Reviewerly/exHarmony huggingface dataset:
LAE: Language-Aware Encoder for Monolingual and Multilingual ASR

Despite the rapid progress in automatic speech recognition (ASR) research, recognizing multilingual speech using a unified ASR system remains highly challenging. Previous works on multilingual speech recognition mainly focus on two directions: recognizing multiple monolingual speech or recognizing code-switched speech that uses different languages interchangeably within a single utterance. However, a pragmatic multilingual recognizer is expected to be compatible with both directions. In this work, a novel language-aware encoder (LAE) architecture is proposed to handle both situations by disentangling language-specific information and generating frame-level language-aware representations during encoding. In the LAE, the primary encoding is implemented by the shared block while the language-specific blocks are used to extract specific representations for each language. To learn language-specific information discriminatively, a language-aware training method is proposed to optimize the language-specific blocks in LAE. Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level and shows superior performance on both monolingual and multilingual ASR tasks. With either a real-recorded or simulated code-switched dataset, the proposed LAE achieves statistically significant improvements on both CTC and neural transducer systems. Code is released
Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Purpose In recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications. Design/methodology/approach The fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work. Findings One case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method. Originality/value The main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.
Multi-Image Compression–Encryption Algorithm Based on Compressed Sensing and Optical Encryption

In order to achieve large-capacity, fast and secure image transmission, a multi-image compression-encryption algorithm based on two-dimensional compressed sensing (2D CS) and optical encryption is proposed in this paper. Firstly, the paper uses compressed sensing to compress and encrypt multiple images simultaneously, and design a new structured measurement matrix. Subsequently, double random phase encoding based on the multi-parameter fractional quaternion Fourier transform is used to encrypt the multiple images for secondary encryption, which improves the security performance of the images. Moreover, a fractional-order chaotic system with more complex chaotic behavior is constructed for image compression and encryption. Experimental results show that the algorithm has strong robustness and security.
Human resource development and turnover intention: organizational commitment's role as a mediating variable

Purpose The purpose of this paper is to examine the effect of human resource development on turnover intention through the mediating role of organizational commitment. Design/methodology/approach This paper used a quantitative research design. Data were collected from 204 flight attendants employees working at Royal Jordanian Airlines Company using an email survey questionnaire. Structural equation modeling (SEM) was adopted to test the hypothesized model. Findings The results assure positive effect of human resource development (HRD) on organizational commitment. Negative effect of both HRD and organizational commitment to turnover intention is observed. The results also confirm that the effect of HRD on turnover intention is negatively mediated by organizational commitment. Originality/value This research paper extends the literature by empirically adducing evidence that organizational commitment negatively mediated the effect of human resource development on turnover intention of the airlines in Jordan.
Video Watermarking Algorithm Based on NSCT, Pseudo 3D-DCT and NMF

Video watermarking is an important means of video and multimedia copyright protection, but the current watermarking algorithm is difficult to ensure high robustness under various attacks. In this paper, a video watermarking algorithm based on NSCT, pseudo 3D-DCT and NMF has been proposed. Combined with NSCT, 3D-DCT and NMF, the algorithm embeds the encrypted QR code copyright watermark into the NMF base matrix to improve the anti-attack ability of the watermark under the condition of invisibility. The experimental results show that the algorithm ensures the invisibility of the watermark with a high signal-to-noise ratio of the video, and meanwhile has high ability and robustness against common single and combined attacks, such as filtering, noise, compression, shear, rotation and so on. The issue that the video watermarking algorithm has poor resistance to various attacks, especially the shearing attack, has been solved in this paper; thus, it can be used for digital multimedia video copyright protection.
Cavity Quantum Electrodynamics Effects with Nitrogen Vacancy Center Spins Coupled to Room Temperature Microwave Resonators

Cavity quantum electrodynamics (CQED) effects, such as Rabi splitting, Rabi oscillations, and superradiance, have been demonstrated with nitrogen vacancy (NV) center spins in diamond coupled to microwave resonators at cryogenic temperature. In this Letter, we explore the possibility to realize strong collective coupling and CQED effects with ensembles of NV spins at room temperature. Our calculations show that thermal excitation of the individual NV spins leads to population of collective Dicke states with low symmetry and a reduced collective coupling to the microwave resonators. Optical pumping can be applied to counteract the thermal excitation of the NV centers and to prepare the spin ensemble in Dicke states with high symmetry. The resulting strong coupling with high-quality resonators enables the study of intriguing CQED effects across the weak-to-strong coupling regime, and may have applications in quantum sensing and quantum information processing.
Technologies in service communication: looking forward

Purpose The aim of the paper is to bring together the state-of-the-art research and theory from the communication and service research fields to examine the implications of new technologies for the future of service communication. Design/methodology/approach The authors apply the media affordances perspective to develop an overarching framework that facilitates theoretical conceptualization and research question formulation on the constantly evolving technology-enabled communication formats. Findings Central to the forward-looking framework of service communication facilitated by new technologies, this paper identifies various affordances at the service frontline where service customers and providers interact with the technologies. Customers are empowered to expand their roles blurring the role distinction between service providers and customers. Depending on what kind of relationships service providers form with the emerging technologies, they may develop new service communication strategies and new interaction possibilities with customers. As a result, the technologies' affordances would facilitate value creation outcomes that can manifest in the external (whether it is in the physical or digital space) and/or internal (one's own mind) spaces. Applying the affordances framework, the authors map out four key areas of future research regarding new technologies in service communication: (1) social media technologies; (2) multisensory reality-enhancing technologies; (3) AI-enabled voice assistants; and (4) AI-driven service robots. Originality/value This paper proposes an original theoretical framework to stimulate and guide future research and theory development regarding the implications of new technologies in the constantly evolving and complex service communication landscape.
On Structural Explanation of Bias in Graph Neural Networks

Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the de facto solution in a variety of decision-making scenarios. However, GNNs could yield biased results against certain demographic subgroups. Some recent works have empirically shown that the biased structure of the input network is a significant source of bias for GNNs. Nevertheless, no studies have systematically scrutinized which part of the input network structure leads to biased predictions for any given node. The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios. In this paper, we study a novel research problem of structural explanation of bias in GNNs. Specifically, we propose a novel post-hoc explanation framework to identify two edge sets that can maximally account for the exhibited bias and maximally contribute to the fairness level of the GNN prediction for any given node, respectively. Such explanations not only provide a comprehensive understanding of bias/fairness of GNN predictions but also have practical significance in building an effective yet fair GNN model. Extensive experiments on real-world datasets validate the effectiveness of the proposed framework towards delivering effective structural explanations for the bias of GNNs. Open-source code can be found at https://github.com/yushundong/REFEREE.
Visual Consensus Modeling for Video-Text Retrieval

In this paper, we propose a novel method to mine the commonsense knowledge shared between the video and text modalities for video-text retrieval, namely visual consensus modeling. Different from the existing works, which learn the video and text representations and their complicated relationships solely based on the pairwise video-text data, we make the first attempt to model the visual consensus by mining the visual concepts from videos and exploiting their co-occurrence patterns within the video and text modalities with no reliance on any additional concept annotations. Specifically, we build a shareable and learnable graph as the visual consensus, where the nodes denoting the mined visual concepts and the edges connecting the nodes representing the co-occurrence relationships between the visual concepts. Extensive experimental results on the public benchmark datasets demonstrate that our proposed method, with the ability to effectively model the visual consensus, achieves state-of-the-art performances on the bidirectional video-text retrieval task. Our code is available at https://github.com/sqiangcao99/VCM.
Interpretable Generative Adversarial Networks

Learning a disentangled representation is still a challenge in the field of the interpretability of generative adversarial networks (GANs). This paper proposes a generic method to modify a traditional GAN into an interpretable GAN, which ensures that filters in an intermediate layer of the generator encode disentangled localized visual concepts. Each filter in the layer is supposed to consistently generate image regions corresponding to the same visual concept when generating different images. The interpretable GAN learns to automatically discover meaningful visual concepts without any annotations of visual concepts. The interpretable GAN enables people to modify a specific visual concept on generated images by manipulating feature maps of the corresponding filters in the layer. Our method can be broadly applied to different types of GANs. Experiments have demonstrated the effectiveness of our method.
MPSN: Motion-aware Pseudo-Siamese Network for indoor video head detection in buildings

Indoor video head detection is an essential component of building occupancy detection. While deep learning models have achieved remarkable progress in general object detection, their performance is limited in complex indoor scenes. Indoor surveillance videos often contain cluttered background objects, among which heads have small scales and diverse poses. In this paper, we propose Motion-aware Pseudo-Siamese Network (MPSN), an end-to-end approach that leverages head motion information to guide deep learning models to extract effective head features in indoor scenarios. By taking the pixel-wise difference between adjacent frames as the auxiliary input, MPSN effectively enhances human head motion information and removes irrelevant background objects. Its performance is compared with that of existing methods. Compared with existing methods, MPSN achieves superior performance on two indoor video datasets. It successfully suppresses static background objects and highlights the moving instances, especially human heads in indoor videos. Different methods to capture head motion also were compared. MPSN demonstrates simplicity and flexibility compared to other methods. We validate its robustness through adversarial experiments with a mathematical solution of small perturbations for robust model selection. To confirm its potential in building control systems, we apply MPSN to occupancy counting. The code is available at https://github.com/pl-share/MPSN.
Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model.
Resume Classification using various Machine Learning Algorithms

With the onset of the epidemic, everything has gone online, and individuals have been compelled to work from home. There is a need to automate the hiring process in order to enhance efficiency and decrease manual labour that may be done electronically. If resume categorization were done online, it would significantly save paperwork and human error. The recruiting process has several steps, but the first is resume categorization and verification. Automating the first stage would greatly assist the interview process in terms of speedy applicant selection. Classification of resumes will be performed using Machine Learning Algorithms such as Nave Bayes, Random Forest, and SVM, which will aid in the extraction of skills and show diverse capabilities under appropriate job profile classes. While the abilities are being extracted, an appropriate job profile may be retrieved from the categorised and pre-processed data and shown on the interviewer’s screen. During video interviews, this will aid the interviewer in the selection of candidates.
A framework for automating deployment and evaluation of blockchain networks

A blockchain network is a distributed system established by mutually distrusting participants to operate a blockchain, enabling them to manage critical information such as account balances or asset ownership without a centralised third party. Blockchain network deployment and evaluation have become prevalent due to the emerging blockchain use cases by enterprises, governments, and Internet of Things (IoT) applications, which demand private blockchains rather than participating in public ones. A blockchain network architecture drives deployment and evaluation activities. Nevertheless, practitioners must learn and perform error-prone activities to transform architecture into a blockchain network and evaluate it. Therefore, it is beneficial to automate these activities so that practitioners can focus on the architecture design, a valuable and hard-to-automate activity. The key challenges of such an automation framework are keeping up with the advances in blockchain technologies and the increasing complexity of blockchain network architecture. This paper proposes NVAL, a software framework that implements a novel architecture-driven, community-supported approach to automate blockchain network deployment and evaluation. NVAL accepts blockchain network architecture as input. It supports complex multi-channel blockchain networks, an increasingly prevalent architecture for private blockchain. The framework keeps up with blockchain technologies by leveraging platform-specific automation programmes developed by a practitioner community via runtime composition to handle new networks. We evaluated NVAL with a case study and showed that the framework requires only seven automation programmes to deploy 65 blockchain networks with 12 diverse architectures and generate 295 evaluation datasets. Furthermore, it consumes only 95.5 ms to plan and orchestrate the deployment and evaluation, which is minuscule compared to the total time required for deploying and benchmarking a blockchain network.
Speech GAU: A Single Head Attention for Mandarin Speech Recognition for Air Traffic Control

The rise of end-to-end (E2E) speech recognition technology in recent years has overturned the design pattern of cascading multiple subtasks in classical speech recognition and achieved direct mapping of speech input signals to text labels. In this study, a new E2E framework, ResNet–GAU–CTC, is proposed to implement Mandarin speech recognition for air traffic control (ATC). A deep residual network (ResNet) utilizes the translation invariance and local correlation of a convolutional neural network (CNN) to extract the time-frequency domain information of speech signals. A gated attention unit (GAU) utilizes a gated single-head attention mechanism to better capture the long-range dependencies of sequences, thus attaining a larger receptive field and contextual information, as well as a faster training convergence rate. The connectionist temporal classification (CTC) criterion eliminates the need for forced frame-level alignments. To address the problems of scarce data resources and unique pronunciation norms and contexts in the ATC field, transfer learning and data augmentation techniques were applied to enhance the robustness of the network and improve the generalization ability of the model. The character error rate (CER) of our model was 11.1% on the expanded Aishell corpus, and it decreased to 8.0% on the ATC corpus.
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization

Unsupervised domain adaptation (UDA) for semantic seg-mentation has been well-studied in recent years. However, most existing works largely neglect the local regional consis-tency across different domains, and are less robust to changes in outdoor environments. In this paper, we propose a novel and fully end-to-end trainable approach, called regional contrastive consistency regularization (RCCR) for domain adaptive semantic segmentation. Our core idea is to pull the sim-ilar regional features extracted from the same location of dif-ferent images, i.e., the original image and augmented image, to be closer, and meanwhile push the features from the dif-ferent locations of the two images to be separated. We pro-pose a region-wise contrastive loss with two sampling strate-gies to realize effective regional consistency. Besides, we present momentum projection heads, where the teacher pro-jection head is the exponential moving average of the student. Finally, a memory bank mechanism is designed to learn more robust and stable region-wise features under varying environ-ments. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods.
A Survey on NIST Selected Third Round Candidates for Post Quantum Cryptography

The tremendous increase in the volume of data over internet for many applications such as banking, hospitals, insurance, online trading, messengers, voice communications and others has resulted in increased security threats. Due to the evaluation in the quantum computer generation, the classical cryptography algorithm will be broken in very less amount of time and lead to the security breaches and malicious attacks. The National Institute of Standard and Technology (NIST) has invited a new generation of Quantum Safe cryptography. First two rounds are completed successfully, and the finalist has been announced. This paper provides an in-depth examination of NIST third round selected candidates which are going to be the standard Post Quantum Cryptography (PQC) algorithm. Also, the limitations and attacking concerns related to that PQC algorithms are presented.
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation

Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides considering the relevance between recommendations and trigger item, the recommendations should also be diversified to avoid information cocoons. However, existing diversified recommendation methods mainly focus on item-level diversity which is insufficient when the recommended items are all relevant to the target item. Moreover, redundant or noisy item features might affect the performance of simple feature-aware recommendation approaches. Faced with these issues, we propose a Feature Disentanglement Self-Balancing Re-ranking framework (FDSB) to capture feature-aware diversity. The framework consists of two major modules, namely disentangled attention encoder (DAE) and self-balanced multi-aspect ranker. In DAE, we use multi-head attention to learn disentangled aspects from rich item features. In the ranker, we develop an aspect-specific ranking mechanism that is able to adaptively balance the relevance and diversity for each aspect. In experiments, we conduct offline evaluation on the collected dataset and deploy FDSB on KuaiShou app for online A/B test on the function of relevant recommendation. The significant improvements on both recommendation quality and user experience verify the effectiveness of our approach.