Reviewerly/exHarmony huggingface dataset:
YOLOV5s-Face face detection algorithm

Aiming at the problems of missed detection and false detection of face detection in the current face detection model in complex environments, YOLOV5s-Face face detection algorithm is proposed. It is improved based on YOLOV5s. First, the anchor box's size is modified using the K-means algorithm, which determine the priority anchor box size, and improve the fitness of the anchor box and the actual object. Secondly, the SE attention mechanism is embedded in the backbone network structure of YOLOV5s. It might improve the network's capacity for feature extraction. Finally, this research employs four-scale feature detection to enhance the network's detection performance on small face targets. The outcomes of the experiments demonstrate that the YOLOV5s-Face algorithm has an increase of 3.9 percentage points and 4 percentage points in the mAP and Recall of the YOLOV5s algorithm respectively. YOLOV5s-Face algorithm can better detect face.
Fast Steering Mirror Model Identification Based on Impulse Response Hankel Matrix

In the theoretical modeling process of the voice-coil motor fast steering mirror system, the simplification of its structure leads to low model accuracy, which in turn affects the analysis of subsequent control algorithms. To address the problem, we propose to identify the model of the voice-coil motor fast steering mirror using the Hankel matrix-based identification method. This model can better identify the system with strong nonlinear factors caused by having flexible modes. In this paper, we use the pseudo-random m-sequence as the input signal. By this method, we can identify the system and compare the fitting situation of the subspace identification method. The results show that the identification method we used identified the model more accurately, with a maximum reduction of additive uncertainty of about 96.808%, a multiplicative uncertainty of about 93.5406%, this method is more suitable for fast reflector systems with flexible modal structures.
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains

The ability of reasoning over evidence has received increasing attention in question answering (QA). Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence. However, existing text-based complex question answering datasets fail to provide explicit reasoning process, while it’s important for retrieval effectiveness and reasoning interpretability. Therefore, we present a benchmark ReasonChainQA with explanatory and explicit evidence chains. ReasonChainQA consists of two subtasks: answer generation and evidence chains extraction, it also contains higher diversity for multi-hop questions with varying depths, 12 reasoning types and 78 relations. To obtain high-quality textual evidences for answering complex question. Additional experiment on supervised and unsupervised retrieval fully indicates the significance of ReasonChainQA. Dataset and codes will be made publicly available upon accepted.
UAV Multi-target Surveillance Cruise Trajectory Planning Based on DQN Algorithm

With the continuous development of artificial intelligence, it has become increasingly common to use UAVs to survey the environment to complete tasks. In this paper, a deep reinforcement learning method(DQN) is used to monitor multiple target objects with unmanned aerial vehicles when the environment is unknown. To this end, the flight decision problem of the UAV is constructed as a Markov model, and the neural network layers relative to the number of experience pools are set for sampling training. The simulation results show that the UAV can independently plan a trajectory to monitor all targets, and the trajectory route of this scheme is better than the DQN algorithm.
Third-party construction intrusion detection of natural gas pipelines based on improved YOLOv5

With the continuous development of unmanned aerial vehicle (UAV) detection technology, UAV line detection has been well applied in the oil and gas storage and transportation industry. Regularly patrol the high-risk areas through UAV, and monitor whether there are third-party intrusion events near oil and gas pipelines, including construction machineries such as excavators and bulldozers. When the third-party intrusion event occurs near the pipeline, an alarm will be issued, and the line patrol personnel will investigate in time. In this paper, the single-stage target detection algorithm based on deep learning is combined with the UAV patrol line of natural gas pipelines, and an algorithm model based on improved YOLOv5 is proposed. The Recurrent Criss-Cross Attention module is added to the backbone network to improve the feature extraction capability. The feature fusion module uses the TridentNet network to improve the performance of the multi-scale feature fusion of the model. WBF post-processing is used to replace the original NMS, improving the prediction algorithm's regression accuracy. Experiments show that the improvement has effectively improved the detection effect of third-party intrusion of natural gas pipelines.
A CNN-Based Multi-stage Framework for Renal Multi-structure Segmentation

Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images has significant therapeutic implications. 3D visual model of kidney, renal tumor, renal vein and renal artery helps clinicians make accurate preoperative planning. In this paper, we utilize a modified nnU-Net named nnHra-Net network, and propose a multi-stage framework with coarse-to-fine and ensemble learning strategy to precisely segment the multi-structure of kidney. Our method is quantitatively evaluated on a public dataset from MICCAI 2022 Kidney Parsing for Renal Cancer Treatment Challenge (KiPA2022), with mean Dice similarity coefficient (DSC) as 95.91%, 90.65%, 88.60% and 85.36% for the kidneys, kidney tumors, arteries and veins respectively, wining the stable and top performance on both open and close testing in the challenge.
Adversarial Learning-Based Sentiment Analysis for Socially Implemented IoMT Systems

Sentiment analysis is an important task in social computing and behavior analysis, and is a typical indicator of social health. It is a challenging mission to predict the sentiment of people in socially implemented Internet of Medical Things (IoMT) systems. The existing methods have several defects, and a typical defect is that most methods ignore the fact that there is much noise in IoMT systems and it is far not enough only to develop classification models for one type of data. In socially implemented IoMT systems, many methods treat the review text as plain text but ignore the potential knowledge structure. To solve those problems, in this article, we propose a novel solution, which is composed of adversarial learning and a hierarchical attention mechanism. We construct a hierarchical attention mechanism to learn the knowledge structure of a text. We propose to apply the attention mechanism both at the word level and sentence level, enabling us to learn the knowledge from each word and each sentence. We propose to use adversarial learning to learn new knowledge as non-random perturbations, which promotes the model’s robustness. We evaluate our method on several large-scale real-world datasets, covering a wide range of cases of sentiment analysis. Experimental results demonstrate that our method achieves superior performance compared to state-of-the-art methods.
Emotion Prediction Oriented Method With Multiple Supervisions for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotion-cause pairs from two perspectives of emotion and cause. However, emotion extraction is more crucial to the ECPE task than cause extraction. Motivated by this analysis, we propose an end-to-end emotion-cause extraction approach oriented toward emotion prediction (EPO-ECPE), aiming to fully exploit the potential of emotion prediction to enhance emotion-cause pair extraction. Considering the strong dependence between emotion prediction and emotion-cause pair extraction, we propose a synchronization mechanism to share their improvement in the training process. That is, the improvement of emotion prediction can facilitate the emotion-cause pair extraction, and then the results of emotion-cause pair extraction can also be used to improve the accuracy of emotion prediction simultaneously. For the emotion-cause pair extraction, we divide it into genuine pair supervision and fake pair supervision, where the genuine pair supervision learns from the pairs with more possibility to be emotion-cause pairs. In contrast, fake pair supervision learns from other pairs. In this way, the emotion-cause pairs can be extracted directly from the genuine pair, thereby reducing the difficulty of extraction. Experimental results show that our approach outperforms the 13 compared systems and achieves new state-of-the-art performance.
Quantifying the Effects of Ground Truth Annotation Quality on Object Detection and Instance Segmentation Performance

Fully-supervised object detection and instance segmentation models have accomplished notable results on large-scale computer vision benchmark datasets. However, fully-supervised machine learning algorithms’ performances are immensely dependent on the quality of the training data. Preparing computer vision datasets for object detection and instance segmentation is a labor-intensive task requiring each instance in an image to be annotated. In practice, this often results in the quality of bounding box and polygon mask annotations being suboptimal. This paper quantifies empirically the ground truth annotation quality and COCO’s mean average precision (mAP) performance by introducing two separate noise measures, uniform and radial, into the ground truth bounding box and polygon mask annotations for the COCO and Cityscapes datasets. Mask-RCNN models are trained on various levels of noise measures to investigate the performance of each level of noise. The results showed degradation of mAP as the level of both noise measures increased. For object detection and instance segmentation respectively, using the highest level of noise measure resulted in a mAP degradation of 0.185 & 0.208 for uniform noise with reductions of 0.118 & 0.064 for radial noise on the COCO dataset. As for the Cityscapes datasets, reductions of mAP performance of 0.147 & 0.142 for uniform noise and 0.101 & 0.033 for radial noise were recorded. Furthermore, a decrease in average precision is seen across all classes, with the exception of the class motorcycle. The reductions between classes vary, indicating the effects of annotation uncertainty are class-dependent.
Fridolin: participatory design and evaluation of a nutrition chatbot for older adults

Abstract In recent years, emerging approaches to chatbot-guided food coaching and dietary management, while innovative and promising in nature, have often lacked long-term studies. Therefore, with this work, we pursued a participatory approach within a design case study to the co-design and development of a nutrition chatbot for elderly people. Overall, 15 participants were directly involved in the study, of which 12 participated in the initial co-design phase, seven in the first real-world evaluation study over four weeks, and three in the second evaluation study over seven weeks. We contribute to the fields of Human-Computer Interaction by showing how the long-term use of such a chatbot in the area of nutrition looks like, which design implications arise for the development of nutrition chatbots, and how a participatory design approach can be realized to design, evaluate and develop nutrition chatbots.
Quantifying Continental Crust Thickness Using the Machine Learning Method

Abstract Crustal thickness plays a key role in many geological processes. However, it remains challenging to quantify crustal thickness in the geological past. Here we propose an Extremely Randomized Trees algorithm‐based machine learning model to recover crustal thickness of old geological regions. The model is trained using major oxide and trace element compositions of 1,480 young intermediate to felsic rocks from global arcs and collisional orogens and geophysical measurements of crustal thickness. The model provides better estimations of crustal thickness than the commonly used methods based on Sr/Y and (La/Yb) N when applied to the testing data. The validity of this model is further demonstrated by its applications to the Kohistan–Ladakh, Gangdese and Talkeetna arcs, where paleocrustal thicknesses have been well constrained. We then use this model to construct the Mesozoic crustal thickness evolution of the Erguna Block in the southeast of the Mongol–Okhotsk suture belt. The closure time of the suture zone is still debated. Our results suggest that the crustal thickness of the Erguna Block increased from 43 ± 9 km at 210 Ma to 62 ± 7 km at 180 Ma, remained constant between 180 and 150 Ma, and then thinned to 36 ± 4 km at 120 Ma. These results suggest that the Mongol–Okhotsk Ocean closed in the Early–Middle Jurassic and the thickened crust was stretched during the Cretaceous. We show that the thick crust and compression‐extension transition seem to be favorable for the formation of porphyry copper deposits in the Erguna Block during the Late Jurassic.
Refining neural network predictions using background knowledge

Abstract Recent work has shown learning systems can use logical background knowledge to compensate for a lack of labeled training data. Many methods work by creating a loss function that encodes this knowledge. However, often the logic is discarded after training, even if it is still helpful at test time. Instead, we ensure neural network predictions satisfy the knowledge by refining the predictions with an extra computation step. We introduce differentiable refinement functions that find a corrected prediction close to the original prediction. We study how to effectively and efficiently compute these refinement functions. Using a new algorithm called iterative local refinement (ILR), we combine refinement functions to find refined predictions for logical formulas of any complexity. ILR finds refinements on complex SAT formulas in significantly fewer iterations and frequently finds solutions where gradient descent can not. Finally, ILR produces competitive results in the MNIST addition task.
Client Selection With Staleness Compensation in Asynchronous Federated Learning

As a nascent privacy-preserving machine learning (ML) paradigm, federated learning (FL) leverages distributed clients at the network edge to collaboratively train an ML model. Asynchronous FL overcomes the straggler issue in synchronous FL. However, asynchronous FL incurs the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staleness</i> problem, which degrades the training performance of FL over wireless networks. To tackle the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staleness</i> problem, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staleness</i> compensation algorithm to improve the training performance of FL in terms of convergence and test accuracy. By including the first-order term in Taylor expansion of the gradient function, the proposed algorithm compensates the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staleness</i> in asynchronous FL. To further minimize training latency, we model the client selection for asynchronous FL as a multi-armed bandit problem. We develop an online client selection algorithm to minimize training latency without prior knowledge of the channel condition or local computing status. Simulation results show that the proposed algorithm outperforms the baseline algorithms in both test accuracy and training latency.
Fed-IoUT: Opportunities and Challenges of Federated Learning in the Internet of Underwater Things

The Internet of Underwater Things (IoUT), as an emerging technology, plays an important role in the development of marine resources. With the growth of IoUT equipment, data from various sources has increased dramatically. Multiple challenges, such as data privacy, communication overhead, and data heterogeneity, arise with the application of new underwater Internet of Things (IoT) scenarios. To solve these challenges, we propose an IoUT framework called Fed-IoUT, which utilizes Federated Learning (FL) paradigm. FL, with its advantages of data privacy protection, low communication overhead, and balanced computing resources, is perfectly suited to our scenario. Fed-IoUT can make full use of the large amount of data collected by IoUT equipment, reduce data communication overhead, and protect the privacy of observing data. In this article, we also introduced the application of Fed-IoUT in relevant fields from five aspects: water quality monitoring, underwater life detection, flood forecasting, underwater detection, and marine energy transfer. And then, we put forward the limitations of development under the current conditions and made a comprehensive outlook for the future.
Facial reconstruction using 3-D computerized method: A scoping review of Methods, current Status, and future developments

Facial reconstruction (otherwise known as facial approximation) is an alternative method that has been widely accepted in forensic anthropological and archaeological circumstances. This method is considered useful for creating the virtual face of a person based on skull remain. Three-dimensional (3-D) traditional facial reconstruction (known as sculpture or manual method) has been recognized for more than a century; however, it was declared to be subjective and required anthropological training. Until recently, with the progression of computational technologies, many studies attempted to develop a more appropriate method, so-called the 3-D computerized facial reconstruction. This method also relied on anatomical knowledge of the face-skull relationship, divided into semi- and automated based computational method. The 3-D computerized facial reconstruction makes it more rapid, more flexible, and more realistic to generate multiple representations of faces. Moreover, new tools and technology are continuously generating fascinating and sound research as well as encouraging multidisciplinary collaboration. This has led to a paradigm shift in the 3-D computerized facial reconstruction to a new finding and new technique based on artificial intelligence in academia. Based on the last 10-years scientific-published documents, this article aims to explain the overview of the 3-D computerized facial reconstruction and progression as well as an issue relating to future directions to encourage further improvement.
Automatic modulation recognition based on a multiscale network with statistical features

Automatic modulation recognition (AMR) can be used in dynamic spectrum access (DSA) techniques to reduce the pressure on spectrum resources. In this paper, we propose a multiscale convolution-based network model called MSNet-SF, which combines traditional statistical features and deep learning (DL) to balance recognition accuracy and complexity. In the model, the feature information is extracted by two multiscale modules, which consist of unit convolution and three different sizes of convolution kernels arranged in parallel. Additionally, the sparse connectivity of unit convolution enables the network to be more lightweight. Five statistical features (four higher-order cumulants (HOCs) and one zero-centered normalized instantaneous magnitude tightness) are also input into the model and are fully fused with the main feature map by multiplication to achieve complementarity of long-term and short-term features. This approach yields a large performance gain at a small cost and greatly reduces the confusion between QAM16 and QAM64. Simulation results in the RML2018.10A dataset show that the average recognition accuracy of the model improved by 4% after adding the statistical features and achieved an accuracy of more than 97% from 12 dB.
Texture and geochemistry of sphalerite from the Chitudian Pb-Zn-Ag deposit, southern margin of the North China Craton: Implications for the enrichments of Cd, Ga, and In

The Chitudian Pb-Zn-Ag deposit, located in the southern margin of the North China Craton (SNCC), is a typical magmatic-hydrothermal deposit, where sphalerite is the predominate ore mineral with significant concentrations of critical metals, including Ga, In, and Cd. However, previous studies paid little attention to the critical metals in the deposit. This study carried out in-suit elemental analysis on the sphalerite from the Chitudian deposit, aiming to constrain the spatial distribution and enrichment mechanism of critical metals. In the Chitudian deposit, five types of sphalerite were identified (Z-Sp, Y-Sp1, Y-Sp2, X-Sp1, and X-Sp2), which differ in color, texture, and chemical composition. The concentrations of Cd, Ga, and In of these sphalerites range from 1440 to 5145 ppm, 0.53 to 528 ppm, and 0.75 to 2113 ppm, respectively. In detail, Cd is mainly concentrated in the sphalerites from the hydrothermal vein-type orebodies relative to skarn-type orebodies. Indium is progressively depleted from granite-proximal locations to granite-distal locations, and the Fe-rich sphalerite is an excellent host of In. Gallium enrichment is only observed in the tectonically deformed location of the sulfide ore vein, where the Ga-rich sphalerite is poor in Fe and shows a complex texture. Microanalysis conducted on the Ga-rich sphalerite suggested that Ga, together with In, Sn, and Cu, are removed from the deformed sphalerite by mineral-fluid interaction and subsequently sequestered by the late sphalerite cement. Considering that the Ga-rich sphalerite is close to the Ga-bearing coal bed of the Meiyaogou Formation, it is likely that the coal bed could be a potential source for Ga. These results highlight that tectonic deformation followed by hydrothermal fluid overprinting is favorable for the enrichment of critical metals in sphalerite-rich deposits.
Multilingual Sentiment Analysis using Deep-Learning Architectures

Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets shared by the people on social media can be mined to infer the valuable ‘mindset’ of the people which is much required for any ruling government in the world. The objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. The idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. The pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). The larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. The non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries’ sentiment and their corresponding reliance on Russian oil is performed.