Reviewerly/exHarmony huggingface dataset:
Hyper Metamorphism: Hyper Secure and Trustworthy 5G Networks using Blockchain with IoT

5G wireless networks, Internet of Things (IoT), Blockchain and autonomous communications are transforming from being the tech buzzwords towards a hyper-connected, high-quality life and business applications. Integration of the research trends into a problem-solving, working, impact-full application requires efficient systems. With swift 5G wireless networks, the autonomous device communications share local and global resources with each other. However, autonomous sharing requires a certain level of trust management among devices. Social Internet of Things (SIoT) envisions a decentralized approach to quantify device trust, stored in the devices. In this article, we present and propose a hybrid network where the trust value must be globalized by utilizing the blockchain. We discuss a hyper-connected, co-existing paradigm of autonomous trust management locally and globally. Each device can calculate, manage and share trust values under a tightly scrutinized and secure system. Our physical experiments deduce that higher reward (ether value) from 5.3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> to 216.3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> reduces transaction time from 25 to 5 seconds.The increase in simultaneous requests also plays a role in increasing latency from 250 to 275 ms for 1 to 10 requests. However, the cost of the proposed solution is the transaction latency increases of approximately 2s.
Performance Enhancement Kit for Frame 51 Machine With DLN Combustor

Abstract A Performance Enhancement Kit (PEK) is introduced to infuse the latest technology to MS5001PA fleet there-by existing customers benefit from increased output, extended inspection intervals, improved reliability, and operability. The kit includes advanced-technology combustion system with improved design of hot gas path (HGP) components. The components in the KIT are designed for either enhanced output of 6% or for an increased interval between repairs or increased ‘Mean Time Between Maintenance’ (MTBM) from 24khrs to 35khrs. Initially PEK was introduced as part of MS5001PA in 2016 for standard combustor. Present study shows its application to Dry Low NOx (DLN) combustion system without affecting Maintenance Interval (MI). PEK DLN configuration alters the combustion and in-turn the Stage 1 Nozzle inlet profile. High fidelity CFD computations were performed to evaluate impact of these altered profile on HGP components, especially the thermal distribution and its impact on durability. Adiabatic wall temperatures obtained from CFD were used in developing heat transfer and flow temperature in the flow path locations. These were used to perform thermo-structural assessment for HGP components using ‘Finite Element Analysis’ (FEA) tools. With this evaluation, durability of HGP components in terms of LCF, creep was thoroughly assessed to ensure part life is meeting MI. Detailed high-fidelity FEA shows that, creep, oxidation, and ‘Low Cycle Fatigue’ (LCF) meet the design requirements for both life extension as well as the power increase conditions with DLN combustor profile. To gain confidence, MS5001PA durability results were benchmarked with field experience of ‘Standard’ (STD) and DLN configuration which shows coherent results with Creep and LCF. This gives confidence in thermal profile which was developed for PEK STD and DLN configurations. Based on detailed thermo-structural assessment, the maintenance factor was set to 1. This paper presents the latest methodology used in CFD for aero flow prediction and high-fidelity Finite Element (FE) assessment used for durability prediction of HGP components.
Literature Review: Bulletin Media in Learning as a Literacy Resource in Strengthening the National Literacy Movement Program

Through a survey of numerous articles connected to bulletin media in learning, this study intends to provide information on the utilization and development of bulletins as a learning medium. This study is classified as a literature review. The information data used was collected from scientific journals accredited by the Science and Technology Index (SINTA). The information was gathered by analyzing published data from 2016 through 2022. Researchers examined, analyzed, and collaborated to evaluate, assemble, and review the results. Choosing nine journal papers that investigate the use and development of bulletins as learning media. 9 articles were picked at random to represent the chosen theme research, namely the use and development of bulletin media. The literature review includes information on the title of the scientific article, the author, the source of the journal publication, the methods used, the findings, and the review's recommendations in describing specific concepts and explanations that are still missing from the essence of the scientific article. This scientific article review is meant to serve as a source of inspiration for educational progress through the use of various media in molding excellent education in accordance with national goals
Why AI Cannot Think

In the context of the recent interest in large language models (LLMs) and image creation using artificial intelligence, the debate about whether AI is capable of reasoning arises again and again. This paper argues that it is a mistake to understand the outputs of artificial intelligence in terms of an expression of thinking. It draws on both phenomenological arguments (Dreyfus, Cantwell Smith) and arguments from the context of an analytic anthropology (Davidson, McDowell): To be a thinking being, one must be able not only to operate with representations of the world, but also to understand them as representations of the world. To be a thinking being one also has to be a bearer of a form of life.
The Role of Culture in the Intelligence of AI

Artificial intelligence has received a new boost from the recent hype about large lan- guage models. However, to avoid misconceptions, it is better to speak of 'machine intelligence'. In addition to reflecting on current processes, the cultural sector can benefit from very specific machine learning approaches to transfer literary me- thods such as 'distant readings' and find new connections in cultural data. In light of resource and exploitation problems, what is needed is a 'critical technical practice' (Agre) that brings together various actors, productively engages with AI's own logics and error cultures, and uses its potential to cope with the flood of information. Artificial intelligence has received a new boost from the recent hype about large lan- guage models. However, to avoid misconceptions, it is better to speak of 'machi- ne intelligence'. In addition to reflecting on current processes, the cultural sector can benefit from very specific machine learning approaches to transfer literary me- thods such as 'distant readings' and find new connections in cultural data. In light of resource and exploitation problems, what is needed is a 'critical technical practice' (Agre) that brings together various actors, productively engages with AI's own logics and error cultures, and uses its potential to cope with the flood of information.
Digital Curation and AI

In this article, Clemens Neudecker discusses various use cases, from text recognition to image analysis or classification to contextualization, that have already demonstrated how digitization and curation can benefit from the use of AI. But applying black-box technologies from the private sector to cultural data without fully understanding the implications also involves risks. This paper presents two examples of the adoption of AI in cultural heritage from the Staatsbibliothek zu Berlin (Berlin State Library), highlights some of the recent criticisms of data practices in the domain of AI, and offers ideas and suggestions regarding the potential role and contribution of digital curators and cultural heritage institutions for the benefit of AI.
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree

The Combined Target-Assignment and Path-Finding (TAPF) problem requires simultaneously assigning targets to agents and planning collision-free paths for them from their start locations to their assigned targets. As a leading approach to addressing TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each tree. While CBSTA finds optimal solutions, it faces scalability challenges due to the duplicated collision resolution in multiple trees and the expensive computation of K-best assignments. We introduce Incremental Target Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS generates only a single search tree and avoids computing K-best assignments by incrementally computing new 1 -best assignments during the search. We show that ITA-CBS, in theory, is guaranteed to find optimal solutions and, in practice, runs faster than CBS-TA in 96.1% of 6,334 test cases.
Handling New Class in Online Label Shift

In many real-world applications, data are continuously accumulated within open environments. For instance, in disease diagnosis, the prevalence of diseases can vary across seasons, and new types of diseases can emerge. This paper investigates the problem of learning from unlabeled data where the label distribution evolves over time, and meanwhile, previously unseen new class appears in the data stream. To handle the new class in online label shift, we first design a novel risk estimator by unbiased risk rewriting and mixture proportion estimation. Subsequently, we employ the online ensemble paradigm for model updating to handle unknown distribution shifts. The proposed approach enjoys a theoretical guarantee of dynamic regret, ensuring its effectiveness in adapting to the changing label distribution and the presence of the new class in streams. Experiments conducted on diverse benchmark datasets and two real-world applications demonstrate the effectiveness of the proposed algorithm.
Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

Latent factor models are the dominant backbones of contemporary recommender systems (RSs) given their performance advantages, where a unique vector embedding with a fixed dimensionality (e.g., 128) is required to represent each entity (commonly a user/item). Due to the large number of users and items on e-commerce sites, the embedding table is arguably the least memory-efficient component of RSs. For any lightweight recommender that aims to efficiently scale with the growing size of users/items or to remain applicable in resource-constrained settings, existing solutions either reduce the number of embeddings needed via hashing, or sparsify the full embedding table to switch off selected embedding dimensions. However, as hash collision arises or embeddings become overly sparse, especially when adapting to a tighter memory budget, those lightweight recommenders inevitably have to compromise their accuracy. To this end, we propose a novel compact embedding framework for RSs, namely Compositional Embedding with Regularized Pruning (CERP). Specifically, CERP represents each entity by combining a pair of embeddings from two independent, substantially smaller meta-embedding tables, which are then jointly pruned via a learnable element-wise threshold. In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary. Given the compatibility with agnostic latent factor models, we pair CERP with two popular recommendation models for extensive experiments, where results on two real-world datasets under different memory budgets demonstrate its superiority against state-of-the-art baselines. The codebase of CERP is available in https://github.com/xurong-lianyCERP.
Concentric Ring Loss for Face Forgery Detection

The issue of detecting face forgeries has garnered significant interest in the field of computer vision, primarily driven by the growing social concerns of indistinguishable deepfake images. One of the primary obstacles encountered in the field of deepfake detection is enhancing the discriminative power of learned features. In this paper, we propose a Concentric Ring Loss (CRL) that aims to promote the learning of compressed intra-class features and separated inter-class features inside a model. Specifically, we apply margin penalties in both Euclidean and angular space separately, which serve to increase the separation between real and fake images. Moreover, we introduce a frequency-aware triplet network with a self-developed sample generation strategy, which provides efficient hard triplets for model training. Extensive experiments demonstrate the superiority of our methods over multiple datasets. We show that CRL consistently outperforms the state-of-the-art by a large margin.
Federated Learning for Privacy-Preserving Prediction of Occupational Group Mobility Using Multi-Source Mobile Data

This paper focuses on the mobility prediction problem of specific occupational groups that rely on mobile devices, predicting the mobilities of these groups using data shared across different platforms. While the mobility patterns of general populations have been studied extensively, predicting the movements of specific occupational groups like ride-hailing drivers, who frequently move over long distances, requires a more nuanced approach. This paper introduces FedOGM, a federated learning framework designed to predict the mobility of specific occupational groups while preserving user privacy. The framework utilizes a dynamic bidirectional graph attention network model DyBGAT for predicting the edge weights of an occupational dynamic Origin-Destination graph, representing the mobility behavior. In order to address key challenges in federated learning, FedOGM integrates By leveraging multi-source data from users’ mobile devices and extracting unique occupational features and occupational features into the framework, such as applications usage duration feature, location switching feature, and call duration feature. Assisted by these personalized occupational features, we have devised a client selection algorithm, generated occupational dynamic OD graphs to diminish communication overhead, and proposed a group-level FedAvg aggregation method. Experiments conducted on real-world datasets of ride-hailing drivers, ride-hailing truck drivers, and real estate salespersons, including scenarios with false data, confirm the efficacy of the proposed methods in both mobility prediction and privacy protection.
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness

Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to show that heterogeneous deep ensembles with high ensemble diversity can effectively leverage model learning heterogeneity to boost ensemble robustness. We first show that heterogeneous DNN models trained for solving the same learning problem, e.g., object detection, can significantly strengthen the mean average precision (mAP) through our weighted bounding box ensemble consensus method. Second, we further compose ensembles of heterogeneous models for solving different learning problems, e.g., object detection and semantic segmentation, by introducing the connected component labeling (CCL) based alignment. We show that this two-tier heterogeneity driven ensemble construction method can compose an ensemble team that promotes high ensemble diversity and low negative correlation among member models of the ensemble, strengthening ensemble robustness against both negative examples and adversarial attacks. Third, we provide a formal analysis of the ensemble robustness in terms of negative correlation. Extensive experiments validate the enhanced robustness of heterogeneous ensembles in both benign and adversarial settings. The appendix and source codes are available on GitHub at https://github.com/git-disl/HeteRobust.
MTT-DynGL: Towards Multidimensional Topology-oriented Time-series Dynamic Graphs Learning Model

Dynamic graph learning has received increasing attention in recent years. However, real-world graph data sets are characterized by significant structural complexity, attribute diversity, and temporal variability. Importantly, there are complex and significant influence mechanisms between them. All them pose great challenges to dynamic graph learning (DGL). To address them, we propose a novel dynamic graph learning framework, MTT-DynGL. First, graph attention networks (GAT) is used to efficiently aggregate the topology and multidimensional attribute features on each snapshot. Then, a temporal variation matrix with strength factors is designed to further measure the interaction mechanism between structures and attributes over time. Further, to effectively integrate the above results, a MTT-based dynamic graph learning network is designed. It consists of an MTT integration mechanism and a bidirectional dilated causal convolution network. The former is used to learn temporal variation features in an integrated manner, and the latter is used to improve learning quality and training efficiency. Finally, the effectiveness of our method is verified by multiple experiments.
Feedback Decision Transformer: Offline Reinforcement Learning With Feedback

Recent trajectory optimization methods for offline reinforcement learning ($R L$) define the problem as one of conditional-sequence policy modeling. One of these methods is Decision Transformer (DT), a Transformer-based trajectory optimization approach that achieved competitive results with the current state-of-the-art. Despite its high capabilities, DT underperforms when the training data does not contain full trajectories, or when the recorded behavior does not offer sufficient coverage of the states-actions space. We propose Feedback Decision Transformer (FDT), a data-driven approach that uses limited amounts of high-quality feedback at critical states to significantly improve DT’s performance. Our approach analyzes and estimates the Q-function across the states-actions space, and identifies areas where feedback is likely to be most impactful. Next, we integrate this feedback into our model, and use it to improve our model’s performance. Extensive evaluation and analysis on four Atari games show that FDT significantly outperforms DT in multiple setups and configurations.
SOAC: Supervised Off-Policy Actor-Critic for Recommender Systems

Improving users’ long-term experience in recommender systems (RS) has become a growing concern for recommendation platforms. Reinforcement learning (RL) is an attractive approach because it can plan and optimize long-term returns sequentially. However, directly applying RL as an online learning method in the RS setting can significantly compromise users’ satisfaction and experience. As a result, learning the recommendation policy from logged feedback collected under different policies has emerged as a promising direction. Offline learning enables the agent to utilize off-policy learning techniques. Nevertheless, several challenges need to be addressed, such as distribution shift. In this paper, we propose a novel RL method, called Supervised Off-Policy Actor-Critic (SOAC), for learning the recommendation policy from the logged feedback without exploration. The proposed SOAC addresses challenges, including distribution shift and extrapolation errors, and focuses on improving the ranking of items in a recommendation list. The experimental results demonstrate that SOAC can achieve better recommendation performance than existing supervised RL methods.
Enhancing Social Recommendation with Multi-View BERT Network

With the emergence of online social platforms enabling users to share their opinions with others, there has been a critical need for developing a recommendation system incorporating users’ social connections to learn their preferences. Social relations between users offer potential information about users’ preferences, which alleviates the data sparsity issue and boosts recommendation performance. Several efforts have been made to develop an efficient social recommendation system. Nevertheless, existing approaches have two significant drawbacks: First, they haven’t efficiently explored the complex correlations between the diverse influence of neighbours on users’ item preferences. Second, contemporary systems rely on the unidirectional context of items and fail to embrace bidirectional contexts and long range dependencies while predicting the next user-item interaction. To address the above issues, we propose a novel framework for Social Recommendation called Multi-View Bert Network (MVBN). This model incorporated Bidirectional Encoder Representations from Transformer(BERT) with Multi-Task learning to learn bidirectional context-aware user and item embeddings with neighbourhood sampling. Neighbourhood Sampling technique samples the most influential neighbours for all the items, and our proposed Sequence Header correlates sampled neighbours with users. The main objective of our model is to predict the next item that a user would interact with based on its interaction behaviour. Experiments on three real-world datasets demonstrate that the proposed MVBN model outperforms the state-of-the-art recommendation methods consistently and significantly. Finally, an extensive ablation study is carried out to validate the importance of each component in the system.
On Regularized Sparse Logistic Regression

Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve $\ell_{1}$-regularized logistic regression, there is no equivalently abundant work on solving sparse logistic regression with nonconvex regularization term. In this paper, we propose a unified framework to solve $\ell_{1}$-regularized logistic regression, which can be naturally extended to nonconvex regularization term, as long as certain requirement is satisfied. In addition, we also utilize a different line search criteria to guarantee monotone convergence for various regularization terms. Empirical experiments on binary classification tasks with real-world datasets demonstrate our proposed algorithms are capable of performing classification and feature selection effectively at a lower computational cost.
Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length

Graph embedding methods are effective techniques for representing nodes and their relations in a continuous space. Several studies try to embed graphs in constant curvature manifolds such as Euclidean, hyperbolic, and spherical space. It is critical how to select the best space for the graph embedding, as well as its dimensionality. In this study, we focus on the aforementioned constant curvature manifolds and aim at the dimensionality and curvature selection from the viewpoint of statistical model selection for latent variable models. Thereafter, we introduce universal latent variables models using wrapped normal distributions, which are the extension of Gaussian distribution for Riemannian manifolds. We then propose a novel methodology using decomposed normalized maximum likelihood code-length, which is based on the minimum description length principle. We empirically demonstrated the effectiveness of our method using both artificial and real-world datasets.