Reviewerly/exHarmony huggingface dataset:
Prosody Modelling With Pre-Trained Cross-Utterance Representations for Improved Speech Synthesis

When humans speak multiple utterances in a continuous manner, the prosodic features generated in each utterance are related to those in its neighbouring utterances. Such cross-utterance (CU) dependencies are often ignored by the current neural text-to-speech (TTS) systems, which reduces the naturalness and expressiveness of the synthesized speeches. In this paper, we propose to improve the prosody modelling ability of neural TTS systems using pre-trained CU acoustic and text representations. Such CU acoustic representations are derived using the Wav2Vec 2.0 model (W2V2) from the synthesized audios of the past utterances, while the CU text representations are extracted using the Bidirectional Encoder Representation from Transformers (BERT) model from the scripts of the future utterances. Experimental results on a Mandarin audiobook and an English audiobook showed the naturalness and expressiveness of the synthesized audios were significantly improved by incorporating such pre-trained W2V2 and BERT CU representations into the Fastspeech2 TTS framework.
Metaverse Education: Opportunities and Challenges for Immersive Learning in Virtual Environments

This research identifies key terms related to the Metaverse. It investigates the pros and cons of using this immersive technology for educational purposes. The findings suggest that the Metaverse provides multi-sensory experiences in three-dimensional (3D) environments and enables individuals to interact with other users and physical objects in virtual worlds. Yet, they also indicate that there are a number of challenges that have to be dealt with, including the cost of the infrastructure; the degree of freedom of its users; regulatory concerns, privacy and security of the personal data; as well as possible addictions and mental health issues if individuals are exposed to prolonged use, among others. This contribution implies that there is scope for educators to continue developing the Metaverse’s virtual spaces as it can create infinite possibilities for the development of the students’ knowledge, competences and abilities. At the same time, educators and students ought to be warned and properly informed about its possible dangers.
Lite general network and MagFace CNN for micro-expression spotting in long videos

Facial expressions, especially spontaneous micro-expressions, as an intuitive reflection of human emotions, have come through much concern along with rapid advances in computer vision recently. Micro-expressions are small in amplitude and short in duration and often appear together with macro-expressions, making micro-expression spotting in long videos a challenging task. In this article, we propose intersection over minimum labelling method combined with a Lite General Network and MagFace CNN (LGNMNet) model to predict the possibility of video frames belonging to a micro-expression interval, which balances easy and difficult samples to improve the learning effect of training process. Experimental results show that our method achieves state-of-the-art performance in spotting micro-expressions in long videos of both the CAS(ME)2 and SAMM-LV datasets (with F1-scores of 0.2474 and 0.2555, respectively). Additionally, a new pair-merge way of combining nearby detected apex frames to construct micro-expression intervals in post-processing stage has been devised and analysed, providing a feasible solution for the task of macro- and micro-spotting in long videos.
Bi-directional controlled asymmetric teleportation protocol initiated by a mentor in noisy environments

Abstract In this paper we consider the problem of the bidirectional state transfer of a 2-qubit and a 3-qubit quantum state unknown to the parties, respectively, who intend to send the respective states in the site of the other party. These states are intended for transfer but are not possessed by the intender. The protocol has a Mentor whose role is in the fabrication of entangled channels, distribution of qubits through the noisy environment in order to create entanglement sharing and finally fixing one of the several courses of the protocol. The role of the Mentor is thereby completed. The protocol is overseen by a controller whose action signals the final steps to be taken in the protocol. Three types of noises are considered through three different Kraus operators. There is an analysis of the fidelity of the protocol corresponding to three types of noises.
Quantum Models for Flux-Driven Superconducting Traveling-Wave Parametric Amplifiers with Different Nonlinear Junction Topologies

In this contribution, we investigate different types of Josephson junction arrays that are embedded in coplanar microwave transmission lines, acting as traveling-wave parametric amplifiers. For the realization of traveling-wave parametric amplifiers, different Josephson junction array geometries have been proposed in the literature. Here, we investigate the amplifier’s Josephson nonlinearity along with the gain performance, following a general method to derive the nonlinear wave-mixing coefficients from the current-phase relation of different junction array topologies. These nonlinear coefficients are then incorporated into a corresponding Hamiltonian, which is used in order to solve the Heisenberg equations of motion of the photon number operator, determining the gain of the amplifier.
Aligning social networks and co-designed visions to foster systemic innovation in the Alps

Mountain regions face substantial challenges and opportunities arising from global change. The capacity of mountain regions for (systemic) innovation will be determinant to the success of system transformations envisioned by social actors of mountain communities. By analysing the social networks of two regions in the Alps and relating them to desired future visions of sustainable regional development, we provide insights about innovative capacities in mountain regions and propose how to strengthen these capacities in order to support regional transformations. In particular, we analyse innovation systems' collaboration networks to evaluate the innovative capacity of communities, co-construct visions with mountain stakeholders, and assess innovation requirements to meet these visions. Accordingly, we propose changes to the collaboration networks for addressing regional innovation system (RIS) capacities to help achieve visions through systemic innovation. Our analysis furthers the understanding on the type and magnitude of regional innovation system changes required to more effectively address desired transformative visions in mountain regions. We close by discussing implications of our approach for the assessment of innovative capacity and the transformation of mountain regions.The online version contains supplementary material available at 10.1007/s10113-023-02099-y.
Power Management of Cloud Datacenter in Infrastructure Level via Efficinet Virtual Machine Placement by Utilizing Hybrid Genetic Algorithm

The virtualization technology enables cloud datacenters to co-host multiple virtual machines (VMs) on a single physical machine (PM) to meet the need of different users. Efficient VM placement (VMP) schemes can significantly decrease residual power consumption in cloud infrastructure level. This paper formulates VMP to an integer linear optimization programming problem with power management perspective. To solve this NP-Hard problem, a hybrid genetic algorithm (HGA) is presented. To verify the effectiveness of proposed HGA, it was tested against other state-of-the-arts in different scenarios. The results shows that HGA beats other approaches in terms of power management and reduction of total power consumption significantly.
Pressure Sensor Positioning for Accurate Human Interaction with a Robotic Hand

Sensor positioning involves determining the best location for a sensor to be placed or installed so that it can effectively sense or measure the desired physical or environmental parameters. In this paper, we present a novel approach for finding sensor positions on a robotic hand using machine learning techniques. We focus on pressure sensors and their placement in order to enhance the performance and reliability of gesture recognition tasks. Our study analyzes data from 22 sensors placed at different locations on a right-handed robotic hand, simulating 10 distinct hand gestures. We employ various machine learning algorithms and create a correlation matrix to determine the most relevant sensor positions. The results highlight the significance of sensor optimization in improving the overall efficiency and effectiveness of robotic hand systems. By reducing the number of sensors to 12, we were still differentiate 10 hand gestures with % 99 accuracy.
Response of soil oxides in complex terrain region to environment revealed by hyperspectral observations

Soil oxides are important diagnostic indicators for soil quality assessment, and their content has a positive effect on adsorption of heavy metals, remediation of pollution, and enhancement of organic matter enrichment. Moreover, soil oxides are affected by multiple environmental factors such as soil pH, geomorphological, and elevation, and their distribution is highly regional. In this study, 421 ground soil samples and 2 hyperspectral satellite data were collected. The content distribution of 8 main oxides in soil, including SiO 2 , Al 2 O 3 , Fe 2 O 3 , MgO, CaO, Na 2 O, K 2 O, and Corg. were obtained according to the process of data preprocessing, spectral transformation, feature extraction, band combination, and model establishment. The results indicated that the homogenization spectral transformation method is the most suitable; The calculation accuracy of complex spectral characteristic parameters is inferior to that of characteristic bands; The difference model is more suitable for the extraction of soil oxide content in this study area; The average content of SiO 2 and CaO shows a decreasing trend with the elevation increasing; The average content of SiO 2 and Fe 2 O 3 shows an upward trend with the increase of slope; East is the main geomorphic direction of soil oxide enrichment. SiO 2 , Al 2 O 3 , and MgO are the three soil oxides with the highest correlation. The response relationship between soil oxides, topography and pH is discussed, and the distribution rule of soil oxides and environmental determinants are obtained, which provides a set of technical schemes for digital soil research. This study can extract the content of soil components in a large area, analyze the causes, and provide a new technical scheme for soil investigation and treatment.
Recognition new energy vehicles based on improved YOLOv5

In the field of Intelligent Traffic Systems (ITS), vehicle recognition is a hot research topic. Although different types of vehicles can already be recognized, further identification and statistics of new energy and fuel vehicles in unknown and complex environments remain a challenging task. In this paper, we propose a New Energy Vehicle Recognition and Traffic Flow Statistics (NEVTS) approach. Specifically, we first utilized the You Only Look Once v5 (YOLOv5) algorithm to detect vehicles in the target area, in which we applied Task-Specific Context Decoupling (TSCODE) to decouple the prediction and classification tasks of YOLOv5. This approach significantly enhanced the performance of vehicle detection. Then, track them upon detection. Finally, we use the YOLOv5 algorithm to locate and classify the color of license plates. Green license plates indicate new energy vehicles, while non-green license plates indicate fuel vehicles, which can accurately and efficiently calculate the number of new energy vehicles. The effectiveness of the proposed NEVTS in recognizing new energy vehicles and traffic flow statistics is demonstrated by experimental results. Not only can NEVTS be applied to the recognition of new energy vehicles and traffic flow statistics, but it can also be further employed for traffic timing pattern extraction and traffic situation monitoring and management.
Stylised-facts view of fourth industrial revolution technologies impacting digital learning and workplace environments: ChatGPT and critical reflections

When the 21st century was ushered in, and in the period following its inception, there was a lot of hype about how 21st-century skills, especially the 4Cs (critical thinking, collaboration, communication, and creativity), were going to play a pivotal role for digital learning and workplace environments. Two decades later, these environments are still grappling with the specific changes brought about and the actual role played by these skills in their respective facets. Within these two decades, though, a new hype has emerged about how fourth industrial revolution (4IR) technologies are likely to affect and change the future of digital learning and workplace environments in ways never seen in previous industrial and digital revolutions. Amongst these technologies, artificial intelligence and automation are touted as some of the technologies that will change the future of digital learning and work. Against this background, this paper sets out to critically reflect on the prospects and challenges these two 4IR technologies have for digital learning and work as the 21st century is on the cusp of the third decade. It does so by analysing and discussing AI−/machine−human fused stylised facts based on ChatGPT-generated responses and on a human distillation and reworking of those responses.
Graph pattern detection and structural redundancy reduction to compress named graphs

The flexible paradigm of Resource Description Framework (RDF) has accelerated the raw data published on the web. Therefore, the volume of generated RDF data has increased impressively in the last decade promoting compression to manage and reduce the size of RDF datasets. Universal RDF compressors can be able to detect and remove redundancy at symbolic, syntactic, or semantic levels. However, these compressors rarely exploit the graph patterns as well as structural regularities in real-world datasets. An efficient approach for compressing the RDF datasets based on the structural properties is HDT (Header-Dictionary-Triple). However, it cannot manage the RDF datasets with named graphs, the regularities of the graph structure, and structural redundancies. Because HDT considers all the triples to reside in the same default graph. Though, the triples of an RDF dataset belong to various named graphs. In this study, we have proposed a novel approach to deal with the above-mentioned challenges. We introduce hybrid TI-GI (Triple Interpreter-Graph Interpreter) to manage the RDF datasets with named graphs and use compact RDF serialization. We also propose RDF-RR (RDF-Redundancy Reducer) and object mapping that detects and removes structural redundancies by identifying the common patterns related to the predicates and objects in the RDF datasets. We employ a differential compressor to discover the frequent graph pattern in a single pass by using the data structure-oriented approach of the dataset. Evaluation of real-world datasets affirms that our proposed approach can substantially reduce the size of the experimental RDF datasets at approximately 30.52%, 24.92%, and 26.96% when compared with the existing HDT, HDT-FoQ (HDT-Focused on Querying) and the 2Tp (two Triple Predicate based index) approaches. Moreover, the indexing time of our proposed system is also reduced at approximately 17.89%, 13.70%, and 9.32% when compared with the HDT, HDT-FoQ, and 2Tp approaches.
Shared and individual representation learning with Feature Diversity for Deep MultiView Clustering

Due to the remarkable representation ability of Nonnegative matrix factorization (NMF), its multiview variants have become a crucial kind of multiview representation learning methods. However, the majority of existing methods fail to effectively utilize complex higher-level hierarchical information, shared information, and individual information of multiview data. To address these challenges, we propose the Shared and individual representation learning with feature Diversity for Deep Multiview Clustering (SiDDMVC). Specifically, this method decomposes data of each view through nonnegative matrix factorization in a layer-wise fashion to exploit complex higher-level hierarchical information. The outputs of NMF are then divided into shared and individual representations with shared geometrical constraint and feature diversity constraint. Experimental results on six real-world datasets have shown that SiDDMVC not only achieves superior performance through the use of graph regularization and two diversity constraints, but also surpasses several state-of-the-art multiview clustering methods.
Version 1.3-BANSHEE—A MATLAB toolbox for Non-Parametric Bayesian Networks

This update (version 1.3) of our contribution BANSHEE—A MATLAB toolbox for Non-Parametric Bayesian Networks provides functional updates. Additions to the software package include quantifying fully parametric Bayesian Networks, drawing a user-defined sample size number of (un)conditional samples, sample-based conditioning sampling, and choosing different sample sizes for the model-validation tests based on the Hellinger distance. The use of the t copula in the Cramér–Von Mises test is also added. New detailed examples demonstrating the new features available are also provided.
Compromise privacy in large-batch Federated Learning via model poisoning

Federated Learning (FL) is a distributed learning paradigm in which clients collaboratively train a global model with shared gradients while preserving the privacy of local data. Recent researches found that an adversary can reveal private local data with gradient inversion attacks. However, setting a large batchsize in local training can defense against these attacks effectively by confusing gradients computed on private data. Although some advanced methods have been proposed to improve the performance of gradient inversion attacks on the large-batch training, they are limited to specific model architectures (e.g., fully connected neural networks (FCNNs) with ReLU layers). To address these problems, we propose a novel gradient inversion attack to compromise privacy in large-batch FL via model poisoning. We poison clients' models with malicious parameters, which are constructed purposely to mitigate the confusion of aggregated gradients. For FCNNs, the private data can be perfectly recovered by analyzing gradients of the first fully connected (FC) layer. For convolutional neural networks (CNNs), we extend our proposed method as a hybrid approach, consisting of the analytic method and the optimization-based method. We first recover the feature maps after the convolutional layers and then reconstruct private data by minimizing the loss of data-wise feature map matching. We demonstrate the effectiveness of our proposed method on four datasets and show that it outperforms previous methods for large-batch FL (e.g., 64, 128, 256) and models with different activation layers (e.g., ReLU, Sigmoid and Tanh).
A new oversampling approach based differential evolution on the safe set for highly imbalanced datasets

Oversampling method is used to solve the class imbalanced issues. Some existing oversampling methods do not well remove noisy samples and avoid synthesizing noisy samples. Therefore, we propose a new oversampling approach based differential evolution on the safe set for highly imbalanced datasets (SS_DEBOHID). SS_DEBOHID firstly uses k-nearest neighbors (kNN) method to learn the safe area of minority; then the DEBOHID oversampling method is used to synthesize new minority samples in the safe area. The advantages of SS_DEBOHID include that (a) it generates samples in the safe area to reduce generation of noisy samples and reduce synthetic samples falling into the classification boundary and majority area; (b) it uses the DEBOHID method to synthesize samples and increase the diversity of samples; (c) the method is suitable for highly imbalanced datasets. The proposed method is compared with 10 methods on 43 highly imbalanced datasets and evaluated on AUC and G_Mean metrics. The experimental results show that SS_DEBOHID obtains more than 30 best performing datasets on KNN, SVM, and DT classifiers in terms of AUC and G_mean, respectively. The proposed method outperforms other methods by 8.07% to 24.34% on average AUC metric and by at least 6.96% and up to 45.37% on average G_mean metric. In addition, we validate the efficiency of SS_DEBOHID on 8 high-dimensional and large sample size datasets. The experimental results show that SS_DEBOHID has better classification performance and robustness.
Transformer-based global–local feature learning model for occluded person re-identification

Most recent occluded person re-identification (re-ID) methods usually learn global features directly from pedestrian images, or use additional pose estimation and semantic analysis model to learn local features, while ignoring the relationship between global and local features, thus incorrectly retrieving different pedestrians with similar attributes as the same pedestrian. Moreover, learning local features using auxiliary models brings additional computational cost. In this work, we propose a Transformer-based dual-branch feature learning model for occluded person re-ID. Firstly, we propose a global–local feature interaction module to learn the relationship between global and local features, thus enhancing the richness of information in pedestrian features. Secondly, we randomly erase local areas in the input image to simulate the real occlusion situation, thereby improving the model’s adaptability to the occlusion scene. Finally, a spilt group module is introduced to explore the local distinguishing features of pedestrian. Numerous experiments validate the effectiveness of our proposed method.
High-speed 3-D reconstruction based on phase shift coding and interleaved projection

Fringe projection profilometry (FPP) based on temporal phase unwrapping (TPU) is one of the most important three-dimensional (3-D) structured light (SL) reconstruction techniques. However, there is an inevitable contradiction between the number of auxiliary coded patterns and the measuring speed. To mark a large number of fringe order in high-frequency fringe projection or wide-field scenes, commonly-used strategy is to add more coded patterns, which compress heavily the ratio of phase-shifting patterns carrying the 3-D shape information of object. In this paper, a phase shift coding method is developed to mark fringe order, in which three phase-shifting patterns and one auxiliary coded pattern can obtain high-quality wrapped phase and fringe order at the same time. In addition, an interleaved projection can provide two fringe order with different densities to extend the coding range of fringe order, while ensuring the measuring speed. Experiments with various static and dynamic objects demonstrated that the proposed method with the advantages of flexibility, high-efficiency coding can achieve the reconstructing rate of 30 fps for 8-bit fringe projection. This method should be highly meaningful for practical applications of 3-D face modeling, industrial inspection, intelligent recognition, virtual reality, and so on.