Reviewerly/exHarmony huggingface dataset:
Class-aware sample reweighting optimal transport for multi-source domain adaptation

Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread attention due to their availability to transfer knowledge from multiple source domains to the unlabeled target domain. Optimal transport (OT) has recently been utilized to measure the distance between distributions in virtue of its robustness. This paper proposes a novel OT-based Class-Aware Sample Reweighting (CASR) method to achieve sample-level fine-grained alignment between multi-source and target. Technically, the class-aware sampling strategy ensures class-level conditional alignment during transport by explicitly selecting samples from each domain. Besides, the sample-reweighting module is designed to allocate specific mass to each transmitted sample, which considers the classification reliability and the spatial information correlation to obtain the alignment priority between target and multi-source and further optimize the transport plan. Extensive experiments conducted on several benchmarks show that CASR presents significant advantages compared with other MSDA methods, and the visualization analysis further demonstrates the effectiveness of each proposed module.
Is ChatGPT Leading Generative AI? What is Beyond Expectations?

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Multimodal sentiment analysis based on fusion methods: A survey

Sentiment analysis is an emerging technology that aims to explore people’s attitudes toward an entity. It can be applied in a variety of different fields and scenarios, such as product review analysis, public opinion analysis, psychological disease analysis, and risk assessment analysis. Traditional sentiment analysis only includes the text modality and extracts sentiment information by inferring the semantic relationship within sentences. However, some special expressions, such as irony and exaggeration, are difficult to detect via text alone. Multimodal sentiment analysis contains rich visual and acoustic information in addition to text, and uses fusion analysis to more accurately infer the implied sentiment polarity (positive, neutral, negative). The main challenge in multimodal sentiment analysis is the integration of cross-modal sentiment information, so we focus on introducing the framework and characteristics of different fusion methods. In addition, this article discusses the development status of multimodal sentiment analysis, popular datasets, feature extraction algorithms, application areas, and existing challenges. It is hoped that our work can help researchers understand the current state of research in the field of multimodal sentiment analysis, and be inspired by the useful insights provided in the article to develop effective models.
Developments and Applications of Artificial Intelligence in Music Education

With the continuous developments of information technology, advanced computer technology and information technology have been promoted and used in the field of music. As one of the products of the rapid development of information technology, Artificial Intelligence (AI) involves many interdisciplinary subjects, adding new elements to music education. By analyzing the advantages of AI in music education, this paper systematically summarizes the application of AI in music education and discusses the development prospects of AI in music education. With the aid of AI, the combination of intelligent technology and on-site teaching solves the lack of individuation in the traditional mode and enhances students’ interest in learning.
Image sensing with multilayer nonlinear optical neural networks

Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm breaks this delineation between data collection and analysis by designing optical components to perform not imaging, but encoding. By optically encoding images into a compressed, low-dimensional latent space suitable for efficient post-analysis, these image sensors can operate with fewer pixels and fewer photons, allowing higher-throughput, lower-latency operation. Optical neural networks (ONNs) offer a platform for processing data in the analog, optical domain. ONN-based sensors have however been limited to linear processing, but nonlinearity is a prerequisite for depth, and multilayer NNs significantly outperform shallow NNs on many tasks. Here, we realize a multilayer ONN pre-processor for image sensing, using a commercial image intensifier as a parallel optoelectronic, optical-to-optical nonlinear activation function. We demonstrate that the nonlinear ONN pre-processor can achieve compression ratios of up to 800:1 while still enabling high accuracy across several representative computer-vision tasks, including machine-vision benchmarks, flow-cytometry image classification, and identification of objects in real scenes. In all cases we find that the ONN's nonlinearity and depth allowed it to outperform a purely linear ONN encoder. Although our experiments are specialized to ONN sensors for incoherent-light images, alternative ONN platforms should facilitate a range of ONN sensors. These ONN sensors may surpass conventional sensors by pre-processing optical information in spatial, temporal, and/or spectral dimensions, potentially with coherent and quantum qualities, all natively in the optical domain.
Novel machine learning based approach for analysing the adoption of metaverse in medical training: A UAE case study

The outbreak of the COVID-19 pandemic led to disruptions in the delivery of medical training across borders, posing challenges in observing and practicing advanced surgical techniques with cutting-edge medical equipment from foreign countries. However, the utilization of educational approaches centred on the “Metaverse” concept has emerged as a promising solution to address the escalating demand for virtual medical education. Traditional technologies like Zoom video conferencing were found insufficient for comprehensive medical instruction, prompting the emergence of innovative digital teaching methodologies within the medical community of the United Arab Emirates (UAE). This study aims to investigate how students perceive the effectiveness of the Metaverse system in achieving medical training objectives in the UAE. The research employs a unique conceptual framework that links individual attributes with technological factors. By employing a blend of structural equation modelling (SEM) and machine learning (ML) methodologies, along with the analysis of importance-performance maps (IPMA), the research evaluates the factors that contribute to measuring the viability of the Metaverse system for medical training. This evaluation is conducted using data gathered from a cohort of 879 university students. The findings indicated that the OneR classifier demonstrates the highest accuracy among classifiers in forecasting users' inclination to embrace the Metaverse system for medical training, achieving an 80.7% accuracy rate. Furthermore, the study reveals a strong positive association between perceived usefulness and perceived usability, highlighting the significant impact of personal attributes and technological elements on students' decisions. Notably, individuals with a greater willingness to embrace uncertainty and innovative technologies are more inclined to use the Metaverse system for medical education. In conclusion, this multi-analytical investigation sheds light on the potential of the Metaverse system to enhance medical training and addresses the challenges posed by the COVID-19 pandemic. The findings carry important implications for the field of information systems and provide valuable insights for medical educators seeking effective solutions during times of disruption.
Can language models be used for real-world urban-delivery route optimization?

Language models have contributed to breakthroughs in interdisciplinary research, such as protein design and molecular dynamics understanding. In this study, we reveal that beyond language, representations of other entities, such as human behaviors, that are mappable to learnable sequences can be learned by language models. One compelling example is the real-world delivery route optimization problem. We here propose a novel approach based on the language model to optimize delivery routes on the basis of drivers' historical experiences. Although a broad range of optimization-based approaches have been designed to optimize delivery routes, they do not capture the implicit knowledge of complex delivery operating environments. The model we propose integrates this knowledge in the route optimization process by learning from driving behaviors in experienced drivers. A real-world delivery route that preserves drivers' implicit behavioral patterns is first analogized to a sentence in natural language. Through unsupervised learning, we then learn the vector representations of words and infer the drivers' delivery chains on the basis of the tailored chain-reaction-based algorithm. We also provide insights into the fusion of language models and operations research methods. In our approach, language models are applied to learn drivers' delivery behaviors and infer new deliveries at the delivery zone level, while the classic traveling salesman problem (TSP) model is embedded into the hybrid framework for intra-zone optimization. Numerical experiments performed on real-world data from Amazon's delivery service demonstrate that the proposed approach outperforms pure optimization, supporting the effectiveness, efficiency, and extensibility of our model. As a versatile approach, the proposed framework can easily be extended to various disciplines in which the data follow certain grammar rules. We anticipate that our work will serve as a stepping stone toward the understanding and application of language models in tackling interdisciplinary research problems.
Mobile learning in pre-service teacher education: Examining the use of professional learning networks

Interest in how to use mobile devices to support teaching and learning has increased as technologies have become more sophisticated and ubiquitous. A recent focus in teacher education is the use of mobile devices to support teachers’ professional learning networks (PLNs). This study investigates how pre-service teachers (PSTs) use mobile technologies to support different aspects of their PLN activities. The study uses a qualitative methodology, where data from focus group discussions, artefact collection, and participant journals kept by 11 final year PSTs provided nuanced insights into their mobile learning practices. A validated mobile pedagogical framework (Kearney, Schuck, Burden, &amp; Aubusson, 2012) is used to analyse the data. Findings uncover a deeper understanding of exemplary mobile learning approaches in initial teacher education and have implications for effective preparation of PSTs for career-long professional learning.
Guided Policy Search for Sequential Multitask Learning

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection.A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods.It can also provide asymptotic local convergence guarantees.However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios.This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process.The algorithm's adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly.In this paper, the GPS approach is
Difficulty Controllable Generation of Reading Comprehension Questions

We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG). Taking as input a sentence in the reading comprehension paragraph and some of its text fragments (i.e., answers) that we want to ask questions about, a DQG method needs to generate questions each of which has a given text fragment as its answer, and meanwhile the generation is under the control of specified difficulty labels---the output questions should satisfy the specified difficulty as much as possible. To solve this task, we propose an end-to-end framework to generate questions of designated difficulty levels by exploring a few important intuitions. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that the question generated by our framework not only have better quality under the metrics like BLEU, but also comply with the specified difficulty labels.
ImageTagger: An Open Source Online Platform for Collaborative Image Labeling

The need for labeled training data for object recognition in RoboCup increased due to the spread of deep learning approaches. Creating large sets of training images from different environments and annotating the recorded objects is difficult for a single RoboCup team. This paper presents our tool ImageTagger which facilitates creating and sharing such data sets. The tool is already being successfully used in RoboCup Soccer, and a large amount of labeled data is publicly available. Other leagues are invited to use this tool to create data for their contexts.
Multi-label Categorization of Accounts of Sexism using a Neural Framework

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways.In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in utilizing such data to study and counter sexism better.The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur.To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization.We develop a neural solution for this multilabel classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones.Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework.The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.
Practical fall detection based on IoT technologies: A survey

Fall is the second incident that leads to death over the world. Fall event happens to numerous groups of people consist of elderly, babies and also younger people. Admittedly, fall-related research must be considered as one of the most important aspects of a healthy lifestyle. For this reason, Internet of Things(IoT) is the emerging technology and a powerful candidate to develop fall diagnosis system. In this paper, we have discussed the three stages of fall as Prediction, Prevention, and Detection. We have illustrated Edge, Fog, and Cloud layers as IoT layers to develop a fall diagnosis system. At the end of the paper, we have considered the challenges of fall diagnosis systems and suggested future aspects.
Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media

This paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network.The input to the network was character embeddings combined with emoji embeddings.The approach was evaluated on all the subtasks in SemEval-2019 Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media.The evaluation also showed that even though the capsule networks have not been used commonly in NLP tasks, they can outperform existing state of the art solutions for offensive language detection in social media.
Differentiable Reasoning over a Virtual Knowledge Base

We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus. At each step the operation uses a combination of sparse-matrix TFIDF indices and maximum inner product search (MIPS) on a special index of contextual representations. This module is differentiable, so the full system can be trained completely end-to-end using gradient based methods, starting from natural language inputs. We also describe a pretraining scheme for the index mention encoder by generating hard negative examples using existing knowledge bases. We show that DrKIT improves accuracy by 9 points on 3-hop questions in the MetaQA dataset, cutting the gap between text-based and KB-based state-of-the-art by 70%. DrKIT is also very efficient, processing upto 10x more queries per second than existing state-of-the-art QA systems.
Communication Aspects of the Integration of Wireless IoT Devices with Distributed Ledger Technology

The pervasive need to safely share and store information between devices calls for the replacement of centralized trust architectures with decentralized ones. DLTs are seen as the most promising enabler of decentralized trust, but they still lack technological maturity, and their successful adoption depends on the understanding of the fundamental design trade-offs and their reflection in the actual technical design. This work focuses on the challenges and potential solutions for an effective integration of DLTs in the context of the Internet of Things (IoT). We first introduce the landscape of IoT applications and discuss the limitations and opportunities offered by DLTs. Then we review the technical challenges encountered in the integration of resource-constrained devices with distributed trust networks. We describe the common traits of lightweight synchronization protocols, and propose a novel classification rooted in the IoT perspective. We identify the need for receiving ledger information at the endpoint devices, implying a two-way data exchange that contrasts with the conventional uplink-oriented communication technologies intended for IoT systems.
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability

Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods can be leveraged in adversarial ML. Second, we highlight with MPLens how the vulnerability of pre-trained models under membership inference attack is not uniform across all classes, particularly when the training data is skewed. We show that risk from membership inference attacks is routinely increased when models use skewed training data. Finally, we investigate the effectiveness of differential privacy as a mitigation technique against membership inference attacks. We discuss the trade-offs of implementing such a mitigation strategy with respect to the model complexity, the learning task complexity, the dataset complexity and the privacy parameter settings.
Cybersecurity Challenges Associated With the Internet of Things in a Post-Quantum World

The Internet of Things (IoT) is an emerging networking paradigm connecting billions of devices securely to the Internet. Another emerging paradigm is quantum computing which - while opening new compute opportunities - was shown to jeopardise most cybersecurity protocols. In this study, we discuss techniques able to provide security in a post-quantum IoT. Specifically, we examine how the third-generation partnership project (3GPP) IoT security solutions fair in a post-quantum environment. Also, we analyse the security features of fifth-generation (5G) networks, propose improvements and discuss the manner in which a quantum computer can compromise security. Our results prove the existence of multiple vulnerabilities in the current IoT architecture and implementations. With advances in quantum computing having rendered the current security algorithms unsafe, more advanced techniques should be established to mitigate such risks. To this end, we present promising lattice-driven cryptographic techniques which we prove quantum resistance.
Efficient Minimum Word Error Rate Training of RNN-Transducer for End-to-End Speech Recognition

In this work, we propose a novel and efficient minimum word error rate (MWER) training method for RNN-Transducer (RNN-T). Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores for expected edit-distance computation, in our proposed method, we re-calculate and sum scores of all the possible alignments for each hypothesis in N-best lists. The hypothesis probability scores and back-propagated gradients are calculated efficiently using the forward-backward algorithm. Moreover, the proposed method allows us to decouple the decoding and training processes, and thus we can perform offline parallel-decoding and MWER training for each subset iteratively. Experimental results show that this proposed semi-on-the-fly method can speed up the on-the-fly method by 6 times and result in a similar WER improvement (3.6%) over a baseline RNN-T model. The proposed MWER training can also effectively reduce high-deletion errors (9.2% WER-reduction) introduced by RNN-T models when EOS is added for endpointer. Further improvement can be achieved if we use a proposed RNN-T rescoring method to re-rank hypotheses and use external RNN-LM to perform additional rescoring. The best system achieves a 5% relative improvement on an English test-set of real far-field recordings and a 11.6% WER reduction on music-domain utterances.
Dual humanness and trust in conversational AI: A person-centered approach

Conversational Artificial Intelligence (AI) is digital agents that interact with users by natural language. To advance the understanding of trust in conversational AI, this study focused on two humanness factors manifested by conversational AI: speaking and listening. First, we explored users' heterogeneous perception patterns based on the two humanness factors. Next, we examined how this heterogeneity relates to trust in conversational AI. A two-stage survey was conducted to collect data. Latent profile analysis revealed three distinct patterns: para-human perception, para-machine perception, and asymmetric perception. Finite mixture modeling demonstrated that the benefit of humanizing AI's voice for competence-related trust can evaporate once AI's language understanding is perceived as poor. Interestingly, the asymmetry between humanness perceptions in speaking and listening can impede morality-related trust. By adopting a person-centered approach to address the relationship between dual humanness and user trust, this study contributes to the literature on trust in conversational AI and the practice of trust-inducing AI design.