Reviewerly/exHarmony huggingface dataset:
Joint Intent Detection Model for Task-oriented Human-Computer Dialogue System using Asynchronous Training

How to accurately understand low-resource languages is the core of the task-oriented human-computer dialogue system. Language understanding consists of two sub-tasks, i.e., intent detection and slot filling. Intent detection still faces challenges due to semantic ambiguity and implicit intentions with users’ input. Moreover, separately modeling intent detection and slot filling significantly decrease the correctness and relevance between questions and answers. To address these issues, we propose a joint intent detection method using asynchronous training strategy. The proposed method firstly encodes local text information extracted by CNN and relationship information among words emphasized by attention structure. Later, a joint intent detection model with asynchronous training strategy is proposed by either fusing hidden states of intent detection and slot filling layers, or adopting the key information to fine-tune the whole network, greatly increasing the relevance of intent detection and slot filling subtasks. The accuracy achieved by the proposed method tested on an open-source airline travel dataset and a self-collected electricity service dataset, i.e., ATIS and ECSF, are 97.49% and 89.68%, respectively, which proves the effectiveness of joint learning and asynchronous training.
Bandwidth-efficient multi-task AI inference with dynamic task importance for the Internet of Things in edge computing

Over the past years, artificial intelligence (AI) models have been utilized for the Internet of Things (IoT) in applications such as remote assistance based on augmented reality (AR) in smart factories, as well as powerline inspection and precision agriculture missions performed by unmanned aerial vehicles (UAVs). Due to the limited battery capacity and computing power of these devices (e.g., AR glasses and UAVs), edge computing is recognized as a means to empower the Internet of Things (IoT) with AI. Considering that multiple AI model inference tasks (e.g., point cloud classification and fault detection) are typically performed on the same stream of sensory data (e.g., UAV camera feed), we propose TORC (Tasks-Oriented Edge Computing) to reduce the bandwidth requirement. By incorporating AI into data transmission, the lightweight framework of TORC preserves edge computing servers’ ability to reconstruct/restore data into the original form, ensuring the proper coexistence of AI inference tasks and traditional non-AI tasks like human inspection, as well as simultaneous localization and mapping. It encodes and decodes sensory data with neural networks, whose training is driven by the AI inference tasks, in order to reduce bandwidth consumption and latency without impairing the accuracy of the AI inference tasks. Additionally, taking into account the mobility of the IoT and changes in the environment, TORC can adapt to variation in the bandwidth budget, as well as the temporally dynamic importance of AI inference tasks, without the need to train multiple neural networks for each setting. As a demonstration, empirical results conducted on the Cityscapes dataset and tasks related to autonomous driving show that, at the same level of accuracy, TORC reduces the bandwidth consumption by up to 48% and latency by up to 26%.
ADEPT

Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achieve ultra-fast and efficient tensor operations for neural network (NN) acceleration. Current PTC designs are either manually constructed or based on matrix decomposition theory, which lacks the adaptability to meet various hardware constraints and device specifications. To our best knowledge, automatic PTC design methodology is still unexplored. It will be promising to move beyond the manual design paradigm and "nurture" photonic neurocomputing with AI and design automation. Therefore, in this work, for the first time, we propose a fully differentiable framework, dubbed ADEPT, that can efficiently search PTC designs adaptive to various circuit footprint constraints and foundry PDKs. Extensive experiments show superior flexibility and effectiveness of the proposed ADEPT framework to explore a large PTC design space. On various NN models and benchmarks, our searched PTC topology outperforms prior manually-designed structures with competitive matrix representability, 2×-30× higher footprint compactness, and better noise robustness, demonstrating a new paradigm in photonic neural chip design. The code of ADEPT is available at link using the TorchONN library.
How cloud accounting and integrated services based on AI can impact accounting companies?

Abstract Artificial intelligence solutions are not present only in big companies, but the new trend is to start changing the accounting profession also in small and medium companies. In order to shorten the period in which they will have access to artificial intelligence solutions, the artificial intelligence needs to be offered in a more accessible form. This paper represents the first step in a more in depth research about the way available resources for an organization to implement artificial intelligence solutions in their financial departments. This decision has the potential to bring fundamental changes to accounting and auditing, and in the entire financial markets. This paper explores the potential of artificial solutions adaptation in accounting, identifying the steps already taken in this field of activity which will help them to implement further changes. We investigate mainly the organizational changes brought by cloud computing and its potential for the accounting field to adapt to further technological solutions. We assessed the cloud computing’s effect on the accounting profession, and the potential role of these professions in further implementing more intelligent solutions, as well as the main advantages and disadvantages in order to understand the reasons for high interest in migration to cloud computing, especially during the COVID-19 crisis.
The relationship between transformational leadership, job satisfaction, and organizational commitment in Jordanian nurses

Background: Leaders influence and inspire others, develop strategies, organize the work and coordinate resources. Leadership style may affect nurses' performance directly and it may have positive or negative effects on nursing performance. We investigated the relationship between transformational leadership (TL), job satisfaction, and organizational commitment (OC) in Jordanian nurses.&#x0D; Methods: The study utilized a correlational, cross-sectional design. A sample of 253 registered nurses was recruited using a convenience sampling method from two government hospitals in Jordan. The Global Transformational Leadership (GTL) scale, job satisfaction survey, and Organizational Commitment Scale (OCS) were used to collect the data.&#x0D; Results: A total of 253 participants completed the study. The results showed that there was a significant and positive association between TL and job satisfaction, (r = 0.297, P = 0.000). There was a positive relationship between TL and OC (r = 0.200, P = 0.001).&#x0D; Conclusion: Considering the outcomes of the current study, it can be concluded that transformational leadership is one of the important leadership styles that has the potential to support nurses and improved nurses’ job satisfaction and organizational commitment in Jordanian hospitals.&#x0D; Abbreviations: OC - Organizational commitment; TL - Transformational leadership; GTL - Global Transformational Leadership Scale; JSS - Job Satisfaction Survey; OCS - Organizational Commitment Survey&#x0D; Keywords: Transformational leadership; Job satisfaction; Organizational commitment; Nurses; Jordan.&#x0D; Citation: Othman T, Khrais H. The relationship between transformational leadership, job satisfaction, and organizational commitment in Jordanian nurses. Anaesth. pain intensive care 2022;26(3):304-309; DOI: 10.35975/apic.v26i3.1896&#x0D; Received: November 23, 2021, Reviewed: March 09, 2022, Accepted: March 21, 2022&#x0D;
Regulatable and Hardware-Based Proof of Stake to Approach Nothing At Stake and Long Range Attacks

Proof of Stake powered blockchains account for general trends in existing consensus mechanisms. However, existing PoS protocols are vulnerable to the nothing at stake and long range attacks, which allow attackers to gain unfair shares based on costless simulations and malicious sale information. In a decentralized setting, these securities are limited as each node is unregulated. To address these problems, we introduce a proof-of-hardware-stake (PohS) consensus mechanism and a regulatory mechanism based on a consortium blockchain. Our approach is implemented in a sharding blockchain to scale the consensus. Since any node on the network cannot fake information, the blockchain trustless won not be decreased by the reliance on a consortium blockchain. Under the competing rule of PohS consensus mechanism, adversaries can issue the long range attack with at most <inline-formula><tex-math notation="LaTeX">$10^{-3}$</tex-math></inline-formula> probability. The regulatory mechanism implements the regulation of costless simulations at the nothing stake attack. We prove that our design is secure (<i>e.g.,</i> 51&#x0025; and selfish mining) against adversarial stakes less than 51&#x0025; by adopting the square root of stakes for competition. Our proof also highlights the security of double-spending and long range attacks. Simulations are conducted to compare the efficiency of our approach with Ethereum and Ouroboros.
A Combination of Visual and Temporal Trajectory Features for Cognitive Assessment in Smart Home

The rapid increase of the elderly population and new advances in pervasive computing technologies allow innovative tools and applications to support independent living for frail people and identify early symptoms of health problems, including neurodegenerative disorders. Among several studies reported in the literature, monitoring locomotion traces to detect symp-toms of cognitive impairment has gained increasing attention. Therefore, in this work, we propose a novel technique for the recognition of locomotion patterns related to cognitive decline based on sensor data acquired in smart homes. In particular, we introduce a vision-based method to graphically represent indoor trajectories with random rotation, using different handcrafted features designed for image analysis tasks and combined with features extracted directly from spatio-temporal sequences of movements. Experiments on a real-world dataset acquired in a smart-home test-bed show that the proposed approach achieves promising results.
Mind the context—The relevance of personality for face-to-face and computer-mediated communication

A large body of research has examined the link between personality and face-to-face (FtF) communication knowledge, skills, abilities, and other characteristics (KSAOs). With the rise of digital media, text-based computer-mediated (CM) communication KSAOs have gained increasing attention. We conducted two studies to investigate how personality relates to KSAOs in the different contexts of FtF and CM communication. Contrasting perspectives hypothesize that the results in the FtF and CM contexts would be very similar or distinctly different. In Study 1 ( n = 454), an online panel study, the Big Five personality dimensions were assessed and their relationships to FtF and CM communication KSAOs were investigated. Structural equation models and relative weight regression analyses showed that these personality dimensions, mostly extraversion and neuroticism, explained more variance in FtF as compared to CM communication KSAOs. Study 2 ( n = 173), conducted in a laboratory context, showed similar results compared to Study 1. In addition, when the Big Five personality dimensions were assessed with a CM frame of reference, more variance was explained in CM than in FtF communication KSAOs. These results point to the importance of considering context effects in communication and in personality research: FtF and CM communication KSAOs need to be differentiated. If not properly contextualized, the relevance of personality and communication competencies in predicting criteria may be underestimated due to contextual mismatches.
Conditional mixture modeling and model-based clustering

Due to a potentially high number of parameters, finite mixture models are often at the risk of overparameterization even for a moderate number of components. This can lead to overfitting individual components and result in mixture order underestimation. One of the most popular approaches to address this issue is to reduce the number of parameters by considering parsimonious models. The vast majority of techniques in this direction focuses on the reparameterization of covariance matrices associated with mixture components. We propose an alternative approach based on the parsimonious parameterization of location parameters that enjoys remarkable modeling flexibility especially in the presence of non-compact clusters. Due to an attractive closed form formulation, speedy parameter estimation is available by means of the EM algorithm. The utility of the proposed method is illustrated on synthetic and well-known classification data sets.
A CNN Bi-LSTM based Multimodal Continuous Hand Gesture Recognition

Multimodal gesture recognition is one of the important tasks in the field of Human-Computer Interaction. This paper proposes a Convolutional Neural Network-Bidirectional Long Short-Term Memory network based multimodal gesture recognition technique for continuous hand gestures. The modalities like RGB modality, optical flow modality and the segmented hand data are used as input to the feature extractor network. Apart from these modalities, this paper proposes the feature extraction from temporal differences of RGB modality. The pretrained Googlenet Caffe model is used to learn the features from these modalities. These features are concatenated for performing the fusion process. For the classification task, the Bidirectional Long Short-Term Memory network is used. The proposed method is tested on the IPN Hand dataset. Initially each modality is used individually. Thereafter their combinations are used for further improvement in classification accuracy. The combination of segmented hand modality, optical flow modality and temporal difference of RGB modality provided the best result with a Levenshtein accuracy of 62.22%. The simulation results show that the proposed approach outperforms the benchmark techniques for multimodal gesture recognition.
Parallel voice conversion with limited training data using stochastic variational deep kernel learning

There are two types of voice conversion methods: statistical and deep learning-based. Although statistical methods can train with limited data, they face challenges, including spectral oversmoothing and time-domain discontinuity. On the other hand, extensively researched deep learning-based methods rely primarily on massive amounts of data, which limits their practical applicability. Given that voice conversion is an engineering problem with limited training data, it is crucial to develop techniques that can produce satisfactory results in terms of quality and similarity in the absence of a large amount of data. This paper proposes a voice conversion model based on stochastic variational deep kernel learning (SVDKL), which works with limited training data. The model allows the use of both the deep neural network's expressive capability and the high flexibility of the Gaussian process, which is a Bayesian and non-parametric method. The model utilizes a cascade of a deep neural network and a conventional kernel as the covariance function, which enables it to estimate non-smooth and more complex functions. Furthermore, the model's sparse variational Gaussian process solves the scalability problem of exact inference and enables the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which takes into account both data fitting and model complexity. Considering model complexity reduces the training data by increasing the robustness to overfitting. To evaluate the proposed scheme, we examined the model's performance with as little as approximately 80 s of training data. The results indicated that our method obtains a higher mean opinion score, smaller spectral distortion, and better preference tests than the state-of-the-art limited data methods.
The Banking Industry in the Ecosystem of Digital Currencies and Digital Central Bank Currencies

Purpose: This chapter aims to lay out the issues regarding the world of digital currencies, private and central bank digital currency (CBDC). In that connection, the authors want to, as much as possible, systematically present the terminology, examples of various digital currencies and the technology behind that phenomenon. The chapter also highlights the occurrence of CBDC and the possible implications of its introduction to day-to-day commercial banking practice, possibly taking the payment systems and transactions alternation, balance sheet and profits’ issues into consideration.Need for the study: Digital currencies already have and are also soon going to have an enormous impact on society as such, where payments for everyday goods and services are taken on a whole new platform and level, in the sense of how the payments are made and payment systems are constructed, as also in the sense of quantity, as the number and sum-wise payments carried out via such platforms are growing.Methodology: A triangulation method, a mixed qualitative methodological approach was implemented, so the research offers a synthesis of previously published contributions in this field, followed by deductive and inductive reasoning interconnected with descriptive and comparative analyses.Findings: As digital currency already have a vast impact on payment systems and modes of payment, the CBDC, an imperative of today and not the matter of the future, will have implications for commercial banks, probably in the field of lowering banks’ commissions, no big customer data-selling ability, accumulating the deposits and deposit policies and credit policies due to higher funding costs for banks. There is an interwovenness among the central bank activities, bank customer’s behaviour and commercial bank activities. Therefore, the change of payment and spending behaviour of customers because of central banks’ introducing novelties will also have consequences for the banking industry.Practical implications: The choice to handle cash or digital currency will be obsolete, and an individual’s or a firm’s financial knowledge must be upgraded in the field of new money using angles. The issue of digital currencies and CBDCs are no longer a matter of choice but are becoming a new reality. Therefore, it is necessary for the common public, economy and banking system, especially now carrying out most of payments and transfers of money, to study this field and foresee the possible consequences and risks emerging.
ResNet-Enabled cGAN Model for Channel Estimation in Massive MIMO System

Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been developed to reduce signal processing complexity. This DL framework uses conditional generative adversarial networks (cGANs) and various convolutional neural networks, namely reverse residual network (reverse ResNet), squeeze-and-excitation ResNet (SE ResNet), ResUNet++, and reverse SE ResNet, as the generator model of cGAN for extracting the features from the quantized received signals. The simulation results of this paper show that the trained residual block-based generator model of cGAN has better channel generation performance than the standard generator model in terms of mean square error.
SDoS: Selfish Mining-Based Denial-of-Service Attack

In this paper, we focus on mining attacks targeting the Proof of Work (PoW) consensus mechanism in blockchain-based systems. Specifically, we model mining as a game and propose a mining attack &#x2013; the Selfish mining-based denial of service (SDoS) attack. By studying the choices (mining or stopping) of honest miners under the attack and the adversary&#x2019;s revenue, we demonstrate that selfish mining is incentive-compatible with game-level denial of service attack, and that SDoS can be more threatening than existing mining attacks. Even under the worst assumption, the adversary only needs to master more than 19.6% of the total mining power to increase the revenue, and can launch a 51% attack with much less than 50%. In addition, we show that honest miners may make decisions based on the overall or current utility, and choosing the current utility is more beneficial to the adversary.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2-D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images, such as rotations and reflections. To solve this problem, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on each orientation, which allows it to effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layer-wise symmetry constraints. Finally, based on our novel framework, extensive experiments conducted on real-world clinical data demonstrate that a group equivariant Res-UNet (called GER-UNet) outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods in the tasks of hepatic tumor segmentation, COVID-19 lung infection segmentation, and retinal vessel detection. More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs, and delineating organs on other medical imaging modalities.