Reviewerly/exHarmony huggingface dataset:
Unveiling the Power of Deep Learning: Insights into Advanced Neural n Network Architectures

Deep learning has emerged as a powerful paradigm within artificial intelligence, revolutionizing various fields with its ability to automatically discover intricate patterns and representations from vast amounts of data. In this article, we delve into the latest advancements in deep learning, focusing on the exploration of advanced neural network architectures. Through a comprehensive review and analysis, we uncover the underlying principles and methodologies driving the success of these architectures, elucidating their capabilities and limitations. From convolutional neural networks (CNNs) for image recognition to recurrent neural networks (RNNs) for sequential data processing, we highlight key innovations and breakthroughs that have propelled the field forward. Furthermore, we discuss recent trends such as attention mechanisms, transformer models, and graph neural networks, showcasing their potential applications and impact across diverse domains. By unraveling the power of deep learning and its advanced neural network architectures, this article aims to provide valuable insights into the state-of-the-art techniques driving AI research and development.
Scaling adaptive quantum simulation algorithms via operator pool tiling

A systematic and easily generalizable method to perform adaptive variational quantum simulations on lattice models, termed operator tiling, is proposed, in which a small problem instance is first studied to learn the most relevant operators. State preparation circuits using operators identified from solving the small problem instance are then iteratively tiled to larger problem instances. The method is demonstrated on strongly correlated spin models in one and two dimensions and shown to yield effective ans\"atze.
A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Abstract Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, “The food is tasty but restaurant is untidy”, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an $$aspect$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>aspect</mml:mi> </mml:mrow> </mml:math> and $$context$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>context</mml:mi> </mml:mrow> </mml:math> words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.
GROWING STUDENT LITERACY ABILITY THROUGH THE SCHOOL LITERACY MOVEMENT

In today's rapidly evolving world, literacy has become an essential skill for individuals to navigate and succeed in various aspects of life. Literacy is not limited to reading and writing but also encompasses critical thinking, comprehension, and communication skills. Recognizing the significance of literacy, many educational institutions and organizations have initiated programs and movements to enhance the literacy capabilities of students. One such movement is the School Literacy Movement. This study aims to explore gerakan literasi sekolah and its impact on the growth of students' literacy skills.
THE ROLE OF GOVERNMENT POLICY IN FOSTERING ENTREPRENEURSHIP: A USA AND AFRICA REVIEW

This Review explores the crucial role of government policies in fostering entrepreneurship by examining the experiences of the United States (USA) and various African nations. Entrepreneurship is a driving force for economic growth, job creation, and innovation, and government policies play a pivotal role in shaping the entrepreneurial landscape. In the USA, a robust history of entrepreneurial success is attributed in part to supportive government policies. The Review delves into specific policies that have contributed to fostering entrepreneurship, including tax incentives, access to funding through Small Business Administration (SBA) programs, and initiatives promoting innovation and research. The analysis extends to the regulatory environment, emphasizing the importance of flexible regulations that encourage innovation while ensuring consumer protection. Turning attention to Africa, the Review explores the diversity of government approaches across the continent. It considers initiatives implemented by African nations to promote entrepreneurship, such as the establishment of entrepreneurial ecosystems, funding mechanisms for startups, and efforts to streamline regulatory frameworks. The review acknowledges the unique challenges faced by African entrepreneurs, including limited access to capital and infrastructure constraints, and evaluates how government policies aim to address these issues. The comparative analysis highlights lessons that African nations can draw from the USA's successful policies, such as the importance of fostering a culture of innovation, providing targeted financial support, and creating an enabling regulatory environment. Moreover, it emphasizes the need for context-specific policies that consider the unique socio-economic conditions of each African nation. In conclusion, the Review underscores the critical role of government policy in nurturing entrepreneurship, drawing insights from the USA's experience and the varied approaches across African nations. It calls for a nuanced understanding of the diverse African entrepreneurial landscape and encourages policymakers to tailor initiatives that catalyze entrepreneurship, driving economic development across the continent.&#x0D; Keywords: Role, Government, Policy, Fostering, Entrepreneurship.
A CRITICAL REVIEW OF ERP SYSTEMS IMPLEMENTATION IN MULTINATIONAL CORPORATIONS: TRENDS, CHALLENGES, AND FUTURE DIRECTIONS

Enterprise Resource Planning (ERP) systems have become indispensable tools for managing complex business processes in multinational corporations (MNCs). This paper presents a critical review of the trends, challenges, and future directions associated with ERP systems implementation in the context of MNCs. The study synthesizes existing literature to offer a comprehensive understanding of the evolving landscape of ERP adoption in a globalized business environment. The trends in ERP implementation within MNCs underscore the increasing reliance on integrated software solutions to enhance operational efficiency and facilitate seamless communication across diverse geographical locations. Notably, the integration of emerging technologies such as artificial intelligence, machine learning, and blockchain into ERP systems has gained traction, presenting new opportunities and complexities for MNCs. However, the implementation of ERP systems in MNCs is not without challenges. Cultural diversity, varying regulatory frameworks, and differences in business practices across regions pose significant obstacles to successful deployment. Additionally, the sheer scale and complexity of MNCs' operations demand careful consideration of customization needs, scalability, and alignment with organizational goals. The critical review highlights the need for a strategic approach to ERP implementation in MNCs. Successful cases often involve top management commitment, extensive user training, and a phased implementation strategy that accommodates regional variations. Furthermore, the study emphasizes the importance of post-implementation evaluation and continuous improvement to address evolving business requirements. Looking ahead, future directions in ERP systems implementation within MNCs are shaped by the rapid evolution of technology and the dynamic nature of global business. The integration of cloud-based solutions, the advancement of Industry 4.0 technologies, and the increasing emphasis on sustainability are anticipated to influence the next wave of ERP adoption in MNCs. This critical review contributes valuable insights into the current state of ERP systems implementation in multinational corporations, addressing trends, challenges, and future directions. It provides a roadmap for practitioners, researchers, and policymakers to navigate the complexities of ERP adoption in the ever-evolving landscape of global business.&#x0D; Keywords: ERP, MNCs, Industry 4.0, Multinational, Corporation, Review.
A cross-talk robust multichannel VAD model for multiparty agent  interactions trained using synthetic re-recordings

In this work, we propose a novel cross-talk rejection framework for a multi-channel multi-talker setup for a live multiparty interactive show. Our far-field audio setup is required to be hands-free during live interaction and comprises four adjacent talkers with directional microphones in the same space. Such setups often introduce heavy cross-talk between channels, resulting in reduced automatic speech recognition (ASR) and natural language understanding (NLU) performance. To address this problem, we propose voice activity detection (VAD) model for all talkers using multichannel information, which is then used to filter audio for downstream tasks. We adopt a synthetic training data generation approach through playback and re-recording for such scenarios, simulating challenging speech overlap conditions. We train our models on this synthetic data and demonstrate that our approach outperforms single-channel VAD models and energy-based multi-channel VAD algorithm in various acoustic environments. In addition to VAD results, we also present multiparty ASR evaluation results to highlight the impact of using our VAD model for filtering audio in downstream tasks by significantly reducing the insertion error.
How to validate average calibration for machine learning regression  tasks ?

Average calibration of the uncertainties of machine learning regression tasks can be tested in two ways. One way is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV) or mean squared uncertainty. The alternative is to compare the mean squared z-scores or scaled errors (ZMS) to 1. Both approaches might lead to different conclusion, as illustrated on an ensemble of datasets from the recent machine learning uncertainty quantification literature. It is shown here that the CE is very sensitive to the distribution of uncertainties, and notably to the presence of outlying uncertainties, and that it cannot be used reliably for calibration testing. By contrast, the ZMS statistic does not present this sensitivity issue and offers the most reliable approach in this context. Implications for the validation of conditional calibration are discussed.
Human Activity Recognition System with Smartphones Using Machine Learning

Abstract: Human activity recognition (HAR) is a crucial task in the field of physical activity monitoring. It involves the identification of a person's activities and movements using sensors. The accuracy of HAR systems plays a significant role in enhancing physical health, preventing accidents and injuries, and improving security systems. One of the most common approaches to HAR is using smartphones as sensors. However, smartphones have limited processing power and battery life, which can impact the performance of HAR systems. To overcome these challenges, we can apply different machine learning models and compare their performances. We begin by selecting a standard dataset, the HAR dataset from the Machine Learning Repository. We then apply several Machine learning models, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).To compare the performance of these models, we train and test each model on the HAR dataset. We also select the best set of parameters for each model using grid search. Our results show that the Support Vector Machine (SVM) performed the best (average accuracy 96.33%), significantly outperforming the other models. We can confirm the statistical significance of these results by employing statistical significance test methods.The SVM model demonstrated superior performance in recognizing human activities. This highlights the potential of machine learning models in revolutionizing the field of HAR. However, further research is needed to address the challenges of limited processing power and battery life in smartphones and to explore other potential applications of HAR systems.
Low-Rank Graph Contrastive Learning for Node Classification

Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph data, would considerably degrade the performance of GNNs revealed by recent studies. In this work, we propose a novel and robust GNN encoder, Low-Rank Graph Contrastive Learning (LR-GCL). Our method performs transductive node classification in two steps. First, a low-rank GCL encoder named LR-GCL is trained by prototypical contrastive learning with low-rank regularization. Next, using the features produced by LR-GCL, a linear transductive classification algorithm is used to classify the unlabeled nodes in the graph. Our LR-GCL is inspired by the low frequency property of the graph data and its labels, and it is also theoretically motivated by our sharp generalization bound for transductive learning. To the best of our knowledge, our theoretical result is among the first to theoretically demonstrate the advantage of low-rank learning in graph contrastive learning supported by strong empirical performance. Extensive experiments on public benchmarks demonstrate the superior performance of LR-GCL and the robustness of the learned node representations. The code of LR-GCL is available at \url{https://anonymous.4open.science/r/Low-Rank_Graph_Contrastive_Learning-64A6/}.
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning  Using Weight Regularization

A common phenomena confining the representation quality in Self-Supervised Learning (SSL) is dimensional collapse (also known as rank degeneration), where the learned representations are mapped to a low dimensional subspace of the representation space. The State-of-the-Art SSL methods have shown to suffer from dimensional collapse and fall behind maintaining full rank. Recent approaches to prevent this problem have proposed using contrastive losses, regularization techniques, or architectural tricks. We propose WERank, a new regularizer on the weight parameters of the network to prevent rank degeneration at different layers of the network. We provide empirical evidence and mathematical justification to demonstrate the effectiveness of the proposed regularization method in preventing dimensional collapse. We verify the impact of WERank on graph SSL where dimensional collapse is more pronounced due to the lack of proper data augmentation. We empirically demonstrate that WERank is effective in helping BYOL to achieve higher rank during SSL pre-training and consequently downstream accuracy during evaluation probing. Ablation studies and experimental analysis shed lights on the underlying factors behind the performance gains of the proposed approach.
Probabilistic Reasoning in Generative Large Language Models

This paper considers the challenges that Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values. This type of reasoning is relevant to a variety of contexts ranging from everyday conversations to medical decision-making. Despite improvements in the mathematical reasoning capabilities of LLMs, they still exhibit significant difficulties when it comes to probabilistic reasoning. To deal with this problem, we first introduce the Bayesian Linguistic Inference Dataset (BLInD), a new dataset specifically designed to test the probabilistic reasoning capabilities of LLMs. We then leverage this new dataset to thoroughly illustrate the specific limitations of LLMs for tasks involving probabilistic reasoning and present several strategies that map the problem to different formal representations, including Python code, probabilistic inference algorithms, and probabilistic logical programming. We conclude by providing an evaluation of our methods on BLInD and on an adaptation of a causal reasoning question-answering dataset, which further shows their practical effectiveness.
Scalable Graph Self-Supervised Learning

In regularization Self-Supervised Learning (SSL) methods for graphs, computational complexity increases with the number of nodes in graphs and embedding dimensions. To mitigate the scalability of non-contrastive graph SSL, we propose a novel approach to reduce the cost of computing the covariance matrix for the pre-training loss function with volume-maximization terms. Our work focuses on reducing the cost associated with the loss computation via graph node or dimension sampling. We provide theoretical insight into why dimension sampling would result in accurate loss computations and support it with mathematical derivation of the novel approach. We develop our experimental setup on the node-level graph prediction tasks, where SSL pre-training has shown to be difficult due to the large size of real world graphs. Our experiments demonstrate that the cost associated with the loss computation can be reduced via node or dimension sampling without lowering the downstream performance. Our results demonstrate that sampling mostly results in improved downstream performance. Ablation studies and experimental analysis are provided to untangle the role of the different factors in the experimental setup.
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain  Generalization and Catastrophic Forgetting in CLIP

Foundations models are presented as generalists that often perform well over a myriad of tasks. Fine-tuning these models, even on limited data, provides an additional boost in task-specific performance but often at the cost of their wider generalization, an effect termed catastrophic forgetting. In this paper, we analyze the relation between task difficulty in the CLIP model and the performance of several simple parameter-efficient fine-tuning methods through the lens of domain generalization and catastrophic forgetting. We provide evidence that the silhouette score of the zero-shot image and text embeddings is a better measure of task difficulty than the average cosine similarity of correct image/label embeddings, and discuss observable relationships between task difficulty, fine-tuning method, domain generalization, and catastrophic forgetting. Additionally, the averaged results across tasks and performance measures demonstrate that a simplified method that trains only a subset of attention weights, which we call A-CLIP, yields a balance between domain generalization and catastrophic forgetting.
Concatenate codes, save qubits

The essential requirement for fault-tolerant quantum computation (FTQC) is the total protocol design to achieve a fair balance of all the critical factors relevant to its practical realization, such as the space overhead, the threshold, and the modularity. A major obstacle in realizing FTQC with conventional protocols, such as those based on the surface code and the concatenated Steane code, has been the space overhead, i.e., the required number of physical qubits per logical qubit. Protocols based on high-rate quantum low-density parity-check (LDPC) codes gather considerable attention as a way to reduce the space overhead, but problematically, the existing fault-tolerant protocols for such quantum LDPC codes sacrifice the other factors. Here we construct a new fault-tolerant protocol to meet these requirements simultaneously based on more recent progress on the techniques for concatenated codes rather than quantum LDPC codes, achieving a constant space overhead, a high threshold, and flexibility in modular architecture designs. In particular, under a physical error rate of $0.1\%$, our protocol reduces the space overhead to achieve the logical CNOT error rates $10^{-10}$ and $10^{-24}$ by more than $90 \%$ and $97 \%$, respectively, compared to the protocol for the surface code. Furthermore, our protocol achieves the threshold of $2.4 \%$ under a conventional circuit-level error model, substantially outperforming that of the surface code. The use of concatenated codes also naturally introduces abstraction layers essential for the modularity of FTQC architectures. These results indicate that the code-concatenation approach opens a way to significantly save qubits in realizing FTQC while fulfilling the other essential requirements for the practical protocol design.
Pareto-Optimal Algorithms for Learning in Games

We study the problem of characterizing optimal learning algorithms for playing repeated games against an adversary with unknown payoffs. In this problem, the first player (called the learner) commits to a learning algorithm against a second player (called the optimizer), and the optimizer best-responds by choosing the optimal dynamic strategy for their (unknown but well-defined) payoff. Classic learning algorithms (such as no-regret algorithms) provide some counterfactual guarantees for the learner, but might perform much more poorly than other learning algorithms against particular optimizer payoffs. In this paper, we introduce the notion of asymptotically Pareto-optimal learning algorithms. Intuitively, if a learning algorithm is Pareto-optimal, then there is no other algorithm which performs asymptotically at least as well against all optimizers and performs strictly better (by at least $\Omega(T)$) against some optimizer. We show that well-known no-regret algorithms such as Multiplicative Weights and Follow The Regularized Leader are Pareto-dominated. However, while no-regret is not enough to ensure Pareto-optimality, we show that a strictly stronger property, no-swap-regret, is a sufficient condition for Pareto-optimality. Proving these results requires us to address various technical challenges specific to repeated play, including the fact that there is no simple characterization of how optimizers who are rational in the long-term best-respond against a learning algorithm over multiple rounds of play. To address this, we introduce the idea of the asymptotic menu of a learning algorithm: the convex closure of all correlated distributions over strategy profiles that are asymptotically implementable by an adversary. We show that all no-swap-regret algorithms share the same asymptotic menu, implying that all no-swap-regret algorithms are ``strategically equivalent''.