Reviewerly/exHarmony huggingface dataset:
Machine Learning Enhanced Blockchain Consensus with Transaction Prioritization for Smart Cities

In the given technology-driven era, smart cities are the next frontier of technology, aiming at improving the quality of people's lives. Many research works focus on future smart cities with a holistic approach towards smart city development. In this paper, we introduce such future smart cities that leverage blockchain technology in areas like data security, energy and waste management, governance, transport, supply chain, including emergency events, and environmental monitoring. Blockchain, being a decentralized immutable ledger, has the potential to promote the development of smart cities by guaranteeing transparency, data security, interoperability, and privacy. Particularly, using blockchain in emergency events will provide interoperability between many parties involved in the response, will increase timeliness of services, and establish transparency. In that case, if a current fee-based or first-come-first-serve-based processing is used, emergency events may get delayed in being processed due to competition, and thus, threatening people's lives. Thus, there is a need for transaction prioritization based on the priority of information and quick creation of blocks (variable interval block creation mechanism). Also, since the leaders ensure transaction prioritization while generating blocks, leader rotation and proper election procedure become important for the transaction prioritization process to take place honestly and efficiently. In our consensus protocol, we deploy a machine learning (ML) algorithm to achieve efficient leader election and design a novel dynamic block creation algorithm. Also, to ensure honest assessment from the followers on the blocks generated by the leaders, a peer-prediction-based verification mechanism is proposed. Both security analysis and simulation experiments are carried out to demonstrate the robustness and accuracy of our proposed scheme.
PROBLEMATIKA PEMBELAJARAN JARAK JAUH DI MIN 3 TANGERANG

Distance learning during the covid-19 pandemic was implemented by all schools so that several problems were found that were faced by teachers, students and parents. Distance learning, the role of parents is very necessary to support the smooth teaching and learning activities. A learning method in which the teaching process occurs separately from the learning process so that communication between teaching staff and students must be facilitated by printed materials, electronic media, and other media. During the COVID-19 pandemic, teachers are required to learn about information technology so that in providing learning it is not boring and creates enthusiasm for learning for students
Development as an Intellectual Process

This chapter contributes to rethinking African politics by viewing the continent’s development as an intellectual process in which resources are created for the overall satisfaction of human needs. Central to this process is the importance of developing resources over their distribution or consumption volume. Therefore, development as an intellectual process in which society’s resources are created to ensure a better life for its citizens is a central task. The best, most effective, and progressive way to do justice to this task is to put the people at the centre of development in advocacy, debate, and policy. The dominant and yet incorrect view that Africa’s economic sectors such as mining and agriculture are drivers of its economy and development should be replaced with the indispensable role of its human capital under the leadership of intellectuals in its economy and development. This role should be recognised in theory and practice. The point is that economic sectors derive their importance to economy and development from human beings using the knowledge, skills, and determination in deploying their resources to achieve tactical and strategic objectives. Therefore, African intellectuals, creators of leaders and institutions and organisations, producers and disseminators of knowledge and forces dominating the possession and utilisation of human capital without whose organic input no organisation or social pressure can achieve and sustain its strategic objectives, are indispensable to a rethinking of African politics of development they must lead. Their weapons in this role are their leadership of and role in institutions and organisations and individual knowledge and talents as proud continental assets through the mediation of parties under their leadership in deploying Africa’s natural resources, mainly mineral and agricultural resources, inputting the continent and its people first in terms of development.
Evaluación de la calidad de los cursos virtuales en la Universidad Abierta para Adultos, UAPA

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization

Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans and intelligent systems under unfavorable conditions (e.g., complete darkness). However, previously developed methods have poor colorization performance for small sample classes. Moreover, reducing the high confidence noise in pseudo-labels and addressing the problem of image gradient disappearance during translation are still under-explored, and keeping edges from being distorted during translation is also challenging. To address the aforementioned issues, we propose a novel learning framework called Memory-guided cOllaboRative atteNtion Generative Adversarial Network (MornGAN), which is inspired by the analogical reasoning mechanisms of humans. Specifically, a memory-guided sample selection strategy and adaptive collaborative attention loss are devised to enhance the semantic preservation of small sample categories. In addition, we propose an online semantic distillation module to mine and refine the pseudo-labels of NTIR images. Further, conditional gradient repair loss is introduced for reducing edge distortion during translation. Extensive experiments on the NTIR2DC task show that the proposed MornGAN significantly outperforms other image-to-image translation methods in terms of semantic preservation and edge consistency, which helps improve the object detection accuracy remarkably.
Adversarial robustness of VAEs through the lens of local geometry

In an unsupervised attack on variational autoencoders (VAEs), an adversary finds a small perturbation in an input sample that significantly changes its latent space encoding, thereby compromising the reconstruction for a fixed decoder. A known reason for such vulnerability is the distortions in the latent space resulting from a mismatch between approximated latent posterior and a prior distribution. Consequently, a slight change in an input sample can move its encoding to a low/zero density region in the latent space resulting in an unconstrained generation. This paper demonstrates that an optimal way for an adversary to attack VAEs is to exploit a directional bias of a stochastic pullback metric tensor induced by the encoder and decoder networks. The pullback metric tensor of an encoder measures the change in infinitesimal latent volume from an input to a latent space. Thus, it can be viewed as a lens to analyse the effect of input perturbations leading to latent space distortions. We propose robustness evaluation scores using the eigenspectrum of a pullback metric tensor. Moreover, we empirically show that the scores correlate with the robustness parameter $\beta$ of the $\beta-$VAE. Since increasing $\beta$ also degrades reconstruction quality, we demonstrate a simple alternative using \textit{mixup} training to fill the empty regions in the latent space, thus improving robustness with improved reconstruction.
Imperceptible and Robust Backdoor Attack in 3D Point Cloud

With the thriving of deep learning in processing point cloud data, recent works show that backdoor attacks pose a severe security threat to 3D vision applications. The attacker injects the backdoor into the 3D model by poisoning a few training samples with trigger, such that the backdoored model performs well on clean samples but behaves maliciously when the trigger pattern appears. Existing attacks often insert some additional points into the point cloud as the trigger, or utilize a linear transformation (e.g., rotation) to construct the poisoned point cloud. However, the effects of these poisoned samples are likely to be weakened or even eliminated by some commonly used pre-processing techniques for 3D point cloud, e.g., outlier removal or rotation augmentation. In this paper, we propose a novel imperceptible and robust backdoor attack (IRBA) to tackle this challenge. We utilize a nonlinear and local transformation, called weighted local transformation (WLT), to construct poisoned samples with unique transformations. As there are several hyper-parameters and randomness in WLT, it is difficult to produce two similar transformations. Consequently, poisoned samples with unique transformations are likely to be resistant to aforementioned pre-processing techniques. Besides, as the controllability and smoothness of the distortion caused by a fixed WLT, the generated poisoned samples are also imperceptible to human inspection. Extensive experiments on three benchmark datasets and four models show that IRBA achieves 80%+ ASR in most cases even with pre-processing techniques, which is significantly higher than previous state-of-the-art attacks.
Leveraging Distributional Bias for Reactive Collision Avoidance under Uncertainty: A Kernel Embedding Approach

Many commodity sensors that measure the robot and dynamic obstacle's state have non-Gaussian noise characteristics. Yet, many current approaches treat the underlying-uncertainty in motion and perception as Gaussian, primarily to ensure computational tractability. On the other hand, existing planners working with non-Gaussian uncertainty do not shed light on leveraging distributional characteristics of motion and perception noise, such as bias for efficient collision avoidance. This paper fills this gap by interpreting reactive collision avoidance as a distribution matching problem between the collision constraint violations and Dirac Delta distribution. To ensure fast reactivity in the planner, we embed each distribution in Reproducing Kernel Hilbert Space and reformulate the distribution matching as minimizing the Maximum Mean Discrepancy (MMD) between the two distributions. We show that evaluating the MMD for a given control input boils down to just matrix-matrix products. We leverage this insight to develop a simple control sampling approach for reactive collision avoidance with dynamic and uncertain obstacles. We advance the state-of-the-art in two respects. First, we conduct an extensive empirical study to show that our planner can infer distributional bias from sample-level information. Consequently, it uses this insight to guide the robot to good homotopy. We also highlight how a Gaussian approximation of the underlying uncertainty can lose the bias estimate and guide the robot to unfavorable states with a high collision probability. Second, we show tangible comparative advantages of the proposed distribution matching approach for collision avoidance with previous non-parametric and Gaussian approximated methods of reactive collision avoidance.
Learning Human Cognitive Appraisal Through Reinforcement Memory Unit

We propose a novel memory-enhancing mechanism for recurrent neural networks that exploits the effect of human cognitive appraisal in sequential assessment tasks. We conceptualize the memory-enhancing mechanism as Reinforcement Memory Unit (RMU) that contains an appraisal state together with two positive and negative reinforcement memories. The two reinforcement memories are decayed or strengthened by stronger stimulus. Thereafter the appraisal state is updated through the competition of positive and negative reinforcement memories. Therefore, RMU can learn the appraisal variation under violent changing of the stimuli for estimating human affective experience. As shown in the experiments of video quality assessment and video quality of experience tasks, the proposed reinforcement memory unit achieves superior performance among recurrent neural networks, that demonstrates the effectiveness of RMU for modeling human cognitive appraisal.
Character Education Oriented Discovery Model in Description Text Learning in Smp/Mts

The monotonous and boring learning conditions for students occur because there is no proper learning approach.Then, it was found that students had difficulties in learning descriptive text, because they had little vocabulary, and limited ideas to write about.In addition, one thing that is very important in the learning process is the growth of character values.A nation will fall down if its young generation has low morale, easily gives up, almost lost honesty, lacks discipline, lacks responsibility, and thins out the sense of Nationalism.Of the existing problems, of course, must find a solution, that's why research is done.This study aims to determine the design of the discovery model that is oriented towards character education in learning description texts in SMP/MTs.In this study we used the Research and Development (R&D) method with a Design Based Research (DBR) approach.This study involved 2 experts as validators, and 4 teachers as model users, namely Indonesian language teachers.The teachers came from State Junior High Schools, Private Junior High Schools, State MTs, and Private MTs.The results showed that the design of the discovery model oriented to character education in descriptive text learning is a new learning model that combines discovery learning models with character education, as well as descriptive text teaching materials containing character values.Based on the model syntax validation data, in general the assessment of the two validators concluded that the syntax of the character education-oriented discovery learning model obtained is a very feasible category.Furthermore, the feasibility test on the learning objectives of the descriptive text, the teachers gave an assessment that it is above the minimum completeness criteria, which means that it is very feasible to use.The results of the study indicate that the teacher of the discovery model oriented to character education in learning description texts in SMP/MTs is very much needed.
Accelerated parallel non-conjugate sampling for Bayesian non-parametric models

Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing features from some prior distribution. In special cases, where the integration is tractable, we can sample new feature assignments according to a predictive likelihood. We present a novel method to accelerate the mixing of latent variable model inference by proposing feature locations based on the data, as opposed to the prior. First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference. Next, we propose an approximate inference strategy to perform accelerated inference in parallel. A two-stage algorithm that combines the two approaches provides a computationally attractive method that can quickly reach local convergence to the posterior distribution of our model, while allowing us to exploit parallelization.
Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems

One of the major problems of most quantum computing applications is that the required number of qubits to solve a practical problem is much larger than that of today's quantum hardware. We propose an algorithm, called large-system sampling approximation (LSSA), to solve Ising problems with sizes up to $N_{\rm{gb}}2^{N_{\rm{gb}}}$ by an $N_{\rm{gb}}$-qubit gate-based quantum computer, and with sizes up to $N_{\rm{an}}2^{N_{\rm{gb}}}$ by a hybrid computational architecture of an $N_{\rm{an}}$-qubit quantum annealer and an $N_{\rm{gb}}$-qubit gate-based quantum computer. By dividing the full-system problem into smaller subsystem problems, the LSSA algorithm then solves the subsystem problems by either gate-based quantum computers or quantum annealers, optimizes the amplitude contributions of the solutions of the different subsystems with the full-problem Hamiltonian by the variational quantum eigensolver (VQE) on a gate-based quantum computer, and determines the approximated ground-state configuration. We apply the level-1 approximation of LSSA to solving fully-connected random Ising problems up to 160 variables using a 5-qubit gate-based quantum computer, and solving portfolio optimization problems up to 4096 variables using a 100-qubit quantum annealer and a 7-qubit gate-based quantum computer. We demonstrate the use of the level-2 approximation of LSSA to solve the portfolio optimization problems up to 5120 ($N_{\rm{gb}}2^{2N_{\rm{gb}}}$) variables with pretty good performance by using just a 5-qubit ($N_{\rm{gb}}$-qubit) gate-based quantum computer. The completely new computational concept of the hybrid gate-based and annealing quantum computing architecture opens a promising possibility to investigate large-size Ising problems and combinatorial optimization problems, making practical applications by quantum computing possible in the near future.
impact of cultural differences on LINE app communication among Thai and Chinese employees

This research explored the impact of cultural differences using LINE application as a communication channel toward the components of employee engagement. This research added more contributions to using LINE application and employee engagement which found that both factors--job demands and employee support--are associated positively with the intensity of social media use. The survey data were gathered from 300 Thai lecturers and 300 Chinese lecturers from a private university in Thailand. Partial least squares structural equation modeling was employed for data analysis. The results from the regression analysis showed that the LINE app used for personal related internal communication has negative effects on vigor, dedication and absorption for the Thai respondents. As for the Chinese respondents, it has positive effects on vigor, dedication and absorption. This means that in the Chinese context the more they are engaged with their personal whereabouts the more active they are in the workplace.
Real-time CPU-based View Synthesis for Omnidirectional Video

In this paper, the authors describe the real-time CPU-based implementation of the virtual view synthesis algorithm for high-resolution omnidirectional content. The proposed method allows a user of the immersive video virtually navigating within the scene captured by a multiview system comprised of 360-degree or 180-degree cameras. The proposed method does not require using powerful graphic cards as other state-of-the-art real-time synthesis methods. Instead, the emerge of consumer-grade multithreaded CPUs and CPU-based virtual view synthesis, allows further development of cheap, consumer immersive video systems. The proposed method was compared with the state-of-the-art view synthesis algorithm – RVS, both in terms of quality of synthesized views and computational time required for the synthesis, presenting the usefulness of the proposed method.
Directional Deduction

We present some new methods for logical deduction, based on ideas from ground theory. Roughly speaking, in our calculi a typical deduction will proceed as follows: we first analyse the premiss down to its ultimate grounds; then we discard information irrelevant to the conclusion; and then we synthesize the conclusion up from its ultimate grounds. We give a series of calculi for: classical propositional logic (Chapter 1); classical predicate logic (Chapter 2); modal propositional logic and modal predicate logic (Chapter 3); and some relevantistic fragments of these various systems (Chapters 4 and 5). In connection with these fragments we develop also some new semantic constructions, of 'truthmaker semantics' type.
Transformer Vs. MLP-Mixer: Exponential Expressive Gap For NLP Problems

Vision-Transformers are widely used in various vision tasks. Meanwhile, there is another line of works starting with the MLP-mixer trying to achieve similar performance using mlp-based architectures. Interestingly, until now those mlp-based architectures have not been adapted for NLP tasks. Additionally, until now, mlp-based architectures have failed to achieve state-of-the-art performance in vision tasks. In this paper, we analyze the expressive power of mlp-based architectures in modeling dependencies between multiple different inputs simultaneously, and show an exponential gap between the attention and the mlp-based mechanisms. Our results suggest a theoretical explanation for the mlp inability to compete with attention-based mechanisms in NLP problems, they also suggest that the performance gap in vision tasks may be due to the mlp relative weakness in modeling dependencies between multiple different locations, and that combining smart input permutations with mlp architectures may not be enough to close the performance gap alone.