Reviewerly/exHarmony huggingface dataset:
Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding

A fundamental question in designing lossy data compression schemes is how well one can do in comparison with the rate-distortion function, which describes the known theoretical limits of lossy compression. Motivated by the empirical success of deep neural network (DNN) compressors on large, realworld data, we investigate methods to estimate the rate-distortion function on such data, which would allow comparison of DNN compressors with optimality. While one could use the empirical distribution of the data and apply the Blahut-Arimoto algorithm, this approach presents several computational challenges and inaccuracies when the datasets are large and high-dimensional, such as the case of modern image datasets. Instead, we reformulate the rate-distortion objective, and solve the resulting functional optimization problem using neural networks.We apply the resulting rate-distortion estimator, called NERD, on popular image datasets, and provide evidence that NERD can accurately estimate the rate-distortion function. Using our estimate, we show that the rate-distortion achievable by DNN compressors are within several bits of the rate-distortion function for realworld datasets. Additionally, NERD provides access to the ratedistortion achieving channel, as well as samples from its output marginal. Therefore, using recent results in reverse channel coding, we describe how NERD can be used to construct an operational one-shot lossy compression scheme with guarantees on the achievable rate and distortion. Experimental results demonstrate competitive performance with DNN compressors.
Playing With Others Using Headphones: Musicians Prefer Binaural Audio With Head Tracking Over Stereo

Immersive listening systems have grown significantly over the past decade and are now an established area of scientific, artistic, and industrial research. However, scarce research has been conducted on musicians' preferences for playing through headphones over binaural spatialization systems with the addition of head tracking, as opposed to classical stereophonic systems. This comparison is essential to optimally support the playing experience with others for cases of remote collaborative playing, individual instrumental practice, individual recreational music-making using backing tracks, and studio recording sessions. In this article, we study the preferences of playing musicians for a stereophonic system versus a binaural head-tracking system composed of Ambisonics technology and binaural synthesis with generalized head-related transfer functions. We conducted two experiments, each with 30 expert musicians, where participants were asked to rate and compare the 2 listening conditions while playing their instrument either seated or standing. Overall, the quantitative and qualitative results indicated a generalized preference for the binaural system with head tracking over the stereophonic system, with higher ratings for localization, immersion, social presence, realism, and connection with other musicians. Moreover, participants moved their heads significantly more in the binaural conditions. This phenomenon may be explained by the higher engagement and arousal due to the improved auditory experience, or alternatively by the presence of embodied music cognition mechanisms that cause a higher degree of exploration to better understand the action–perception loop. These findings highlight the need for progressing current commercial hardware and software systems used by musicians while playing over headphones.
Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution

The goal of the “2019 Automatic Speaker Verification Spoofing and Countermeasures Challenge” (ASVspoof) was to make it easier to create systems that could identify voice spoofing attacks with high levels of accuracy. However, model complexity and latency requirements were not emphasized in the competition, despite the fact that they are stringent requirements for implementation in the real world. The majority of the top-performing solutions from the competition used an ensemble technique that merged numerous sophisticated deep learning models to maximize detection accuracy. Those approaches struggle with real-world deployment restrictions for voice assistants which would have restricted resources. We merged skip connection (from ResNet) and max feature map (from Light CNN) to create a compact system, and we tested its performance using the ASVspoof 2019 dataset. Our single model achieved a replay attack detection equal error rate (EER) of 0.30% on the evaluation set using an optimized constant Q transform (CQT) feature, outperforming the top ensemble system in the competition, which scored an EER of 0.39%. We experimented using depthwise separable convolutions (from MobileNet) to reduce model sizes; this resulted in an 84.3 percent reduction in parameter count (from 286K to 45K), while maintaining similar performance (EER of 0.36%). Additionally, we used Grad-CAM to clarify which spectrogram regions significantly contribute to the detection of fake data.
Quad-Channel Contrastive Prototype Networks for Open-Set Recognition in Domain-Specific Tasks

A traditional deep neural network-based classifier assumes that only training classes appear during testing in closed-world settings. In most real-world applications, an open-set environment is more realistic than a conventional approach where unseen classes are potentially present during the model’s lifetime. Open-set recognition (OSR) provides the model with the capability to address this issue by reducing open-set risk, in which unknown classes could be recognized as known classes. Unfortunately, many proposed open-set techniques evaluate performance using “toy” datasets and do not consider transfer learning, which has become common practice in deriving a strong performance from deep learning models. We propose a quad-channel contrastive prototype network (QC-CPN) using quad-channel views of the input with contrastive prototype loss for real-world applications. These open-set techniques also require the tuning of new hyperparameters to justify their performance, so we first employ evolutionary simulated annealing (EvoSA) to find good hyperparameters and evaluate their performance with our proposed approach. The comparison results show that QC-CPN effectively outperforms other state-of-the-art techniques in rejecting unseen classes in a domain-specific dataset using the same backbone (MNetV3-Large) and could become a strong baseline for future study.
Cybersecurity Awareness Assessment among Trainees of the Technical and Vocational Training Corporation

People are the weakest link in the cybersecurity chain when viewed in the context of technological advancement. People become vulnerable to trickery through contemporary technical developments such as social media platforms. Information accessibility and flow have increased rapidly and effectively; however, due to this increase, new electronic risks, or so-called cybercrime, such as phishing, scams, and hacking, lead to privacy breaches and hardware sabotage. Therefore, ensuring data privacy is vital, particularly in an educational institute where students constitute the large majority of users. Students or trainees violate cybersecurity policies due to their lack of awareness about the cybersecurity environment and the consequences of cybercrime. This paper aims to assess the level of awareness of cybersecurity, users’ activities, and user responses to cybersecurity issues. This paper collected data based on a distributed questionnaire among trainees in the Technical and Vocational Training Corporation (TVTC) to demonstrate the necessity of increasing user awareness and training. In this study, quantitative research techniques were utilized to analyze the responses from trainees using tests such as the Chi-Squared test. Proof of the reliability of the survey was provided using Cronbach’s alpha test. This research identifies the deficiencies in cybersecurity awareness among TVTC trainees. After analyzing the gathered data, recommendations for tackling these shortcomings were offered, with the aim of enhancing trainees’ decision-making skills regarding privacy and security using the Nudge model.
Hydraulic Data Preprocessing for Machine Learning-Based Intrusion Detection in Cyber-Physical Systems

The protection of critical infrastructure such as water treatment and water distribution systems is crucial for a functioning economy. The use of cyber-physical systems in these systems presents numerous vulnerabilities to attackers. To enhance security, intrusion detection systems play a crucial role in limiting damage from successful attacks. Machine learning can enhance security by analysing data patterns, but several attributes of the data can negatively impact the performance of the machine learning model. Data in critical water system infrastructure can be difficult to work with due to their complexity, variability, irregularities, and sensitivity. The data involve various measurements and can vary over time due to changes in environmental conditions and operational changes. Irregular patterns and small changes can have significant impacts on analysis and decision making, requiring effective data preprocessing techniques to handle the complexities and ensure accurate analysis. This paper explores data preprocessing techniques using a water treatment system dataset as a case study and provides preprocessing techniques specific to processing data in industrial control to yield a more informative dataset. The results showed significant improvement in accuracy, F1 score, and time to detection when using the preprocessed dataset.
Ultra‐Compact and NonVolatile Nanophotonic Neural Networks

Abstract A nanophotonic neural network (N‐PNN) architecture is proposed with compact nanophotonic scattering units and a hybrid structure of silicon and nonvolatile arrayed Sb 2 Se 3 . This PNN can execute deep neural networks (DNN) classification and identification tasks with a broad operation bandwidth and very compact footprint. The reconstruction of the convolutional kernel core is realized by digitally switching the phase state of the Sb 2 Se 3 array. Based on a three‐dimensional finite‐difference time‐domain analysis, the core unit received only 4.92 × 2.34 µm 2 footprint. The convolution kernel unit weights are reconfigured with high‐accuracy (7‐bit) image processing and recognition in the wavelength C‐band (1530–1570 nm). Furthermore, various deep‐learning tasks (speech, digital patterns, and clothing patterns) are investigated. The accuracy of the classification and recognition efficiency reached almost the same level as that of a 64‐bit computer. The size of the N‐PNN is almost two orders of magnitude smaller than that of classic Mach–Zehnder interferometer meshes. It is conducive for scalability, high‐radix DNN, and optoelectronic fusion of photonic integrated circuits and electronic integrated circuits.
Reducing molecular electronic Hamiltonian simulation cost for linear combination of unitaries approaches

We consider different Linear Combination of Unitaries (LCU) decompositions for molecular electronic structure Hamiltonians. Using these LCU decompositions for Hamiltonian simulation on a quantum computer, the main figure of merit is the 1-norm of their coefficients, which is associated with the quantum circuit complexity. It is derived that the lowest possible LCU 1-norm for a given Hamiltonian is half of its spectral range. This lowest norm decomposition is practically unattainable for general Hamiltonians; therefore, multiple practical techniques to generate LCU decompositions are proposed and assessed. A technique using symmetries to reduce the 1-norm further is also introduced. In addition to considering LCU in the Schr\"odinger picture, we extend it to the interaction picture, which substantially further reduces the 1-norm.
Research on the acceptance of collaborative robots for the industry 5.0 era -- The mediating effect of perceived competence and the moderating effect of robot use self-efficacy

Collaborative robots (Cobots), an important component of the Industry 5.0 era, have been rapidly entering a variety of industrial application scenarios. However, employees working with them are reluctant to accept cobots into the workplace. Therefore, the traditional technology acceptance model (TAM) is unsuitable for research on the acceptance of cobots with artificial intelligence and the human-robot interaction process. In addition, anthropomorphism cannot explain the lower employee acceptance with the increase of cobots anthropomorphic from the mechanistic level. Therefore, based on the human-robot interaction phenomenon in the emerging industrial field, combined with the Uncanny Valley effect and intergroup threat theory, 300 subjects were invited to conduct an empirical study using experimental vignette methodology (EVM). The findings are as follows: 1) Perceived competence plays a mediating role in the relationship between cobots anthropomorphic and acceptance of cobots; 2) Perceived competence and perceived threat serially mediates the relationship between cobots anthropomorphic and acceptance of cobots; 3) The cobot use self-efficacy plays a moderating role in the relationship between perceived competence and perceived threat. The research results provide a mechanistic explanation for alleviating the low acceptance of cobots, give measures and methods to improve acceptance of cobots and provide solutions for the promotion and application of cobots in the industrial field.
A Secure and Reliable Supply chain management approach integrated with IoT and Blockchain

Nowadays, secure and reliable management of logistics is highly needed. Logistics is the delivery of goods from producers to legitimate consumers in accurate amounts and good conditions. The use of low-capable sensor nodes in smart logistics makes it vulnerable to many security threats. Smart logistics necessitated the delivery of suitable information to the authorized person at the appropriate time and place, which is only feasible with a stable infrastructure. This paper presents an authentication scheme together with blockchain technology to provide a secure supply chain management system. The presented authentication mechanism is based on standard KERBOROS scheme which is a ticket-based scheme. The scheme is evaluated by BAN logic which shows that it is an effective scheme in terms of improved response time and encryption/decryption time along with key generation time.
Digital competence of teachers in the use of ICT for research work: development of an instrument from a PLS-SEM approach

All spheres of our life are being affected using technology, particularly its integration in the research processes carried out by teachers. The success of the integration of specific digital resources in research work can be affected by several factors, such as: digital skills for finding information, managing it, analyzing it, and communicating results; digital flow; anxiety in the use of ICT; digital ethics; quality of digital resources; and finally, the behavioral intention to integrate ICT. The purpose of this study is to examine the factors that influence the integration of ICT in the research process of the Higher Education teacher, and the relation between them. An online survey was used to collect data, and 1740 participants. This study used a causal model through partial least squares structural equations modeling (PLS-SEM). With this, the hypotheses established between the integration of ICT and its possible incident factors were verified. The findings revealed a significant influence path from factor integration to digital skills, ethics, flow digital, and behavior intention. Although, resource quality and ICT anxiety had significant effects on the causal model, they did not have a large impact on teachers' integration of digital resources. The total of these factors corresponded to 48.20% of the variance in the integration of the researcher of the specific digital resources to be used in the research process. These results confirm that this model is effective in explaining the technological integration of teachers to use ICT in research work.
An effective fraud detection using competitive swarm optimization based deep neural network

Frauds have no persistent patterns. They constantly alter their behaviour, necessitating the use of unsupervised learning. Fraudsters gain access to latest technology that enables them to commit scams via internet transactions. Consumers' habitual behaviour is assumed by fraudsters, and fraud trends very quickly. Fraud detection systems must utilize unsupervised learning to identify online payments since some fraudsters initially use online channels before switching towards other methods. The goals of this research are to construct a deep convolutional neural network model to detect anomalies from regular patterns produced by competitive swarm optimization, with a particular emphasis on fraud scenarios that cannot be detected using prior records or supervised learning (CSO). An unsupervised learning method called the suggested CSO-DCNN employs ReLu by making the inputs and outputs equal. The CSO-DCNN is classifies the fraud activity using real-time and other available dataset that is compared with the existing algorithms. The experimental results shows that the suggested CSO-DCNNobtains98.20%, 99.77%, 95.23% of accuracy for credit card, insurance and Mortgage Data set.
An Efficient Deep Learning for Thai Sentiment Analysis

The number of reviews from customers on travel websites and platforms is quickly increasing. They provide people with the ability to write reviews about their experience with respect to service quality, location, room, and cleanliness, thereby helping others before booking hotels. Many people fail to consider hotel bookings because the numerous reviews take a long time to read, and many are in a non-native language. Thus, hotel businesses need an efficient process to analyze and categorize the polarity of reviews as positive, negative, or neutral. In particular, low-resource languages such as Thai have greater limitations in terms of resources to classify sentiment polarity. In this paper, a sentiment analysis method is proposed for Thai sentiment classification in the hotel domain. Firstly, the Word2Vec technique (the continuous bag-of-words (CBOW) and skip-gram approaches) was applied to create word embeddings of different vector dimensions. Secondly, each word embedding model was combined with deep learning (DL) models to observe the impact of each word vector dimension result. We compared the performance of nine DL models (CNN, LSTM, Bi-LSTM, GRU, Bi-GRU, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU) with different numbers of layers to evaluate their performance in polarity classification. The dataset was classified using the FastText and BERT pre-trained models to carry out the sentiment polarity classification. Finally, our experimental results show that the WangchanBERTa model slightly improved the accuracy, producing a value of 0.9225, and the skip-gram and CNN model combination outperformed other DL models, reaching an accuracy of 0.9170. From the experiments, we found that the word vector dimensions, hyperparameter values, and the number of layers of the DL models affected the performance of sentiment classification. Our research provides guidance for setting suitable hyperparameter values to improve the accuracy of sentiment classification for the Thai language in the hotel domain.
Examining the Impact of Frontline Service Robots Service Competence on Hotel Frontline Employees from a Collaboration Perspective

With the increasing adoption of frontline service robots (FLSRs) in hospitality workplaces, collaboration between frontline employees (FLEs) and FLSRs has become a necessity. The existing literature focuses on the customer perspective of FLSRs; however, this study explains the mechanisms through which employees’ willingness to collaborate with FLSRs are built. By incorporating robot service capability and perceived risk as external variables into a technology acceptance model, this study investigated the mechanisms of FLEs’ willingness to collaborate with FLSRs. The results showed that the service capability of FLSRs plays a significant role in increasing FLEs’ willingness to collaborate, whereas perceived risk decreases their willingness to collaborate. These results indicate that the level of service capability of FLSRs and the management of perceived risk are important in shaping FLEs’ positive attitudes toward collaborating with FLSRs. Therefore, this study extends the literature by investigating how FLEs and FLSRs relate to each other from a collaboration perspective.
Complexity of Implementing Trotter Steps

Quantum dynamics can be simulated on a quantum computer by exponentiating elementary terms from the Hamiltonian in a sequential manner. However, such an implementation of Trotter steps has gate complexity depending on the total Hamiltonian term number, comparing unfavorably to algorithms using more advanced techniques. We develop methods to perform faster Trotter steps with complexity sublinear in the number of terms. We achieve this for a class of Hamiltonians whose interaction strength decays with distance according to power law. Our methods include one based on a recursive block encoding and one based on an average-cost simulation, overcoming the normalization-factor barrier of these advanced quantum simulation techniques. We also realize faster Trotter steps when certain blocks of Hamiltonian coefficients have low rank. Combining with a tighter error analysis, we show that it suffices to use $\left(\eta^{1/3}n^{1/3}+\frac{n^{2/3}}{\eta^{2/3}}\right)n^{1+o(1)}$ gates to simulate uniform electron gas with $n$ spin orbitals and $\eta$ electrons in second quantization in real space, asymptotically improving over the best previous work. We obtain an analogous result when the external potential of nuclei is introduced under the Born-Oppenheimer approximation. We prove a circuit lower bound when the Hamiltonian coefficients take a continuum range of values, showing that generic $n$-qubit $2$-local Hamiltonians with commuting terms require at least $\Omega(n^2)$ gates to evolve with accuracy $\epsilon=\Omega(1/poly(n))$ for time $t=\Omega(\epsilon)$. Our proof is based on a gate-efficient reduction from the approximate synthesis of diagonal unitaries within the Hamming weight-$2$ subspace, which may be of independent interest. Our result thus suggests the use of Hamiltonian structural properties as both necessary and sufficient to implement Trotter steps with lower gate complexity.
A self-organizing fuzzy neural network with hybrid learning algorithm for nonlinear system modeling

Fuzzy neural networks (FNNs) integrating the advantages of fuzzy systems and neural networks are useful techniques for nonlinear system modeling. However, how to determine the structure and parameters to guarantee satisfactory modeling performance still remains a challenge. In this study, a self-organizing FNN with hybrid learning algorithm (SOFNN-HLA) is developed for nonlinear system modeling. First, a growing-and-pruning constructive scheme is proposed based on the network learning accuracy and the rule firing strength. New fuzzy rules can be developed with appropriate initial parameters based on the idea of an error-correction algorithm to improve the learning performance. Meanwhile, some redundant rules with low firing strength would be pruned to ensure a compact structure. Second, a hybrid learning algorithm combining an improved second-order algorithm and the least square method is developed for parameter adjustment. In this hybrid learning algorithm, linear parameters and nonlinear parameters are tackled separately to enhance the learning efficiency. Finally, the effectiveness of SOFNN-HLA is validated by two benchmark simulations and one real problem from wastewater treatment processes. The results show that the proposed SOFNN-HLA can achieve desirable generalization performance with a compact structure for nonlinear system modeling.