incorporates various decentralized power sources and manages the distribution of electricity. In microgrid, prosumers play a significant role in trading electricity, and it is essential to establish an effective power trading mechanism to incentivize their active participation. This study proposes a power trading mechanism for prosumers in microgrids, which incorporates blockchain technology to protect their rights and interests. The mechanism employs reinforcement learning to optimize trading decisions and develops a reputation mechanism to evaluate prosumers' trustworthiness based on their past transactions. The proposed strategy encourages prosumers to conduct more transactions with honest peers by introducing reputation value rewards into the benefit function. The simulation results indicate that the proposed strategy outperforms traditional approaches and reputation value significantly impacts prosumers' utility. Overall, the proposed strategy aims to promote honest transactions and enhance prosumers' participation in microgrid power transactions.
Designing conditions for coexistence

• A novel ontology that challenges the inherent anthropocentrism of design practice. • Brings together diverse expertise from design, architecture, humanities, and business. • Intersects theoretical and practical knowledge to inform the future of design. • Advances five principles for Designing Conditions for Coexistence. • Design as an exploratory practice capable of building more-than-human relationships.
Enhancing Resilience in Coastal Regions from a Socio-Ecological Perspective: A Case Study of Andhra Pradesh, India

Addressing the second priority of the Sendai Framework on Disaster Risk Reduction (SFDRR) for strengthening disaster risk governance, this paper considers coastal regions from a Socio-Ecological System (SES) perspective. It identifies the drivers of risk and critical linkages between various key components of coastal SESs that can potentially influence the resilience of coastal regions. The study considers that, in a coastal area, where the system is complex with numerous stakeholders, any decision taken in the form of developmental policy or as an adaptive practice is critical for system changes, thereby making risk anticipation and its management difficult. To further conduct a detailed investigation of this subject, seven settlements along the coastal Andhra Pradesh (India) region were methodically selected. Herein, focus group discussions were conducted to prepare fuzzy cognitive maps (FCM), which were further analyzed through the mental modeler online platform. By analyzing the structural metrics of the FCM, saltwater intrusion was identified as a significant system component. Furthermore, the study also analyzed the effects of change on the characteristics of selected SES components regarding the effectiveness of existing governance strategies and, conversely, the impact of implementing governance strategies on the identified critical components of SES. By critically examining the various scenarios thus generated, the research identifies various components, generating vulnerabilities in human systems. The derived results were cross-validated through community consultation, and the temporal variations in the state of system components were mapped. The study is expected to provide considerable input to policymakers for enhancing decision-making in coastal regions from an SES perspective.
Heterogeneous attention based transformer for sign language translation

Sign language translation (SLT) has attracted significant interest both from research and industry, enabling convenient communications with the deaf-mute community. While recent transformer-based models have shown improved sign translation performance, it is still under-explored how to design an efficient transformer-based deep network architecture that effectively extracts joint visual-text features by exploiting multi-level spatial and temporal contextual information. In this paper, we propose heterogeneous attention based transformer(HAT), a novel SLT model to generate attentions from diverse spatial and temporal contextual levels. Specifically, the proposed light dual-stream sparse attention-based module yields more effective visual-text representations compared to conventional transformers. Extensive experiments demonstrate that our HAT achieves state-of-the-art performance on the challenging PHOENIX2014T benchmark dataset with a BLEU-4 score of 25.33 on the test set.
Prediction and aggregation of regional PV and wind generation based on neural computation and real measurements

With the increasing integration of renewable energy sources (RES) in electricity grid networks, the power supply system has been changed to be decentralised. The complexity of the grids increases. So the estimation and prediction of regional renewable power integration is essential for grid stability and planning. However, the power prediction from single virtual power plant (VPP) is no longer suitable for the large scale regional renewable power generation. This paper proposed a prediction model based on neural computation. Various architectures of artificial neural network (ANN) are studied. Different input features are investigated and uncertainty of the prediction results are assessed. The relative mean absolute error (rMAE) to installed capacity can reach 2.5 % in summer and 0.5 % in winter for PV power prediction. By using the reduced mean values of input categories features the rMAE can reach 1.7 % and 4.9 % for PV and wind prediction for the whole year respectively.
A Model for Qur’anic Sign Language Recognition Based on Deep Learning Algorithms

Deaf and dumb Muslims cannot reach advanced levels of education due to the impact of obstruction on their educational attainment. This leads to their inability to learn, recite, and understand the meanings and interpretations of the Holy Qur’an as easily as ordinary people, which also prevents them from applying Islamic rituals such as prayer that require learning and reading the Holy Qur’an. In this paper, we propose a new model for Qur’anic sign language recognition based on convolutional neural networks through data preparation, preprocessing, feature extraction, and classification stages. The proposed model is aimed at recognizing the movements of the Arabic sign language by recognizing the hand gestures that refer to the dashed Qur’anic letters in order to help the deaf and dumb learn their Islamic rituals. The experiments have been conducted on a part of a large Arabic sign language dataset called ArSL2018, which represents the 14 dashed letters in the Holy Qur’an, so that this part contains only 24,137 images. The experimental results demonstrate that the proposed model performs better than the other existing models.
Principles of a unified open personalized computer-integrated learning environment for the Junior Academy of Sciences of Ukraine

This study aims to determine the conceptual foundations of a unified personalized open computer-integrated learning environment for the Junior Academy of Sciences of Ukraine (JASU). The research tasks include identifying priority directions for developing students' research competence, analyzing the innovative activities of JASU in implementing cloud-based ICT (Information and Communication Technology), and defining the objectives and structure of the "Learning Interaction Support Server" software complex as the basis for a unified personalized open computer-integrated learning environment for JASU. The research object focuses on the process of developing students' research competence within JASU, while the subject encompasses the concept of a unified personalized open computer-integrated learning environment for JASU based on network ICT services.Research methods employed in this study involve analyzing regulatory documents, scientific publications, articles, monographs, conference proceedings, and seminars. Additionally, an analysis of the extracurricular education system and the structure and activities of JASU was conducted.The research findings entail identifying priority directions for fostering innovative thinking and culture, designing an acmeological educational space considering the innovative development of education, personal demands, societal and state needs. Furthermore, the innovative activities of JASU were examined, and models for developing students' research competence within JASU were proposed. The implementation of cloud technologies, as the most promising innovative direction for the development of network ICT services, was discussed. Moreover, the tasks and structure of the "Learning Interaction Support Server" software complex—an adaptive distributed information and knowledge management system and personalized networked virtual electronic platforms for educational actors—were determined.The main conclusions and recommendations highlight that the creation of a unified personalized open computer-integrated learning environment for JASU will ensure the presence of all educational process participants, regardless of time and location. It will also promote the development and effective utilization of managed educational information resources, including personalized databases for students and educators. Ultimately, the motivation and productivity of scientific research will be significantly enhanced.