Reviewerly/exHarmony huggingface dataset:
Learning-Based Rate Control for Video-Based Point Cloud Compression

Due to limited transmission resources and storage capacity, efficient rate control is important in Video-based Point Cloud Compression (V-PCC). In this paper, we propose a learning-based rate control method to improve the rate-distortion (RD) performance of V-PCC. A low-latency synchronous rate control structure is designed to reduce the overhead of pre-coding. The basic unit (BU) parameters are predicted accurately based on our proposed CNN-LSTM neural network, instead of the online updating approach, which can be inaccurate due to low consistency between adjacent 2D frames in V-PCC. When determining the quantization parameters for the BU, a patch-based clipping method is proposed to avoid unnecessary clipping. This approach is able to improve the RD performance and subjective dynamic point cloud quality. Experiments show that our proposed rate control method outperforms present approaches.
Adaptive Action Assessment

Action assessment, the process of evaluating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how well</i> an action is performed, is an important task in human action analysis. Action assessment has experienced considerable development based on visual cues; however, existing methods neglect to adaptively learn different architectures for varied types of actions and are therefore limited in achieving high-performance assessment for each type of action. In fact, every type of action has specific evaluation criteria, and human experts are trained for years to correctly evaluate a single type of action. Therefore, it is difficult for a single assessment architecture to achieve high performance for all types of actions. However, manually designing an assessment architecture for each specific type of action is very difficult and impracticable. This work addresses this problem by adaptively designing different assessment architectures for different types of actions, and the proposed approach is therefore called the adaptive action assessment. In order to facilitate our adaptive action assessment by exploiting the specific joint interactions for each type of action, a set of graph-based joint relations is learned for each type of action by means of trainable joint relation graphs built according to the human skeleton structure, and the learned joint relation graphs can visually interpret the assessment process. In addition, we introduce using a normalized mean squared error loss (N-MSE loss) and a Pearson loss that perform automatic score normalization to operate adaptive assessment training. The experiments on four benchmarks for action assessment demonstrate the effectiveness and feasibility of the proposed method. We also demonstrate the visual interpretability of our model by visualizing the details of the assessment process.
Hybrid quantum-classical algorithm for computing imaginary-time correlation functions

Quantitative descriptions of strongly correlated materials pose a considerable challenge in condensed matter physics and chemistry. A promising approach to address this problem is quantum embedding methods. In particular, the dynamical mean-field theory (DMFT) maps the original system to an effective quantum impurity model comprising correlated orbitals embedded in an electron bath. The biggest bottleneck in DMFT calculations is numerically solving the quantum impurity model, i.e., computing Green's function. Past studies have proposed theoretical methods to compute Green's function of a quantum impurity model in polynomial time using a quantum computer. So far, however, efficient methods for computing the imaginary-time Green's functions have not been established despite the advantages of the imaginary-time formulation. We propose a quantum-classical hybrid algorithm for computing imaginary-time Green's functions on quantum devices with limited hardware resources by applying the variational quantum simulation. Using a quantum circuit simulator, we verified this algorithm by computing Green's functions for a dimer model as well as a four-site impurity model obtained by DMFT calculations of the single-band Hubbard model, although our method can be applied to general imaginary-time correlation functions.
Fisher Information Universally Identifies Quantum Resources

We show that both the classical as well as the quantum definitions of the Fisher information faithfully identify resourceful quantum states in general quantum resource theories, in the sense that they can always distinguish between states with and without a given resource. This shows that all quantum resources confer an advantage in metrology, and establishes the Fisher information as a universal tool to probe the resourcefulness of quantum states. We provide bounds on the extent of this advantage, as well as a simple criterion to test whether different resources are useful for the estimation of unitarily encoded parameters. Finally, we extend the results to show that the Fisher information is also able to identify the dynamical resourcefulness of quantum operations.
COVID-19 Pandemic and Its Brunt on Digital Transformation and Cybersecurity

COVID-19 has collapsed all the sectors in the world and created new strategies for business houses; as a result, all the industries have transformed their operating model from traditional to working from home, and it has paved the way to the ‘new normal'. However, governments have imposed various restrictions in connection with COVID-19, and business houses have determined and asked the employees to stay at home and to discharge their duties and responsibilities under the work-from-home concept; thereby, the technology-enabled working environment has become popular and played an important role in the case of personal and working life. As a result, a massive digital transformation was found in the business landscape, and this also brought cyber threats to the business organizations as all the business activities have been done virtually. With this backdrop, this chapter addresses how COVID-19 has transformed various sectors, areas where the digital transformation has taken place during COVID-19, the need for digital transformation, and cybersecurity.
Automated decision-making

Purpose This paper aims to analyse three decision-making approaches that involve humans and artificial autonomous agents, namely, human “in the loop”, “on the loop” and “out of the loop” and identifies the decision characteristics that determine the choice of a decision-making approach. Design/methodology/approach This is a conceptual paper that analyses the relationships between the human and the artificial autonomous agents in the decision-making process from the perspectives of the agency theory, sustainability, legislation, economics and operations management. Findings The paper concludes that the human “out of the loop” approach is most suitable for quick, standardised, frequent decisions with low negative consequences of a wrong decision by the artificial intelligence taken within a well-defined context. Complex decisions with high outcome uncertainty that involve significant ethical issues require human participation in the form of a human “in the loop” or “on the loop” approach. Decisions that require high transparency need to be left to humans. Originality/value The paper evaluates the decision-making approaches from the perspectives of the agency theory, sustainability, legislation, economics and operations management and identifies the decision characteristics that determine the choice of a decision-making approach.
A Bayesian Network Model of Causal Learning

Associationist theories of causal induction model learning as the acquisition of associative weights between cues and outcomes. An important deficit of this class of models is its insensitivity to the causal role of cues. A number of recent experimental findings have shown that human learners differentiate between cues that represent causes and cues that represent effects. Our Bayesian network model overcomes this restriction. The model starts learning with initial structural assumptions about the causal model underlying the learning domain. This causal model guides the estimation of causal strength, and suggests integration schemas for multiple cues. In this way, causal models effectively reduce the potential computational complexity inherent in even relatively simple learning tasks. The Bayesian model is applied to a number of experimental findings, including studies on estimation of causal strength, cue competition, base rate use, and learning linearly and nonlinearly separable categories.
Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

Handwritten Character Recognition is most challenging area of research, in which for various aspects a little enhancement can be always achieved. It is due to the irregularity of writing and shapes of different class user’s orientation affects the recognition rate. In this paper we have taken the complexity of Odia handwritten character recognition and successfully resolve with Principal Component Analysis (PCA). Here we had adopted a model in which the importance of symmetric axis chords in recognition of unconstrained handwritten characters is established. This symmetric axis chords are drawn along both row-wise and column-wise among the points one end to other. In addition to we have calculated the statistical feature as Euclidian distance, Hamilton distance which drawn from the midpoint of the symmetric chord to nearest pixel of the character. Apart from it we have also reported the angular values from the centroid of the image to the character pixel. This empirical model also harnessed the PCA over the feature set and perform the dimension reduction to the feature set which later termed as the key feature set. A certain series of experiment was carried on for the proper implementation of proposed technique, henceforth we have taken the standard Handwritten Database from various research institutes. Lastly on simulation analysis Radial Basis Function Neural Network (RBFNN) has been reported as to achieve high recognition rate through Gaussian kernel and a comparison among them has also reported here with.
Effects of robot restaurants’ food quality, service quality and high-tech atmosphere perception on customers’ behavioral intentions

Purpose Robot chefs and robot waiters have emerged in the restaurant industry. Based on the curiosity theory and the stimulus–organism–response paradigm, this study aims to understand the influence of robot restaurant attributes on customer behavioral intention before purchase. Design/methodology/approach Based on research data from 289 respondents comprising undergraduates, postgraduates and non-students in China, the theoretical model is tested via the partial least squares technique. Findings Food quality perception directly affects regular patronage intention, but it has no significant influence on experience intention. Service quality perception and high-tech atmosphere perception positively affect experience intention and regular patronage intention through the mediator of interest in robot restaurants. Originality/value Different from previous studies focusing on human employee restaurants, this study, to the best of the authors’ knowledge, is the first to systematically investigate the influence of robot restaurant attributes on customer behavioral intention, and it finds that these attributes have their own uniqueness vis-à-vis influencing customer behavioral intention.
Activation of indistinguishability-based quantum coherence for enhanced metrological applications with particle statistics imprint

Significance Quantum coherence has a fundamentally different origin for nonidentical and identical particles since for the latter a unique contribution exists due to indistinguishability. Here we experimentally show how to exploit, in a controllable fashion, the contribution to quantum coherence stemming from spatial indistinguishability. Our experiment also directly proves, on the same footing, the different role of particle statistics (bosons or fermions) in supplying coherence-enabled advantage for quantum metrology. Ultimately, our results provide insights toward viable quantum-enhanced technologies based on tunable indistinguishability of identical building blocks.
Design of cultural emperor penguin optimizer for energy-efficient resource scheduling in green cloud computing environment

In recent times, energy related issues have become challenging with the increasing size of data centers. Energy related issues problems are becoming more and more serious with the growing size of data centers. Green cloud computing (GCC) becomes a recent computing platform which aimed to handle energy utilization in cloud data centers. Load balancing is generally employed to optimize resource usage, throughput, and delay. Aiming at the reduction of energy utilization at the data centers of GCC, this paper designs an energy efficient resource scheduling using Cultural emperor penguin optimizer (CEPO) algorithm, called EERS-CEPO in GCC environment. The proposed model is aimed to distribute work load amongst several data centers or other resources and thereby avoiding overload of individual resources. The CEPO algorithm is designed based on the fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), which boosts the exploitation capabilities of EPO algorithm using the CA, shows the novelty of the work. The EERS-CEPO algorithm has derived a fitness function to optimally schedule the resources in data centers, minimize the operational and maintenance cost of the GCC, and thereby decrease the energy utilization and heat generation. To ensure the improvised performance of the EERS-CEPO algorithm, a wide range of experiments is performed and the experimental outcomes highlighted the better performance over the recent state of art techniques.
Graph Rationalization with Environment-based Augmentations

Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizability and interpretability of neural networks on vision and language data. In graph applications such as molecule and polymer property prediction, identifying representative subgraph structures named as graph rationales plays an essential role in the performance of graph neural networks. Existing graph pooling and/or distribution intervention methods suffer from lack of examples to learn to identify optimal graph rationales. In this work, we introduce a new augmentation operation called environment replacement that automatically creates virtual data examples to improve rationale identification. We propose an efficient framework that performs rationale-environment separation and representation learning on the real and augmented examples in latent spaces to avoid the high complexity of explicit graph decoding and encoding. Comparing against recent techniques, experiments on seven molecular and four polymer real datasets demonstrate the effectiveness and efficiency of the proposed augmentation-based graph rationalization framework.
Seven lessons from the e-Peso pilot plan: The possibility of a Central Bank Digital Currency

The decision to introduce a central bank digital currency (CBDC) is part of the new challenges that central banks are facing as technology keeps moving. The e-Peso pilot plan, implemented between 2017 and 2018, could provide some key findings for central banks. In this sense, we can learn seven lessons from the e-Peso pilot plan: (i) reputation is key for central banks’ decision to introduce a CBDC; (ii) financial inclusion and cultural reasons are the main motivations; (iii) the technological solution as simple as possible; (iv) security aspects and traceable transfers are central for operational risk problems; (v) a token was a good solution for CBDC implementation; (vi) digital money was used for small payments and transfers; and (vii) CBDCs complement the existing means of payment. The conclusions highlight that CBDC choices are based not only on technical considerations but also on the cultural implications money use. The adoption of this new means of payment will be incremental but not reversible.
Comprehensive Fair Meta-learned Recommender System

In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation scenarios, i.e. learning to learn the user preference by only a few past interaction items. The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively. They aim at deriving general knowledge across preference learning of various users, so as to rapidly adapt to the future new user with the learned prior and a small amount of training data. However, previous works have shown that recommender systems are generally vulnerable to bias and unfairness. Despite the success of meta-learning at improving the recommendation performance with cold-start, the fairness issues are largely overlooked. In this paper, we propose a comprehensive fair meta-learning framework, named CLOVER, for ensuring the fairness of meta-learned recommendation models. We systematically study three kinds of fairness - individual fairness, counterfactual fairness, and group fairness in the recommender systems, and propose to satisfy all three kinds via a multi-task adversarial learning scheme. Our framework offers a generic training paradigm that is applicable to different meta-learned recommender systems. We demonstrate the effectiveness of CLOVER on the representative meta-learned user preference estimator on three real-world data sets. Empirical results show that CLOVER achieves comprehensive fairness without deteriorating the overall cold-start recommendation performance.
A sophisticated and provably grayscale image watermarking system using DWT-SVD domain

Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today's digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high-high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.
Digital learning environment: pedagogical сomprehension

The aim of the article is to comprehend the digital learning environment. Phylogeny of concepts: information and educational space, educational environment, information and educational environment is traced. The change in relations in educational activities due to the digitalization is shown as a new educational result.
Efficient Schemes for Committing Authenticated Encryption

This paper provides efficient authenticated-encryption (AE) schemes in which a ciphertext is a commitment to the key. These are extended, at minimal additional cost, to schemes where the ciphertext is a commitment to all encryption inputs, meaning key, nonce, associated data and message. Our primary schemes are modifications of GCM (for basic, unique-nonce AE security) and AES-GCM-SIV (for misuse-resistant AE security) and add both forms of commitment without any increase in ciphertext size. We also give more generic, but somewhat more costly, solutions.
Identification of Intrusion Events Based on Distributed Optical Fiber Sensing in Complex Environment

With the development of economy, the construction of modern infrastructures has been gradually improved. Some infrastructures, such as factories and airports, have large construction volume and high security requirements. Once an intrusion occurs, it will pose a great threat to the safety of personnel and property. Therefore, the perimeter security system based on Internet of Things (IoT) devices and technology is important. Distributed optical fiber sensor (DOFS) is an emerging infrastructure for IoT. Owing to multipoint sensing at the same time, it has attracted extensive attention in the field of perimeter security. However, with the ambient interference, intrusion signals and ambient signals form mixed signals, which brings great difficulty to the efficient and correct identification of intrusion signals. In order to establish a perimeter security system in complex environment, this article proposes an intrusion detection scheme based on DOFS, IoT, and pattern separation technology. The scheme realizes the separation of ambient signals and intrusion signals for the first time. Experiments have proved that this method can separate and recover intrusion signals from mixtures with the average SDR and SI-SDR of 12.35 dB and 11.61 dB, which effectively lays a foundation for subsequent feature extraction and identification. Besides, the response time is only about 0.5 s. This work makes a significant step toward achieving high performance of the perimeter security system in complex environment.
Data Privacy and Trustworthy Machine Learning

The privacy risks of machine learning models is a major concern when training them on sensitive and personal data. We discuss the tradeoffs between data privacy and the remaining goals of trustworthy machine learning (notably, fairness, robustness, and explainability).
Detail-Enhanced Wavelet Residual Network for Single Image Super-Resolution

Single-image super-resolution (SR) is vital in all areas of computer vision, due to the capability of the technology to generate high-resolution (HR) images. Conventional SR approaches do not consider high-frequency detail information during the reconstruction, resulting in high-frequency details of the image unreal, distorted in the reconstructed SR image. In this study, a novel detail-enhanced wavelet residual network (DeWRNet) is proposed to individually deal with the low- and high-frequency of sub-images and resolve the problem of the details over smooth with a novel low-to-high frequency transmission (L2HFT) and detail enhancement (DE) mechanism. Unlike traditional SR approaches, which directly predict high-resolution images, the proposed DeWRNet decomposes the image into low- and high-frequency ones through stationary wavelet transform, and trains low- and high-frequency sub-images with different models. Furthermore, while reconstructing high-frequency details, low-frequency structure is also provided to further restore and enhance high-frequency details by the proposed L2HFT and DE mechanism. Finally, the joint-loss function is used to optimize low- and high-frequency results in different degree of weighting. In addition to correct restoration, image details are further enhanced by adjusting different hyperparameters during training. Compared with the state-of-the-art approaches, the experimental results indicate that the proposed DeWRNet achieves a better performance and has excellent visual presentation, especially in image edges and texture details.