Reviewerly/exHarmony huggingface dataset:
Cooperative modular reinforcement learning for large discrete action space problem

Deep reinforcement learning (DRL) has achieved remarkable results on high-dimension state tasks. However, it suffers in hard convergence and low sample efficiency when solving large discrete action space problems. To meet these challenges, we develop a cooperative modular reinforcement learning (CMRL) method to distributedly solve the problems with a large discrete action space. A general yet effective task decomposition method is proposed to decompose the complex decision task in a large action space into multiple decision sub-tasks in small action subsets, using a rule-based action division method. The CMRL method consisting of multiple Critic networks is proposed to settle the multiple sub-tasks, where each Critic network learns a decomposed value function to obtain the local optimal action in a sub-task. The global optimal action is cooperatively chosen by all local optimal actions. Moreover, we propose a new parallel training mechanism, which trains multiple Critic networks with different models and multi-data in parallel. Mathematical properties are proposed to analyze the rationality and superiority of CMRL. Four different simulation experiments are conducted to verify the generality and effectiveness of CMRL for large action space problems. The results show that CMRL has superior performance on training efficiency compared with classical and latest DRL methods while maintaining the accuracy of the solution.
Applications of IoT and Blockchain in Smart Agriculture: Architectures and Challenges

Internet of Things (IoT) technologies have created a big revolution in the ICT industries. It’s has penetrated in almost all sectors including healthcare, manufacturing, education, agriculture, etc. However, there are many challenges facing the IoT implementations due to its centralized server/client model. Many of the technologies in IoT is based on centralized model which introduce a new set of technical limitations to manage them. These includes traffic congestion when the server couldn’t support the large number of request from clients and when the server is down, client request cannot be met. Shifting IoT systems into the decentralized systems is one of the potential solution and Blockchain is the best option for decentralization. Blockchain technology decentralizes computation and management processes and can solve many IoT issues including security. A generalized IoT-based Blockchain architecture for Smart Agriculture application is proposed. Furthermore, the opportunities and challenges based on this architecture is discussed in detail.
Protection of Data in Edge and Cloud Computing

The massive growth of IoT and the rise of inventions in this field have led people to depend heavily on those technologies. This change has directed users to place their data, whether it is confidential or not, in specific storage known as the cloud. Cloud Computing enables users to save their data in the cloud. When IoT devices started generating large amounts of data, known as Big Data, the cloud couldn’t handle them due to its limited bandwidth and resources, so storing the data was moved to the endpoints of the network replacing cloud computing with edge computing. Edge computing allows users to store the data at the edge of the network. This is a promising technology now as it provides the best features of real-time and parallel processing and content perception. However, just like cloud computing the security of edge computing has risen a lot of concerns. Cloud security and Edge security have the most crucial concepts that gather data to process. Several security frameworks for cloud and edge computing have been discussed and invented. In our paper, we discuss the different challenges and solutions to said frameworks. We also compare the old and new frameworks of security according to their security encryption methods, confidentiality handling of data, and splitting of data through the packets.
Enhancing claim classification with feature extraction from anomaly‐detection‐derived routine and peculiarity profiles

Abstract Usage‐based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection (AD) to vehicles' trip summaries, we develop a method allowing to derive a “routine” and a “peculiarity” anomaly profile for each vehicle. To this end, AD algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared with the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared with trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve the classification.
Safe reinforcement learning under temporal logic with reward design and quantum action selection

This paper proposes an advanced Reinforcement Learning (RL) method, incorporating reward-shaping, safety value functions, and a quantum action selection algorithm. The method is model-free and can synthesize a finite policy that maximizes the probability of satisfying a complex task. Although RL is a promising approach, it suffers from unsafe traps and sparse rewards and becomes impractical when applied to real-world problems. To improve safety during training, we introduce a concept of safety values, which results in a model-based adaptive scenario due to online updates of transition probabilities. On the other hand, a high-level complex task is usually formulated via formal languages, including Linear Temporal Logic (LTL). Another novelty of this work is using an Embedded Limit-Deterministic Generalized Büchi Automaton (E-LDGBA) to represent an LTL formula. The obtained deterministic policy can generalize the tasks over infinite and finite horizons. We design an automaton-based reward, and the theoretical analysis shows that an agent can accomplish task specifications with the maximum probability by following the optimal policy. Furthermore, a reward shaping process is developed to avoid sparse rewards and enforce the RL convergence while keeping the optimal policies invariant. In addition, inspired by quantum computing, we propose a quantum action selection algorithm to replace the existing [Formula: see text]-greedy algorithm for the balance of exploration and exploitation during learning. Simulations demonstrate how the proposed framework can achieve good performance by dramatically reducing the times to visit unsafe states while converging optimal policies.
Conducting Phenomenon-Driven Research Using Virtual Reality and the Metaverse

We argue and provide evidence that VR is particularly suited to conduct phenomenon-driven empirical research that theories of management and organizations neither adequately predict nor explain. We focus on four areas. First, we demonstrate the capabilities and accessibility of VR technology. Second, we show how VR can be used as a platform to expand our ability to study poorly-understood phenomena in new ways. This includes improving our ability to study phenomena that are either difficult or impossible to examine using more traditional methodological approaches. Third, we discuss opportunities for using VR to conduct research in several illustrative management subfields (i.e., organizational behavior, strategy, entrepreneurship). Further, we discuss how the metaverse opens up almost limitless opportunities for future research. Finally, we provide recommendations and also discuss pitfalls to avoid when using VR as a research platform.
RETRACTED: A BERT encoding with Recurrent Neural Network and Long-Short Term Memory for breast cancer image classification

Computer-aided diagnostic systems developed with Artificial Intelligence (AI) are a major advancement in healthcare analytics, assisting radiologists with a second opinion on cancer diagnosis. In this study, we explore two modalities of breast images, ultrasound, and histology, for their classification into cancerous and non-cancerous categories. Traditionally Convolution Neural Networks (CNN) have done a commendable job of extracting features using convolution kernels from images with great accuracy. Also, the Bidirectional Encoder Representations from Transformers (BERT) has been widely used in Natural Language Processing (NLP) for feature encoding and downstream tasks like segmentation and classification. We extract the power of Vision Transformers (ViT) and implement transfer learning using BERT pre-training of Image Transformers (BEiT) as a feature encoding technique. We use the encoded features for classification with Recurrent Neural Network – Long short Term Memory (RNN-LSTM). The classification is performed on two modalities of breast image datasets: BUSI1311 and breast histopathological images. Both modalities yielded competitive accuracies. The BUSI1311 dataset produced 99 percent accuracy compared to 91 percent accuracy for breast histology images.
Mineral content estimation for salt lakes on the Tibetan plateau based on the genetic algorithm-based feature selection method using Sentinel-2 imagery: A case study of the Bieruoze Co and Guopu Co lakes

Salt lakes on the Tibetan Plateau (TP) are rich in lithium (Li), boron (B) and other mineral resources, and accurate assessment of the mineral content and spatial distribution of the brine in those salt lakes is important to guide the development and utilization of their mineral resources. There are few studies estimating the mineral content of salt lakes on the TP due to the lack of in situ investigation data. This study introduced an intelligent prediction model combining a feature selection algorithm with a machine learning algorithm using Sentinel-2 satellite data to estimate the Li, B, and TDS contents of Bieruoze Co and Guopu Co lakes on the TP. First, to enrich the spectral information, four mathematical transformations (reciprocal, logarithmic, reciprocal of logarithm, and first-order derivative) were applied to the original bands. Then, feature selection was performed using the genetic algorithm (GA) to select the optimal input variables for the model. Finally, prediction models were constructed by partial least squares regression (PLSR), multiple linear regression (MLR), and random forest (RF). The results showed that: 1) The spectral mathematical transformation provided rich spectral information for the mineral content estimation. 2) The performance of the estimation model constructed by the feature optimization method using GA was better than that of the estimation model constructed based on all spectral bands. Based on GA for feature optimization, the MAPE of GA-RF for estimating Li, B and TDS contents on the testing set was reduced by 77.52%, 28.54% and 36.79%, respectively. 3) Compared with the GA-MLR and GA-PLSR models, GA-RF estimated Li ( R 2 =0.99, RMSE=1.15 mg L -1 , MAPE=3.00%), B ( R 2 =0.97, RMSE=10.65 mg L -1 , MAPE=2.73%), and TDS ( R 2 =0.93, RMSE=0.60 g L -1 , MAPE=1.82%) all obtained the optimal performance. This study showed that the combination of the GA-based feature selection method and the RF model has excellent performance and applicability for monitoring the content of multiple minerals using Sentinel-2 imagery in salt lakes on the TP.
CBCS: A Scalable Consortium Blockchain Architecture Based on World State Collaborative Storage

In the big data environment, data are characterized by a large volume, various types, and rapid changes. The consortium blockchain applied in this environment faces the problem of excessive storage of the ledger, and the ledger handling different types of business needs to be isolated to ensure the ledger’s security. To this end, this paper proposes a scalable consortium blockchain architecture based on world state collaborative storage (CBCS). First, a business world state database update method is designed based on sparse Merkle multiproofs, where the collaborative storage of world state is realized under the premise of mutual isolation of the ledger between business domains. Then, a world state consistency verification method based on the rank B+ tree is designed to verify the consistency of the business world state in business domains by the checking sidechain, and a main-side chain cross-anchoring structure is designed to realize secure anchoring of the mainchain and the checking sidechain. Meanwhile, a blockchain transaction trusted tracing method based on two-level certification is developed to enable business nodes to obtain complete blockchain transactions. Finally, the feasibility and efficiency of the proposed mechanism to solve the storage scalability problem in the consortium blockchain are verified through experiments.
FEM-BEM Vibroacoustic Simulations of Motion Driven Cymbal-Drumstick Interactions

The transient acoustic dynamics of a splash cymbal are investigated via the Finite Element Method-Boundary Element Method. Real three-dimensional motion data recorded from the interaction of drummer–drumstick–cymbal provide the initial and the loading conditions to the simulated interaction of the drumstick–cymbal Finite Element Models. Progressively intensified free strokes are used as loading conditions for both experiment and simulation. The velocity values of the moving drumstick in various drumming conditions are monitored, recorded, and analysed to provide input data into the time domain simulations. The synergy of motion capturing and numerical methods allows computing the sound generated by the combined interaction of the vibroacoustic behaviour of the cymbal with the motor-interaction of the performer. The proposed methodology promotes a novel perspective in musical instrument design, optimization, and manufacturing considering performance discrepancies intentionally introduced by performers.
Center-Aware 3D Object Detection with Attention Mechanism Based on Roadside LiDAR

Infrastructure 3D Object Detection is a pivotal component of Vehicle-Infrastructure Cooperated Autonomous Driving (VICAD). As turning objects account for a high proportion of traffic at intersections, anchor-free representation in the bird’s-eye view (BEV) is more suitable for roadside 3D detection. In this work, we propose CetrRoad, a simple yet effective center-aware detector with transformer-based detection head for roadside 3D object detection with single LiDAR (Light Detection and Ranging). CetrRoad firstly utilizes a voxel-based roadside LiDAR feature encoder module that voxelizes and projects the raw point cloud into BEV with dense feature representation, following a one-stage center proposal module that initializes center candidates of objects based on the top N points in the BEV target heatmap with unnormalized 2D Gaussian. Then, taking attending center proposals as query embedding, a detection head with multi-head self-attention and multi-scale multi-head deformable cross attention can refine and predict 3D bounding boxes for different classes moving/parked at the intersection. Extensive experiments and analyses demonstrate that our method achieves state-of-the-art performance on the DAIR-V2X-I benchmark with an acceptable training time cost, especially for Car and Cyclist. CetrRoad also reaches comparable results with the multi-modal fusion method for Pedestrian. An ablation study demonstrates that center-aware query as input can provide denser supervision than a purified feature map in the attention-based detection head. Moreover, we were able to intuitively observe that in complex traffic environment, our proposed model could produce more accurate 3D detection results than other compared methods with fewer false positives, which is helpful for other downstream VICAD tasks.
Parallel Algorithm for Connected-Component Analysis Using CUDA

In this article, we introduce a parallel algorithm for connected-component analysis (CCA) on GPUs which drastically reduces the volume of data to transfer from GPU to the host. CCA algorithms targeting GPUs typically store the extracted features in arrays large enough to potentially hold the maximum possible number of objects for the given image size. Transferring these large arrays to the host requires large portions of the overall execution time. Therefore, we propose an algorithm which uses a CUDA kernel to merge trees of connected component feature structs. During the tree merging, various connected-component properties, such as total area, centroid and bounding box, are extracted and accumulated. The tree structure then enables us to only transfer features of valid objects to the host for further processing or storing. Our benchmarks show that this implementation significantly reduces memory transfer volume for processing results on the host whilst maintaining similar performance to state-of-the-art CCA algorithms.
Dual-Encoder Transformer for Short-Term Photovoltaic Power Prediction Using Satellite Remote-Sensing Data

The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short-term prediction, which is crucial for reliable power system operation. Existing methods focus on coupling satellite images with ground measurements to extract features using deep neural networks. However, a flexible predictive framework capable of handling these two data structures is still not well developed. The spatial and temporal features are merely concatenated and passed to the following layer of a neural network, which is incapable of utilizing the correlation between them. Therefore, we propose a novel dual-encoder transformer (DualET) for short-term PV power prediction. The dual encoders contain wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively. Moreover, we propose a cross-domain attention module to learn the correlation between the temporal features and cloud information and modify the attention modules with the spare form and Fourier transform to improve their performance. The experiments on real-world datasets, including PV station data and satellite images, show that our model achieves better results than other models for short-term PV power prediction.
Time-to-space progressive network using overlap skeleton contexts for action recognition

In the previous GCNs-based methods for action recognition, non-local spatial-temporal relationships between nodes are not evaluated well. Furthermore, the computational cost for long-term temporal modeling is rarely considered. In this paper, we construct a novel Time-to-Space Progressive Network (T2SPNet) for skeleton-based action recognition. First, based on the relationship theory in sociology, we propose the Spatial Overlap Contexts-enhanced (SOC) stream and Spatial-Temporal Overlap Contexts-enhanced (STOC) stream. These streams are updated by aggregating the characteristics of nodes that have similar overlap contexts dynamically. Second, our proposed Enhanced Temporal Response-based Distillation (ETRD) method transfers the non-local temporal knowledge from the teacher STOC stream to Student SOC (S-SOC) stream. ETRD addresses the lack of temporal knowledge in SOC stream. Third, the progressive strategy with three stages is designed. The tasks in the early stages construct S-SOC stream with lower computational cost. In the final stage, lighter S-SOC stream replaces complex STOC stream. We compare our T2SPNet with the previous GCNs-based and Transformer-based methods on the large-scale datasets: NTU RGB + D 60 dataset, Kinetics-Skeleton dataset and NTU RGB + D 120 dataset. The competitive results verify the effectiveness of our network.
SI4IoT: A methodology based on models and services for the integration of IoT systems

The Internet of Things (IoT) is a technology that is growing faster every day due to the large number of platforms and end-devices that are becoming connected to each other. As part of this wide and diverse scenario, developers are now facing various challenges, such as heterogeneity, diversity of communication protocols, discovery of things, and coordination of services, among others. A paradigm that can help to tackle these issues is the model engineering since it allows different elements to be reused which can simplify the work of developers. In this paper, we propose SI4IoT (Service Integration for IoT), a methodology based on MDE (Model-Driven Engineering) for the development of IoT systems. This methodology enables automatic code generation, making it easier for developers to design sophisticated new IoT applications. We focus on a DSL (Domain-Specific Language), a graphic editor, and a set of M2T (Model-to-Text) transformations that generate code for software artifacts on Arduino, Node-Red, Ballerina, and NCL-Lua for deployment on hardware nodes, web services, and DTV (Digital TV). Our proposal consists of a model for the integration of services made up of three layers: physical, logical, and application. To validate our proposal, a Smart Home scenario has been considered, with sensors and actuators which, when combined, allow control of lights and heating. In addition, it allows the user to receive information about their home on television based on the REST services that have been created for the IoT nodes.
Socially assistive robots in health and social care: Acceptance and cultural factors. Results from an exploratory international online survey

This study explored the views of an international sample of registered nurses and midwives working in health and social care concerning socially assistive robots (SARs), and the relationship between dimensions of culture and rejection of the idea that SARs had benefits in these settings.An online survey was used to obtain rankings of (among other topics) the extent to which SARs have benefits for health and social care. It also asked for free text responses regarding any concerns about SARs.Most respondents were overwhelmingly positive about SARs' benefits. A small minority strongly rejected this idea, and qualitative analysis of the objections raised by them revealed three major themes: things might go wrong, depersonalization, and patient-related concerns. However, many participants who were highly accepting of the benefits of SARs expressed similar objections. Cultural dimensions of long-term orientation and uncertainty avoidance feature prominently in technology acceptance research. Therefore, the relationship between the proportion of respondents from each country who felt that SARs had no benefits and each country's ratings on long-term orientation and uncertainty avoidance were also examined. A significant positive correlation was found for long-term orientation, but not for uncertainty avoidance.Most respondents were positive about the benefits of SARs, and similar concerns about their use were expressed both by those who strongly accepted the idea that they had benefits and those who did not. Some evidence was found to suggest that cultural factors were related to rejecting the idea that SARs had benefits.