Reviewerly/exHarmony huggingface dataset:
Semantic segmentation-based system for fall detection and post-fall posture classification

Fall is one of the most critical issues faced by elders in their daily life. The consequences of falls range from fatal injuries, severe injuries to no injuries. Therefore, an effective system for detecting falls and treating post-fall injuries is important. Unlike wearable sensors, camera-based systems seem more comfortable and flexible for daily life monitoring. However, monitoring human activities in real settings using cameras is not only challenging but also pose privacy issues. To mitigate this problem, we propose a surveillance camera-based framework for fall detection and post-fall classification where the human silhouette is extracted and used instead of raw images. The human silhouette is obtained using a pixel-level classification based on a Multi-Scale Skip Connection Segmentation Network (MSSkip), which is shown to achieve state-of-the-art performance on the validation set from PASCAL VOC 2012 dataset for the class Person with an IoU of 90%. The temporal and spatial variations of the human poses are fed to a Convolutional Long Short Term Memory (ConvLSTM) network to detect whether or not a fall has occurred. The proposed fall detection method achieved an F1-score of 97.68% on the UP Fall dataset, and state-of-art performance on the UR Fall detection database. For post-fall posture classification, the Xception network is shown to achieve an F1-score of 97.85% on our customized post-fall dataset.
The Quantum Internet: Enhancing Classical Internet Services One Qubit at A Time

Nowadays, the classical Internet has mainly envisioned as the underlying communication infrastructure of the Quantum Internet, aimed at providing services such as signaling and coordination messages. However, the interplay between classical and Quantum Internet is complex and its understanding is pivotal for an effective design of the Quantum Internet protocol stack. The aim of the paper is to shed the light on this interplay, by highlighting that such an interplay is indeed bidirectional rather than unidirectional. And the Quantum Internet exhibits the potential of supporting and even enhancing classical Internet functionalities.
Exploiting an Elitist Barnacles Mating Optimizer implementation for substitution box optimization

Barnacles Mating Optimizer (BMO) is a new metaheuristic algorithm that suffers from slow convergence and poor efficiency due to its limited capability in exploiting the search space and exploring new promising regions. Addressing these shortcomings, this paper introduces Elitist Barnacles Mating Optimizer (eBMO). Unlike BMO, eBMO exploits the elite exponential probability (Pelite) to decide whether to intensify search process via swap operator or to diversify search by randomly exploring new regions. Furthermore, eBMO uses Chebyshev map instead of random numbers to generate quality S-boxes. Experimental results of eBMO on the generation of 8 × 8 substitution-box are competitive against other existing works.
Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.
Bi-path Combination YOLO for Real-time Few-shot Object Detection

Few-shot object detection (FSOD) has more attention in recent years as the quantitative limitation of instances during the model training. Previous works based on meta learning and transfer learning focus on the detection precision but ignore the inferring speed, which is difficult to apply in amounts of applications. In this letter, to keep a high inferring speed and a comparable detection precision, we propose a real-time detector entitled Bi-path Combination You Only Look Once (BC-YOLO) for FSOD. BC-YOLO can be categorized as a transfer learning based one-stage object detector with a two-phase training scheme. It is particularly composed of bi-path parallel detection branches which detect base and novel class objects respectively and commonly detect objects with a discriminator in the inferring stage. Moreover, to elevate the model generalization trained from few-shot objects, we further propose an Attentive DropBlock algorithm to make the detector focus on the entire details of objects instead of the local discriminative regions. Extensive experiments on PASCAL VOC 2007 and MS COCO 2014 datasets demonstrate that our method can achieve a better tradeoff between speed and precision than state-of-the-art methods.
Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network

With the gradually increasing demand for high-resolution images, image super-resolution (SR) technology has become more and more important in our daily life. In general, high resolution is often accomplished by increasing the accuracy and density of the sensor. However, such an approach is too expensive on the design and equipment. Particularly, increasing the sensor density of satellites incurs great risks. Inspired by EEGAN, some parts of networks: Ultra-Dense Subnet (UDSN) and Edge-Enhanced Subnet (EESN) are modified. The UDSN is used to extract features and obtain high-resolution images which look clear but are deteriorated by artifacts in the intermediate stage, while the EESN is used to purify, enhance and extract the image contours and uses mask processing to eliminate the image corrupted by noise. Then, the restored intermediate image and the enhanced edge are combined to become a high-resolution image with clear contents and high authenticity. Finally, we use Kaggle open source, AID, WHU-RS19, and SpaceWill datasets to perform the test and compare the SR results among different models. It shows that our proposed approach outperforms other state-of-the-art SR models on satellite images.
Enhancing Session-Based Recommendations with Popularity-Aware Graph Neural Networks

Real-time and reliable recommendations are essential for anonymous users in session-based recommendation systems.Graph neural network-based algorithms are attracting more researchers due to their simplicity and efficiency.However, current methods overlook the influence of edge frequency on feature aggregation in graph modeling and fail to account for the impact of item popularity on user interest.To address these issues, a novel approach called Popularity-Aware Graph Neural Networks for Session-based Recommendations is proposed.This study integrates both edge frequency and item popularity into the modeling process to enhance the learning of item features and user interests.A graph that includes the number of edge occurrences is constructed, and a graph neural network with an attention mechanism is utilized to learn user interests and item features by aggregating information from the graph.Finally, the session's final representation is learned based on the occurrence frequency of items.The proposed study evaluates the model on two classical ecommerce datasets and demonstrates its superiority over existing methods.
Image encryption algorithm based on a new chaotic system with Rubik's cube transform and Brownian motion model

In this paper a new two-dimensional chaotic map based on Logistic map and Chebyshev map is proposed. The performance analysis and evaluation show the proposed map has a very wide range of chaos and good ergodicity. Further, a new image encryption algorithm is designed using the chaotic map with Rubik's Cube transform and Brownian motion model. The encryption process is 'confusion - Rubik's cube transformation - diffusion'. In the confusion stage, a method combining Brownian motion theory and chaotic sequence is proposed. Then combining the advantages of chaotic system and Rubik's cube, a Rubik's Cube transformation algorithm is proposed. The scrambled pixel matrix and the chaotic matrix are spliced into a hexahedron structure, and the Rubik's Cube is dynamically scrambled through a pseudo-random sequence. Finally, the XOR operation is performed on the basis of the two-dimensional Hénon chaotic map. The experimental results show that the algorithm has the advantages of large key space, strong robustness and high security, and can resist common cryptanalysis attacks.
A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the algorithm’s robustness in handling various optimization problems. Finally, the proposed algorithm is formulated as a training algorithm of ANN to optimize its neuron weights, biases, and selection of activation function based on the given classification dataset. The ANN model trained by the proposed algorithm is reported to outperform those trained by existing PSO variants in terms of classification accuracy when solving the majority of selected datasets, suggesting its potential applications in challenging real-world problems, such as intelligent condition monitoring of complex industrial systems.
Adoption of Metaverse in South East Asia

Despite massive support for blockchain startups, countries in Southeast Asia have yet to fully launch themselves into metaverse. The appeal of playing lucrative crypto games such as Axie Infinity, where active players can earn up to US$15 per day, may provide these countries the incentive to kickstart many of the business opportunities in metaverse gaming. Metaverse is a potential hype. The international audience has started riding on the back of metaverse's success. Countries in Southeast Asia (SEA) are at with their respective development within the metaverse space. It is important to recognise the level of development of metaverse in these countries. Among those who felt negative about the Metaverse, the most common reason was their concerns about data security and privacy. This research intends to understand the development of metaverse in countries in SEA – Vietnam, Indonesia and Malaysia. Through understanding the development of metaverse in these countries, these countries can properly position themselves to reap the most benefit out of this hype.
CNNs/ViTs-CNNs/ViTs: Mutual distillation for unsupervised domain adaptation

Unsupervised Domain Adaptation (UDA) is a popular machine learning technique to reduce the distribution discrepancy among domains. In previous UDA methods, only convolutional neural networks (CNNs) or vision transformers (ViTs) are used as the backbone. Therefore, these methods are subjected to inherent characteristics of single-CNNs or single-ViTs. In fact, CNNs are more biased towards local textures, while ViTs are inclined to learn the shape information of images. In this paper, we consider that the model combining the above two bias properties is closer to the human visual neural system and achieves more robust performance. A novel mutual distillation method for UDA without any assistant networks is proposed. Mutual distillation between two backbones with complementary properties (i.e. CNNs & ViTs) can promote each other, leading to better domain knowledge transfer. Additionally, traditional domain-mixup approaches can only mix limited cross-domain information through linear interpolation. To encourage more cross-domain information interaction between the two backbones and bridge the domain gap, we propose a patch-mixup method, building a train of mixed intermediate domains composed of augmented patches. Meanwhile, a cross-domain semantic alignment loss is proposed to align the semantic information of these domains. Extensive experiments show that our method achieves state-of-the-art results on several standard UDA benchmarks, such as Office-31, Office-Home, ImageCLEF-DA and VisDA-2017.
An improved stochastic configuration network for concentration prediction in wastewater treatment process

A learner model with fast learning and compact architecture is expected for industrial data modeling. To achieve these goals during stochastic configuration networks (SCNs) construction, we propose an improved version of SCNs in this paper. Unlike the original SCNs, the improved one employs a new inequality constraint in the construction process. In addition, to speed up the construction efficiency of SCNs, a node selection method is proposed to adaptively select nodes from a candidate pool. Moreover, to reduce the redundant nodes of the built SCNs model, we further compress the model based on the singular value decomposition algorithm. The improved SCNs are compared with other methods over four datasets and then applied to the ammonia–nitrogen concentration prediction task in the wastewater treatment process. Experimental results indicate that the proposed method has good potential for industrial data analytics.
Co‐Creating Ethical Practices and Approaches for Fieldwork

Abstract This article builds on perspectives shared at a Town Hall held during the 2021 AGU Fall Meeting focused on valuable lessons learned after publicized incidents of unethical field sampling. We present ideas and recommendations that are applicable across all Earth science disciplines. The major recommendation centers on equally valuing Indigenous and Western perspectives and knowledges, with actions that include building relationships with Indigenous communities, teaching ethical sampling protocols in geoscience curricula, and broadly implementing sharable sample archives. Co‐creating guidelines and practices with Indigenous partnership and expertise will ensure that equity is essential to geoethics and that geoscience is for society.
Space Noncooperative Object Active Tracking With Deep Reinforcement Learning

Actively tracking an arbitrary space noncooperative object relied on visual sensor remains a challenging problem. In this article, we provide an open-source benchmark for space noncooperative object visual tracking including simulated environment, evaluation toolkit, and a position-based visual servoing (PBVS) baseline algorithm, which can facilitate the research in this topic especially for those methods based on deep reinforcement learning. We also present an end-to-end active visual tracker based on deep Q-learning, named as DRLAVT, which learns approximately optimal policy merely took color or RGBD images as input. To the best of authors knowledge, it is the first intelligent agent used for active visual tracking in aerospace domain. The experiment results show that our DRLAVT achieves an excellent robustness and real-time performance compared with the PBVS baseline, benefitted from the design of complex neural network and efficient reward function. In addition, the multiple targets training adopted in this article effectively guarantees the transferability of DRLAVT by forcing the agent to learn optimal control policy with respect to motion patterns of the target.
Scalable clustering by aggregating representatives in hierarchical groups

Appropriately handling the scalability of clustering is a long-standing challenge for the study of clustering techniques and is of fundamental interest to researchers in the community of data mining and knowledge discovery. In comparison to other clustering methods, hierarchical clustering demonstrates better interpretability of clustering results but poor scalability while handling large-scale data. Thus, more comprehensive studies on this problem need to be conducted. This paper develops a new scalable hierarchical clustering model called Election Tree, which can detect the most representative point for each sub-cluster via the process of node election in split data and adjust the members in sub-clusters by the operations of node merging and swap. Extensive experiments on real-world datasets reveal that the proposed computational framework has better clustering accuracy as opposed to the competing baseline methods. Meanwhile, with respect to the scalability tests on incremental synthetic datasets, the results show that the new model has a significantly lower time consumption than the state-of-the-art hierarchical clustering models such as PERCH, GRINCH, SCC and other classic baselines.
A geospatial assessment of the techno-economic wind and solar potential of Mongolia

Even though the country's geographic and climatic characteristics are favourable for renewable energy technology, Mongolia's power infrastructure has a large carbon footprint. Therefore, it is crucial to determine Mongolia's economic potential for solar and wind energy. The technological and financial potential of solar and wind energy in Mongolia is determined in a two-step approach while considering the geographical feasibility. In order to include physical-geographical limits such as slope and socio-geographical constraints such as protected areas in the assessment of renewable energy potential, a GIS-based technique for assessing the appropriateness of possible renewable energy sites is first created. This is followed by an energy potential tool which calculates the technical and economic potential. The combined technical wind and solar potential is estimated to be 7.25 TW capacity and 12.17 PWh/year of electricity. The results indicate an economic wind potential capacity of 0 to 73.36 GW (0 to 123.10 TWh/year). The economic potential of ground-mounted PV is 5.12 TW (9.57 PWh/year) and rooftop PV is 1.11 GW (1.92 TWh/year). In addition, the estimated area suitable for wind power plants is 325 431 km2, ground-mounted PV is 118 484 km2 and rooftop PV is about 27 km2.
Decentralized Identity Management Using Blockchain

This article explores the usage of decentralised identity (DID) management using blockchain in global organisations to support secure usage of information resources. Blockchain as technology was initially introduced as a cryptocurrency and there have been challenges in its adoption for enterprise applications such as identity management. DID is emerging as one of the strong blockchain adoption use cases. Industry pioneers and users across domains have started exploring DID use cases, which help better protect their personal data and application access control as compared to traditional, central, or federated identity management models. In this exploratory work, the authors employ qualitative secondary case-based study research methodology to understand the challenges of the current digital identity management landscape and explore the possible benefits of DID as an emerging identity management paradigm. They propose a conceptual cube framework for analysing and studying various DID platforms thereby contributing to both the theory and practice of digitally secure identity.
Marketing in the Metaverse and the Need for Consumer Protections

Over the next five to ten years, the metaverse is likely to transform how consumers interact with digital content, transitioning society from flat media viewed in the third person to immersive experiences engaged in the first person. This will greatly impact the marketing industry, transforming the basic tools, techniques, and tactics from flat artifacts such as images and videos, to immersive and interactive promotional experiences. In the metaverse, marketing campaigns will likely include extensive use of Virtual Product Placements (VPPs) and Virtual Spokespeople (VSPs). Such methods will be highly effective forms of advertising, for they will target users through natural, personal, and immersive means. At the same time, these methods can easily be used and abused in predatory ways. This paper reviews the most likely marketing techniques of the metaverse, outlines the potential risks to consumers, and makes recommendations for policymakers and business leaders that could protect the public.
Adversarial security mitigations of mmWave beamforming prediction models using defensive distillation and adversarial retraining

The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable to adversarial attacks, such as Fast Gradient Sign Method , Basic Iterative Method , Projected Gradient Descent , and Momentum Iterative Method , because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods defend the DNN models against adversarial attacks in next-generation wireless networks.
Anomaly Detection in Critical-Infrastructures using Autoencoders: A Survey

In critical infrastructures, timely detection of anomalies is essential to detect failures, avoid catastrophic damages, and improve resilience. Neural Network models are one of the state-of-the-art approaches used for anomaly detection. Among Neural Network architectures used these days, Autoencoders (AEs) have gained significant attention due to their advantages such as unsupervised learning, dimensionality reduction, non-linear feature extraction, the ease of integration with other neural network algorithms, and ease of use. Therefore, in this paper, we present: 1) anomaly detection and types of anomaly detection, 2) recent advancements in AEs typically used in anomaly detection, 3) AE-based Anomaly Detection (AE-AD) in selected critical infrastructures such as smart grids, intelligent transportation systems, and smart buildings, and 4) future research opportunities. We hope that this systematic survey of AE-based anomaly detection approaches will help the community prioritize research efforts to address pressing issues in critical infrastructures.