Reviewerly/exHarmony huggingface dataset:
CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans

Managing a work-life balance has always been challenging, especially after the recent trend of working from home, which has made maintaining one's fitness and diet regime strenuous. Failing to adhere to a fitness and diet plan has been shown to cause long-term effects on a person's health including obesity and shortened lifespans. Currently, people plan their fitness and diet plans around their schedule, but these tasks are challenging to maintain. Users tend to give up these plans due to a lack of time and planning. To help with better planning for a fitness and diet regime, we propose, CHARLIE, a chatbot that works around a user's schedule to intelligently recommend diets and fitness goals based on their calendar. CHARLIE is a recommendation system that suggests the users' a certain meal and fitness activities based on the amount of time they have left in their day and the number of calories they lose.
A Survey on the Internet of Musical Things: Environment Challenges, Standards, Services, and Future Visions

The Internet of Musical Things (IoMusT) is a multidisciplinary research field capable of fostering new applications and services for musical practice from the junction of digital and physical domains through information and communication technologies. Despite advantages, this area lacks standardization and formal definitions, especially in elaborating its environments. In this paper, we surveyed more than 80 articles to provide an overview of the main technological components necessary to allow the interconnection between devices that enable IoMusT concepts and applications. Our goal is to provide consistent material to guide researchers and developers on this topic. Finally, open questions and research challenges are also analyzed and discussed.
MediLinker: a blockchain-based decentralized health information management platform for patient-centric healthcare

Patients' control over how their health information is stored has been an ongoing issue in health informatics. Currently, most patients' health information is stored in centralized but siloed health information systems of healthcare institutions, rarely connected to or interoperable with other institutions outside of their specific health system. This centralized approach to the storage of health information is susceptible to breaches, though it can be mitigated using technology that allows for decentralized access. One promising technology that offers the possibility of decentralization, data protection, and interoperability is blockchain. In 2019, our interdisciplinary team from the University of Texas at Austin's Dell Medical School, School of Information, Department of Electrical and Computer Engineering, and Information Technology Services developed MediLinker-a blockchain-based decentralized health information management platform for patient-centric healthcare. This paper provides an overview of MediLinker and outlines its ongoing and future development and implementation. Overall, this paper contributes insights into the opportunities and challenges in developing and implementing blockchain-based technologies in healthcare.
A semantic modular framework for events topic modeling in social media

The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural language processing has received researchers’ attention, and various algorithms have been developed for this goal. In this paper, we propose a Semantic Modular Model (SMM) consisting of 5 different modules, namely Distributional Denoising Autoencoder, Incremental Clustering, Semantic Denoising, Defragmentation, and Ranking and Processing. The proposed model aims to (1) cluster various documents and ignore the documents that might not contribute to the identification of events, (2) identify more important and descriptive keywords. Compared to the state-of-the-art methods, the results show that the proposed model has a higher performance in identifying events with lower ranks and extracting keywords for more important events in three English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed method outperformed the best-reported results in the mean keyword-precision metric by 7.9%.
Design of All-Optical Subtractors Utilized with Plasmonic Ring Resonators for Optical Computing

In this paper, a novel plasmonic all-optical half-subtractor and full-subtractor are designed for optical computing. The structure of plasmonic subtractors consists of a metal–insulator–metal (MIM) waveguide and rectangular ring resonators covered by a graphene layer. Due to the nonlinear optical properties of graphene, the states of the plasmonic resonators can be controlled by the pump intensity of a pump beam focused on the graphene layer. The resonators can work as all-optical switches with an ultra-fast response time to constitute optical logic devices according to the directed logic mechanism. A finite-difference time-domain method is utilized to numerically investigate the transmission of the output signals which represent the results of subtraction operations. Simulation results obtained indicate that the proposed plasmonic devices have the ability to implement half-subtraction and full-subtraction with a small feature size and fast response time, and provide a new concept and method for the design and realization of optical computing devices.
MIGA: A Unified Multi-Task Generation Framework for Conversational Text-to-SQL

Conversational text-to-SQL is designed to translate multi-turn natural language questions into their corresponding SQL queries. Most advanced conversational text-to-SQL methods are incompatible with generative pre-trained language models (PLMs), such as T5. In this paper, we present a two-stage unified MultI-task Generation frAmework (MIGA) that leverages PLMs’ ability to tackle conversational text-to-SQL. In the pre-training stage, MIGA first decomposes the main task into several related sub-tasks and then unifies them into the same sequence-to-sequence (Seq2Seq) paradigm with task-specific natural language prompts to boost the main task from multi-task training. Later in the fine-tuning stage, we propose four SQL perturbations to alleviate the error propagation problem. MIGA tends to achieve state-of-the-art performance on two benchmarks (SparC and CoSQL). We also provide extensive analyses and discussions to shed light on some new perspectives for conversational text-to-SQL.
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing

Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of speech recognition, machine translation and speech synthesis. To ensure the translated speech to be well aligned with the corresponding video, the length/duration of the translated speech should be as close as possible to that of the original speech, which requires strict length control. Previous works usually control the number of words or characters generated by the machine translation model to be similar to the source sentence, without considering the isochronicity of speech as the speech duration of words/characters in different languages varies. In this paper, we propose VideoDubber, a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech. Specifically, we control the speech length of generated sentence by guiding the prediction of each word with the duration information, including the speech duration of itself as well as how much duration is left for the remaining words. We design experiments on four language directions (German -&gt; English, Spanish -&gt; English, Chinese English), and the results show that VideoDubber achieves better length control ability on the generated speech than baseline methods. To make up the lack of real-world datasets, we also construct a real-world test set collected from films to provide comprehensive evaluations on the video dubbing task.
CCT-Unet: A U-Shaped Network Based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI

The accurate segmentation of prostate region in magnetic resonance imaging (MRI) can provide reliable basis for artificially intelligent diagnosis of prostate cancer. Transformer-based models have been increasingly used in image analysis due to their ability to acquire long-term global contextual features. Although Transformer can provide feature representations of the overall appearance and contour representations at long distance, it does not perform well on small-scale datasets of prostate MRI due to its insensitivity to local variation such as the heterogeneity of the grayscale intensities in the peripheral zone and transition zone across patients; meanwhile, the convolutional neural network (CNN) could retain these local features well. Therefore, a robust prostate segmentation model that can aggregate the characteristics of CNN and Transformer is desired. In this work, a U-shaped network based on the convolution coupled Transformer is proposed for segmentation of peripheral and transition zones in prostate MRI, named the convolution coupled Transformer U-Net (CCT-Unet). The convolutional embedding block is first designed for encoding high-resolution input to retain the edge detail of the image. Then the convolution coupled Transformer block is proposed to enhance the ability of local feature extraction and capture long-term correlation that encompass anatomical information. The feature conversion module is also proposed to alleviate the semantic gap in the process of jumping connection. Extensive experiments have been conducted to compare our CCT-Unet with several state-of-the-art methods on both the ProstateX open dataset and the self-bulit Huashan dataset, and the results have consistently shown the accuracy and robustness of our CCT-Unet in MRI prostate segmentation.
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning

Self-supervised learning (SSL) techniques have recently been integrated into the few-shot learning (FSL) framework and have shown promising results in improving the few-shot image classification performance. However, existing SSL approaches used in FSL typically seek the supervision signals from the global embedding of every single image. Therefore, during the episodic training of FSL, these methods cannot capture and fully utilize the local visual information in image samples and the data structure information of the whole episode, which are beneficial to FSL. To this end, we propose to augment the few-shot learning objective with a novel self-supervised Episodic Spatial Pretext Task (ESPT). Specifically, for each few-shot episode, we generate its corresponding transformed episode by applying a random geometric transformation to all the images in it. Based on these, our ESPT objective is defined as maximizing the local spatial relationship consistency between the original episode and the transformed one. With this definition, the ESPT-augmented FSL objective promotes learning more transferable feature representations that capture the local spatial features of different images and their inter-relational structural information in each input episode, thus enabling the model to generalize better to new categories with only a few samples. Extensive experiments indicate that our ESPT method achieves new state-of-the-art performance for few-shot image classification on three mainstay benchmark datasets. The source code will be available at: https://github.com/Whut-YiRong/ESPT.
Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout

Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this paper, we report our experience developing and deploying the Federated Opportunistic Block Dropout (FedOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FedOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.
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 fB and a debiased model fD. fB is trained to focus on bias aligned samples (i.e., overfitted to the bias) while fD is mainly trained with bias conflicting samples by concentrating on samples which fB fails to learn, leading fD to be less susceptible to the dataset bias. While the state of the art debiasing techniques have aimed to better train fD, we focus on training fB, an overlooked component until now. Our empirical analysis reveals that removing the bias conflicting samples from the training set for fB is important for improving the debiasing performance of fD. This is due to the fact that the bias conflicting samples work as noisy samples for amplifying the bias for fB 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 fB. 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.
Open Metaverse with Open Software

The metaverse is an increasingly popular social interaction, gaming, and education platform. In recent years there has been a growing interest in the Metaverse by various commercial firms in order to promote economic transactions and the development of applications in the world of digital and virtual interaction. With the widespread adoption of the metaverse concept, there is a growing need for open standards and open-source code related to the metaverse to ensure interoperability and enable innovation. Open standards enable different systems to communicate and share data, while open-source code promotes collaboration and transparency in development. This manuscript describes the importance of open standards and open-source code for developing metaverse technologies. We discuss the benefits of open standards for interoperability and innovation and examine case studies of successful open-source metaverse projects. We also discuss challenges in implementing open standards and open source code in metaverse development, including intellectual property concerns and governance issues. The open standards and open source code are essential for the metaverse’s growth and sustainability. The developers and policymakers should prioritize these principles to ensure a the development of inclusive and open virtual environment. Our work aims to generate immersive, multiverse reality scenarios using dedicated graphics platforms for generating virtual environments.
Zak transform as a framework for quantum computation with the Gottesman-Kitaev-Preskill code

The Gottesman-Kitaev-Preskill (GKP) code encodes a qubit into a bosonic mode using periodic wave functions. This periodicity makes the GKP code a natural setting for the Zak transform, which is tailor-made to provide a simple description for periodic functions. We review the Zak transform and its connection to a Zak basis of states in Hilbert space, decompose the shift operators that underpin the stabilizers and the correctable errors, and find that Zak transforms of the position wave function appear naturally in GKP error correction. We construct a bosonic subsystem decomposition (SSD)---the modular variable SSD---by dividing a mode's Hilbert space, expressed in the Zak basis, into that of a virtual qubit and a virtual gauge mode. Tracing over the gauge mode gives a logical-qubit state, and preceding the trace with a particular logical-gauge interaction gives a different logical state---that associated to GKP error correction.
Multi-label feature selection based on HSIC and sparrow search algorithm

Feature selection has always been an important topic in machine learning and data mining. In multi-label learning tasks, each sample in the dataset is associated with multiple labels, and labels are usually related to each other. At the same time, multi-label learning has the problem of "curse of dimensionality". Feature selection therefore becomes a difficult task. To solve this problem, this paper proposes a multi-label feature selection method based on the Hilbert-Schmidt independence criterion (HSIC) and sparrow search algorithm (SSA). It uses SSA for feature search and HSIC as feature selection criterion to describe the dependence between features and all labels, so as to select the optimal feature subset. Experimental results demonstrate the effectiveness of the proposed method.
Hierarchies of Frequentist Bounds for Quantum Metrology: From Cramér-Rao to Barankin

We derive lower bounds on the variance of estimators in quantum metrology by choosing test observables that define constraints on the unbiasedness of the estimator. The quantum bounds are obtained by analytical optimization over all possible quantum measurements and estimators that satisfy the given constraints. We obtain hierarchies of increasingly tight bounds that include the quantum Cram\'er-Rao bound at the lowest order. In the opposite limit, the quantum Barankin bound is the variance of the locally best unbiased estimator in quantum metrology. Our results reveal generalizations of the quantum Fisher information that are able to avoid regularity conditions and identify threshold behavior in quantum measurements with mixed states, caused by finite data.
The Implementation of Literacy Activities in Primary School

School Literacy Movement is a program which has been voiced by the Indonesian Minister of Education and Culture since 2015. This program aimed to promote and support reading culture in school due to consistent unsatisfactory result of Program of International Student Assessment indicate the need for special effort to improve the young generation’s literacy skills. This study aimed at analyzing such efforts in the school level. The study employed a qualitative research design which explored the types of literacy activities implemented in the school to support literacy movement program. Classroom observation and interview were conducted to collect the data from Bali Kiddy School. There were 2 teachers who gave their consent to be involved in this study. The data were analyzed descriptively. These phases include categorizing the data, presenting the findings, and evaluating and validating the data. The finding reveals that the schools have some strategies to engage students to read non-subject books, write reading response journal, do spelling and writing activities. To encourage children's literacy development, building a literacy environment where they are highly exposed to the language is essential.
Influence of APP-Assisted Teaching on Teaching Quality in Mobile Learning

At present, a large number of education APPs have been developed and widely applied, making mobile learning one of the more popular learning methods under influence of the epidemic. Undeniably, control of college students is indeed an issue that needs to be considered to prevent non-learning behaviors of students in APP learning. Education APPs are more suitable to assist traditional education and to help teachers’ teaching and students’ learning. Based on the theory of fragmented learning and activity learning, a questionnaire on the influence of APP-assisted teaching technology on teaching quality of mobile learning was designed, and mediating effect of team interdependence on the influence of APP-assisted teaching technology on teaching quality of mobile learning was measured. Kruskal-Wallis test was used to measure difference between APP usage quantity and teaching quality. Results show that convenience, individuation and immediacy of APP-assisted teaching technology have significant influence on teaching quality of mobile learning. Team interdependence plays a part in mediating effect of APP-assisted teaching technology on teaching quality of mobile learning. Different APP usage quantity samples all show a significant effect on teaching quality (p&lt;0.05). Conclusions have important reference value for learners to rely on education APPs to carry out fragmented, efficient and personalized learning, to improve education APPs as a teaching method to assist traditional teaching, and to avoid learners falling into “technology-oriented” misunderstanding of over-reliance on APPs for learning.