Reviewerly/exHarmony huggingface dataset:
Teacher’s Instructional Strategies: Exploring the Lived Experiences of Teachers under Thragom Cluster Schools, Trashi Yangtse

This study adopted a qualitative method to explore the instructional practices of teachers in three primary schools under Thragom cluster, Trashi Yangtse Dzongkhag. The study was aimed at gaining an understanding of teachers’ beliefs and how their attitude shaped their teaching strategies that impacted students’ learning outcomes. The samples for this study included twelve participants (six teachers and six students) from the three schools. Findings from the study indicated that teachers were well acquainted with 21st-century skills. However, there were disparities between teachers' pedagogical philosophy and actual pedagogical practices involved in the teaching and learning processes. Teachers' teaching styles and behaviors were also found to have a significant impact on student's academic achievement. Students' preferences of learning through ICT-based lessons were found to be effective in improving their understanding.
An Approach for Enhancing Privacy and Protection in Decentralized Multi-Authority Characteristic-based on Encryption in Cloud Computing

The Decentralizing multi-authority Attribute- Based secret writing has applied for finding issues arising from sharing various confidential company information in resources like cloud computing. The dispersivemulti-authority trait- Grounded secret jotting systems that wont accept the central authority, conspiracy resistance may be achieved employing a transnational symbol. thus, identity must be managed encyclopedically, which ends in pivotal issues of sequestration and security. A theme is developed which do not use the central authority to manage druggies and keys and solely easy trust relations ought to get shaped by participating the general public key between every Authority. stoner individualities square measure distinctive by combining a stoner’s identity with the Attribute Authority wherever the stoner is set up. Once a crucial request must be created to an authority outside the sphere, the request must be performed by the authority within the current sphere rather of by the druggies, so, stoner individualities stay nonpublic to the Attribute Authority outside the sphere, which can enhance sequestration and security and also the crucial supplying. The theme relies on order direct brigades. an suggestion of security is bestowed that uses the binary System secret jotting methodology. Once the trait authorities in each sphere are members of a hierarchicalmulti-authority ABE, the goal for future work must be to develop a system that combines the plan presented in this study with the hierarchicalmulti-authority ABE.
A Context-Free Grammar for Parsing Manipuri Language

Parsing, i.e., identifying the underlying hierarchical structure of natural language expressions is important for several natural language processing applications.In recent times Machine Learning (ML) approaches have been developed for this study for many languages.Most of the effective techniques require an annotated corpus of the language for training and validation.For the Manipuri language of the Tibeto-Burman family, neither such a corpus nor a grammar framework to automatically analyse and represent the structure of sentences exists yet.This study proposes a Context-Free Grammar (CFG) that provides the framework to represent the structure of Manipuri sentences.This paves the way for parsing Manipuri sentences using CFG-based parsers for various applications and to conveniently build a Treebank for developing ML-based parsers for Manipuri.The rules of the proposed CFG are handcrafted after extensive analysis of the structure of Manipuri sentences.The grammar covers simple, compound, complex and compound-complex sentences.For evaluation, we induce an Earley's parser with the proposed CFG and test it over a collection of sentences that covers the possible varieties of structure.A recognition rate of 83.20% achieved in these experiments indicates the effectiveness of the proposed grammar.
The second BMF investigation: mindset, motivation, and book reading interest

Le, T. T., Vuong, T. T., Ho, M. T., &amp;amp; La, V. P. (2022). The second BMF investigation: mindset, motivation, and book reading interest. The Mindsponge and BMF Analytics for Innovative Thinking in Social Sciences and Humanities, De Gruyter, 205-254.
The Development of Mobile Applications for Language Learning: A Systematic Review of Theoretical Frameworks

This systematic review was conducted to examine theories that were adopted or considered in the development of mobile applications for language learning. It aims to provide a holistic overview of major theoretical principles that underpin each developmental study to identify trends and gaps in the synthesised literature. The studies were collected from the Scopus and Web of Science databases as they were the main sources of reputable journals. Primary searches between 2011 and 2020 revealed approximately 158 studies related to the topic under investigation. After further filtering based on the inclusion criteria and removal of duplicates, 39 studies matched the research criteria and were used for further analysis. The analysis revealed that researchers tend to choose to solve problems pertaining to vocabulary learning and learners’ motivation through the development of mobile applications for language learning. They preferred to use constructivist-based theories such as situated learning and collaborative learning in guiding their development though behaviourist principles are also dominant. However, very few studies used theories related to language acquisition and learning in the design and development process. Hence, this gap should be given priority in future developmental research within the same scope as the generic learning theories may not be accurately addressing the language learning problems.
Analyzing Receptivity of Indian Respondents for Introduction of a Regulated Cryptocurrency Market

There have been increasing concerns regarding the cryptocurrency market for several reasons, regarding its decentralized system, and impact on the current financial market. Thus, the introduction of a regulated cryptocurrency market has sparked the public’s interest. This research study aimed to evaluate Indian respondents’ knowledge of cryptocurrencies and their receptivity (on the basis of several factors) towards the introduction of a regulated cryptocurrency market in India by conducting an online survey using a mixed-method research approach. Among the eight different factors examined, the study found the level of liquidity to be the highest-rated factor amongst respondents in influencing their receptivity. This finding suggests that the Indian public prefers a fast-flowing and smooth trading market, like the cryptocurrency market. Another top-rated factor was the level of security, possibly due to the increasing concern of cyberattacks in the financial markets.&#x0D; Conversely, anonymous usage, inflation risk, and operation cost were the least influential, highlighting the Indian public’s unique characteristics. Data revealed that respondents who were finance professionals and had IT experience were generally more receptive to the idea of a regulated cryptocurrency market in India. As a result, additional nuanced marketing strategies targeted at different sectors, especially the finance and IT sectors, as identified through the results include: introducing a regulated cryptocurrency market to the Indian public by highlighting their concern for the level of security and attracting them by highlighting how its regulation by authorities would keep in check the threats it poses.
Electronic Information Security Model of Nonlinear Differential Equations

Abstract The electronic information security model has the characteristics of nonlinearity and uncertainty. There will be great limitations if we use traditional mathematical theory for processing. Therefore, the paper proposes an electronic circuit information security simulation method utilizing nonlinear differential equations. The method uses a fractional reactance approximation circuit to build a nonlinear differential operator. The model modifies the circuit impedance function in electronic information security to form a transfer function. Then with the help of this function, we build the block diagram of nonlinear differential equation simulation. Experimental research shows that the algorithm proposed in this paper is simple, and the amount of data is small. At the same time, the experiment also verifies the correctness and effectiveness of the processing method.
Securing Cloud Data Under Key Exposure

Abstract: Recent news reveal a powerful attacker which breaks data confidentiality by acquiring cryptographic keys, by means of coercion or backdoors in cryptographic software. Once the encryption key is exposed, the only viable measure to preserve data confidentiality is to limit the attacker’s access to the ciphertext. This may be achieved, for example, by spreading ciphertext blocks across servers in multiple administrative domains—thus assuming that the adversary cannot compromise all of them. Nevertheless, if data is encrypted with existing schemes, an adversary equipped with the encryption key, can still compromise a single server and decrypt the ciphertext blocks stored therein. In this paper, we study data confidentiality against an adversary which knows the encryption key and has access to a large fraction of the ciphertext blocks[3]. To this end, we propose Bastion, a novel and efficient scheme that guarantees data confidentiality even if the encryption key is leaked and the adversary has access to almost all ciphertext blocks. We analyze the security of Bastion, and we evaluate its performance by means of a prototype implementation. We also discuss practical insights with respect to the integration of Bastion in commercial dispersed storage systems.
Breast Cancer Prognosis using Machine Learning Ensemble Techniques

According to WHO, breast cancer is the disease that affects people the most frequently and most dangerously in the world. Researchers are paying more attention to breast cancer because of how deadly it is and how early detection can prevent it. Since the advent of supervised machine learning algorithms, the early detection of breast cancer has advanced. The usage of several machine learning techniques as well as ensemble algorithms is demonstrated in the study. The outcomes were extremely precise, allowing for the best-possible cancer prediction. The paper's modest goal is to save people suffering from the disease by enabling them to know if the detected tumour is cancerous or non-cancerous, being Malignant. It focuses on early diagnosis of breast cancer. This paper would be useful and aiding for those who are novel researchers in prediction and diagnosis of breast cancer using machine learning.
Deep Reinforcement Learning-Based Cooperative Robot Using Facial Feedback

We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning.Metaphorically, the adaptation allows us to find needles in haystacks in the form of very predictive but rarely seen features.Our paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions to control the gradient steps of the algorithm.We describe and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal function that can be chosen in hindsight.We give several efficient algorithms for empirical risk minimization problems with common and important regularization functions and domain constraints.We experimentally study our theoretical analysis and show that adaptive subgradient methods outperform state-of-the-art, yet non-adaptive, subgradient algorithms.
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting

Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings. When dealing with estimated skeleton data in real-world videos, such methods perform poorly due to the large variations across subjects and camera viewpoints. To address this issue, we introduce ViA, a novel View-Invariant Autoencoder for self-supervised skeleton action representation learning. ViA leverages motion retargeting between different human performers as a pretext task, in order to disentangle the latent action-specific `Motion' features on top of the visual representation of a 2D or 3D skeleton sequence. Such `Motion' features are invariant to skeleton geometry and camera view and allow ViA to facilitate both, cross-subject and cross-view action classification tasks. We conduct a study focusing on transfer-learning for skeleton-based action recognition with self-supervised pre-training on real-world data (e.g., Posetics). Our results showcase that skeleton representations learned from ViA are generic enough to improve upon state-of-the-art action classification accuracy, not only on 3D laboratory datasets such as NTU-RGB+D 60 and NTU-RGB+D 120, but also on real-world datasets where only 2D data are accurately estimated, e.g., Toyota Smarthome, UAV-Human and Penn Action.
Probabilistic buckling of imperfect hemispherical shells containing a distribution of defects

The buckling of spherical shells is plagued by a strong sensitivity to imperfections. Traditionally, imperfect shells tend to be characterized empirically by the knockdown factor, the ratio between the measured buckling strength and the corresponding classic prediction for a perfect shell. Recently, it has been demonstrated that the knockdown factor of a shell containing a single imperfection can be predicted when there is detailed a priori knowledge of the defect geometry. Still, addressing the analogous problem for a shell containing many defects remains an open question. Here, we use finite element simulations, which we validate against precision experiments, to investigate hemispherical shells containing a well-defined distribution of imperfections. Our goal is to characterize the resulting knockdown factor statistics. First, we study the buckling of shells containing only two defects, uncovering nontrivial regimes of interactions that echo existing findings for cylindrical shells. Then, we construct statistical ensembles of imperfect shells, whose defect amplitudes are sampled from a lognormal distribution. We find that a 3-parameter Weibull distribution is an excellent description for the measured statistics of knockdown factors, suggesting that shell buckling can be regarded as an extreme-value statistics phenomenon.
Revisiting the Importance of Amplifying Bias for Debiasing

In image classification, "debiasing" aims to train a classifier to be less susceptible to dataset bias, the strong correlation between peripheral attributes of data samples and a target class. For example, even if the frog class in the dataset mainly consists of frog images with a swamp background (i.e., bias-aligned samples), a debiased classifier should be able to correctly classify a frog at a beach (i.e., bias-conflicting samples). Recent debiasing approaches commonly use two components for debiasing, a biased model $f_B$ and a debiased model $f_D$. $f_B$ is trained to focus on bias-aligned samples (i.e., overfitted to the bias) while $f_D$ is mainly trained with bias-conflicting samples by concentrating on samples which $f_B$ fails to learn, leading $f_D$ to be less susceptible to the dataset bias. While the state-of-the-art debiasing techniques have aimed to better train $f_D$, we focus on training $f_B$, an overlooked component until now. Our empirical analysis reveals that removing the bias-conflicting samples from the training set for $f_B$ is important for improving the debiasing performance of $f_D$. This is due to the fact that the bias-conflicting samples work as noisy samples for amplifying the bias for $f_B$ since those samples do not include the bias attribute. To this end, we propose a simple yet effective data sample selection method which removes the bias-conflicting samples to construct a bias-amplified dataset for training $f_B$. Our data sample selection method can be directly applied to existing reweighting-based debiasing approaches, obtaining consistent performance boost and achieving the state-of-the-art performance on both synthetic and real-world datasets.
Greedy Causal Discovery is Geometric

Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality. Algorithms that greedily transform one candidate DAG into another given a fixed set of moves have been particularly successful, for example the GES, GIES, and MMHC algorithms. In 2010, Studen\'y, Hemmecke and Lindner introduced the characteristic imset polytope, $\operatorname{CIM}_p$, whose vertices correspond to Markov equivalence classes, as a way of transforming causal discovery into a linear optimization problem. We show that the moves of the aforementioned algorithms are included within classes of edges of $\operatorname{CIM}_p$ and that restrictions placed on the skeleton of the candidate DAGs correspond to faces of $\operatorname{CIM}_p$. Thus, we observe that GES, GIES, and MMHC all have geometric realizations as greedy edge-walks along $\operatorname{CIM}_p$. Furthermore, the identified edges of $\operatorname{CIM}_p$ strictly generalize the moves of these algorithms. Exploiting this generalization, we introduce a greedy simplex-type algorithm called \emph{greedy CIM}, and a hybrid variant, \emph{skeletal greedy CIM}, that outperforms current competitors among hybrid and constraint-based algorithms.
Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition

This paper looks at semi-supervised learning (SSL) for image-based text recognition. One of the most popular SSL approaches is pseudo-labeling (PL). PL approaches assign labels to unlabeled data before re-training the model with a combination of labeled and pseudo-labeled data. However, PL methods are severely degraded by noise and are prone to over-fitting to noisy labels, due to the inclusion of erroneous high confidence pseudo-labels generated from poorly calibrated models, thus, rendering threshold-based selection ineffective. Moreover, the combinatorial complexity of the hypothesis space and the error accumulation due to multiple incorrect autoregressive steps posit pseudo-labeling challenging for sequence models. To this end, we propose a pseudo-label generation and an uncertainty-based data selection framework for semi-supervised text recognition. We first use Beam-Search inference to yield highly probable hypotheses to assign pseudo-labels to the unlabelled examples. Then we adopt an ensemble of models, sampled by applying dropout, to obtain a robust estimate of the uncertainty associated with the prediction, considering both the character-level and word-level predictive distribution to select good quality pseudo-labels. Extensive experiments on several benchmark handwriting and scene-text datasets show that our method outperforms the baseline approaches and the previous state-of-the-art semi-supervised text-recognition methods.
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction

In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices and communicating only model changes to the server. There are two main challenges: (i) the client heterogeneity, making FL algorithms that use the weighted averaging to aggregate model updates from the clients have slow progress and unsatisfactory learning results; and (ii) the difficulty of tuning the server learning rate with trial-and-error methodology due to the big computation time and resources needed for each experiment. To address these challenges, we propose a simple online meta-learning method to learn a strategy of aggregating the model updates, which adaptively weighs the importance of the clients based on their attributes and adjust the step sizes of the update. We perform extensive evaluations on public datasets. Our method significantly outperforms the state-of-the-art in both the speed of convergence and the quality of the final learning results.
Monotonic Gaussian process for physics-constrained machine learning with materials science applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce and noisy, and monotonicity is supported by strong physical evidence.
On Almost-Sure Intention Deception Planning that Exploits Imperfect Observers

Intention deception involves computing a strategy which deceives the opponent into a wrong belief about the agent's intention or objective. This paper studies a class of probabilistic planning problems with intention deception and investigates how a defender's limited sensing modality can be exploited by an attacker to achieve its attack objective almost surely (with probability one) while hiding its intention. In particular, we model the attack planning in a stochastic system modeled as a Markov decision process (MDP). The attacker is to reach some target states while avoiding unsafe states in the system and knows that his behavior is monitored by a defender with partial observations. Given partial state observations for the defender, we develop qualitative intention deception planning algorithms that construct attack strategies to play against an action-visible defender and an action-invisible defender, respectively. The synthesized attack strategy not only ensures the attack objective is satisfied almost surely but also deceives the defender into believing that the observed behavior is generated by a normal/legitimate user and thus failing to detect the presence of an attack. We show the proposed algorithms are correct and complete and illustrate the deceptive planning methods with examples.