Reviewerly/exHarmony huggingface dataset:
The Role of Computer-Assisted Language Learning (CALL) in Language Teachers’ Professional Development

In the 1950s, there were considerable changes in both second language instruction and learning. The transition from teacher-centred to student-centred learning has totally altered the structure of second language teaching, and the process is still going strong. Consideration of incorporating new methods, technology, and devices has become an integral aspect of language classrooms. The involvement of teachers in this phenomenon is critical and unavoidable. Teachers are now eager to understand their students' individual requirements and are looking for methods and activities that might bring about a revolution in the realm of second language teaching. They select the most appropriate and pertinent strategy based on the personalities and attitudes of their students. Due to which, it is essential to give their professional growth top priority so they are prepared to handle today's problems and decide what is best for their students. The study uses a qualitative interview approach to ascertain how technology influences language instructors' professional development with a focus on computer-assisted language learning (CALL). Recommendations are made to improve the professional development of language teachers based on the respondents' answers to the interview questions.
A Comparative Study of Punjabi and English Word Order: SOV Vs SVO

This study intends to compare the two languages, Punjabi and English, in terms of their apparently different word orders (i.e., SOV and SVO) under the rubric of Linear Correspondence Axiom (LCA): languages may have a similar underlying word order SVO despite of the apparent variation. In the backdrop of Universal Grammar (i.e., assuming universal principles underlying apparent variation of languages: Principles and Parameters), this study employs the Minimalist framework of Chomsky where LCA is assumed to hold true at least in its empirical conclusions. This study concludes that Punjabi also underlies SVO order at base. Its variation from SVO languages like English is caused by the movement of object to ‘specifier’-I position or Spec Agro position, and the movement of verb+ auxiliary complex to I position. Furthermore, SOV languages also exhibit grammatical structures with SVO word order to strengthen the line of reasoning that basic word order for such languages is SVO. A more extensive study into movement operations and possibility of flexible word orders in languages may help the future studies in reaching correct conclusion with respect to word order.
Stylometric Appraisal of Coelho’s Selected Translated Novels: A Digital Humanities Study

To quantify the style markers of any writer is a laborious and erroneous task in close reading, while distant reading with the Voyant Summary tool presents stylometric features precisely. The current study employs the Voyant summary tool to analyse data through the lens of Stefan Sinclair and Geoffery Rockwell's Hermeneutica Theory (2016). The Voyant Summary tool performs text mining of Coelho's style markers; total words, unique words, vocabulary density, the average length of sentences and the most frequent words. Coelho's vocabulary density ranges from 0.075 to 0.263 words. Colhelo’s average length of sentences ranges from 13 to 26.2 words per sentence. The dialogic writing style is evident with the word 'said’. Moreover, themes of love and life are the most repeated ones. To enhance the readability of his works, he refrains from archaic vocabulary. The Zahir shows the lowest vocabulary density, while Adultery reveals the highest vocabulary density.
To Explore the Relationship between Attitude toward Science and Achievement among Students in Tehsil Sarai Alamgir

The main purpose of the current research was to explore the relationship between students’ attitudes toward science and its relationship with achievement in it at elementary level in tehsil Sarai Alamgir district Gujrat. It was descriptive-correlational research based on survey design. Data were collected from randomly selected 1,166 students by self-developed questionnaire. The data were analyzed by applying inferential and descriptive statistics in software named SPSS. After analysis, significant positive correlation coefficient between students’ attitude towards science and achievement was found among students regarding their group of gender. It was found that the students possessing high level of attitude performed “excellent”, students possessing medium level of attitude performed “good” and students possessing low level of attitude toward science performed “average” in science exam. It was also found that majority of the male had high level of perception about science and the female students had medium level of perception about science.
Provable Data Subset Selection For Efficient Neural Network Training

Radial basis function neural networks (\emph{RBFNN}) are {well-known} for their capability to approximate any continuous function on a closed bounded set with arbitrary precision given enough hidden neurons. In this paper, we introduce the first algorithm to construct coresets for \emph{RBFNNs}, i.e., small weighted subsets that approximate the loss of the input data on any radial basis function network and thus approximate any function defined by an \emph{RBFNN} on the larger input data. In particular, we construct coresets for radial basis and Laplacian loss functions. We then use our coresets to obtain a provable data subset selection algorithm for training deep neural networks. Since our coresets approximate every function, they also approximate the gradient of each weight in a neural network, which is a particular function on the input. We then perform empirical evaluations on function approximation and dataset subset selection on popular network architectures and data sets, demonstrating the efficacy and accuracy of our coreset construction.
The Relationship Between Sociodemography and Nutritional Status with the Cognitive Level of Elementary School Children in Padang Timur Based on Cognitive Test Battery for Individuals with or without Intellectual Disabilities (CIID)

Background: Poor cognitive performance in children has been associated with several risk factors associated with low socioeconomic status, such as the level of parental education and particularly low maternal education, malnutrition, micronutrient deficiencies and a less stimulating environment for children. The CIID has the same principles as other nonverbal IQ tests, correlates well with the WISC and has excellent psychometric qualities. Aim: to investigate the relationship between sociodemography and nutritional status with cognitive level of elementary school children in Padang Timur District using CIID. Methods: Analytical research with a cross sectional study design from February to May 2022 on elementary school children in Padang Timur, Padang city. Results: There is a relationship between the cognitive level of elementary school children with nutritional status, mother's education, family income and father's education. Conclusion: The most important factor in influencing a child's cognitive level is nutritional status. Keywords: cognitive, sociodemographic, nutritional status
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.
Critical Periods and Developmental Neurosimulation

Simulating development has a number of benefits for understanding the acquisition of intelligent behavior. Yet development is not simply a generic form of generativity or emergence. Development is an inherently embodied and interactive process that unfolds in a limited time span. While embodied development provides a basis for grounding intelligent behavior, it also serves as a means to differentiate various behaviors with respect to the origins of phenotypic characteristics. Agentive development requires both morphogenetic and behavioral acquisitions which can be dependent upon one another. On the other hand, development requires innate and invariant features which interact with but are distinct from the environment. We propose a set of models that define critical period acquisition in an embodied developmental context. Critical periods are periods of enhanced acquisition that shape future learning and experience. Leading to contingencies that affect the ability of an agent to integrate environmental information, critical periods also require the acquisition of the phenotype itself. leading to contingencies that affect the ability of an agent to integrate environmental information. This work is presented in light of the Developmental Neurosimulation paradigm, in addition to understanding agentive development from a biologically-inspired perspective.
Rationality-bounded adaptive learning in multi-agent dynamic games

This paper presents an adaptive learning framework that aims to enable agents with bounded rationality to learn from dynamic games and making decisions accordingly. Within this framework, agents lack complete knowledge of future payoffs but are capable of forming beliefs based on their recognitive abilities and past experiences. Each region involved dynamically adjusts its strategy by considering the tradeoff between the current payoff and belief updated. Based on this premise, we propose the Boundedly Rational Multiagent Learning (BRML) algorithm and provide proof of its convergence under given conditions. To demonstrate the practicality of the BRML algorithm, we employ it to two critical dynamic games: repeated cooperative and non-cooperative games. Our results indicate that groups with bounded rationality can achieve coordination through the behavior of agents with high recognitive ability. Furthermore, Pareto optimality is successfully attained in the coordination problems through the application of this multiagent adaptive learning framework.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning

In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning. This task is particularly challenging due to the complex dynamics of fluids and the need to achieve multi-modal goals. The policy is required to successfully reach both position goals and water amount goals, which leads to a large convoluted goal state space. To overcome these challenges, we introduce Goal Sampling Adaptation for Scooping (GOATS), a curriculum reinforcement learning method that can learn an effective and generalizable policy for robot scooping tasks. Specifically, we use a goal-factorized reward formulation and interpolate position goal distributions and amount goal distributions to create curriculum throughout the learning process. As a result, our proposed method can outperform the baselines in simulation and achieves 5.46% and 8.71% amount errors on bowl scooping and bucket scooping tasks, respectively, under 1000 variations of initial water states in the tank and a large goal state space. Besides being effective in simulation environments, our method can efficiently adapt to noisy real-robot water-scooping scenarios with diverse physical configurations and unseen settings, demonstrating superior efficacy and generalizability. The videos of this work are available on our project page: https://sites.google.com/view/goatscooping.
Side channel attack performance of correlation power analysis method in noise

This article explores the use of artificial noise to defend against power analysis and power analysis-based side-channel attacks on AES encryption. The study covers both hardware and open-source software components for performing power analysis and provides an analysis of attack performance. It also explains how security measures against side-channel attacks can be implemented without disrupting system operation.
Pre-copy live virtual machine migration techniques in cloud computing using HDWHM algorithm

Live Virtual Machine (VM) migration is a legacy of server virtualization in cloud computing. One of the difficulties in a Virtual Machine (VM) Environment is load balancing, and the pre-copy migration strategy is both time-consuming and expensive. In this paper, we propose an efficient VM migration strategy to compute the parameters for Linear Adam Algorithm overload detection and Interquartile Range (IQR) under-loaded identification, the Central Processing Unit (CPU) utilization is sufficient. VMs require constant network traffic, storage, CPU, and memory demands. This study recommends using the novel Hamming Distance-Based Weighted Harmonic Mean (HDWHM) strategy for pre-copy live VM migration. We tested the suggested strategy using datasets from CloudSim-3.0.3 on a real PlanetLab. Simulations show that the suggested approach decreases host downtimes, energy consumption, VM migrations, and SLA degradation.
DATA INTEGRITY PROOFS ON CLOUD COMPUTING USING BLOCKCHAIN

Cloud data deduplication can save cloud storage resources and network communication bandwidth, and improve cloud storage efficiency.In our project we develop the series of deduplication technologies that are proposed to achieve cloud data deduplication.In past, the traditional duplication technologies and scheme mainly implements data deduplication without providing data integrity verification, so it cannot be verified whether the cloud service provider correct stores user data.In our project we proposes a data integrity verification scheme of deduplication for cloud ciphertexts, including cloud ciphertext deduplication and cloud data integrity verification.In our project Data Integrity Proofs on Cloud Computing using Blockchain we adopted the signature method.In addition, our project ensures the privacy of cloud data, and satisfies the user data is secure and our result shows the higher efficiency.
Slava Ukraini: Exploring Identity Activism in Support of Ukraine via the Ukraine Flag Emoji on Twitter

Identity Activism is a new phenomenon afforded by the massive popularity of social media. It consists of the prominent display of a social movement symbol within a space reserved for description of the self. The 2022 Russian invasion of Ukraine provides a contemporary (yet unfortunate) opportunity to observe this phenomenon. Here, we introduce and explore this concept in the context of the recent Twitter trend of displaying the Ukraine flag emoji in bios and names to signal support of Ukraine. We explore several questions, including: how has the popularity of this trend changed over time, are users who display the flag more likely to be connected to others who do, and what types of users are and are not participating. We find that Ukraine flag emoji prevalence in both names and bios increased many-fold in late February 2022, with it becoming the 11th most prevalent emoji in bios and the 3rd most prevalent emoji in names during March. We also find evidence that users who display the flag in their bio or name are more likely to follow and be followed by others who also do so, as compared to users who do not. Finally, we observe that users who share politically left-leaning messages were most likely to display the emoji. Those who share account information from alternative social media sites and non-personal accounts appear least likely. These findings give us insight into how users participate in Identity Activism, what connections exist between participating users, and, in this particular case, what types of users participate.
Tek Modlu ve Çok Modlu Kıyaslama Fonksiyonlarını Kullanan Biyoloji Tabanlı Metasezgisel Optimizasyon Algoritmalarının Performans Karşılaştırması

Optimization is used in almost every aspect of our lives today and makes our lives easier. Optimization is generally studied as classical and heuristic optimization techniques. Classical optimization methods are not effective in real-world engineering problems. These methods, by their nature, require a mathematical model. Metaheuristic optimization methods have started to be used frequently today in the solution of these problems when a mathematical model cannot be created or a solution cannot be produced in an effective time even if it is created. These methods, by their nature, cannot produce effective results in all engineering problems. Therefore, new metaheuristic optimization methods are constantly being researched. In this study, quality test functions have been used to compare the performances of five algorithms that have been developed in recent years and produce effective results. The results obtained from these functions are shared in this study. It has been observed that the Artificial Hummingbird Optimization Algorithm (AHA) gives better results than other metaheuristic algorithms.
E-voting using blockchain Comparative Analysis

Electronic voting (e-voting) is a digital method of casting and counting votes in an election.Blockchain, a decentralized digital ledger technology, has been suggested as a potential solution to the challenges of e-voting, such as security, transparency, and trust.This comparative analysis examines the use of blockchain technology in e-voting systems, comparing it with traditional e-voting systems and highlighting the advantages and disadvantages of each.First, we discuss the basic concepts of blockchain technology and e-voting; we analysed the advantages and disadvantages of blockchain-based e-voting compared to traditional e-voting.We also examine the challenges of implementing blockchain-based e-voting, including scalability, interoperability, and regulatory compliance.Finally, we consider the potential for blockchain-based e-voting to enhance the democratic process by increasing voter turnout, reducing fraud, and improving the integrity of elections.
The Relationship of Corporate Governance and Profitability with Corporate Social Responsibility Disclosure in Pakistan

The objective of this research study is to investigate the impact of corporate governance attributes and profitability on corporate social responsibility disclosure (CSRD) among listed non-financial firms in Pakistan. The current study gathered data from the published annual reports of the 66 non-financial firms listed on the Pakistan Stock Exchange (PSX) from 2013 to 2016. The study employed a fixed effect model or a random effect model for data analysis, along with some pre-requisite diagnostic tests. The results of the study demonstrate the following outcomes: First, the board size, managerial ownership, and return on assets are positively linked with corporate social responsibility disclosure; second, return on equity shows a significant negative relationship with corporate social responsibility disclosure. Importantly, this study suggests that corporate governance attributes and firm profitability are the most significant components in improving corporate social responsibility disclosure. The research study has some important implications for academicians, auditors, and policymakers in improving corporate social responsibility reporting disclosure.
BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset

While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33,695 human annotated document samples from six domains - i) books and magazines, ii) public domain govt. documents, iii) liberation war documents, iv) newspapers, v) historical newspapers, and vi) property deeds, with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models.
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE

Videos depict the change of complex dynamical systems over time in the form of discrete image sequences. Generating controllable videos by learning the dynamical system is an important yet underexplored topic in the computer vision community. This paper presents a novel framework, TiV-ODE, to generate highly controllable videos from a static image and a text caption. Specifically, our framework leverages the ability of Neural Ordinary Differential Equations~(Neural ODEs) to represent complex dynamical systems as a set of nonlinear ordinary differential equations. The resulting framework is capable of generating videos with both desired dynamics and content. Experiments demonstrate the ability of the proposed method in generating highly controllable and visually consistent videos, and its capability of modeling dynamical systems. Overall, this work is a significant step towards developing advanced controllable video generation models that can handle complex and dynamic scenes.
Data-dependent Generalization Bounds via Variable-Size Compressibility

In this paper, we establish novel data-dependent upper bounds on the generalization error through the lens of a "variable-size compressibility" framework that we introduce newly here. In this framework, the generalization error of an algorithm is linked to a variable-size 'compression rate' of its input data. This is shown to yield bounds that depend on the empirical measure of the given input data at hand, rather than its unknown distribution. Our new generalization bounds that we establish are tail bounds, tail bounds on the expectation, and in-expectations bounds. Moreover, it is shown that our framework also allows to derive general bounds on any function of the input data and output hypothesis random variables. In particular, these general bounds are shown to subsume and possibly improve over several existing PAC-Bayes and data-dependent intrinsic dimension-based bounds that are recovered as special cases, thus unveiling a unifying character of our approach. For instance, a new data-dependent intrinsic dimension-based bound is established, which connects the generalization error to the optimization trajectories and reveals various interesting connections with the rate-distortion dimension of a process, the R\'enyi information dimension of a process, and the metric mean dimension.