Reviewerly/exHarmony huggingface dataset:
Deep learning based automatic modulation recognition: Models, datasets, and challenges

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed.
Special Relativity Search: A novel metaheuristic method based on special relativity physics

In this work, a novel metaheuristic optimization algorithm called Special Relativity Search (SRS) is proposed. The SRS is inspired by the interaction of particles in an electromagnetic field. Particle interactions are calculated using the Lorentz force, and the equation of motion is developed using angular frequency. The magnetic force between particles is perpendicular to the velocity of charged particles and the magnetic field, which causes particles to move in a circular trajectory. In this method, for the first time, the theory of special relativity physics is utilized to determine the coordinates of charged particles in each rotation. The SRS main step equation is developed using the two phenomena of length contraction and time dilation. Charged particles are members of the initial population that is randomly generated, and their charge is determined based on their fitness. To show the efficiency and robustness of the SRS in solving optimization problems, 83 benchmark functions, which are a wide range of mathematical problems, are selected and optimized based on the values of Best, Mean, Median, and Standard Deviation (SD). The statistical test of the Wilcoxon Signed Ranks (WSR) is performed to fairly compare the results of this new method with other popular metaheuristic algorithms. The test results show that in most cases SRS is superior to other methods from the state-of-the-art. The results of evaluated optimization problems show that SRS is more effective and efficient compared to some other well-known metaheuristic methods in solving optimization problems.
Authentic volumetric avatars from a phone scan

Creating photorealistic avatars of existing people currently requires extensive person-specific data capture, which is usually only accessible to the VFX industry and not the general public. Our work aims to address this drawback by relying only on a short mobile phone capture to obtain a drivable 3D head avatar that matches a person's likeness faithfully. In contrast to existing approaches, our architecture avoids the complex task of directly modeling the entire manifold of human appearance, aiming instead to generate an avatar model that can be specialized to novel identities using only small amounts of data. The model dispenses with low-dimensional latent spaces that are commonly employed for hallucinating novel identities, and instead, uses a conditional representation that can extract person-specific information at multiple scales from a high resolution registered neutral phone scan. We achieve high quality results through the use of a novel universal avatar prior that has been trained on high resolution multi-view video captures of facial performances of hundreds of human subjects. By fine-tuning the model using inverse rendering we achieve increased realism and personalize its range of motion. The output of our approach is not only a high-fidelity 3D head avatar that matches the person's facial shape and appearance, but one that can also be driven using a jointly discovered shared global expression space with disentangled controls for gaze direction. Via a series of experiments we demonstrate that our avatars are faithful representations of the subject's likeness. Compared to other state-of-the-art methods for lightweight avatar creation, our approach exhibits superior visual quality and animateability.
Metaverse, token economies, and non-communicable diseases

Rapid advancements in technology and computing power, combined with the convergence of Blockchain, artificial intelligence, Internet of Things, extended reality, gamification, and decentralized finance, have led to recent growth in interest in the metaverse. Indeed today, there are more than 160 metaverse projects, and Citi estimates it could be a 13 trillion USD industry by 2030. While many have not yet grasped the relevance of the metaverse for industries, the opportunity to leverage the metaverse for health and wellness is substantial. In obesity and non-communicable disease (NCDs), there is potential to deploy the metaverse using gamification and incentives, as well as for education and care. This perspective article explores how the metaverse could be used to prevent and manage obesity and NCDs.
A survey on unsupervised learning for wearable sensor-based activity recognition

Human Activity Recognition (HAR) is an essential task in various applications such as pervasive healthcare, smart environment, and security and surveillance. The need to develop accurate HAR systems has motivated researchers to propose various recognition models, feature extraction methods, and datasets. A lot of comprehensive surveys have been done on vision-based HAR, while few surveys have been done on sensor-based HAR. The few existing surveys on sensor-based HAR have focused on reviewing various feature extraction methods, the adoption of deep learning in activity recognition, and existing wearable acceleration sensors, among other areas. In recent times, state-of-the-art HAR models have been developed using wearable sensors due to the numerous advantages it offers over other modalities. However, one limitation of wearable sensors is the difficulty of annotating datasets during or after collection, as it tends to be laborious, time-consuming, and expensive. For this reason, recent state-of-the-art activity recognition models are being proposed using fully unlabelled datasets, an approach which is described as unsupervised learning. However, no existing sensor-based HAR surveys have focused on reviewing this recent adoption. To this end, this survey contributes by reviewing the evolution of activity recognition models, collating various types of activities, compiling over thirty activity recognition datasets, and reviewing the existing state-of-the-art models to leveraging fully unlabelled datasets in activity recognition. Also, this survey is the first attempt at a comprehensive review on the adoption of unsupervised learning in wearable sensor-based activity recognition. This will give researchers in this area a solid background and knowledge of the existing state-of-the-art models and an insight into the grand research areas that can still be explored.
Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers

Cloud Computing services can be accessed anytime, anywhere via the Internet. The overwhelming growth of cloud data centers over the past decade has increased their costs as energy demands have risen. As a result, higher carbon dioxide emissions and other greenhouse gasses are putting a strain on our ecosystem. The main objective of this study is to reduce the power consumption in cloud computing with no or negligible trade-offs in quality of service. This paper presents a new algorithm called the energy efficiency heuristic using virtual machine consolidation to minimize the high energy consumption in the cloud. By setting two thresholds, hosts are classified into three main classes. The designed model reallocates virtual machines from one physical host to another to minimize energy consumption. The results of the proposed algorithm have been obtained in terms of virtual machine migrations, performance degradation caused by migration, service level agreement violations, and execution time, showing a significant improvement over state-of-the-art techniques.
Blockchain Technology for Secure Supply Chain Management: A Comprehensive Review

Supply chain management (SCM) is a core corporate activity responsible for moving commodities and services from one point to another through various stakeholders. The traditional SCM is based on a centralized approach managed at the central headquarter, and all other sub-offices get instructions from the main office. Some major issues with present SCM systems are security, transactional transparency, traceability, stakeholder involvement, product counterfeiting, additional delays, fraud, and instabilities. Blockchain (BC) emerges as a technology that can manage the data and build trust efficiently and transparently. It can also aid in transaction authorization and verification in the supply chain or payments without a third party. To address the present SCM issues, BC technology is a feasible solution. Motivated by the aforementioned considerations, in this paper, we present a survey on the adoption of BC in SCM. This paper undertakes a comprehensive analysis of the literature on BC characteristics, implementations, and business consequences in various SCM. This Blockchain-centered study, in particular, discloses the research state and delineates future research directions by studying and analyzing 97 up-to-date publications highlighting BC&#x2019;s supply chain uses. Transparency and traceability, information sharing, product anti-counterfeiting, and building trust are the major aspects propelling BC&#x2019;s implementation in SCM. Further, we analyzed various applications of SCM in which BC can be used as a probable technology to secure all transactions. Then, we have highlighted open issues and research challenges for adopting BC technology in SCM that open the doors for beginners eager to start work in this amazing area.
A Survey on Incomplete Multiview Clustering

Conventional multiview clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multiview clustering methods. Clustering on such incomplete multiview data is referred to as incomplete multiview clustering (IMC). In view of the promising application prospects, the research of IMC has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of IMC. Importantly, we provide some frameworks to unify the corresponding IMC methods and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the IMC field are offered for researchers. The related codes are released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/DarrenZZhang/Survey_IMC</uri> .
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving

How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (e.g., object detection, motion forecasting). However, in the context of end-to-end driving, we find that imitation learning based on existing sensor fusion methods underperforms in complex driving scenarios with a high density of dynamic agents. Therefore, we propose TransFuser, a mechanism to integrate image and LiDAR representations using self-attention. Our approach uses transformer modules at multiple resolutions to fuse perspective view and bird's eye view feature maps. We experimentally validate its efficacy on a challenging new benchmark with long routes and dense traffic, as well as the official leaderboard of the CARLA urban driving simulator. At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin. Compared to geometry-based fusion, TransFuser reduces the average collisions per kilometer by 48%.
What value does blockchain bring to the imported fresh food supply chain?

During the COVID-19 pandemic, blockchain has been widely used to trace imported fresh food information from sources to destinations. Motivated by observations of real-world practice, we studied the role that blockchain played in imported fresh food supply chains. We first developed the basic models to examine the cases without and with blockchain. We derived the optimal pricing decisions for the supply chain and the conditions under which using blockchain was profitable under each model. To assess the robustness of the results, we then analyzed how risk attitudes affected the optimal supply chain decisions. We found some interesting results: When the effect of assuaging consumers’ safety concerns brought by blockchain was not obvious, blockchain was more likely to help the manufacturer and retailer increase their profits; otherwise, the value of blockchain was not significant. Besides, the risk-averse manufacturer and retailer would decrease their prices in response to the risks of demand fluctuations. Also, the blockchain platform would benefit from the risk-averse manufacturer and retailer but suffer from risk-averse consumers.
An active contour model driven by adaptive local pre-fitting energy function based on Jeffreys divergence for image segmentation

Active contour model (ACM) has been a competitive tool in image segmentation because of its desired segmentation result and accuracy. Nevertheless, it may become unstable while handling images with uneven intensity, different initial contours and noise. In addition, the computation process of the majority of existing ACMs is complex, which makes it time-consuming and less efficient. To resolve above issues, this paper puts forward an active contour approach driven by adaptive local pre-fitting energy function based on Jeffreys divergence (APFJD) for image segmentation. Although the computation process of the proposed model is also complex, the authors design pre-fitting functions that are computed ahead of iteration process, which reduces a great amount of computation time and increases segmentation accuracy. The key idea of utilizing pre-fitting energy is to firstly select a local region at a specific point of the entire image region and compute its median intensity. Next, this local region is grouped into two sub-regions based on this pre-computed median intensity. Afterwards, the mean intensities of these two sub-regions are calculated by averaging their image intensities respectively. Lastly, the same process is repeated at next point until all points in the whole image region are computed. After that, these two pre-fitting functions will be incorporated into a Jeffreys divergence model to construct the proposed energy function. To further improve system stability and robustness, a regularization function is defined to optimize and normalize the data-driven term and level set function. Compared with local region-based models and recently developed models, the proposed APFJD model not only greatly decreases computation cost, but also improves segmentation accuracy. Experimental results also confirm that this model is robust to initial contours with different positions as well as Gaussian noise, and effectively segments images with unevenly distributed intensity.
VR Games in Cultural Heritage: A Systematic Review of the Emerging Fields of Virtual Reality and Culture Games

In recent years, the use of VR games in cultural heritage has been growing. VR Games have increasingly found their way into museums and exhibitions, highlighting the increasing cultural value associated with games and the institutionalization of game culture. In particular, serious VR games have a variety of benefits for educational purposes. There are several studies that deployed VR games to improve visitor experiences in several contexts. However, there are not sufficient studies in the field that examine the benefits and drawbacks of VR gaming. This lack of classification studies is regarded as an obstacle to developing more effective games and proposing guidance on the best way of using them in cultural heritage. This review aims to analyze how VR games are used in cultural heritage settings, to explore the evolution and opportunities of this emerging field, the challenges and tensions these innovations present, and to collectively advance this work to benefit visitor experiences.
Characterizing the OpenSea NFT Marketplace

Non Fungible Tokens (NFTs) are unique digital identifiers used to represent ownership of various cryptoassets such as music, artwork, collectibles, game assets and allow for an unalterable and provable chain of creation and ownership. The market for NFTs exploded in 2021, and much of the public is being exposed to this new digital ownership medium for the first time. While uptake of this new medium is relatively slow, we feel the prospects of blockchain technology are up-and-coming. NFTs are still largely misunderstood by the wider community, and as such, this provides a fascinating opportunity to explore the NFT marketplace in greater depth. We analyzed sales data from OpenSea - the NFT marketplace with the most extensive user base and sales volume. Our study considered 5.25 million sales that occurred between January 1, 2019, and December 31, 2021. We first lead by presenting an overview of our data collection process and summarizing key statistics of the data set. We examine user behaviour in the market to show that a small subset of heavy-hitters is driving massive growth. Secondly, we review the economic activity within the network to show how these power users drive extreme price volatility within the Art and Collectible categories. Lastly, we review the network of buyers and sellers to show that despite the sparsity that exists in the network, communities of users are forming, and most power users tend to congregate in these structures. These findings shed light on areas of the NFT marketplace that have been relatively unexamined and provide a multi-level analysis of a multi-billion dollar market.
Meta-Based Self-Training and Re-Weighting for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) means to identify fine-grained aspects, opinions, and sentiment polarities. Recent ABSA research focuses on utilizing multi-task learning (MTL) to achieve less computational costs and better performance. However, there are certain limits in MTL-based ABSA. For example, unbalanced labels and sub-task learning difficulties may result in the biases that some labels and sub-tasks are overfitting, while the others are underfitting. To address these issues, inspired by neuro-symbolic learning systems, we propose a meta-based self-training method with a meta-weighter (MSM). We believe that a generalizable model can be achieved by appropriate symbolic representation selection (in-domain knowledge) and effective learning control (regulation) in a neural system. Thus, MSM trains a teacher model to generate in-domain knowledge (e.g., unlabeled data selection and pseudo-label generation), where the generated pseudo-labels are used by a student model for supervised learning. Then, the meta-weighter of MSM is jointly trained with the student model to provide each instance with sub-task-specific weights to coordinate their convergence rates, balancing class labels, and alleviating noise impacts introduced from self-training. The following experiments indicate that MSM can utilize 50% labeled data to achieve comparable results to state-of-arts models in ABSA and outperform them with all labeled data.
IoT Group Key Management using Incremental Gaussian Mixture Model

Sensors and the internet are used in the Internet of Things (IoT) to link physical and computer networks. Future IoT development will be heavily influenced by machine learning and cloud computing. Wi-Fi and ethernet are the most common methods of connecting IoT devices to the internet today. Because of the great range and low latency of the 5G cellular network, most gadgets will choose it in the future. This study discusses how to overcome the limitations of early key management systems and provides a key mechanism that is more effective in the Internet of Things context. Prior to this, the network had been deployed and was being operated by interactions using a mathematical structure known as the blom’s key pre- distribution approach. In the event that a new user needs to join the network, the system generates a random group and distributes the secret key. Extra time is required for both processing and communication, as is evident from the outset. To reduce the time it takes to maintain the key distribution within a group or network, this article outlines the use of machine learning principles. Subgroups based on features will likewise be created by this method In addition, the nodes are grouped into viewpoint subgroups, and the secret key is successfully sent among the nodes.
Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation

We propose a robust and accurate method for estimating the 3D poses of two hands in close interaction from a single color image. This is a very challenging problem, as large occlusions and many confusions between the joints may happen. State-of-the-art methods solve this problem by regressing a heatmap for each joint, which requires solving two problems simultaneously: localizing the joints and recognizing them. In this work, we propose to separate these tasks by relying on a CNN to first localize joints as 2D keypoints, and on self-attention between the CNN features at these keypoints to associate them with the corresponding hand joint. The resulting architecture, which we call “Keypoint Transformer”, is highly efficient as it achieves state-of-the-art performance with roughly half the number of model parameters on the InterHand2.6M dataset. We also show it can be easily extended to estimate the 3D pose of an object manipulated by one or two hands with high performance. Moreover, we created a new dataset of more than 75,000 images of two hands manipulating an object fully annotated in 3D and will make it publicly available.