Reviewerly/exHarmony huggingface dataset:
STUDENTS’ PERCEPTIONS TOWARD THE USE OF WORDWALL AS A FORMATIVE ASSESSMENT IN ELT

ABSTRACT&#x0D; This study is to investigate students' perceptions about using Wordwall media as a formative assessment tool in learning English at SMP Negeri 37 Semarang. The subject of this study is 32 students of class VII H of SMP Negeri 37 Semarang in 2023. The data collection method uses questionnaires and interviews. Questionnaires were distributed to determine students' understanding and interviews were conducted to obtain in-depth responses so that the researchers could better describe and analyze students' perceptions of the Wordwall application as a learning tool. The results of this study show that almost all students are interested in using Wordwall as a formative assessment tool because it is easy to use, has an attractive design, and a variety of questions that made students want to use it. It can be concluded that the Wordwall can be used as an assessment tool in the future.&#x0D;
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method

The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment. However, they typically simulate the missing content by manual-designed error masks, thus failing to fill in the realistic video loss in video communication (e.g., telepresence, live streaming, and internet video) and multimedia forensics. To address this, we introduce the bitstream-corrupted video (BSCV) benchmark, the first benchmark dataset with more than 28,000 video clips, which can be used for bitstream-corrupted video recovery in the real world. The BSCV is a collection of 1) a proposed three-parameter corruption model for video bitstream, 2) a large-scale dataset containing rich error patterns, multiple corruption levels, and flexible dataset branches, and 3) a plug-and-play module in video recovery framework that serves as a benchmark. We evaluate state-of-the-art video inpainting methods on the BSCV dataset, demonstrating existing approaches' limitations and our framework's advantages in solving the bitstream-corrupted video recovery problem. The benchmark and dataset are released at https://github.com/LIUTIGHE/BSCV-Dataset.
Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer’s Disease with 3D Amyloid-PET

Early diagnosis of Alzheimer’s disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET, which measures the accumulation of amyloid plaques in the brain—a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET. However, commonly used models are trained in a fully supervised learning manner, and they are inevitably biased toward the given label information. To this end, we propose a selfsupervised contrastive learning method to accurately predict the conversion to AD for individuals with mild cognitive impairment (MCI) with 3D amyloid-PET. The proposed method, SMoCo, uses both labeled and unlabeled data to capture general semantic representations underlying the images. As the downstream task is given as classification of converters vs. non-converters, unlike the general self-supervised learning problem that aims to generate task-agnostic representations, SMoCo additionally utilizes the label information in the pre-training. To demonstrate the performance of our method, we conducted experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The results confirmed that the proposed method is capable of providing appropriate data representations, resulting in accurate classification. SMoCo showed the best classification performance over the existing methods, with AUROC = 85.17%, accuracy = 81.09%, sensitivity = 77.39%, and specificity = 82.17%. While SSL has demonstrated great success in other application domains of computer vision, this study provided the initial investigation of using a proposed self-supervised contrastive learning model, SMoCo, to effectively predict MCI conversion to AD based on 3D amyloid-PET.
Confidence Intervals for the F1 Score: A Comparison of Four Methods

In Natural Language Processing (NLP), binary classification algorithms are often evaluated using the F1 score. Because the sample F1 score is an estimate of the population F1 score, it is not sufficient to report the sample F1 score without an indication of how accurate it is. Confidence intervals are an indication of how accurate the sample F1 score is. However, most studies either do not report them or report them using methods that demonstrate poor statistical properties. In the present study, I review current analytical methods (i.e., Clopper-Pearson method and Wald method) to construct confidence intervals for the population F1 score, propose two new analytical methods (i.e., Wilson direct method and Wilson indirect method) to do so, and compare these methods based on their coverage probabilities and interval lengths, as well as whether these methods suffer from overshoot and degeneracy. Theoretical results demonstrate that both proposed methods do not suffer from overshoot and degeneracy. Experimental results suggest that both proposed methods perform better, as compared to current methods, in terms of coverage probabilities and interval lengths. I illustrate both current and proposed methods on two suggestion mining tasks. I discuss the practical implications of these results, and suggest areas for future research.
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation

Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing. Vision-based learning systems aim to eliminate the need for environment instrumentation by building an implicit understanding of the world based on raw pixels, but navigating the contact-rich high-dimensional search space from solely sparse visual reward signals significantly exacerbates the challenge of exploration. The applicability of such systems is thus typically restricted to simulated or heavily engineered environments since agent exploration in the real-world without the guidance of explicit state estimation and dense rewards can lead to unsafe behavior and safety faults that are catastrophic. In this study, we isolate the root causes behind these limitations to develop a system, called MoDem-V2, capable of learning contact-rich manipulation directly in the uninstrumented real world. Building on the latest algorithmic advancements in model-based reinforcement learning (MBRL), demo-bootstrapping, and effective exploration, MoDem-V2 can acquire contact-rich dexterous manipulation skills directly in the real world. We identify key ingredients for leveraging demonstrations in model learning while respecting real-world safety considerations -- exploration centering, agency handover, and actor-critic ensembles. We empirically demonstrate the contribution of these ingredients in four complex visuo-motor manipulation problems in both simulation and the real world. To the best of our knowledge, our work presents the first successful system for demonstration-augmented visual MBRL trained directly in the real world. Visit https://sites.google.com/view/modem-v2 for videos and more details.
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM

Distributed optimization is a fundamental framework for collaborative inference and decision making in decentralized multi-agent systems. The operation is modeled as the joint minimization of a shared objective which typically depends on observations gathered locally by each agent. Distributed optimization algorithms, such as the common D-ADMM, tackle this task by iteratively combining local computations and message exchanges. One of the main challenges associated with distributed optimization, and particularly with D-ADMM, is that it requires a large number of communications, i.e., messages exchanged between the agents, to reach consensus. This can make D-ADMM costly in power, latency, and channel resources. In this work we propose unfolded D-ADMM, which follows the emerging deep unfolding methodology to enable D-ADMM to operate reliably with a predefined and small number of messages exchanged by each agent. Unfolded D-ADMM fully preserves the operation of D-ADMM, while leveraging data to tune the hyperparameters of each iteration of the algorithm. These hyperparameters can either be agent-specific, aiming at achieving the best performance within a fixed number of iterations over a given network, or shared among the agents, allowing to learn to distributedly optimize over different networks. For both settings, our unfolded D-ADMM operates with limited communications, while preserving the interpretability and flexibility of the original D-ADMM algorithm. We specialize unfolded D-ADMM for two representative settings: a distributed estimation task, considering a sparse recovery setup, and a distributed learning scenario, where multiple agents collaborate in learning a machine learning model. Our numerical results demonstrate that the proposed approach dramatically reduces the number of communications utilized by D-ADMM, without compromising on its performance.
A post-selection algorithm for improving dynamic ensemble selection methods

Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the proposal of several DES approaches, no particular DES technique is the best choice for different problems. Thus, we hypothesize that selecting the best DES approach per query instance can lead to better accuracy. To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics. Experimental results show that using accuracy as a metric to select the ensembles, PS-DES performs better than individual DES techniques. PS-DES source code is available in a GitHub repository
Diversifying Question Generation over Knowledge Base via External Natural Questions

Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing ability of humans, we contend that diverse texts should convey the same semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the above diversity since they calculate the ratio of unique n-grams in the generated question itself, which leans more towards measuring duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG demonstrate that our proposed approach generates highly diverse questions and improves the performance of question answering tasks.
Synchronizing Full-Body Avatar Transforms with WebRTC DataChannel on Educational Metaverse

Full-body avatars are suggested to be beneficial for communication in virtual environments, and consistency between users' voices and gestures is considered essential to ensure communication quality. This paper propose extending the functionality of a web-based VR platform to support the use of full-body avatars and delegated avatar transforms synchronization to WebRTC DataChannel to enhance the consistency between voices and gestures. Finally, we conducted a preliminary validation to confirm the consistency.
Randomized term grouping over physical law on digital quantum simulation

We introduce a randomized algorithm based on qDrift to compute Hamiltonian dynamics on digital quantum computers. We frame it as physDrift because conservation laws in physics are obeyed during evolution of arbitrary quantum states. Empirically we achieved better spectral error reduction with hydrogen chain model compared to previous protocols. Noisy model are investigated as well and we characterised them in the circuit with different schemes, i.e. an attenuation of the measured expectation value is fixed by keeping the circuit depth the same and depolarising error is simulated with randomly applied Pauli gates. This makes it our proposal particularly feasible for implementing and testing on present-day noisy hardware.
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) models have recently attracted a great deal of interest from a variety of application sectors. Despite significant developments in this area, there are still no standardized methods or approaches for understanding AI model outputs. A systematic and cohesive framework is also increasingly necessary to incorporate new techniques like discriminative and generative models to close the gap. This paper contributes to the discourse on XAI by presenting an empirical evaluation based on a novel framework: Sampling - Variational Auto Encoder (VAE) - Ensemble Anomaly Detection (SVEAD). It is a hybrid architecture where VAE combined with ensemble stacking and SHapley Additive exPlanations are used for imbalanced classification. The finding reveals that combining ensemble stacking, VAE, and SHAP can. not only lead to better model performance but also provide an easily explainable framework. This work has used SHAP combined with Permutation Importance and Individual Conditional Expectations to create a powerful interpretability of the model. The finding has an important implication in the real world, where the need for XAI is paramount to boost confidence in AI applications.
Arabic Question-Answering System Based on Deep Learning Models

An information retrieval mechanism with particular capabilities is the question-answering system. The QAS is interested in pertinent responses that are provided in natural language to the suggested questions. It is becoming more and more important in education, notably as a method of automated subjective exam scoring. The classification of questions, information retrieval, and response retrieval are the three basic parts of most QAS systems. By classifying the submitted questions according to their nature, the classifying of questions plays a crucial role in QASs. Retrieval of knowledge is used to find the answer to a question since further processing cannot be done to locate the solution if a document lacks the right answers. The answer to the user’s query is then obtained by performing the retrieving of an answer. A review of the NLP as well as all relevant information on question-answering systems is provided in this research article. There are millions of pieces of knowledge available, but making the proper information accessible when needed is very important. Getting the proper documents to read and further getting a direct answer to one's question from the set of documents is a challenging task. An answering system is concerned with building systems that automatically answer questions posed by humans in a natural language. A QA system is usually programmed to pull answers from a structured database or an unstructured collection of natural language documents. This paper is related to the long answers; it introduces a completely unique way of grading using simple RRN models and advanced RNN models, such as LSTM and GRU. We were ready to build an RRN, LSTM, and GRU neural network for predicting the right answers after training. We got an accuracy on RRN up to 94%, LSTM up to 91%, and GRU up to 99%. And on testing, we got on RRN up to 85%, LSTM up to 84%, and GRU up to 95%.
On the expressivity of embedding quantum kernels

One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quantum kernels are typically evaluated by explicitly constructing quantum feature states and then taking their inner product, here called embedding quantum kernels. Since classical kernels are usually evaluated without using the feature vectors explicitly, we wonder how expressive embedding quantum kernels are. In this work, we raise the fundamental question: can all quantum kernels be expressed as the inner product of quantum feature states? Our first result is positive: Invoking computational universality, we find that for any kernel function there always exists a corresponding quantum feature map and an embedding quantum kernel. The more operational reading of the question is concerned with efficient constructions, however. In a second part, we formalize the question of universality of efficient embedding quantum kernels. For shift-invariant kernels, we use the technique of random Fourier features to show that they are universal within the broad class of all kernels which allow a variant of efficient Fourier sampling. We then extend this result to a new class of so-called composition kernels, which we show also contains projected quantum kernels introduced in recent works. After proving the universality of embedding quantum kernels for both shift-invariant and composition kernels, we identify the directions towards new, more exotic, and unexplored quantum kernel families, for which it still remains open whether they correspond to efficient embedding quantum kernels.
A Projected Upper Bound for Mining High Utility Patterns from Interval-Based Event Sequences

High utility pattern mining is an interesting yet challenging problem. The intrinsic computational cost of the problem will impose further challenges if efficiency in addition to the efficacy of a solution is sought. Recently, this problem was studied on interval-based event sequences with a constraint on the length and size of the patterns. However, the proposed solution lacks adequate efficiency. To address this issue, we propose a projected upper bound on the utility of the patterns discovered from sequences of interval-based events. To show its effectiveness, the upper bound is utilized by a pruning strategy employed by the HUIPMiner algorithm. Experimental results show that the new upper bound improves HUIPMiner performance in terms of both execution time and memory usage.
IoT Motion Tracking System for Workout Performance Evaluation: A Case Study on Dumbbell

An intelligent sports training system based on Internet of Things (IoT) technology is proposed to build a low-cost, easy-to-use home exercise guidance solution, which can provide reliable exercise guidance when gymnasiums are inaccessible for users. The proposed intelligent system includes an inertial measurement microelectromechanical system with Bluetooth low-energy data transmission technology, a smart dumbbell with an acceleration sensor, an application on the smartphone terminal, and a computing central server in the clouds. Two-loop Kalman filters, dynamic motion segmentation method, and neural network are developed to demonstrate and evaluate the users dumbbell exercise modes. Six dumbbell exercise postures and 10 exercise cycles for eight participants are collected for system validation in the experimental study. The experimental results demonstrate that the proposed system can effectively and accurately segment multiple types of dumbbell movements (98.9% accuracy), recognize movements with high reliability (98.3% accuracy), and distinguish standard and non-standard movements (89% accuracy). Finally, this system with an intelligent algorithm software and hardware can be expanded to other similar types of sporting excises.
Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer

Viewport prediction is a crucial aspect of tile-based 360° video streaming system. However, existing trajectory based methods lack of robustness, also oversimplify the process of information construction and fusion between different modality inputs, leading to the error accumulation problem. In this paper, we propose a tile classification based viewport prediction method with Multi-modal Fusion Transformer, namely MFTR. Specifically, MFTR utilizes transformer-based networks to extract the long-range dependencies within each modality, then mine intra- and inter-modality relations to capture the combined impact of user historical inputs and video contents on future viewport selection. In addition, MFTR categorizes future tiles into two categories: user interested or not, and selects future viewport as the region that contains most user interested tiles. Comparing with predicting head trajectories, choosing future viewport based on tile's binary classification results exhibits better robustness and interpretability. To evaluate our proposed MFTR, we conduct extensive experiments on two widely used PVS-HM and Xu-Gaze dataset. MFTR shows superior performance over state-of-the-art methods in terms of average prediction accuracy and overlap ratio, also presents competitive computation efficiency.
Optimization Approaches in Meta-Learning Models

This book chapter provides a comprehensive overview of optimization approaches in meta-learning, focusing on techniques and their applications. Meta-learning is a subfield of machine learning that emphasizes acquiring knowledge from previous tasks and applying the same to new tasks in order to develop the models with improved learning process. Optimization plays a crucial role in meta-learning models by enabling the effective acquisition and utilization of knowledge across tasks. This chapter provides an overview of various optimization approaches employed in meta-learning models which entail changing the model's input parameters or learning algorithms to facilitate effective learning across various tasks or domains. The methods tackle the problem of effective learning without compromising with accuracy and precision in performance focusing on the benefits of meta-learning frameworks in practical situations which may be considered as the real-world applications of these approaches.
American Sign Language Recognition and its Conversion from Text to Speech

Abstract: Speech impairment is a complicated condition that impairs a person's capacity for verbal and audible communication. Those who are impacted frequently use sign language and other alternate forms of communication. While sign language has gained popularity, bridging the communication gap between those who sign and those who don't remains a challenge. Our project endeavors to address this issue by developing an innovative application that offers real-time sign language-to-text translation. This technology aims to facilitate seamless communication between those who use sign language and those who do not. To achieve this, we have constructed a cutting-edge sign language recognition system as part of our project. Our system primarily utilizes American Sign Language (ASL) as its foundation. To accurately detect gestures, we employ a Convolutional Neural Network (CNN) with Inception V3 as the underlying model. This project's core objective is to harness Machine Learning Techniques for converting ASL hand gestures into text and vice versa. We go beyond mere translation by enabling real-time American Sign Language interpretation through single-hand gestures. Furthermore, our system can recognize ASL words, converting them into text before rendering them into audible speech.