Reviewerly/exHarmony huggingface dataset:
DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion

Singing voice conversion (SVC) is one promising technique that can enrich the way of human-computer interaction by en-dowing a computer the ability to produce high-fidelity and expressive singing voice. In this paper, we propose DiffSVC, an SVC system based on denoising diffusion probabilistic model. DiffSVC uses phonetic posteriorgrams (PPGs) as con-tent features. A denoising module is trained in DiffSVC, which takes destroyed mel spectrogram produced by the dif-fusion/forward process and its corresponding step information as input to predict the added Gaussian noise. We use PPGs, fundamental frequency features and loudness features as auxiliary inputs to assist the denoising process. Experi-ments show that DiffSVC can achieve superior conversion performance in terms of naturalness and voice similarity to current state-of-the-art SVC approaches.
Comparison of Four Transfer Learning and Hybrid CNN Models on Three Types of Lung Cancer

The abnormal growth of cells in the lungs led to lung cancer is the second most fatal cancer in the world according to the World Health Organization(WHO) reported in 2020. Lung cancer starts affecting one single lymph node of the lungs and can spread to other body organs, even the brain. Early classification of the types of cancer might ease the way of treatment, which can save millions of lives every year. The faster and precise classification of lung cancer types is the motivation of this study.Our dataset consists of 15000 CT scan images of three types: Lung benign tissue, Lung adenocarcinoma, and Lung squamous cell carcinoma. Five artificial deep neural networks (2-D CNN with SVM, ResNet-50, InceptionResNetV2, Inception-V3, and VGG-19) have been applied in the classification of three types of lung cancer in this research. Various measures including accuracy, precision, recall, and F1 score have been applied to assess the performance of these models. Inception-V3 has been shown the best validation accuracy of 99.13% among the implemented transfer learning and CNN-SVM approaches. Moreover, the comparative analysis of this paper concludes that Inception-V3 is the best-suited model for accurately classifying the types of lung cancer.
A Deep Reinforcement-Learning Approach for Inverse Kinematics Solution of a High Degree of Freedom Robotic Manipulator

The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due to the complexity of derivation, difficulty of computation, and redundancy, traditional IK solutions pose numerous challenges to the operation of a variety of robotic manipulators. This paper develops a Deep Reinforcement Learning (RL) approach for solving the IK problem of a 7-Degree of Freedom (DOF) robotic manipulator using Product of Exponentials (PoE) as a Forward Kinematics (FK) computation tool and the Deep Q-Network (DQN) as an IK solver. The selected approach is architecturally simpler, making it faster and easier to implement, as well as more stable, because it is less sensitive to hyperparameters than continuous action spaces algorithms. The algorithm is designed to produce joint-space trajectories from a given end-effector trajectory. Different network architectures were explored and the output of the DQN was implemented experimentally on a Sawyer robotic arm. The DQN was able to find different trajectories corresponding to a specified Cartesian path of the end-effector. The network agent was able to learn random Bézier and straight-line end-effector trajectories in a reasonable time frame with good accuracy, demonstrating that even though DQN is mainly used in discrete solution spaces, it could be applied to generate joint space trajectories.
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without revealing the local data. Gradient compression could be applied to FL to alleviate the communication overheads but the existing schemes still face challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we evaluate the contributions of the compressed local gradients with respect to different compression ratios. Furthermore, we investigate a learning accuracy-energy efficiency tradeoff problem and the optimal compression ratio and computing frequency are derived for each device. Experimental results show that given the 80&#x0025; test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32&#x0025; of the total energy consumption of the devices.
Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma.In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand.This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points.In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm.Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.
3-D Facial Feature Reconstruction and Learning Network for Facial Expression Recognition in the Wild

Facial expression recognition (FER) in the wild unavoidably suffers from the effects of face posture, illumination, and partial occlusion. In this article, we attempt to alleviate the above negative effects and improve the performance of FER in the wild based on 3-D face feature reconstruction and learning. Three-dimensional face reconstruction not only can effectively make up for the facial apparent information missing inform a 2-D face images but can also extract accurate 3-D facial geometric information in self-occlusion and extreme illumination scenarios. Therefore, we propose a novel end-to-end trainable 3-D face feature reconstruction and learning network (3-DF-RLN) is proposed for FER in the wild. In 3-DF-RLN, the 2-D implicitly frontalized face apparent data and 3-D facial landmarks are reconstructed by a 3-D face reconstruction module and input to two feature extraction pathways. The appearance pathway learns apparent features from the reconstructed 2-D face apparent data using a convolutional neural network. The geometry pathway learns the geometric features from the reconstructed 3-D facial landmarks using a graph convolutional network. Finally, FER is achieved via the fusion of the two pathways. Extensive experiments were conducted to evaluate the proposed method with three benchmark databases, including Multi-PIE, RAF-DB, and AffectNet. The results show that the proposed 3-DF-RLN model has better FER performance, both in the lab and in the wild. In addition, the face graph from the geometry pathway reveals the correlations between facial landmarks in FER.
Sentimental Analysis of Movie Reviews using Soft Voting Ensemble-based Machine Learning

Sentimental analysis helps to classify a subject's sentiments (e.g., positive, negative, or neutral) automatically towards a specific topic, product, news, or any movie. Machine learning is a powerful technique of artificial intelligence (AI) to control the increasing demand for accurate sentimental analysis. The analysis of sentiment on social networks, such as Facebook or Twitter, has become a powerful source of learning about the user's opinion and it has a wide range of applications in the same field. However, the accuracy and efficiency of sentimental analysis are being impeded by different challenges faced in the field of Natural language processing (NLP). In this paper, we have proposed a state-of-the-art soft voting ensemble (SVE) approach to perform sentimental analysis of movie reviews. Five different well-known machine learning (ML) classifiers have been used for this purpose, namely Logistic Regression (LR), Naïve Bayes (NB), XGBoost (XGB), Random Forest (RF), and Multilayer Perceptron (MLP). Our proposed ensemble approach outperformed all other classifiers by giving an overall accuracy, precision, recall, and f1-score of 89.9%, 90.0%, 90.0%, and 90.0%, respectively.
MSRD-CNN: Multi-Scale Residual Deep CNN for General-Purpose Image Manipulation Detection

The authenticity of digital images is a major concern in multimedia forensics due to the availability of advanced photo editing tools/devices.In the literature, several image forensic methods are available to detect specific image processing or editing operations.However, it remains a challenging task to design a universal forensic method that can detect multiple image editing operations.In this paper, a novel Multi-Scale Residual Deep CNN (MSRD-CNN) is designed to learn the image manipulation features adaptively for multiple image manipulation detection.Our network comprises of three stages: pre-processing, hierarchical high-level feature extraction, and classification.Firstly, a multi-scale residual module is employed in pre-processing stage to extract the prediction error or noise features adaptively.Afterwards, the obtained noise features are processed by feature extraction network having multiple Feature Extraction Blocks (FEBs) for the extraction of high-level image tampering features.Lastly, the resultant feature map is provided to the fully-connected dense layer for classification.The experiment results show that our model surpasses the existing schemes even under anti-forensic attacks, when evaluated on large-scale datasets by considering multiple image processing operations.The proposed network provides overall classification accuracies of 97.07% and 97.48% for BOSSBase and Dresden datasets, respectively.
Federated Learning: Challenges, Methods, and Future Directions

Federated learning includes mobile phones for cooperative learning and training and contains limited data on device. Federated learning allocates the machine learning development over to the node (mobile device). There are three kinds of federated learning, i.e., horizontal, vertical, and transfer federated learning. Types of federated learning are based on client-server and peer-peer architecture. Federated learning has potential to serve in various applications such as healthcare, transportation, mobile network, etc. Currently, mobile communication works in a heterogeneous environment and standard. There must be required a standard methodology for wide scope of machine learning such as distributed environment, data security and heterogeneous network support. In this chapter, we will talk about the types, architecture, difficulties, and opportunities of federated learning. This chapter also provides a brief description on the future direction in the field of federated learning.
ScanQA: 3D Question Answering for Spatial Scene Understanding

We propose a new 3D spatial understanding task for 3D question answering (3D-QA). In the 3D-QA task, models receive visual information from the entire 3D scene of a rich RGB-D indoor scan and answer given textual questions about the 3D scene. Unlike the 2D-question answering of visual question answering, the conventional 2D-QA models suffer from problems with spatial understanding of object alignment and directions and fail in object localization from the textual questions in 3D-QA. We propose a baseline model for 3D-QA, called the ScanQA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ATR-DBI/ScanQA, which learns a fused descriptor from 3D object proposals and encoded sentence embeddings. This learned descriptor correlates language expressions with the underlying geometric features of the 3D scan and facilitates the regression of 3D bounding boxes to determine the described objects in textual questions. We collected human-edited question-answer pairs with free-form answers grounded in 3D objects in each 3D scene. Our new ScanQA dataset contains over 41k question-answer pairs from 800 indoor scenes obtained from the ScanNet dataset. To the best of our knowledge, ScanQA is the first large-scale effort to perform object-grounded question answering in 3D environments.
Securing Digital Ledger Technologies-Enabled IoT Devices: Taxonomy, Challenges, and Solutions

With the faster maturity and stability of digitization, connectivity and edge technologies, the number of the Internet of Things (IoT) devices and sensors is flourishing fast in important junctions such as homes, hotels, hospitals, retail stores, manufacturing floors, railway stations, airports, oil wells, warehouses, etc. However, in this extremely connected world, the security implications for IoT devices are getting worse with the constant rise in malicious cyberattacks. The challenge is how to secure IoT sensors, services and data. The blockchain technology, a prominent distributed ledger technology (DLT), is being pronounced as the way forward for safeguarding IoT devices and data. The Directed Acyclic Graph (DAG)-based DLT has the inherent potential to realize the benefits of blockchain with better performance. IOTA is a DAG-based blockchain implementation for the IoT era. The Tangle, the IOTA’s network immutably records the exchange of data and value. It ensures that the information is trustworthy and cannot be tampered with nor destroyed. In this work, we depict a thorough analysis of the existing security studies for IOTA. Then, we identify the gaps and the limitations of these security solution schemes, and finally, propose future security research recommendations that can potentially fill these gaps to secure DLT-enabled IoT devices.
The Effectiveness of Image Augmentation in Breast Cancer Type Classification Using Deep Learning

The past few decades have witnessed a surge in applying machine learning methods in the medical fields to assist in diagnosing various medical conditions. Among all the methods, the convolutional neural network (CNN) has archived the best performance, and researchers have promised many CNN variants to edge the performance in different specialized areas. It has been shown that image augmentation can help improve the CNNs' performance in the image classification task. In this study, we explore how image augmentation methods may help CNN models improve classification performance in predicting biopsy slides benign (not dangerous) or malignant (uncontrolled and may cause death) from breast cancer tumor tissue. We perform a qualitative study on the effectiveness of some basic image augmentation methods.
TQM through the integration of blockchain with ISO 9001:2015 standard based quality management system

Total Quality Management (TQM) facilitates the achievement of continuous quality improvement by implementing several strategies, techniques, system and tools. Out of them, ISO 9001 standard based Quality Management System (QMS) was adopted widely by the organisations for achieving continuous quality improvement. Yet the outcome has not been very effective due to the absence of immutability and traceability in the theory and practices of this approach. This deficiency can be overcome if blockchain technology is used. Hence, the need of the hour is to integrate blockchain technology with ISO 9001:2015 standard based QMS. In order to meet this need, a framework has been presented in this paper. This framework facilitates the usage of smart contracts and hyperledger for achieving traceability and immutability while operating the ISO 9001:2015 standard based QMS. Further, the application of Internet of Things and artificial intelligence are indicated for implementing this integrated framework to achieve the goals of implementing TQM. The paper is concluded by mentioning that implementation in organisations in which ISO 9001:2015 standard is under operation would facilitate to check the practical viability of the framework proposed in this paper.
A survey of resource provisioning problem in cloud brokers

With the rapid development of cloud computing the cloud market has become heterogeneous. In order to avoid this problem becoming an obstacle for users to migrate to cloud computing, cloud brokers have emerged as a third party to provide more advantageous cloud services to users, which can help users ignore the differences between multiple cloud providers. For cloud brokers, whose fundamental function is provisioning resources which are typically not owned by themselves, it is challenging to select and manage resources in a complex and heterogeneous cloud environment. This paper presents a survey of the resource provisioning problem in cloud brokers, by firstly introducing a representative study of cloud broker architecture, then classifying the resource provisioning problem into resource selection problem and resource management problem and discussing them separately. In this survey, a new taxonomy of cloud broker research is proposed, and an analysis of existing research is given at the end along with an outlook on future research directions and some open problems in cloud brokerage. We hope this paper can provide help and guidance for researchers interested in cloud brokers.
Question-Driven Graph Fusion Network for Visual Question Answering

Existing Visual Question Answering (VQA) models have ex-plored various visual relationships between objects in the im-age to answer complex questions, which inevitably introduces irrelevant information brought by inaccurate object detection and text grounding. To address the problem, we propose a Question-Driven Graph Fusion Network (QD-GFN). It first models semantic, spatial, and implicit visual relations in images by three graph attention networks, then question in-formation is utilized to guide the aggregation process of the three graphs, further, our QD-GFN adopts an object filtering mechanism to remove question-irrelevant objects contained in the image. Experiment results demonstrate that our QD-GFN outperforms the prior state-of-the-art on both VQA 2.0 and VQA-CP v2 datasets. Further analysis shows that both the novel graph aggregation method and object filtering mecha-nism play a significant role in improving the performance of the model.
Mobile Devices Strategies in Blockchain-Based Federated Learning: A Dynamic Game Perspective

Leveraging various mobile devices to train the shared model collaboratively, federated learning (FL) can improve the privacy and security of 6G communication. To economically encourage the participation of heterogeneous mobile devices, an incentive mechanism and a fair trading platform are needed. In this paper, we implement a blockchain-based FL system and propose an incentive mechanism to establish a decentralized and transparent trading platform. Moreover, to better understand the mobile devices’ behaviors, we provide economic analysis for this market. Specifically, we propose two strategy models for mobile devices, namely the discrete strategy model (DSM) and the continuous strategy model (CSM). Also, we formulate the interactions among the non-cooperative mobile devices as a dynamic game, where they adjust their strategies iteratively to maximize the individual payoff based on others’ previous strategies. We further prove the existence in Nash equilibrium (NE) of two different models and propose algorithms to achieve them. Simulation results demonstrate the convergence of the proposed algorithms and show that the CSM can effectively increase the mobile devices’ payoffs to 128.1 percent at most compared with DSM.
BNAS-v2: Memory-Efficient and Performance-Collapse-Prevented Broad Neural Architecture Search

In this article, we propose BNAS-v2 to further improve the efficiency of broad neural architecture search (BNAS), which employs a broad convolutional neural network (BCNN) as the search space. In BNAS, the single-path sampling-updating strategy of an overparameterized BCNN leads to terrible unfair training issue, which restricts the efficiency improvement. To mitigate the unfair training issue, we employ a continuous relaxation strategy to optimize all paths of the overparameterized BCNN simultaneously. However, continuous relaxation leads to a performance collapse issue that leads to the unsatisfactory performance of the learned BCNN. For that, we propose the confident learning rate (CLR) and introduce the combination of partial channel connections and edge normalization. Experimental results show that 1) BNAS-v2 delivers state-of-the-art search efficiency on both CIFAR-10 (0.05 GPU days, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> faster than BNAS) and ImageNet (0.19 GPU days) with better or competitive performance; 2) the above two solutions are effectively alleviating the performance collapse issue; and 3) BNAS-v2 achieves powerful generalization ability on multiple transfer tasks, e.g., MNIST, FashionMNIST, NORB, and SVHN. The code is available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/zixiangding/BNASv2</uri></monospace> .
Multitask Allocation Framework With Spatial Dislocation Collision Avoidance for Multiple Aerial Robots

Multitask allocation and trajectory planning for multiple unmanned aerial vehicles (UAVs) have been extensively used in various real-world applications. This article presents a framework of multi-UAV multitask allocation and trajectory planning with collision avoidance. The scenario of interest is one where multiple UAVs are launched in order to investigate selected targets in a massive wildfire disaster relief terrain. Initially, one UAV is launched to search wildfire locations and wildfire lines by a developed informative path planning algorithm. An informative exploratory search mechanism is developed that provides the exploration trajectories to precisely locate the wildfire positions in the wildfire environments. Afterward, with the investigated environmental information including GPS coordinates of wildfire positions and distribution as targets, UAVs are deployed to multiple target positions. In order to perform effective collision avoidance, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. Concurrent multitask allocation, trajectory planning, and collision avoidance are successfully carried out with unequal numbers of UAVs and targets. The proposed framework has been validated by simulation studies and comparative analyzes.