Reviewerly/exHarmony huggingface dataset:
Synthesizing Quantum-Circuit Optimizers

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing. (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.
AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation

We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation. Current deep learning based methods rely on local convolutions for feature learning and suffer from not being scale-invariant, thus requiring training data to be augmented through random flipping and re-scaling. On the other hand, AdaFNIO, learns the features in the frames, independent of input resolution, through token mixing and global convolution in the Fourier space or the spectral domain by using Fast Fourier Transform (FFT). We show that AdaFNIO can produce visually smooth and accurate results. To evaluate the visual quality of our interpolated frames, we calculate the structural similarity index (SSIM) and Peak Signal to Noise Ratio (PSNR) between the generated frame and the ground truth frame. We provide the quantitative performance of our model on Vimeo-90K dataset, DAVIS, UCF101 and DISFA+ dataset.
Bode Integral Limitation For Irrational Systems

Bode integrals of sensitivity and sensitivity-like functions along with complementary sensitivity and complementary sensitivity-like functions are conventionally used for describing performance limitations of a feedback control system. In this paper, we investigate the Bode integral and evaluate what happens when a fractional order Proportional-Integral-Derivative (PID) controller is used in a feedback control system. We extend our analysis to when fractal PID controllers are applied to irrational systems. We split this into two cases: when the sequence of infinitely many right half plane open-loop poles doesn't have any limit points and when it does have a limit point. In both cases, we prove that the structure of the Bode Integral is similar to the classical version under certain conditions of convergence. We also provide a sufficient condition for the controller to lower the Bode sensitivity integral.
Efektivitas Pembelajaran Berbasis Daring dalam Pandangan Siswa di Dusun Sebero Desa Dalegan Panceng Gresik

The purpose of this study was to conduct learning effectiveness in Sebero hamlet by using online or online. The subjects in the journal are research and the web that discuss online-based learning. The scope of this journal covers school teachers and elementary school children, as well as the role of government in pursuing online learning today. The development of this education is very influential with the success of an information with good communication. And technology that plays a role in today's environment and education is also used for online learning, online learning is useful for information and communication between students connecting with the internet which is usually done forever. This online media uses various research subjects such as WhatsApp, Google Classroom, Edmodo, and other websites. The method we use is to interview several students in Sebero Village.
Implementasi Media Komik dalam Meningkatkan Ketrampilan Menulis Puisi Siswa MI Al-Hidayah

The purpose of this study was to find out how the implementation of comics media in improving students' poetry writing skills. This study uses a descriptive qualitative research approach, while the type of research is a case study research using research instruments, namely the researchers themselves and using observation, interview, and documentation research data mining techniques. The data analysis model used is the Miles and Huberman Interactive model, which consists of three stages. First data collection, second data reduction, third data presentation/data display and verification or conclusion. Data analysis was carried out continuously until the end of the study. The subject of the study was the students of MI Al-Hidayah Serah Panceng Gresik. The respondents in this study amounted to 24 students. The independent variables of this study were the implementation of comics and media, while poetry writing skills were the dependent variable. The results of this study indicate that the implementation of comics media is very effective in improving the poetry writing skills of MI AL-Hidayah students for the 2020-2021 academic year.
Benchmarking optimality of time series classification methods in distinguishing diffusions

Statistical optimality benchmarking is crucial for analyzing and designing time series classification (TSC) algorithms. This study proposes to benchmark the optimality of TSC algorithms in distinguishing diffusion processes by the likelihood ratio test (LRT). The LRT is an optimal classifier by the Neyman-Pearson lemma. The LRT benchmarks are computationally efficient because the LRT does not need training, and the diffusion processes can be efficiently simulated and are flexible to reflect the specific features of real-world applications. We demonstrate the benchmarking with three widely-used TSC algorithms: random forest, ResNet, and ROCKET. These algorithms can achieve the LRT optimality for univariate time series and multivariate Gaussian processes. However, these model-agnostic algorithms are suboptimal in classifying high-dimensional nonlinear multivariate time series. Additionally, the LRT benchmark provides tools to analyze the dependence of classification accuracy on the time length, dimension, temporal sampling frequency, and randomness of the time series.
Question Answering and Question Generation for Finnish

Recent advances in the field of language modeling have improved the state-of-the-art in question answering (QA) and question generation (QG). However, the development of modern neural models, their benchmarks, and datasets for training them has mainly focused on English. Finnish, like many other languages, faces a shortage of large QA/QG model training resources, which has prevented experimenting with state-of-the-art QA/QG fine-tuning methods. We present the first neural QA and QG models that work with Finnish. To train the models, we automatically translate the SQuAD dataset and then use normalization methods to reduce the amount of problematic data created during the translation. Using the synthetic data, together with the Finnish partition of the TyDi-QA dataset, we fine-tune several transformer-based models to both QA and QG and evaluate their performance. To the best of our knowledge, the resulting dataset is the first large-scale QA/QG resource for Finnish. This paper also sets the initial benchmarks for Finnish-language QA and QG.
Estimating Conditional Distributions with Neural Networks using R package deeptrafo

Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow backend with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data.
Robust and Accurate Cylinder Triangulation

In this paper we present methods for triangulation of infinite cylinders from image line silhouettes. We show numerically that linear estimation of a general quadric surface is inherently a badly posed problem. Instead we propose to constrain the conic section to a circle, and give algebraic constraints on the dual conic, that models this manifold. Using these constraints we derive a fast minimal solver based on three image silhouette lines, that can be used to bootstrap robust estimation schemes such as RANSAC. We also present a constrained least squares solver that can incorporate all available image lines for accurate estimation. The algorithms are tested on both synthetic and real data, where they are shown to give accurate results, compared to previous methods.
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam

Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks. In this work, we combine data augmentation approaches with a Teacher-Student training scheme to address this issue in a cross-lingual transfer setting for fine-tuning state-of-the-art pre-trained multilingual language models such as mBERT and XLM-R. We evaluate our method on transliterated Hindi and Malayalam, also introducing new datasets for benchmarking on real-world scenarios: one on sentiment classification in transliterated Malayalam, and another on crisis tweet classification in transliterated Hindi and Malayalam (related to the 2013 North India and 2018 Kerala floods). Our method yielded an average improvement of +5.6% on mBERT and +4.7% on XLM-R in F1 scores over their strong baselines.
Min-Max-Jump distance and its applications

We explore three applications of Min-Max-Jump distance (MMJ distance). MMJ-based K-means revises K-means with MMJ distance. MMJ-based Silhouette coefficient revises Silhouette coefficient with MMJ distance. We also tested the Clustering with Neural Network and Index (CNNI) model with MMJ-based Silhouette coefficient. In the last application, we tested using Min-Max-Jump distance for predicting labels of new points, after a clustering analysis of data. Result shows Min-Max-Jump distance achieves good performances in all the three proposed applications. In addition, we devise several algorithms for calculating or estimating the distance.
A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers

Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes. This process is relevant when external constraints limit the performance of an industrial predictive system. Under such circumstances, copying enables the retention of original prediction capabilities while adapting to new demands. Previous research has focused on the single-pass implementation for copying. This paper introduces a novel sequential approach that significantly reduces the amount of computational resources needed to train or maintain a copy, leading to reduced maintenance costs for companies using machine learning models in production. The effectiveness of the sequential approach is demonstrated through experiments with synthetic and real-world datasets, showing significant reductions in time and resources, while maintaining or improving accuracy.
Promosi Industri Fashion Clothing

Abstract. Screamous clothing has been established since 2004 and is able to maintain its existence ntil now. To assist its promotional activities, Screamous utilizes the Instagram TV feature by making web series titled “Tracing Forward”. The series consist of 8 episodes and air on Instagram TV. This web series is specially made for followers or viewers who are 17-34 years old on average. The purpose of this research is to find out how the concept, implementation, obstacles and evaluation of the promotional activities. This study employs a qualitative method with a descriptive study approach. The data were collected through interviews, observations, documentations and literature review. The results of this study describe the implementation of promotions carried out by Screamous clothing on Instagram by utilizing the Instagram TV feature through the web series "Tracing Forward". This research discusses about; 1) The concept of promotion of Screamous clothing in the form of a web series “Tracing Forward”. 2) The process of implementing Screamous clothing promotions in the “Tracing Forward” web series. 3) The internal and external obstacles during the creation and delivery of the web series. 4) Evaluation carried out after the activity took place. The promotion through the web series “Tracing Forward” on Instagram @Screamous_55 went well and achieved the goals expected by the company.&#x0D; &#x0D; &#x0D; Abstrak. Screamous merupakan sebuah clothing yang sudah berdiri sejak tahun 2004 dan mampu mempertahankan eksistensinya hingga hari ini. Dengan media sosial Instagram, clothing Screamous memanfaatkan fitur Instagram TV untuk membantu kegiatan promosinya melalui konten web series berjudul “Tracing Forward” dalam 8 episode yang tayang di Instagram TV dan dibuat oleh clothing Screamous. Adapun tujuan penelitian ini adalah untuk mengetahui bagaimana konsep, implementasi, hambatan serta evaluasi dari pelaksanaan. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi deskriptif. Metode yang digunakan pengumpulan data yang digunakan adalah wawancara, observasi, dokumentasi dan studi pustaka. Hasil penelitian ini menggambarkan pelaksanaan promosi yang dilakukan oleh clothing Screamous di Instagram dengan memanfaatkan fitur Instagram TV melalui web series “Tracing Forward”. Web series ini dibuat secara khusus untuk para followers atau viewers yang rata-rata berusia 17-34 tahun. Peneltian ini membahas mengenai; 1) Konsep promosi clothing Screamous dalam bentuk web series”Tracing Forward”; 2) Proses implementasi promosi clothing Screamous pada web series “Tracing Forward”; 3) Terdapat hambatan internal dan eksternal pada saat pembuatan dan penayangan web series; 4) Evaluasi yang dilakukan setelah kegiatan berlangsung. Pelaksanaan promosi melalui web series “Tracing Forward” di Instagram @Screamous_55 berjalan dengan baik dan mencapai tujuan yang diharapkan oleh perusahaan.
Consistent epistemic planning without communication for MADRL

Abstract Multi-agent cooperation needs to reason about beliefs in the partially observable environment without communication, but the traditional Multi-agent Deep Reinforcement Learning (MADRL) algorithm struggles to handle the uncertainty of agents. Multi-agent Epistemic planning (MEP) tries to let the agent find a best plan to complete the cooperation task, so as to more effectively solve the uncertainty. However, inconsistent planning arises if the MADRL only adds MEP. We propose a MADRL-based policy network architecture called SMM-MEPP: Shared Mental Model - Multi-agent Epistemic Planning Policy. Firstly, Multi-agent Epistemic Planning and MADRL are investigated to build the "Perception-Planning-Action" multi-agent epistemic planning framework. Then, mental model in psychology is introduced and descript as a neural network. Thirdly, parameter sharing mechanism is utilized to achieve the shared mental model and maintain the consistency of epistemic planning. Finally, we apply the SMM-MEPP architecture to three advanced MADRL algorithms (i.e., MAAC, MADDPG and MAPPO) and conduct comparative experiments in multi-agent cooperation tasks. Experiments show that the proposed method can bring consistent planning for multiple agents, and improves convergence speed or training effect in partially observable environment without communication.
Effective Pedagogical Aspects of the Development of Creative Qualities in Students

The current state of society requires students at educational institutions of any level and direction not only to master knowledge and skills that will be useful in their future professions but also have the ability to conduct an active dialogue with colleagues and management, the ability to clearly and persuasively express their point of view, and the ability to be mobile, active, and creative.This study aims to discuss the effective pedagogical approach on creativity of the students.The taken results of this observation contribute to the manifestation of the future specialist's selfdevelopment, self-realization, and the embodiment of his or her own ideas, which are aimed at originality.The student's creative abilities develop in all types of activities that are important for every student.The activation of student creativity is designed not only to awaken and maintain interest in various disciplines and modules, but also, most importantly, to help students realize the need to actualize their own creative abilities in educational and professional activities, ultimately leading to the formation of a graduate specialist competitive on the global education system.
The Destructive Effect of Corruption on Economic Growth in Mali : a Nonlinear Model Analysis

A growing body of empirical work has raised awareness of the destructive effect of corruption on economic development, although no existing study has considered the threshold value at which corruption impedes economic growth. This study assesses the effect of corruption on economic growth by adopting a non-linear approach to determining the corruption threshold. By analyzing the effect of corruption on economic growth in Mali over the period 1988 to 2021, this study examines whether there is evidence of an inverted U-shaped relationship between economic growth and corruption. The threshold effect of corruption is assessed using an augmented quadratic model. The results confirm the existence of an inverted U-shaped relationship between corruption and economic growth on the one hand and show the existence of a long-run relationship between these variables on the other hand, thus validating our initial hypothesis. Furthermore, the results show that economic growth peaks when the corruption perception index reaches 0.07 points. The main lessons to be drawn from these results are that below this threshold, corruption becomes a lubricant for economic growth, while above this threshold, it constitutes a brake on economic growth in Mali.
EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET

&lt;span lang="id"&gt;Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing. However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extracts scalar values using the Eye Aspect Ratio (EAR), and characterises eye proximity in each frame. For each video frame, the proposed method calculates the location of the facial landmark and extracts the vertical distance between the eyelids using the position of the facial landmark. Blinks are detected by using the EAR threshold value and recognising the pattern of EAR values in a short temporal window. According to the results from a common data set, it is shown that the proposed approach is more efficient than state-of-the-art techniques.&lt;/span&gt;
Computational Complexity Reduction of Secure BP Learning Algorithm with Divided Data

Many studies have been conducted on how to perform learning while maintaining data security. One of them is the secure distributed processing with decomposition data. The feature of this method is that it achieves high confidentiality by decomposing data and parameters in machine learning and performing distributed processing. On the other hand, realization of machine learning by distributed and integrated processing of decomposed data and parameters leads to an increase in computational complexity and degradation of computational accuracy. In this paper, we propose an improved BP learning method that suppresses the increase in computational complexity with the increase in the number of servers, and demonstrate its effectiveness.
Providing a method for security analysis and evaluation of infrastructure services in cloud computing

Abstract Today, cloud security is necessary to improve the service allocation, continuity of operations, storage management, information security and. In this way, analyzing the server in question and then defining a formal model by applying the security features is essential. In this paper, infrastructure services are described as services in cloud computing and each vulnerability is investigated. According to Petri's modeling tools, a new approach to assessing the security of infrastructure systems as a service is provided and analyzed. With the help of this method, the security of infrastructure systems as a service in cloud computing can be compared and evaluated. Consequently, using Petri's modeling tool, one of the most famous commercial systems of the infrastructure is modeled as a service called OpenStack, and the behavior of the attacks is evaluated. Applying this method to the OpenStack system demonstrates that our work is safe and that researchers, designers and cloud service providers are expected to make significant gains in delivering results.