Reviewerly/exHarmony huggingface dataset:
Social Media Communication Using TOPSIS Method

Our social media engagement a focus on trust in research We pay.Hence, the social and emotional development of this generation Most of it happens on the internet and cell phones.The entities we call "the media", Society is a provider of infrastructure for life have been involved for over a century, and the world A specific and Invested in a privileged way.Media and information infrastructures are expanding, converging, and every day in an era deeply embedded in the fabric of life, Between "Media" and "Society".Understanding dialectics is very urgent.Media Being social is obvious is emphasized.Research significance: Social media communication focuses on social relationships and relationships Pays, therefore trust in other contexts creating is a bit different.This study is a qualitative one that took the approach and seventeen major Australian Crises in organizations, social communication corporate use of media explored.Analysis of Face book and Twitter messages conducted.Social media users, for example, Videos documenting the disaster on YouTube or with peers on social networking sites allows uploading and viewing -of their website even on compatible mobile phones.Crisis In times of social networking sites like Twitter or micro blogging services such as blogs Scholars have studied the activities.Face book, Twitter, YouTube, and many more social network Sites where users share online content, similar Connect with like-minded people and allow allows Its strength -rapid spread and Content proliferation and informal conversations Ability to facilitate -use it in a professional environment A powerful tool.Methodology: Among the many MCDA/MCTM methods developed to solve real-world decision-making problems, the technique of order prioritization by ideal solution (TOPSIS) continues to perform satisfactorily in various application areas.In this paper, we conduct a state-of-the-art literature review of taxonomy research on TOPSIS applications and methodologies.Alternative
Development of a Blockchain-Based Application for Digital Certificates in Education

Traditional academic certificates have mostly been issued in paper or other physical format and maintained in a registry or a database for a long period of time by a certification authority. The blockchain technology has enabled creation of a decentralized, transaction-based systems, where any transaction ever completed is recorded in a verifiable and permanent way. In this paper, we propose a blockchain-based school management system, named "Ethernal Digital Certificate". Its main purpose is maintaining the data considering summer school events and its participants, as well as issuing certificates for those events. The data is stored on Polygon Supernets blockchain platform. The certificates are issued in the form of NFTs for students who have successfully completed all the courses on an event and they are kept on students' wallets. Once issued, the certificates can't be sold and transferred from one wallet to another. Since the system is based on the blockchain technology and therefore decentralized, there is no need for any third-party organization to verify the certificates.
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multiple methods to enrich the training data. However, introducing intricate design choices or heuristics makes it hard to understand which elements of these methods are indeed crucial for improving robustness. In this work, we take a step back and follow a principled approach to achieve robustness to common corruptions. We propose PRIME, a general data augmentation scheme that relies on simple yet rich families of max-entropy image transformations. PRIME outperforms the prior art in terms of corruption robustness, while its simplicity and plug-and-play nature enable combination with other methods to further boost their robustness. We analyze PRIME to shed light on the importance of the mixing strategy on synthesizing corrupted images, and to reveal the robustness-accuracy trade-offs arising in the context of common corruptions. Finally, we show that the computational efficiency of our method allows it to be easily used in both on-line and off-line data augmentation schemes. Our code is available at https://github.com/amodas/PRIME-augmentations .
Using Conceptual Models in Agile Software Development: A Possible Solution to Requirements Engineering Challenges in Agile Projects

Studies on requirements engineering with Agile methods for software development have shown difficulties in managing the quality of the requirements and communicating with users.Some of these studies have proposed conceptual modeling as a solution to these problems.However, the effort that is required to create conceptual models conflicts with Agile values.In this paper, we propose an approach for using conceptual models in projects while adhering to Agile principles.This approach focuses on projects in which requirements are expressed as user stories that are the main artifacts of the requirements used for software development with Agile methods.First, the paper presents a literature review in which we have systematically searched for the challenges to requirements engineering with Agile methods.Next, we report on a survey study in which we interviewed 16 experts in the Agile methodology to confirm the identified challenges and find new ones that are not covered in the literature.Based on a thematic analysis of the challenges, we argue that most of them map to the two main purposes of using conceptual models in software development: improving communication and understanding requirements.To effectively use conceptual models in projects that use the Agile methodology, several conditions must be met, which we make explicit in the paper.The paper ends by illustrating how these conditions can be met demonstrating the models that can be automatically generated from a given set of user stories.This demonstration was subsequently used to obtain feedback from the experts on the perceived benefits of conceptual models in addressing the challenges of requirements engineering.
Difficulty-Aware Simulator for Open Set Recognition

Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we present a novel framework, DIfficulty-Aware Simulator (DIAS), that generates fakes with diverse difficulty levels to simulate the real world. We first investigate fakes from generative adversarial network (GAN) in the classifier’s viewpoint and observe that these are not severely challenging. This leads us to define the criteria for difficulty by regarding samples generated with GANs having moderate-difficulty. To produce hard-difficulty examples, we introduce Copycat, imitating the behavior of the classifier. Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively. As a result, DIAS outperforms state-of-the-art methods with both metrics of AUROC and F-score. Our code is available at https://github.com/wjun0830/Difficulty-Aware-Simulator .
Towards Hard-Positive Query Mining for DETR-Based Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is a core task for high-level image understanding. Recently, Detection Transformer (DETR)-based HOI detectors have become popular due to their superior performance and efficient structure. However, these approaches typically adopt fixed HOI queries for all testing images, which is vulnerable to the location change of objects in one specific image. Accordingly, in this paper, we propose to enhance DETR’s robustness by mining hard-positive queries, which are forced to make correct predictions using partial visual cues. First, we explicitly compose hard-positive queries according to the ground-truth (GT) position of labeled human-object pairs for each training image. Specifically, we shift the GT bounding boxes of each labeled human-object pair so that the shifted boxes cover only a certain portion of the GT ones. We encode the coordinates of the shifted boxes for each labeled human-object pair into an HOI query. Second, we implicitly construct another set of hard-positive queries by masking the top scores in cross-attention maps of the decoder layers. The masked attention maps then only cover partial important cues for HOI predictions. Finally, an alternate strategy is proposed that efficiently combines both types of hard queries. In each iteration, both DETR’s learnable queries and one selected type of hard-positive queries are adopted for loss computation. Experimental results show that our proposed approach can be widely applied to existing DETR-based HOI detectors. Moreover, we consistently achieve state-of-the-art performance on three benchmarks: HICO-DET, V-COCO, and HOI-A. Code is available at https://github.com/MuchHair/HQM .
Context-Consistent Semantic Image Editing with Style-Preserved Modulation

Semantic image editing utilizes local semantic label maps to generate the desired content in the edited region. A recent work borrows SPADE block to achieve semantic image editing. However, it cannot produce pleasing results due to style discrepancy between the edited region and surrounding pixels. We attribute this to the fact that SPADE only uses an image-independent local semantic layout but ignores the image-specific styles included in the known pixels. To address this issue, we propose a style-preserved modulation (SPM) comprising two modulations processes: The first modulation incorporates the contextual style and semantic layout, and then generates two fused modulation parameters. The second modulation employs the fused parameters to modulate feature maps. By using such two modulations, SPM can inject the given semantic layout while preserving the image-specific context style. Moreover, we design a progressive architecture for generating the edited content in a coarse-to-fine manner. The proposed method can obtain context-consistent results and significantly alleviate the unpleasant boundary between the generated regions and the known pixels.
Deep-Learning-Based Blockchain for Secure Zero Touch Networks

The recent technological advancements in wireless communication systems and the Internet of Things (IoT) have accelerated the development of zero touch networks (ZTNs). ZTNs provide self-monitoring, self-configuring, and automated service-level policies that cannot be fulfilled by the traditional network management and orchestration approaches. Despite the hype, the majority of data exchange between participating entities occurs over insecure public channels, which present a number of possible security risks and attacks. Toward this end, we first analyze the attack surface on IoT-enabled ZTNs and the inherent architectural flaws for such threats. After an overview of attack surface, this article presents a new deep-learning- and blockchain-assisted case study for secure data sharing in ZTNs. Specifically, first, we design a novel variational autoencoder (VAE) and attention-based gated recurrent units (AGRU)-based intrusion detection system (IDS) for ZTNs. Second, a novel authentication protocol that combines blockchain, smart contracts (SCs), elliptic curve cryptography (ECC), and a proof of authority (PoA) consensus mechanism is developed to improve secure data sharing in ZTNs. The extensive experimental results show the effectiveness of the proposed approach. Lastly, this work discusses critical issues, opportunities, and open research directions to solve these challenges.
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning

Class-Incremental Learning (CIL) struggles with catastrophic forgetting when learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging without access to the training data of previously learned classes. Though recent DFCIL works introduce techniques such as model inversion to synthesize data for previous classes, they fail to overcome forgetting due to the severe domain gap between the synthetic and real data. To address this issue, this paper proposes relation-guided representation learning (RRL) for DFCIL, dubbed R-DFCIL. In RRL, we introduce relational knowledge distillation to flexibly transfer the structural relation of new data from the old model to the current model. Our RRL-boosted DFCIL can guide the current model to learn representations of new classes better compatible with representations of previous classes, which greatly reduces forgetting while improving plasticity. To avoid the mutual interference between representation and classifier learning, we employ local rather than global classification loss during RRL. After RRL, the classification head is refined with global class-balanced classification loss to address the data imbalance issue as well as learn the decision boundaries between new and previous classes. Extensive experiments on CIFAR100, Tiny-ImageNet200, and ImageNet100 demonstrate that our R-DFCIL significantly surpasses previous approaches and achieves a new state-of-the-art performance for DFCIL. Code is available at https://github.com/jianzhangcs/R-DFCIL .
Development of a Generalizable Framework for Machine Learning-based Evaluation of Written Explanations of Reaction Mechanisms from the Post-secondary Organic Chemistry Curriculum

To assess the understanding of reaction mechanisms it is necessary to ask learners what the lines, letters, arrows, and other symbols mean. Regurgitation of mechanistic representations is an insufficient measure of learning. In this chapter, we report a framework for assessing understanding through the evaluation of written explanations of what is happening and why for a broad array of reaction mechanisms taught in the postsecondary organic chemistry curriculum. We outline levels of explanation sophistication that can be used to identify opportunities for developing deeper and more meaningful understanding. This work builds on educational research on how organic chemistry students learn and develop expertise in using reaction mechanisms to predict and explain chemical transformations. Purposely considering mechanisms from a mechanistic step or mechanistic component perspective (for example, understanding of a proton transfer) has the potential to spark fresh insights for new and innovative means to facilitate learning. Our long-term goal for this work is to operationalize the framework by applying machine learning techniques to analyze written responses which will provide targeted feedback to educators and learners as they develop understanding of reaction mechanisms.
Long Movie Clip Classification with State-Space Video Models

Most modern video recognition models are designed to operate on short video clips (e.g., 5–10 s in length). Thus, it is challenging to apply such models to long movie understanding tasks, which typically require sophisticated long-range temporal reasoning. The recently introduced video transformers partially address this issue by using long-range temporal self-attention. However, due to the quadratic cost of self-attention, such models are often costly and impractical to use. Instead, we propose ViS4mer, an efficient long-range video model that combines the strengths of self-attention and the recently introduced structured state-space sequence (S4) layer. Our model uses a standard Transformer encoder for short-range spatiotemporal feature extraction, and a multi-scale temporal S4 decoder for subsequent long-range temporal reasoning. By progressively reducing the spatiotemporal feature resolution and channel dimension at each decoder layer, ViS4mer learns complex long-range spatiotemporal dependencies in a video. Furthermore, ViS4mer is $$2.63{\times }$$ faster and requires $$8{\times }$$ less GPU memory than the corresponding pure self-attention-based model. Additionally, ViS4mer achieves state-of-the-art results in 6 out of 9 long-form movie video classification tasks on the Long Video Understanding (LVU) benchmark. Furthermore, we show that our approach successfully generalizes to other domains, achieving competitive results on the Breakfast and the COIN procedural activity datasets. The code is publicly available ( https://github.com/md-mohaiminul/ViS4mer ).
Adaptive Cooperative Load Transportation by a Team of Quadrotors With Multiple Constraint Requirements

Cable-suspended load carried by multiple unman- ned aerial vehicles (UAVs) has applications in many areas. However, most existing aerial load transportation works are tailored to a specific type of load transportation tasks, or assume simplified system models or transportation scenarios. Furthermore, no existing works on this topic can provide a unified framework to address multiple performance and safety constraints during the cooperative transportation operation. In this paper, we propose and investigate a new constrained cooperative control architecture for an UAV team, which are collaboratively carrying a three-dimensional load, subject to multiple user-defined time-varying performance and safety constraint requirements. A unified framework using universal barrier functions has been proposed to deal with different types of constraint requirements. Moreover, control saturation and uncertainties in UAV inertia matrices are dealt with by employing adaptive estimators. Exponential convergence on the distance and attitude tracking errors can be guaranteed by the algorithm. Lastly, we discuss a simulation example that further shows the efficacy of the proposed cooperative control framework.
Ensemble Deep Random Vector Functional Link Neural Network for Regression

Inspired by the ensemble strategy of machine learning, deep random vector functional link (dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different datasets.Our present work first fills the gap of dRVFL and edRVFL work in the field of regression.We test and evaluate the performances of the dRVFLs on regression problems.Subsequently, we propose a novel regularization method [boosted factor (BF)], two dRVFLs variants [edRVFL with skip connection (edRVFL-SC) and edRVFL with random skip connections (edRVFL-RSC)] and one strategy [ensemble skip connection edRVFL (esc-edRVFL)] which show significant improvement over the original dRVFL.The BF is a newly introduced hyperparameter to scale the values of the activated hidden neurons to accommodate the diversity of the data, and it is also able to filter the neurons.edRVFL-SC and edRVFL-RSC are the edRVFL variants with skip connections.In edRVFL-SC, we apply dense skip connections to the edRVFL, which is inspired by the residual architecture in the deep learning area.However, due to the specificity of randomized networks, the simple skip connections are probably leading to the reuse of useless features.To address this problem, we propose a random skip connection-based edRVFL, which can keep the diversity in the latent space.esc-RVFL is an ensemble scheme that utilizes several edRVFL-RSC models trained on the different folds of the training dataset.The esc-edRVFL is identified as the best-performing algorithm through a comprehensive evaluation of 31 UCI datasets.
Word Segmentation on Discovered Phone Units With Dynamic Programming and Self-Supervised Scoring

Recent work on unsupervised speech segmentation has used self-supervised models with phone and word segmentation modules that are trained jointly. This paper instead revisits an older approach to word segmentation: bottom-up phone-like unit discovery is performed first, and symbolic word segmentation is then performed on top of the discovered units (without influencing the lower level). To do this, I propose a new unit discovery model, a new symbolic word segmentation model, and then chain the two models to segment speech. Both models use dynamic programming to minimize segment costs from a self-supervised network with an additional duration penalty that encourages longer units. Concretely, for acoustic unit discovery, duration-penalized dynamic programming (DPDP) is used with a contrastive predictive coding model as the scoring network. For word segmentation, DPDP is applied with an autoencoding recurrent neural as the scoring network. The two models are chained in order to segment speech. This approach gives comparable word segmentation results to state-of-the-art joint self-supervised segmentation models on an English benchmark. On French, Mandarin, German and Wolof data, it outperforms previous systems on the ZeroSpeech benchmarks. Analysis shows that the chained DPDP system segments shorter filler words well, but longer words might require some external top-down signal.
Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

This research introduced a novel simple metaheuristic called as total interaction algorithm (TIA).TIA is a swarm intelligence which relies on the interaction among solutions in the population.The core and distinct concept of TIA is that each solution interacts with all other solutions in every iteration to find the best possible solution.Then, this new alternative replaces the current solution if it is better than the current solution.In this research, TIA is tested to solve 23 functions that represent both unimodal and multimodal problems.TIA is benchmarked with five metaheuristics: particle swarm optimization (PSO), marine predator algorithm (MPA), golden search optimizer (GSO), guided pelican algorithm (GPA), and driving training-based optimizer (DTBO).The result indicates that TIA is superior to other benchmark metaheuristics, especially in solving the high dimension functions.TIA is better than PSO, MPA, GSO, GPA, and DTBO in 22,21,16,11,13 functions.The result also indicates that the increase of the maximum iteration improves the performance of TIA mostly in solving high dimension unimodal functions.Meanwhile, the increase of the population size is less significant to improve its performance.Overall, this research resumes that the interaction with as many as possible individuals is proven better than with only selected individuals as implemented in many other metaheuristics.