Reviewerly/exHarmony huggingface dataset:
Aplikasi Pembelajaran Interaktif Bahasa Arab Dasar untuk Anak Berbasis Android

In the development of this modern era, there have been many technologies that are growing rapidly. As for computers, which are equipment created to facilitate human work when achieving progress both in the manufacture of hardware and software and not only computers, now developments in the field of mobile phones are included in the scope of SmartPhones, which It has been developed a lot starting from features or in the field of hardware and software, there have been many mobile-based applications that have been created and have been used by the wider community today and even become an obligation for all people to use smartphones that are equipped with various features and applications in it. starting from the field of Sales, Learning, business, Health and Education and this time the author will try to make a final project in the field of basic learning aimed at children to start an early learning stage of basic introduction and basic understanding of it's a lesson, the target we will take is learning a foreign language, namely Arabic with the system that we will make is aimed at children to make it easier for them to learn Arabic, easily, clearly and certainly fun
Pengembangan Lembar Kerja Peserta Didik (LKPD) Berbasis Model Pembelajaran Student Facilitator and Explaining (SFAE) pada Pembelajaran Matematka Kelas VIII SMPN 2 Ampek Angkek Tahun Pelajaran 2021/2022

This research is motivated by the problems found in class VIII of SMPN 2 Ampek Angkek, namely textbooks are the only source of learning for students. and the limited number of book packages means that not all students have a book package as a guide and guide in understanding lessons and learning only goes in one direction and is the size of the teacher. Thus making the learning outcomes of students low. to overcome this problem, teachers must have companion learning resources that can be used in the learning process, one of which is the Student Worksheet (LKPD). The designed LKPD is Student Facilitator and Explaining (SFAE) model-based LKPD. This study aims to obtain a valid, practical and effective Student Facilitator and Explaining (SFAE) model-based LKPD product in class VIII SMPN 2 Ampek Angkek. This research is a developmental research or Research and Development (R&amp;D) with the Sugiyono development model. The steps that researchers use are as follows. (1) potential and problems, (2) information gathering, (3) product design, (4) design validity, (5) improvement design, and (6) trials Based on the development research that has been carried out, it is obtained that LKPD has a very valid criterion of 83.4% for practicality, 86.26% for practicality, and 86.26% for effectiveness the very effective criterion is 83%. So it can be concluded that the Mathematics LKPD Based on the Student Facilitator and Explaining (SFAE) Learning model) In Class VIII Mathematics Learning at SMPN 2 Ampek Angkek that is developed is valid, practical, and effective.
Penggunaan Media Benda Konkret terhadap Hasil Belajar Matematika Tema Kegemaran pada Kelas III SDN Pangkemiri I Tulangan Sidoarjo

Theoretically, third grade elementary school students are still in the ability to think concretely and view everything as a whole. Meanwhile, based on observations of class III students at SDN Pangkemiri I Tulangan Sidoarjo, it shows that when teaching, the teacher does not use learning media so that students have difficulty understanding the material presented. The teacher is only guided by the package book and Student Worksheet (LKS) and the role of the teacher is more than that of the students. Data collection techniques carried out are through observation, tests, interviews and documentation. Subjects in the study amounted to 32 students / i. The results showed that the classical completeness of student learning outcomes increased in cycle II.
Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding

In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in PSNR of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community.
On Finite Blocklength Lossy Source Coding

In this monograph, we review recent advances in second-order asymptotics for lossy source coding, which provides approximations to the finite blocklength performance of optimal codes. The monograph is divided into three parts. In part I, we motivate the monograph, present basic definitions, introduce mathematical tools and illustrate the motivation of non-asymptotic and second-order asymptotics via the example of lossless source coding. In part II, we first present existing results for the rate-distortion problem with proof sketches. Subsequently, we present five generations of the rate-distortion problem to tackle various aspects of practical quantization tasks: noisy source, noisy channel, mismatched code, Gauss-Markov source and fixed-to-variable length compression. By presenting theoretical bounds for these settings, we illustrate the effect of noisy observation of the source, the influence of noisy transmission of the compressed information, the effect of using a fixed coding scheme for an arbitrary source and the roles of source memory and variable rate. In part III, we present four multiterminal generalizations of the rate-distortion problem to consider multiple encoders, decoders or source sequences: the Kaspi problem, the successive refinement problem, the Fu-Yeung problem and the Gray-Wyner problem. By presenting theoretical bounds for these multiterminal problems, we illustrate the role of side information, the optimality of stop and transmit, the effect of simultaneous lossless and lossy compression, and the tradeoff between encoders' rates in compressing correlated sources. Finally, we conclude the monograph, mention related results and discuss future directions.
Hamiltonian Neural Networks with Automatic Symmetry Detection

Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we enhance HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples, a pendulum on a cart and a two-body problem from astrodynamics are considered.
Improving Machine Translation with Phrase Pair Injection and Corpus Filtering

In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed approach, we observe an improvement in the Machine Translation (MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi, and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on the FLORES test data. These BLEU score improvements are over the models trained using the whole pseudo-parallel corpus augmented with the parallel corpus.
Multi-Agent Interplay in a Competitive Survival Environment

Solving hard-exploration environments in an important challenge in Reinforcement Learning. Several approaches have been proposed and studied, such as Intrinsic Motivation, co-evolution of agents and tasks, and multi-agent competition. In particular, the interplay between multiple agents has proven to be capable of generating human-relevant emergent behaviour that would be difficult or impossible to learn in single-agent settings. In this work, an extensible competitive environment for multi-agent interplay was developed, which features realistic physics and human-relevant semantics. Moreover, several experiments on different variants of this environment were performed, resulting in some simple emergent strategies and concrete directions for future improvement. The content presented here is part of the author's thesis "Multi-Agent Interplay in a Competitive Survival Environment" for the Master's Degree in Artificial Intelligence and Robotics at Sapienza University of Rome, 2022.
A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams

The growing deployment of human-robot collaborative processes in several industrial applications, such as handling, welding, and assembly, unfolds the pursuit of systems which are able to manage large heterogeneous teams and, at the same time, monitor the execution of complex tasks. In this paper, we present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs), in charge of actions scheduling, while the allocation problem is formulated through a Mixed-Integer Linear Program (MILP), that assigns dynamically individual roles or collaborations to the agents of the team. Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration (e.g. makespan, ergonomics, human preferences). Human preference are identified through a negotiation phase, in which, an human agent can accept/refuse to execute the assigned task.In addition, bilateral communication between humans and the system is achieved through an Augmented Reality (AR) custom user interface that provides intuitive functionalities to assist and coordinate workers in different action phases. The computational complexity of the proposed methodology outperforms literature approaches in industrial sized jobs and teams (problems up to 50 actions and 20 agents in the team with collaborations are solved within 1 s). The different allocated roles, as the cost functions change, highlights the flexibility of the architecture to several production requirements. Finally, the subjective evaluation demonstrating the high usability level and the suitability for the targeted scenario.
Geodesic slice sampling on the sphere

Probability measures on the sphere form an important class of statistical models and are used, for example, in modeling directional data or shapes. Due to their widespread use, but also as an algorithmic building block, efficient sampling of distributions on the sphere is highly desirable. We propose a shrinkage based and an idealized geodesic slice sampling Markov chain, designed to generate approximate samples from distributions on the sphere. In particular, the shrinkage based algorithm works in any dimension, is straight-forward to implement and has no tuning parameters. We verify reversibility and show that under weak regularity conditions geodesic slice sampling is uniformly ergodic. Numerical experiments show that the proposed slice samplers achieve excellent mixing on challenging targets including the Bingham distribution and mixtures of von Mises-Fisher distributions. In these settings our approach outperforms standard samplers such as random-walk Metropolis Hastings and Hamiltonian Monte Carlo.
A Meta-Learning Approach for Software Refactoring

Software refactoring is the process of changing the structure of software without any alteration in its behavior and functionality. Presuming it is carried out in appropriate opportunities, refactoring enhances software quality characteristics such as maintainability and extensibility. Thus far, various studies have addressed the problem of detecting proper opportunities for refactoring. Most of them are based on human expertise and are prone to error and non-meticulous. Fortunately, in recent efforts, machine learning methods have produced outstanding results in finding appropriate opportunities for refactoring. Sad to say, Machine learning methods mostly need plenty of data and, consequently, long processing time. Furthermore, there needs to be more annotated data for many types of refactoring, and data collection is time-consuming and costly. Accordingly, in this paper, we have formulated the problem of detecting appropriate opportunities for refactoring as a few-shot classification problem. We have utilized model-agnostic meta-learning (MAML), a recognized meta-learning algorithm, to learn a neural network on tasks from high-resource data. The trained model, then, is adapted to a model with high accuracy for tasks from low-resource data. Experimental results revealed 91% accuracy, which illustrates the effectiveness and competitiveness of our proposed meta-learning model.
On backpropagating Hessians through ODEs

We discuss the problem of numerically backpropagating Hessians through ordinary differential equations (ODEs) in various contexts and elucidate how different approaches may be favourable in specific situations. We discuss both theoretical and pragmatic aspects such as, respectively, bounds on computational effort and typical impact of framework overhead. Focusing on the approach of hand-implemented ODE-backpropagation, we develop the computation for the Hessian of orbit-nonclosure for a mechanical system. We also clarify the mathematical framework for extending the backward-ODE-evolution of the costate-equation to Hessians, in its most generic form. Some calculations, such as that of the Hessian for orbit non-closure, are performed in a language, defined in terms of a formal grammar, that we introduce to facilitate the tracking of intermediate quantities. As pedagogical examples, we discuss the Hessian of orbit-nonclosure for the higher dimensional harmonic oscillator and conceptually related problems in Newtonian gravitational theory. In particular, applying our approach to the figure-8 three-body orbit, we readily rediscover a distorted-figure-8 solution originally described by Sim\'o. Possible applications may include: improvements to training of `neural ODE'- type deep learning with second-order methods, numerical analysis of quantum corrections around classical paths, and, more broadly, studying options for adjusting an ODE's initial configuration such that the impact on some given objective function is small.
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation

Deep Neural Networks are vulnerable to adversarial attacks. Neural Architecture Search (NAS), one of the driving tools of deep neural networks, demonstrates superior performance in prediction accuracy in various machine learning applications. However, it is unclear how it performs against adversarial attacks. Given the presence of a robust teacher, it would be interesting to investigate if NAS would produce robust neural architecture by inheriting robustness from the teacher. In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation. Unlike previous knowledge distillation methods that encourage close student/teacher output only in the last layer, RNAS-CL automatically searches for the best teacher layer to supervise each student layer. Experimental result evidences the effectiveness of RNAS-CL and shows that RNAS-CL produces small and robust neural architecture.
Soft Thresholding for Visual Image Enhancement

Thresholding converts a greyscale image into a binary image, and is thus often a necessary segmentation step in image processing. For a human viewer however, thresholding usually has a negative impact on the legibility of document images. This report describes a simple method for "smearing out" the threshold and transforming the greyscale image into a different greyscale image. The method is similar to fuzzy thresholding, but is discussed here in the simpler context of greyscale transformations and, unlike fuzzy thresholding, it is independent from the method for finding the threshold. A simple formula is presented for automatically determining the width of the threshold spread. The method can be used, e.g., for enhancing images for the presentation in online facsimile repositories.
What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News

Local news articles are a subset of news that impact users in a geographical area, such as a city, county, or state. Detecting local news (Step 1) and subsequently deciding its geographical location as well as radius of impact (Step 2) are two important steps towards accurate local news recommendation. Naive rule-based methods, such as detecting city names from the news title, tend to give erroneous results due to lack of understanding of the news content. Empowered by the latest development in natural language processing, we develop an integrated pipeline that enables automatic local news detection and content-based local news recommendations. In this paper, we focus on Step 1 of the pipeline, which highlights: (1) a weakly supervised framework incorporated with domain knowledge and auto data processing, and (2) scalability to multi-lingual settings. Compared with Stanford CoreNLP NER model, our pipeline has higher precision and recall evaluated on a real-world and human-labeled dataset. This pipeline has potential to more precise local news to users, helps local businesses get more exposure, and gives people more information about their neighborhood safety.
SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots

Colorectal cancer is the third most common cause of cancer death worldwide. Optical colonoscopy is the gold standard for detecting colorectal cancer; however, about 25 percent of polyps are missed during the procedure. A vision-based autonomous endorobot can improve colonoscopy procedures significantly through systematic, complete screening of the colonic mucosa. The reliable robot navigation needed requires a three-dimensional understanding of the environment and lumen tracking to support autonomous tasks. We propose a novel multi-task model that simultaneously predicts dense depth and lumen segmentation with an ensemble of deep networks. The depth estimation sub-network is trained in a self-supervised fashion guided by view synthesis; the lumen segmentation sub-network is supervised. The two sub-networks are interconnected with pathways that enable information exchange and thereby mutual learning. As the lumen is in the image's deepest visual space, lumen segmentation helps with the depth estimation at the farthest location. In turn, the estimated depth guides the lumen segmentation network as the lumen location defines the farthest scene location. Unlike other environments, view synthesis often fails in the colon because of the deformable wall, textureless surface, specularities, and wide field of view image distortions, all challenges that our pipeline addresses. We conducted qualitative analysis on a synthetic dataset and quantitative analysis on a colon training model and real colonoscopy videos. The experiments show that our model predicts accurate scale-invariant depth maps and lumen segmentation from colonoscopy images in near real-time.
AI Insights into Theoretical Physics and the Swampland Program: A Journey Through the Cosmos with ChatGPT

In this case study, we explore the capabilities and limitations of ChatGPT, a natural language processing model developed by OpenAI, in the field of string theoretical swampland conjectures. We find that it is effective at paraphrasing and explaining concepts in a variety of styles, but not at genuinely connecting concepts. It will provide false information with full confidence and make up statements when necessary. However, its ingenious use of language can be fruitful for identifying analogies and describing visual representations of abstract concepts.
Score-based Causal Representation Learning with Interventions

This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables. Sufficient conditions for DAG recovery are established, and it is shown that a large class of non-linear models in the latent space (e.g., causal mechanisms parameterized by two-layer neural networks) satisfy these conditions. These sufficient conditions ensure that the effect of an intervention can be detected correctly from changes in the score. Capitalizing on this property, recovering a valid transformation is facilitated by the following key property: any valid transformation renders latent variables' score function to necessarily have the minimal variations across different interventional environments. This property is leveraged for perfect recovery of the latent DAG structure using only \emph{soft} interventions. For the special case of stochastic \emph{hard} interventions, with an additional hypothesis testing step, one can also uniquely recover the linear transformation up to scaling and a valid causal ordering.
On Complex Dynamics of a Chemostat Model Under Fractal Fractional Derivative

In this paper, we study the existence of numerical solution and stability of a chemostat model under fractal fractional order derivative. First, we investigate the positivity and roundedness of the solution of the considered system. Second, we find the existence of a solution of the considered system by employing the Banach and Schauder fixed-point theorems. Furthermore, we obtain a sufficient condition that allows the existence of the stabling of solutions by using the numerical-functional analysis. We find that the proposed system exists as a unique positive solution that obeys the criteria of Ulam-Hyers (U-H) and generalized U-H stability. We also establish a numerical analysis for the proposed system by using a numerical scheme based on the Lagrange interpolation procedure. Finally, we provide two numerical examples to verify the correctness of the theoretical results.
A Continuous Time Framework for Discrete Denoising Models

We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chains (CTMCs). The model can be efficiently trained using a continuous time version of the ELBO. We simulate the high dimensional CTMC using techniques developed in chemical physics and exploit our continuous time framework to derive high performance samplers that we show can outperform discrete time methods for discrete data. The continuous time treatment also enables us to derive a novel theoretical result bounding the error between the generated sample distribution and the true data distribution.