Reviewerly/exHarmony huggingface dataset:
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition

Error correction in automatic speech recognition (ASR) aims to correct those incorrect words in sentences generated by ASR models. Since recent ASR models usually have low word error rate (WER), to avoid affecting originally correct tokens, error correction models should only modify incorrect words, and therefore detecting incorrect words is important for error correction. Previous works on error correction either implicitly detect error words through target-source attention or CTC (connectionist temporal classification) loss, or explicitly locate specific deletion/substitution/insertion errors. However, implicit error detection does not provide clear signal about which tokens are incorrect and explicit error detection suffers from low detection accuracy. In this paper, we propose SoftCorrect with a soft error detection mechanism to avoid the limitations of both explicit and implicit error detection. Specifically, we first detect whether a token is correct or not through a probability produced by a dedicatedly designed language model, and then design a constrained CTC loss that only duplicates the detected incorrect tokens to let the decoder focus on the correction of error tokens. Compared with implicit error detection with CTC loss, SoftCorrect provides explicit signal about which words are incorrect and thus does not need to duplicate every token but only incorrect tokens; compared with explicit error detection, SoftCorrect does not detect specific deletion/substitution/insertion errors but just leaves it to CTC loss. Experiments on AISHELL-1 and Aidatatang datasets show that SoftCorrect achieves 26.1% and 9.4% CER reduction respectively, outperforming previous works by a large margin, while still enjoying fast speed of parallel generation.
Graph Component Contrastive Learning for Concept Relatedness Estimation

Concept relatedness estimation (CRE) aims to determine whether two given concepts are related. Existing methods only consider the pairwise relationship between concepts, while overlooking the higher-order relationship that could be encoded in a concept-level graph structure. We discover that this underlying graph satisfies a set of intrinsic properties of CRE, including reflexivity, commutativity, and transitivity. In this paper, we formalize the CRE properties and introduce a graph structure named ConcreteGraph. To address the data scarcity issue in CRE, we introduce a novel data augmentation approach to sample new concept pairs from the graph. As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph. Empirical results on three datasets show significant improvement over the state-of-the-art model. Detailed ablation studies demonstrate that our proposed approach can effectively capture the high-order relationship among concepts.
Decision Support System for Admission of Regional Government Scholarships in Merauke Regency Using the TOPSIS Method

Scholarship is a government program given to high school graduates to be able to continue their studies at the tertiary level. This is as stipulated in the Regent Regulation (PERBUP) No 25 of 2019 concerning "providing scholarships and educational assistance to students of Merauke Regency". In the scholarship acceptance selection activities in 2019, approximately 103 applicants were recorded, and 74 were successfully accepted. The process of selecting prospective scholarship recipients is currently being carried out, namely checking and comparing one by one with the applicant's data. Based on data in 2019, where there are applicants whose report card value data is incomplete but is accepted for a scholarship. This is considered to have an impact on the decision-making results of prospective scholarship recipients in subsequent years. In order to assist KESRA in selecting scholarship recipients, these obstacles can be overcome by using a decision support system. The design of this decision support system is carried out using the TOPSIS method, namely selecting the alternative that is the shortest distance from the positive ideal solution and the furthest distance from the negative ideal solution. The results obtained in this study are a system that can assist the People's Welfare in determining the selection of scholarship recipients based on the three predetermined criteria. Testing the accuracy of the system obtained is 67% and for testing the questionnaire to obtain a percentage of 66.67% which is in the "Agree" category.
Computing Divergences between Discrete Decomposable Models

There are many applications that benefit from computing the exact divergence between 2 discrete probability measures, including machine learning. Unfortunately, in the absence of any assumptions on the structure or independencies within these distributions, computing the divergence between them is an intractable problem in high dimensions. We show that we are able to compute a wide family of functionals and divergences, such as the alpha-beta divergence, between two decomposable models, i.e. chordal Markov networks, in time exponential to the treewidth of these models. The alpha-beta divergence is a family of divergences that include popular divergences such as the Kullback-Leibler divergence, the Hellinger distance, and the chi-squared divergence. Thus, we can accurately compute the exact values of any of this broad class of divergences to the extent to which we can accurately model the two distributions using decomposable models.
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning

Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of multi-agent systems in real-world applications. However, in practice, each individual behavior policy that generates multi-agent joint trajectories usually has a different level of how well it performs. e.g., an agent is a random policy while other agents are medium policies. In the cooperative game with global reward, one agent learned by existing offline MARL often inherits this random policy, jeopardizing the utility of the entire team. In this paper, we investigate offline MARL with explicit consideration on the diversity of agent-wise trajectories and propose a novel framework called Shared Individual Trajectories (SIT) to address this problem. Specifically, an attention-based reward decomposition network assigns the credit to each agent through a differentiable key-value memory mechanism in an offline manner. These decomposed credits are then used to reconstruct the joint offline datasets into prioritized experience replay with individual trajectories, thereafter agents can share their good trajectories and conservatively train their policies with a graph attention network (GAT) based critic. We evaluate our method in both discrete control (i.e., StarCraft II and multi-agent particle environment) and continuous control (i.e., multi-agent mujoco). The results indicate that our method achieves significantly better results in complex and mixed offline multi-agent datasets, especially when the difference of data quality between individual trajectories is large.
Automata Cascades: Expressivity and Sample Complexity

Every automaton can be decomposed into a cascade of basic prime automata. This is the Prime Decomposition Theorem by Krohn and Rhodes. Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex systems made of many components, each implementing a specific functionality. Any automaton can serve as a component; using specific components allows for a fine-grained control of the expressivity of the resulting class of automata; using prime automata as components implies specific expressivity guarantees. Moreover, specifying automata as cascades allows for describing the sample complexity of automata in terms of their components. We show that the sample complexity is linear in the number of components and the maximum complexity of a single component, modulo logarithmic factors. This opens to the possibility of learning automata representing large dynamical systems consisting of many parts interacting with each other. It is in sharp contrast with the established understanding of the sample complexity of automata, described in terms of the overall number of states and input letters, which implies that it is only possible to learn automata where the number of states is linear in the amount of data available. Instead our results show that one can learn automata with a number of states that is exponential in the amount of data available.
Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental Learning

Exemplar rehearsal-based methods with knowledge distillation (KD) have been widely used in class incremental learning (CIL) scenarios. However, they still suffer from performance degradation because of severely distribution discrepancy between training and test set caused by the limited storage memory on previous classes. In this paper, we mathematically model the data distribution and the discrepancy at the incremental stages with mixture uniform distribution (MUD). Then, we propose the asymmetric mix distillation method to uniformly minimize the error of each class from distribution discrepancy perspective. Specifically, we firstly promote mixup in CIL scenarios with the incremental mix samplers and incremental mix factor to calibrate the raw training data distribution. Next, mix distillation label augmentation is incorporated into the data distribution to inherit the knowledge information from the previous models. Based on the above augmented data distribution, our trained model effectively alleviates the performance degradation and extensive experimental results validate that our method exhibits superior performance on CIL benchmarks.
Peningkatan Keterampilan Berpikir Kritis Siswa SMAN 1 Tambun Selatan Melalui Permainan Werewolf Card Game

The 21st century is an era that has a major impact on all aspects of human life, especially in the field of education. In order to improve the level of education in accordance with the needs of the system, this research focuses on improving 4C skills, especially critical thinking skills by involving the Werewolf Card Game learning media, which is expected to be a new innovation in the learning process. The method used in this research is descriptive statistics with a quantitative approach. The findings in this study are that there is a strong influence from the use of the Werewolf Card Game learning media on the critical thinking skills of the students of SMA Negeri 1 Tambun Selatan.
Web Usability Segmentation - Applying K-Means Clustering to the System Usability Scale

With the growing demand in platforms that serve as a medium to connect businesses with clients, as well as for users to interact with each other, there is a high desire for quality user interface (UI) and user experience (UX). System usability plays a vital role in ensuring UX and UI quality. A widely accepted standard for evaluating usability is the system usability scale (SUS) survey which provides an overview on how usable a given system is. Since data-driven techniques and algorithms abound in today's fast-paced world, it is pertinent that system owners leverage on such tools to understand their users and provide quality UX and UI. The data-driven methodology popularly known as segmentation techniques have been applied in numerous fields such as market segmentation, customer relations management, supply chain and production, to better understand the requirements for providing quality customer experience. This study proposes the use of one of such tools, usability segmentation, achieved by applying the k-means clustering algorithm to SUS data. The study contributes to theory and practice.
Speech Sentiment Analysis

Speech is the most common form of communication and conveying emotions between humans.Emotions expressed through speech are very easy for humans to understand but are one the most difficult methods for a machine to understand.In the past many methods have been utilised for speech sentiment analysis but recently with the rapid growth of Machine learning and Artificial Intelligence new techniques have made it possible for machines to mimic human thought processes and reasoning using deep learning techniques and can now work on topics that required human intervention in the past or were not feasible for any machine to perform.Using deep neural networks, machines can learn to automatically extract features from raw data, identify patterns and relationships, and make decisions based on that information, similar to how the human brain processes information.This paper discusses most of the known approaches for speech sentiment analysis and tests out deep learning-based implementation.
Optimal Decision Diagrams for Classification

Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth. Accordingly, decision diagrams are usually less prone to data fragmentation in internal nodes. However, the inherent complexity of training these classifiers acted as a long-standing barrier to their widespread adoption. In this context, we study the training of optimal decision diagrams (ODDs) from a mathematical programming perspective. We introduce a novel mixed-integer linear programming model for training and demonstrate its applicability for many datasets of practical importance. Further, we show how this model can be easily extended for fairness, parsimony, and stability notions. We present numerical analyses showing that our model allows training ODDs in short computational times, and that ODDs achieve better accuracy than optimal decision trees, while allowing for improved stability without significant accuracy losses.
Offline Quantum Reinforcement Learning in a Conservative Manner

Recently, to reap the quantum advantage, empowering reinforcement learning (RL) with quantum computing has attracted much attention, which is dubbed as quantum RL (QRL). However, current QRL algorithms employ an online learning scheme, i.e., the policy that is run on a quantum computer needs to interact with the environment to collect experiences, which could be expensive and dangerous for practical applications. In this paper, we aim to solve this problem in an offline learning manner. To be more specific, we develop the first offline quantum RL (offline QRL) algorithm named CQ2L (Conservative Quantum Q-learning), which learns from offline samples and does not require any interaction with the environment. CQ2L utilizes variational quantum circuits (VQCs), which are improved with data re-uploading and scaling parameters, to represent Q-value functions of agents. To suppress the overestimation of Q-values resulting from offline data, we first employ a double Q-learning framework to reduce the overestimation bias; then a penalty term that encourages generating conservative Q-values is designed. We conduct abundant experiments to demonstrate that the proposed method CQ2L can successfully solve offline QRL tasks that the online counterpart could not.
Exploiting Multiple Abstractions in Episodic RL via Reward Shaping

One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address this problem and improve learning efficiency, we consider a linear hierarchy of abstraction layers of the Markov Decision Process (MDP) underlying the target domain. Each layer is an MDP representing a coarser model of the one immediately below in the hierarchy. In this work, we propose a novel form of Reward Shaping where the solution obtained at the abstract level is used to offer rewards to the more concrete MDP, in such a way that the abstract solution guides the learning in the more complex domain. In contrast with other works in Hierarchical RL, our technique has few requirements in the design of the abstract models and it is also tolerant to modeling errors, thus making the proposed approach practical. We formally analyze the relationship between the abstract models and the exploration heuristic induced in the lower-level domain. Moreover, we prove that the method guarantees optimal convergence and we demonstrate its effectiveness experimentally.
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning

In reinforcement learning (RL), the ability to utilize prior knowledge from previously solved tasks can allow agents to quickly solve new problems. In some cases, these new problems may be approximately solved by composing the solutions of previously solved primitive tasks (task composition). Otherwise, prior knowledge can be used to adjust the reward function for a new problem, in a way that leaves the optimal policy unchanged but enables quicker learning (reward shaping). In this work, we develop a general framework for reward shaping and task composition in entropy-regularized RL. To do so, we derive an exact relation connecting the optimal soft value functions for two entropy-regularized RL problems with different reward functions and dynamics. We show how the derived relation leads to a general result for reward shaping in entropy-regularized RL. We then generalize this approach to derive an exact relation connecting optimal value functions for the composition of multiple tasks in entropy-regularized RL. We validate these theoretical contributions with experiments showing that reward shaping and task composition lead to faster learning in various settings.
General Acyclicity and Cyclicity Notions for the Disjunctive Skolem Chase

The disjunctive skolem chase is a sound, complete, and potentially non-terminating procedure for solving boolean conjunctive query entailment over knowledge bases of disjunctive existential rules. We develop novel acyclicity and cyclicity notions for this procedure; that is, we develop sufficient conditions to determine chase termination and non-termination. Our empirical evaluation shows that our novel notions are significantly more general than existing criteria.
Detecting Multivariate Time Series Anomalies with Zero Known Label

Multivariate time series anomaly detection has been extensively studied under the one-class classification setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach forMultivariate Time series anomaly detection via dynamic Graph and entityaware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges to density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model the accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlow achieves superior anomaly detection performance. Experiments on five public datasets with seven baselines are conducted, MTGFlow outperforms the SOTA methods by up to 5.0 AUROC%.
An Evaluation on the efficiency of E-Mail Spam Detection Using Naive Bayes Classifier

Nowadays, electronic mail is ubiquitous, being used everywhere from the business sector to the classroom. Emails can be broken down into two distinct categories: ham and spam. Email spam, also known as junk email or unwanted email, is a form of email that can be used to harm any user by wasting his or her time, draining system resources, and stealing sensitive data. Every day, the proportion of spam emails increases dramatically. Today's email and IoT service providers face a large and formidable task in detecting and filtering spam. One of the most prominent and widely-known approaches of detecting and avoiding spam is email filtration. It's also one of the most discussed tactics out there. Several machine learning and deep learning techniques, including Naive Bayes, decision trees, neural networks, and random forests, have been employed to reach this objective. After completing a survey of the available machine learning approaches, this article groups them into the most acceptable categories for usage in spam screening on email and IOT platforms. Accuracy, precision, memory requirements, and other metrics are used to thoroughly assess the methodologies. In the final section, we examine both the overall takeaways and directions future studies could go in.
Application of Management Accounting Practice Training Evaluation Software under Accounting Transformation Based on Intelligent OCR Data Clustering

An intelligent projection system based on OCR technology is proposed.Collecting data through cameras, OCR technology recognizes and processes text, so that intelligent OCR has functions such as mobile terminal adaptation, multi-task detection, whole line recognition, image segmentation, positioning, and classification.The application scenarios are more extensive.Text and card identification will be more efficient.Based on the perspective of financial sharing, this paper uses the method of grounded theory to analyze the accounting transformation case of Hebei LT Company.The study found that the two core categories of organizational process transformation and information system construction promoted process reengineering, efficiency and cost, and information construction.
DTEA: optical flow estimation with deep Taylor expansion approximation network

Optical flow is one of the most challenging tasks in computer vision for recovering the three-dimensional structure and motion of objects. Traditional hand-crafted priori methods can generate robust results at the cost of highly computational complexity. While deep optical flow estimation methods are impressive for their performance, especially a fast testing speed, but fail in a good interpretability. To cope with these issues, this paper proposes a novel optical flow estimation method, namely deep Taylor expansion approximation (DTEA). We firstly discuss the relationship between optical flow and Taylor unfolding, then introduce the proposed DTEA network in detail, and finally present experimental results. Quantitative as well as qualitative results of experiments on the <i>FlyingChairs</i> dataset validate the proposed DTEA network is effective, of which the performance can be extensively improved when the depth of the unfolding Taylor approximation is increasing.