Reviewerly/exHarmony huggingface dataset:
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system.A key challenge of OOD detection is to learn discriminative semantic features.Traditional cross-entropy loss only focuses on whether a sample is correctly classified, and does not explicitly distinguish the margins between categories.In this paper, we propose a supervised contrastive learning objective to minimize intra-class variance by pulling together in-domain intents belonging to the same class and maximize inter-class variance by pushing apart samples from different classes.Besides, we employ an adversarial augmentation mechanism to obtain pseudo diverse views of a sample in the latent space.Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for OOD detection. 1* The first two authors contribute equally.Weiran Xu is the corresponding author.
Comparison of MeSH terms and KeyWords Plus terms for more accurate classification in medical research fields. A case study in cannabis research

KeyWords Plus and Medical Subject Headings (MeSH) are widely used in bibliometric studies for topic mapping. The objective of this study is to compare the two description systems in documents about cannabis research to find the concordance between systems and establish whether there is neutrality in topic mapping. A total of 25,593 articles from 1970 to 2019 were drawn from Web of Science's Core Collection and Medline and analyzed. The tidytext library, Zipf's law, topic modeling tools, the contingency coefficient, Cramer's V, and Cohen's kappa were used. The results included 10,107 MeSH terms and 28,870 KeyWords Plus terms. The Zipf distribution of the terms was different for each system in terms of slope and specificity. The documents were classified into seven topics, and the MeSH system proved better at classification. The kappa coefficient between the two systems was 0.477 (for gamma ≥ 0.2); the topics related with human beings presented higher concordance. The use of KeyWords Plus for topic analyses in biomedical areas is not neutral, and this point needs to be taken into account in interpreting results.
Symmetry meets AI

We explore whether Neural Networks (NNs) can {\it discover} the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a {\it decoy task} based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh.
RPPTD: Robust Privacy-Preserving Truth Discovery Scheme

Benefitingfrom the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment.
S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations

Any-to-any voice conversion (VC) aims to convert the timbre of utterances from and to any speakers seen or unseen during training. Various any-to-any VC approaches have been proposed like AUTOVC, AdaINVC, and FragmentVC. AUTOVC, and AdaINVC utilize source and target encoders to disentangle the content and speaker information of the features. FragmentVC utilizes two encoders to encode source and target information and adopts cross attention to align the source and target features with similar phonetic content. Moreover, pre-trained features are adopted. AUTOVC used dvector to extract speaker information, and self-supervised learning (SSL) features like wav2vec 2.0 is used in FragmentVC to extract the phonetic content information. Different from previous works, we proposed S2VC that utilizes Self-Supervised features as both source and target features for VC model. Supervised phoneme posteriororgram (PPG), which is believed to be speaker-independent and widely used in VC to extract content information, is chosen as a strong baseline for SSL features. The objective evaluation and subjective evaluation both show models taking SSL feature CPC as both source and target features outperforms that taking PPG as source feature, suggesting that SSL features have great potential in improving VC.
Utilizing Self-Supervised Representations for MOS Prediction

Speech quality assessment has been a critical issue in speech processing for decades. Existing automatic evaluations usually require clean references or parallel ground truth data, which is infeasible when the amount of data soars. Subjective tests, on the other hand, do not need any additional clean or parallel data and correlates better to human perception. However, such a test is expensive and time-consuming because crowd work is necessary. It thus becomes highly desired to develop an automatic evaluation approach that correlates well with human perception while not requiring ground truth data. In this paper, we use self-supervised pre-trained models for MOS prediction. We show their representations can distinguish between clean and noisy audios. Then, we fine-tune these pre-trained models followed by simple linear layers in an end-to-end manner. The experiment results showed that our framework outperforms the two previous state-of-the-art models by a significant improvement on Voice Conversion Challenge 2018 and achieves comparable or superior performance on Voice Conversion Challenge 2016. We also conducted an ablation study to further investigate how each module benefits the task. The experiment results are implemented and reproducible with publicly available toolkits.
BC-EdgeFL: A Defensive Transmission Model Based on Blockchain-Assisted Reinforced Federated Learning in IIoT Environment

Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.
Improving sentiment analysis accuracy with emoji embedding

Due to the diversity and variability of Chinese syntax and semantics, accurately identifying and distinguishing individual emotions from online texts is challenging. To overcome this limitation, we incorporate a new source of individual sentiment, emojis, which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations. We examined popular sentiment analysis algorithms, including rule-based and classification algorithms, to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance. Emojis were also translated into corresponding sentiment words when constructing features for comparison with those directly generated from emoji label words. In addition, considering different functions of emojis in texts, we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance. We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms, and the algorithm performance can be further increased by taking different emoji usages into consideration. In this study, we developed an improved emoji-embedding model based on Bi-LSTM (namely, CEmo-LSTM), which achieves the highest accuracy (around 0.95) when analyzing online Chinese texts. We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1, 2019 to March 20, 2020 to understand the sentiment evolution of online users during the COVID-19 pandemic. We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions (e.g., horror and sadness). Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.
Attention-based quantum tomography

Abstract With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS ) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems

There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for important tasks in major wireless communication systems: channel estimation and decoding in orthogonal frequency division multiplexing (OFDM) systems, end-to-end autoencoder system design, radio signal classification, and signal authentication. Unfortunately, DNNs can be susceptible to adversarial examples, potentially making such wireless systems fragile and vulnerable to attack. In this work, by designing robust adversarial examples that meet key criteria, we perform a comprehensive study of the threats facing DNN-based wireless systems. We model the problem of adversarial wireless perturbations as an optimization problem that incorporates domain constraints specific to different wireless systems. This allows us to generate wireless adversarial perturbations that can be applied to wireless signals on-the-fly (i.e., with no need to know the target signals a priori), are undetectable from natural wireless noise, and are robust against removal. We show that even in the presence of significant defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems. In particular, the results demonstrate that even when employing well-considered defenses, DNN-based wireless communication systems are vulnerable to adversarial attacks and call into question the employment of DNNs for a number of tasks in robust wireless communication.
Expressing High-Level Scientific Claims with Formal Semantics

The use of semantic technologies is gaining significant traction in science communication with a wide array of applications in disciplines including the life sciences, computer science, and the social sciences. Languages like RDF, OWL, and other formalisms based on formal logic are applied to make scientific knowledge accessible not only to human readers but also to automated systems. These approaches have mostly focused on the structure of scientific publications themselves, on the used scientific methods and equipment, or on the structure of the used datasets. The core claims or hypotheses of scientific work have only been covered in a shallow manner, such as by linking mentioned entities to established identifiers. In this research, we therefore want to find out whether we can use existing semantic formalisms to fully express the content of high-level scientific claims using formal semantics in a systematic way. Analyzing the main claims from a sample of scientific articles from all disciplines, we find that their semantics are more complex than what a straight-forward application of formalisms like RDF or OWL account for, but we managed to elicit a clear semantic pattern which we call the "super-pattern''. We show here how the instantiation of the five slots of this super-pattern leads to a strictly defined statement in higher-order logic. We successfully applied this super-pattern to an enlarged sample of scientific claims. We show that knowledge representation experts, when instructed to independently instantiate the super-pattern with given scientific claims, show a high degree of consistency and convergence given the complexity of the task and the subject. These results therefore open the door on the longer run for allowing researchers to express their high-level scientific findings in a manner they can be automatically interpreted. This in turn will allow for automated consistency checking, question answering, aggregation, and much more.
ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.
Generation of Bell and Greenberger-Horne-Zeilinger states from a hybrid qubit-photon-magnon system

We propose a level-resolved protocol in a hybrid architecture for connecting a superconducting qubit and a magnon mode contained within a microwave cavity (resonator) to generate local and global entangled states, enabling a wide range of applications in quantum communication, quantum metrology, and quantum information processing. Exploiting the high-degree controllability in such a hybrid qubit-photon-magnon system, we derive the effective Hamiltonians at the second-order resonant points by virtue of the strong general Rabi interactions between the resonator and the qubit and between the resonator and the magnon. Consequently, we can efficiently generate the Bell states of the photon-magnon and the qubit-magnon subsystems and the Greenberger-Horne-Zeilinger state of the whole hybrid system. The robustness of our protocol is checked against a nonvanishing tuning time of system frequency and the environmental noise by the Lindblad master equation. Our work makes this hybrid platform of high-degree controllability a high-fidelity candidate for preparing the multiple maximally entangled states.
Explainable Deep Reinforcement Learning: State of the Art and Challenges

Interpretability, explainability, and transparency are key issues to introducing artificial intelligence methods in many critical domains. This is important due to ethical concerns and trust issues strongly connected to reliability, robustness, auditability, and fairness, and has important consequences toward keeping the human in the loop in high levels of automation, especially in critical cases for decision making, where both (human and the machine) play important roles. Although the research community has given much attention to explainability of closed (or black) prediction boxes, there are tremendous needs for explainability of closed-box methods that support agents to act autonomously in the real world. Reinforcement learning methods, and especially their deep versions, are such closed-box methods. In this article, we aim to provide a review of state-of-the-art methods for explainable deep reinforcement learning methods, taking also into account the needs of human operators—that is, of those who make the actual and critical decisions in solving real-world problems. We provide a formal specification of the deep reinforcement learning explainability problems, and we identify the necessary components of a general explainable reinforcement learning framework. Based on these, we provide a comprehensive review of state-of-the-art methods, categorizing them into classes according to the paradigm they follow, the interpretable models they use, and the surface representation of explanations provided. The article concludes by identifying open questions and important challenges.
Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment

Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and prosody, previous efforts typically only model one aspect (e.g., accuracy) at one granularity (e.g., at the phoneme-level). In this work, we explore modeling multi-aspect pronunciation assessment at multiple granularities. Specifically, we train a Goodness Of Pronunciation feature-based Transformer (GOPT) with multi-task learning. Experiments show that GOPT achieves the best results on speechocean762 with a public automatic speech recognition (ASR) acoustic model trained on Librispeech.
Hybrid Mode of Medical Image Watermarking To Enhance Robustness and Imperceptibility

In Modern healthcare systems, diagnostic information of patients is managed digitally. Watermarking strategies plays a significant role to ensure the security, authenticity and management of biomedical images and related patient information. This paper explores different invisible Digital watermarking methods both in spatial and transform / frequency domain and then Hybrid method by combining two techniques to justify better results for medical images. Here, host image considered is a medical image and patient’s information is a watermark which is embedded into it. The embedded watermark is hidden in medical host image and its quality should not be degraded to avoid misdiagnosis by doctors. In this paper, LSB, SVD, DCT, DWT and Hybrid techniques (DWT+SVD) are implemented for embedding and extraction process of invisible medical image watermarking. The performance is evaluated based on PSNR as a measure of imperceptibility and CRC &amp; SSIM as a check of robustness. The attacks introduced are cropping, rotating, and noise like Gaussian, Salt &amp; Paper, Speckle and Poisson. Results reveal that DWT provides higher PSNR values but it is robust for few attacks only. On the other side, SVD provides consistent SSIM for all attacks, although PSNR values are less than that of DWT. Hence a hybrid technique is implemented by incorporating benefits of DWT and SVD both and achieved optimal values of PSNR as well as SSIM to provide better security to medical images by preserving the quality as well.
Modified Mayfly Algorithm for UAV Path Planning

The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.