Reviewerly/exHarmony huggingface dataset:
IDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI LITERASI SISWA SEKOLAH DASAR KELAS RENDAH

This study aims to determine the various types of factors that influence the literacy of low-grade elementary school students. This study uses a literature study research method by examining several journals and references related to the factors that influence the literacy of elementary school students. The results of the various literature reviews will be used to identify several factors that influence student literacy. From the results of a review of various literature studies, the results of factors that affect the low grade elementary school students 'liabilities are the age of students, parents' backgrounds, tutoring that can or additional learning such as tutoring, education, interest or talent, family care level or parent's work
Coupling single atoms to a nanophotonic whispering-gallery-mode resonator via optical guiding

We demonstrate an efficient optical guiding technique for coupling cold atoms in the near field of a planar nanophotonic circuit, and realize large atom-photon coupling to a whispering-gallery mode in a microring resonator with a single-atom cooperativity $C\gtrsim 8$. The guiding potential is created by diffracted light on a nanophotonic waveguide that smoothly connects to a dipole trap in the far field for atom guiding with subwavelength precision. We observe atom-induced transparency for light coupled to a microring, characterize the atom-photon coupling rate, extract guided atom flux, and demonstrate on-chip photon routing by single atoms. Our demonstration promises new applications with cold atoms on a nanophotonic circuit for chiral quantum optics and quantum technologies.
Medical Image Watermarking with Separable Moments

In order to contribute to medical image watermarking methods, we evaluate the performance of several moment families and separable moments. We have modeled watermarking as a multi-objective optimization problem using particle swarm optimization with Conventional Weighted Aggregation. Additionally, we use dither modulation with a uniform quantizer to archive a better rate between imperceptibility and robustness. The proposed scheme was tested under common signal processing attacks. The results confirm the validity of the separable moments since they allowed to achieve in multiple cases a better performance than original moment families. The importance of studying moment families that have lacked of interest is also reinforced, given the relevant performance achieved by q-Krawtchouk moments in comparison to steady moment families in image watermarking.
Automatic Camera Control and Directing with an Ultra-High-Definition Collaborative Recording System

Capturing an event from multiple camera angles can give a viewer the most complete and interesting picture of that event. To be suitable for broadcasting, a human director needs to decide what to show at each point in time. This can become cumbersome with an increasing number of camera angles. The introduction of omnidirectional or wide-angle cameras has allowed for events to be captured more completely, making it even more difficult for the director to pick a good shot. In this paper, a system is presented that, given multiple ultra-high resolution video streams of an event, can generate a visually pleasing sequence of shots that manages to follow the relevant action of an event. Due to the algorithm being general purpose, it can be applied to most scenarios that feature humans. The proposed method allows for online processing when real-time broadcasting is required, as well as offline processing when the quality of the camera operation is the priority. Object detection is used to detect humans and other objects of interest in the input streams. Detected persons of interest, along with a set of rules based on cinematic conventions, are used to determine which video stream to show and what part of that stream is virtually framed. The user can provide a number of settings that determine how these rules are interpreted. The system is able to handle input from different wide-angle video streams by removing lens distortions. Using a user study it is shown, for a number of different scenarios, that the proposed automated director is able to capture an event with aesthetically pleasing video compositions and human-like shot switching behavior.
Curriculum Offline Imitation Learning

Offline reinforcement learning (RL) tasks require the agent to learn from a pre-collected dataset with no further interactions with the environment. Despite the potential to surpass the behavioral policies, RL-based methods are generally impractical due to the training instability and bootstrapping the extrapolation errors, which always require careful hyperparameter tuning via online evaluation. In contrast, offline imitation learning (IL) has no such issues since it learns the policy directly without estimating the value function by bootstrapping. However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies. In this paper, we aim to take advantage of IL but mitigate such a drawback. Observing that behavior cloning is able to imitate neighboring policies with less data, we propose \textit{Curriculum Offline Imitation Learning (COIL)}, which utilizes an experience picking strategy for imitating from adaptive neighboring policies with a higher return, and improves the current policy along curriculum stages. On continuous control benchmarks, we compare COIL against both imitation-based and RL-based methods, showing that it not only avoids just learning a mediocre behavior on mixed datasets but is also even competitive with state-of-the-art offline RL methods.
Basic elements influencing sustainable development of Vietnam high performance basketball

Through the development situation of Vietnam High Achievement Basketball, the thesis analyzes and evaluates the elements influencing the sustainable development of high achievement basketball, thereby giving reasonable orientations and adjustments in high achievement basketball training.
How Data Augmentation affects Optimization for Linear Regression.

Though data augmentation has rapidly emerged as a key tool for optimization in modern machine learning, a clear picture of how augmentation schedules affect optimization and interact with optimization hyperparameters such as learning rate is nascent. In the spirit of classical convex optimization and recent work on implicit bias, the present work analyzes the effect of augmentation on optimization in the simple convex setting of linear regression with MSE loss. We find joint schedules for learning rate and data augmentation scheme under which augmented gradient descent provably converges and characterize the resulting minimum. Our results apply to arbitrary augmentation schemes, revealing complex interactions between learning rates and augmentations even in the convex setting. Our approach interprets augmented (S)GD as a stochastic optimization method for a time-varying sequence of proxy losses. This gives a unified way to analyze learning rate, batch size, and augmentations ranging from additive noise to random projections. From this perspective, our results, which also give rates of convergence, can be viewed as Monro-Robbins type conditions for augmented (S)GD.
Research on the Construction of Public Management Discipline Under the Background of New Liberal Arts

Under the background of new liberal arts, the construction of public management discipline should be based on the social times, the goal of talent cultivation, and the characteristics of the discipline itself, so as to meet the requirements of new liberal arts. In terms of social background, the public management discipline should not only respond to the new problems arising from the technological revolution, but also to the problems of national governance in the new era. In terms of the goal of talent cultivation, the public management discipline should not only cultivate public management professionals but also undertake the basic task of establishing virtue and cultivating talents. In terms of the characteristics of the discipline itself, the public management discipline should highlight the functional advantages of humanistic education, comprehensive discipline, and social service.
Development of Communicative Competencies During Integrated Analysis and Synthesis of a Text

Text is the main tool for designing and managing the learning and education processes. The communicative competencies of the individual are an important component of students' activities including working with documents, the ability to conduct interpersonal and social dialogue, and speak publicly. There is a theory that it is possible to develop a student's personal competence effectively through the integration of academic disciplines which in turn necessitates methods selected by the authors. The purpose of this study is to develop a project-based pedagogical technology that involves complex analytical and synthetic work with the text (CASWT) and contributes to the development of students' communicative competencies. In the course of this study methods of empirical and qualitative observation, analysis and synthesis were used. The practice of combining independent work with text and self-control using subject-specific thematic tests in the Moodle learning environment has been empirically researched. The content of the gained knowledge, faculties, skills, and activities of students after completing project tasks for working with text and communications, which ensure general and special (professional, subject) competencies of applicants for various specialties in compliance with the standards of higher education of Ukraine, is studied. The results of the study are developed graph-diagram which describes logical connections between the main text subjects, observation, analysis and evaluation of the results of work during several years, which allow increasing the quality of performance of the students and their psychological confidence, persuasiveness during studying other disciplines in next years of study.
Analysis of memory consumption by neural networks based on hyperparameters

Deep learning models are trained and deployed in multiple domains. Increasing usage of deep learning models alarms the usage of memory consumed while computation by deep learning models. Existing approaches for reducing memory consumption like model compression, hardware changes are specific. We propose a generic analysis of memory consumption while training deep learning models in comparison with hyperparameters used for training. Hyperparameters which includes the learning rate, batchsize, number of hidden layers and depth of layers decide the model performance, accuracy of the model. We assume the optimizers and type of hidden layers as a known values. The change in hyperparamaters and the number of hidden layers are the variables considered in this proposed approach. For better understanding of the computation cost, this proposed analysis studies the change in memory consumption with respect to hyperparameters as main focus. This results in general analysis of memory consumption changes during training when set of hyperparameters are altered.
Applications of Blockchain Technology in Financial &amp; Personal Data Security

Blockchain technology is defined as a decentralised-systems where the ledger that records the provenance of a digital asset is distributed amongst the users of the blockchain network. Blockchain stores and shares data in a distributed, trusted and immutable manner, removing intermediaries, and not requiring a centralized dependency for checking transactions. Transparency in blockchain provides a less complicated method for accessing ledger-based transactions over networks and makes the system more reliable in terms of data security. The technology is not actively used in today's world due to lack of research and infrastructure and hence the purpose is to modify the algorithms that can be implemented in the existing technology towards the fintech industry and help developers tackle their problems regarding financial data of the users. We surveyed plenty of resources and came across the concepts of distributed hyperledgers, integrated system architecture and secured hashing algorithms [SHA]. Using these concepts as a base we developed algorithms that are interestingly well grounded based on the statistical results gained by us while testing the algorithms. Our research indicates that the proposed algorithms can be implemented into today's technical infrastructure resolving some of the key issues faced by the users using fintech and digital transactions for their day-to-day financial operation.
QVD: Improving Quality – User Story as a Quality Value Driver (QVD) in Software Development

The term "software quality" is widely used, and although it has many definitions, no one definition is universally accepted. Often, the definition refers to specific phases of the software development process and not to software as a whole. In our article, in order to improve the quality of the software, we decided to improve the quality of the phases. To do that, we will focus attention on the drivers that advance quality value and use a new concept called QVD - quality value drivers. We focused on the requirements phase of software development, which probably ranks as the crucial first step. Hence, as a QVD we present the idea of "User Story" (US), a short and simple description of a functionality valuable to a user of a system. In the study, a comparison is made between requirements written by the US method and requirements that are not. After analyzing the results, we concluded that requirements written by the US method have been better understood and evaluated as less difficult to develop. The overall quality rating of their writing is higher than previously. In addition, learning the US method gives the person the ability to better assess the quality of requirements. Finally, improving the writing of the requirements using the US method as QVD improves the quality of the development process, eventually improving the software quality.
Exposing Deepfake with Pixel-wise AR and PPG Correlation from Faint Signals

Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. In spite of an urgent need for Deepfake identification, existing pixel-level detection methods are increasingly unable to resist the growing realism of fake videos and lack generalization. In this paper, we propose a scheme to expose Deepfake through faint signals hidden in face videos. This scheme extracts two types of minute information hidden between face pixels-photoplethysmography (PPG) features and auto-regressive (AR) features, which are used as the basis for forensics in the temporal and spatial domains, respectively. According to the principle of PPG, tracking the absorption of light by blood cells allows remote estimation of the temporal domains heart rate (HR) of face video, and irregular HR fluctuations can be seen as traces of tampering. On the other hand, AR coefficients are able to reflect the inter-pixel correlation, and can also reflect the traces of smoothing caused by up-sampling in the process of generating fake faces. Furthermore, the scheme combines asymmetric convolution block (ACBlock)-based improved densely connected networks (DenseNets) to achieve face video authenticity forensics. Its asymmetric convolutional structure enhances the robustness of network to the input feature image upside-down and left-right flipping, so that the sequence of feature stitching does not affect detection results. Simulation results show that our proposed scheme provides more accurate authenticity detection results on multiple deep forgery datasets and has better generalization compared to the benchmark strategy.
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations

Despite its rise as a prominent solution to the data inefficiency of today's machine learning models, self-supervised learning has yet to be studied from a purely multi-agent perspective. In this work, we propose that aligning internal subjective representations, which naturally arise in a multi-agent setup where agents receive partial observations of the same underlying environmental state, can lead to more data-efficient representations. We propose that multi-agent environments, where agents do not have access to the observations of others but can communicate within a limited range, guarantees a common context that can be leveraged in individual representation learning. The reason is that subjective observations necessarily refer to the same subset of the underlying environmental states and that communication about these states can freely offer a supervised signal. To highlight the importance of communication, we refer to our setting as \textit{socially supervised representation learning}. We present a minimal architecture comprised of a population of autoencoders, where we define loss functions, capturing different aspects of effective communication, and examine their effect on the learned representations. We show that our proposed architecture allows the emergence of aligned representations. The subjectivity introduced by presenting agents with distinct perspectives of the environment state contributes to learning abstract representations that outperform those learned by a single autoencoder and a population of autoencoders, presented with identical perspectives of the environment state. Altogether, our results demonstrate how communication from subjective perspectives can lead to the acquisition of more abstract representations in multi-agent systems, opening promising perspectives for future research at the intersection of representation learning and emergent communication.
Enriching Local and Global Contexts for Temporal Action Localization

Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimination for temporal localization and sufficient visual invariance for action classification. We address this challenge by enriching both the local and global contexts in the popular two-stage temporal localization framework, where action proposals are first generated followed by action classification and temporal boundary regression. Our proposed model, dubbed ContextLoc, can be divided into three sub-networks: L-Net, G-Net and P-Net. L-Net enriches the local context via fine-grained modeling of snippet-level features, which is formulated as a query-and-retrieval process. G-Net enriches the global context via higher-level modeling of the video-level representation. In addition, we introduce a novel context adaptation module to adapt the global context to different proposals. P-Net further models the context-aware inter-proposal relations. We explore two existing models to be the P-Net in our experiments. The efficacy of our proposed method is validated by experimental results on the THUMOS14 (54.3\% at tIoU@0.5) and ActivityNet v1.3 (56.01\% at tIoU@0.5) datasets, which outperforms recent states of the art. Code is available at https://github.com/buxiangzhiren/ContextLoc.
Fourier Optical Convolutional Neural Network Accelerator

Here we report a massively-parallel Fourier-optics convolutional processor accelerated 160x over spatial-light-modulators using digital-mirror-display technology as input and kernel showing an MNIST and CIFAR-10 accuracy of 96% and 54%, respectively.
User-friendly Comparison of Similarity Algorithms on Wikidata

While the similarity between two concept words has been evaluated and studied for decades, much less attention has been devoted to algorithms that can compute the similarity of nodes in very large knowledge graphs, like Wikidata. To facilitate investigations and head-to-head comparisons of similarity algorithms on Wikidata, we present a user-friendly interface that allows flexible computation of similarity between Qnodes in Wikidata. At present, the similarity interface supports four algorithms, based on: graph embeddings (TransE, ComplEx), text embeddings (BERT), and class-based similarity. We demonstrate the behavior of the algorithms on representative examples about semantically similar, related, and entirely unrelated entity pairs. To support anticipated applications that require efficient similarity computations, like entity linking and recommendation, we also provide a REST API that can compute most similar neighbors for any Qnode in Wikidata.
LTD: Low Temperature Distillation for Robust Adversarial Training

Adversarial training has been widely used to enhance the robustness of neural network models against adversarial attacks. Despite the popularity of neural network models, a significant gap exists between the natural and robust accuracy of these models. In this paper, we identify one of the primary reasons for this gap is the common use of one-hot vectors as labels, which hinders the learning process for image recognition. Representing ambiguous images with one-hot vectors is imprecise and may lead the model to suboptimal solutions. To overcome this issue, we propose a novel method called Low Temperature Distillation (LTD) that generates soft labels using the modified knowledge distillation framework. Unlike previous approaches, LTD uses a relatively low temperature in the teacher model and fixed, but different temperatures for the teacher and student models. This modification boosts the model's robustness without encountering the gradient masking problem that has been addressed in defensive distillation. The experimental results demonstrate the effectiveness of the proposed LTD method combined with previous techniques, achieving robust accuracy rates of 58.19%, 31.13%, and 42.08% on CIFAR-10, CIFAR-100, and ImageNet data sets, respectively, without additional unlabeled data.
A class of quantum synchronizable codes of length $2^n$ over ${\rm F}_q$

Quantum synchronizable codes (QSCs) are special quantum error-correcting codes which can be used to correct the effects of quantum noise on qubits and misalignment in block synchronization. In this paper, a new class of quantum synchronizable codes of length $2^n$ are constructed by using the cyclotomic cosets, whose synchronization capabilities always reach the upper bound $2^n$.
Implementing Cooperative Integrated Reading and Composition (CIRC) Method to Enhance the Reading Comprehension of Indonesian EFL Learner

The objective of this research is to reveal the application of the Cooperative, Integrated Reading and Composition (CIRC) method to increase the reading comprehension of Indonesian EFL learners at junior schools. The CIRC method is one of the teaching methods that English teachers adjust to encourage their students to learn reading comprehension (Gupta &amp; Ahuja, 2015); Zarei, 2012). One reason the researcher investigates reading is in the late time, the EFL learners are very less to spend their time on their reading habits (Derinalp, 2021). The EFL learners are very less to spend their time on their reading habits (Fan, 2016). Reading comprehension is one of the important skills of a language. Much information from the text form problematic issues (Riazi, 2017). It needs a complex skill to acquire this language skill. Therefore, the language learner who concerns with transforming information must prepare a strategy to work. This research applied a quasi-experimental method with a non-equivalent control group design. This study took the junior high school students in Parepare, Indonesia, as its subjects. Two groups of students were involved in the study, one as an experimental group and one as the control group. The data were collected using a comprehension test of the reading content. Management of analytical data was tested by the t-test and the accumulation of the percentage value. The results showed that CIRC improves the reading comprehension of Indonesian junior high students.