Reviewerly/exHarmony huggingface dataset:
Faster region based convolution neural network with context iterative refinement for object detection

In this paper, proposed a novel method to improve the localisation precision of identified objects. We present a framework for iteratively enhancing image region recommendations to meet ground truth values in this research. The Faster R–CNN (FR-CNN) seems to be an object recognition deep convolutional network. It gives the user the impression that the network is cohesive and single. The network can provide accurate and timely predictions about the whereabouts of a range of objects. We first build a unified model based on rapid predictions to relocate inaccurate area recommendations. Because the emphasis is on object detection, it may be utilized with a wide range of datasets and is compatible with various FR-CNN architectures. Second, we focus on the application of the joint score function to a variety of picture features. This joint score function depicts the location of the concealed object concerning other objects. The picture data and an updated structured production loss function are the only two inputs that influence the parameters of the joint scoring function. The join-score function and iterative context refinement (CIR) are used to generate our final unified model, which is then classified using Smooth Support Vector Machine (SSVM). We measured accuracy using the mean average precision after training FR-CNN + CIR and SSVM on a low-cost GPU using the PASCAL VOC 2012 dataset. Our results are 3.6 % more exact than rival deep learning algorithms on average.
New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.
Advances in machine learning optimization for classical and quantum photonics

The development and optimization of photonic devices and various other nanostructure electromagnetic devices present a computationally intensive task. Much optimization relies on finite-difference time-domain or finite element analysis simulations, which can become very computationally demanding for finely detailed structures and dramatically reduce the available optimization space. In recent years, various inverse design machine learning (ML) techniques have been successfully applied to realize previously unexplored optimization spaces for photonic and quantum photonic devices. In this review, recent results using conventional optimization methods, such as the adjoint method and particle swarm, are examined along with ML optimization using convolutional neural networks, Bayesian optimizations with deep learning, and reinforcement learning in the context of new applications to photonics and quantum photonics.
Generating high-quality texture via panoramic feature aggregation for large mask inpainting

With the development of image generation and processing techniques, image inpainting techniques based on deep learning have achieved impressive results. Especially the emphasis on global context during inpainting enables the network to generate reasonably coarse inpainting results at low resolutions. However, how to better achieve high-quality texture filling at high resolutions is still a challenging problem. To address this problem, most methods design two-stage networks to achieve structure and texture restoration separately. But in the face of large-scale masks, the generated textures still suffer from blurring and artifacts. Therefore, in order to achieve inpainting of images with large-scale masks and generate fine textures, this paper proposes an end-to-end generative adversarial model for large mask inpainting, called Panoramic Feature Aggregation Network (PFAN). First, this paper designs a Euclidean Attention Mechanism (EAM) which exploits encoder features to generate low-resolution structure restoration. Then a Feature Aggregation Synthesis Block (FASB) is proposed in the decoder to achieve high-resolution complex texture filling. With the global receptive fields of these two modules, texture filling results with satisfactory performance even under large-scale masks. Experiments on CelebA-HQ, Paris Street View and FFHQ datasets show that the proposed method has superior performance.
AD-YOLOv5: An object detection approach for key parts of sika deer based on deep learning

Accurate and rapid fixation of key parts of sika deer is essential for the mechanized and intelligent collection of both deer blood and velvet antler. To solve the low detection accuracy problem of key parts of deer caused by the diversity of deer farm breeding environment and the rapid movement of deer, this paper proposes an AD-YOLOv5 algorithm for detecting key parts of sika deer based on YOLOv5s. Specifically, a new weighted bidirectional feature pyramid network S-BiFPN is first proposed, which both simplifies the structure and effectively performs skip feature fusion to better capture object information at different scales, so that the adaptive feature fusion capability of the model is optimized. Second, this paper proposes to add a SENet module after the C3 module of the 9th Layer in the backbone part of the network, so that the network can adaptively readjust the channel weights of feature maps to enhance the important features for key parts of sika deer and improve the expression ability of the model for key parts. Furthermore, to make the model more stable in the training process, a novel bounding box regression loss function SIoU is led into, which can better learn the position and size of the bounding box by fusing the orientation information between the ground truth box and the predicted box. The effectiveness of the proposed algorithm is demonstrated through ablation experiments. Experimental results show that the mAP@0.5 of the proposed algorithm reaches 97.30 % for the detection of key parts of sika deer, which is 12.85 %, 9.65 %, 9.18 %, 7.13 %, 3.59 %, 4.6 %, 0.90 % and 0.10 % higher than SSD, EfficientDet, YOLOv3, YOLOv4, Faster R-CNN, YOLOv5s, YOLOv7 and YOLOv8s, respectively. This research provides a new idea for detecting key parts of animals in complex environments and lays a theoretical foundation for the development of intelligent fixation devices for sika deer.
Real-time action localization of manual assembly operations using deep learning and augmented inference state machines

The real-time monitoring of assembly operations in manufacturing industries can be used for manufacturing process optimization, which is crucial to manufacturers. It helps to improve productivity by automatically identifying the bottleneck and enhancing product quality by detecting errors and providing feedback to rectify them in real-time. However, developing a robust and reliable assembly monitoring system is not trivial due to the varying length of fine-grained assembly steps and anthropometric variations associated with assembly workers. To tackle the challenge, conventionally, wireless body-worn sensors have been used, leading to operator discomfort, and raising safety concerns. In this work, we propose a novel technique to automatically recognize and localize the assembly steps in real time called the State Machine Integrated Recognition and Localization (SMIRL). SMIRL is comprised of an inference machine and a state machine. They are responsible for detecting and localizing the actions, respectively. SMIRL can measure the duration of individual assembly steps and the entire cycle. Additionally, SMIRL can also detect the mistakes that may occur in an assembly and raise an alert to notify the operators in real time. The mistakes here can be breaking the predefined assembly sequence (Sequence Break) or missing any of the assembly steps (Missed steps). The effectiveness of SMIRL was evaluated against two datasets, with one being from an assembly workstation in industry and the other from a laboratory. The result shows that SMIRL can detect and localize the actions with an Intersection over Union (IoU) score of 87.53%, and identify Sequence Breaks and Missed Steps with F1-Scores of 86.64% and 87.45%, respectively. Through this study, we aim to contribute towards real-time monitoring of human-centric assembly operations to facilitate smart manufacturing.
CIS: Connect It Strong! [Editor's Remarks]

The beginning of a year is a good timing to conclude on what memorable moments have happened in the preceding year. Last summer, I was invited to give talks at the 2023 IEEE CIS Summer School and Summit Forum held at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. Several IEEE CIS officers, Editors-in-Chief, and I gathered to introduce CI techniques and their advancements to beginners in the field—mainly undergraduate and master students. Approximately 400 participants engaged in discussions immensely, and the vibe was very vigorous. I was delighted to share my research results on CI for composing music. Attending this summer school was absolutely a pleasant and fulfilling experience in 2023. I do love to see that CI technologies have been getting more attention among students and young scholars, from the level of this event's participation, and look forward to taking part in various activities held by CIS. Speaking of our Society, Professor Yaochu Jin assumes the CIS President for 2024-2025. We first met at a conference dated back in 2004, and participated or worked together in many CIS activities for two decades since. CIS is such a platform that brings people with similar research interests together to exchange academic ideas and create opportunities for collaboration. It is definitely worthwhile to join CIS and devote time to, because the sense of achievement and rewarding to work with people having similar passion is humongous. Perhaps someone you meet at a CIS conference today could become the CIS President in 20 years, you never know! LOL!
Development of advanced hybrid computational model for description of molecular separation in liquid phase via polymeric membranes

Separation of molecules using membranes has been computationally studied in this work for understanding the separation process and also assessment of integrating machine learning (ML) techniques to computational fluid dynamics (CFD) techniques. We initially performed CFD computations to simulate mass transfer in a membrane-based molecular separation system, and then exported the mass transfer data for training ML models. Indeed, a hybrid model was developed and employed for membrane process in separating a solute from a solution. We have modeled a data set containing more than 14 thousand data rows, which has two inputs r (m) and z (m) and single response, i.e., C (mol/m3) that is solute' content in the solution. Decision tree-based models are used in this modeling. Simple Decision Tree (DT), Boosted Decision Tree (BDT) that is DT alongside AdaBoost, and Random Forest (RF) are the selected models for this research. The ML techniques were further optimized via a novel HHO algorithm. Final assessments show the R2 scores of 0.9991, 0.9874, and 0.9997 respectively for BDT, DT, and RF models. Based on this result and other analysis, RF is selected as the best method in this study for simulation of membrane process. This model has an RMSE error rate of 2.22 × 103.
Multi-domain gate and interactive dual attention for multi-domain dialogue state tracking

Multi-domain dialogue state tracking (MDST) is a crucial component of task-oriented dialogue systems. In the context of multi-turn dialogues between the user and the system, MDST necessitates the continuous keeping track of dialogue states based on the information present in the current dialogue utterance and the dialogue states from the preceding turn. Recent work achieves the successful execution of multi-domain dialogue tasks by adopting an approach that treats each state as an individual label, while regrettably neglecting the potential benefits of incorporating domain-specific information associated with these states. Simultaneous, existing models exhibit a deficiency in effectively modelling the explicit correlations between dialogue contextual semantics and dialogue states. In this paper, we introduce the modules of multi-domain gate and interactive dual attention as novel solutions to address the aforementioned concerns. For the efficient exploitation of domain-specific information within states, we leverage the multi-domain gate as indices to amplify the states pertinent to the current utterance domain while filtering out irrelevant states. Interactive dual attention comprises utterance attention and slot attention, effectively modelling the correlation between dialogue utterances and slots. Additionally, interactive dual attention ensures that each dialogue utterance interacts with the slots once to derive all state updates, thereby ensuring computational efficiency. Specifically, slot attention models the associations between slots by incorporating semantic features to forecast updates in slot values. Meanwhile, utterance attention captures the semantics of dialogue context and integrates it with slot name features to generate dialogue states. All the aforementioned modules are designed based on a slot-independent framework, enabling efficient scalability of slots and circumventing issues related to model input limitations. The experimental results on the multi-domain dialogues dataset MultiWOZ 2.4 demonstrate the superior performance of our model compared to the baselines. Additionally, we conduct a comprehensive analysis of the effectiveness of the multi-domain gate and interactive dual attention modules, elucidating their contribution to the performance of the model through visualization and case studies.
SICNet: Learning selective inter-slice context via Mask-Guided Self-knowledge distillation for NPC segmentation

Accurate segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance (MR) images is crucial for radiotherapy planning. However, vanilla 2D/3D deep convolutional networks fail to gain satisfying NPC segmentation results, since 3D methods suffer from inter-slice discontinuities and 2D methods lack inter-slice context learning. To address this problem, this paper proposes a 2.5D learning Selective Inter-slice Context Network (SICNet) for NPC segmentation. The proposed SICNet comprises a main encoder that extracts target features from the middle slice and an auxiliary encoder that learns inter-slice context from adjacent slices. The features from two encoders are fused by the Inter-slice Context Fusion (ICF) module and then sent to the decoder for predictions. Furthermore, a knowledge distillation module named Mask-Guided Self-knowledge Distillation (MGSD) is proposed to help the auxiliary encoder learn context selectively. Specifically, with the guidance of mask labels, MGSD tells the auxiliary encoder where to look and how to learn, thus achieving selective inter-slice context learning and boosting segmentation accuracy. The proposed SICNet achieves a DSC of 74.38 ± 11.99, an HD95 of 9.31 ± 2.68, and an ASSD of 1.46 ± 0.76 on an in-house NPC dataset, outperforming other state-of-the-art segmentation methods.
Human skin detection: An unsupervised machine learning way

Researchers have been involved for decades in search of an efficient skin detection method. However, current methods have not overcome the significant challenges of skin detection, such as variation of illumination, various skin tones of different ethnic groups, and many others. This research proposed a clustering and region-growing-based skin detection method to overcome these limitations. Together with significant insight, these methods result in a more effective algorithm. The insight concerns the capability to dynamically define the number of clusters in a collection of pixels organized as images. In Clustering for most problem domains, the number of clusters is fixed prior and does not perform effectively over a wide variety of data contents. Therefore, this research paper proposed a skin detection method that validated the above findings. The proposed method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholds or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.
Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control

Ultracold polar molecules have gained much attention as a potential candidate for quantum information processing due to their long coherence time and strong dipole–dipole interaction. In the present study, we explore the dynamics of three polar molecules that are mutually coupled and arranged in an equilateral triangle configuration within a gradient electric field. By employing the pendular states of polar molecules as qubits, we simulate the three-qubit Deutsch-Jozsa algorithm via multi-target optimal control. Through the design of optimal microwave pulses with multiple iterations, our approach can accomplish the operations required to complete the algorithm in a single operational step with high fidelity and large transition probability. Furthermore, we compare and analyze the fidelity and average transition probability with respect to iteration numbers for each unitary operation Uf pulse. It is found that by combining the optimized X, H⊗H⊗H, Uf, and H⊗H gate pulses, the Deutsch-Jozsa algorithm in the three-dipole system can be performed well for distinguishing between constant and balanced functions. Our findings could shed some light on the physical realization of multi-particle quantum algorithm based on polar molecules in external electric fields.
CBDC's FinTech Innovations

As the global financial landscape continues to transform to technological improvements, the emergence of CBDCs represents a significant turning point in financial systems. The central bank of a country issues and controls CBDCs, which are digital representations of sovereign money. Fintech and CBDCs are closely related because of the building pieces of the CBDC operation, which include blockchain or distributed ledgers, digital currency wallets, and dependable payment gateways. The adoption of Central Bank Digital Currencies (CBDCs) has the potential to have a significant impact on the fintech industry and could, in some cases, result in a fintech boom. The goal of this study is to develop a comprehensive knowledge of CBDCs' integration with fintech, their wide-ranging effects on society, and the potential need for digital detoxification techniques to balance off the unwanted effects of growing digitalization which can lead to financial innovation and personal well-being in the digital era by looking at CBDCs from both a technology and human-centric standpoint.
Geometric constructive network with block increments for lightweight data-driven industrial process modeling

Industrial data-driven models may require frequent reconstruction to maintain model performance due to the dynamics, uncertainty, and complexity of industrial processes. The infrastructure of the industrial processes is usually distributed control systems (DCS) with energy-sensitive and resource-constrained. In this context, this article proposes a geometric constructive network with block increments (BI-GCN) to reduce the modeling consumption while achieving comparable accuracy. First, this article proposes a geometric control strategy with block increments, which is capable of adding multiple nodes to the BI-GCN simultaneously. Second, this article demonstrates the universal approximation property of BI-GCN, which in turn guarantees the potential high performance of BI-GCN for modeling tasks. Finally, experiments on benchmark datasets and the grinding process show that BI-GCN can effectively reduce the number of iterations in the modeling process while maintaining comparable accuracy.
Role of Human Resource Management on Strategic Management

Strategic human resource management (SHRM) is a field of study and practice that focuses on the alignment of human resource strategies and practices with the overall strategic objectives of an organization. It recognizes the critical role that human capital plays in driving organizational success and competitive advantage. By integrating HR practices with strategic goals, SHRM aims to maximize the performance, engagement, and development of employees, ultimately contributing to the achievement of organizational objectives. A thorough understanding of SHRM is essential for organizations seeking to optimize their human resources and gain a competitive edge in today's dynamic business environment. In this literature review, the authors delve into the existing research and explore the various aspects of SHRM, shedding light on its theoretical foundations, practical applications, and the potential benefits it offers to organizations.
The impact of extreme dust storms on the national photovoltaic energy supply

This study analyses the consequences of an extreme dust storm that occurred in March 2022 on the Spanish national photovoltaic (PV) energy supply. This event, indeed, substantially raised the particulate matter concentrations and the aerosol optical depths across the country, seriously affecting the surface radiation and posing a substantial threat not only to individual PV systems but also to the national electricity grid. The research, based on the analysis and the forecast of weather, environmental and electrical data, reveals that such event halved the capacity factor of the national PV fleet over a period longer than two weeks. A peak drop as high as 80% was registered, at national level, on the worst day. This underperformance also affected the market share of PV in the national electricity mix, whose monthly average value fell from the predicted 10.9% to 7.1%. Despite the expectations, however, no significant difference in soiling was found compared to the typical losses, thanks to the occurrence of heavier-than-usual rainfall events. This facilitated the recovery of the national PV capacity, which returned to the expected performance factors as soon as the sky cleared.