Reviewerly/exHarmony huggingface dataset:
Triplet teaching graph contrastive networks with self-evolving adaptive augmentation

Unsupervised graph contrastive learning has recently emerged as the solution to the crisis of label information scarcity for graph data in the real world. However, from the general paradigm of graph contrastive learning, most of the existing methods are still flawed in the design and use of augmented views and the design of contrastive targets. Therefore, the works on how to generate reasonable augmented views and utilize them canonically and how to construct efficient and comprehensive contrastive objectives are very meaningful. Based on the teaching concept, this paper proposes a new triplet teaching graph contrastive network with self-evolving adaptive augmentation. Firstly, after carefully analyzing the internal relationships between different augmented perspectives, we present a triple teaching graph neural network framework based on the improved triplet idea. It creates contrastive objectives depending on different contrastive angle levels, providing thorough guidance for graph encoders. Secondly, a self-evolving adaptive graph augmentation scheme based on topology and feature information is proposed. It is worth mentioning that with the continuous deepening of the training process, the scheme can utilize the learnable self-attention mechanism to constantly supply our network framework with an increasing number of reliable augmented views as input. Finally, when designing the contrastive objectives, we introduce a stochastic hybrid module to mine the unexploited information, which opportunely complements the contrastive sample space formed by our network framework. Furthermore, extensive experiments on multiple real-world node classification datasets demonstrate that our model can generate better-quality node embedding for downstream tasks. The implementation of this paper is available at https://github.com/PaperMiao/T-GCSA.
Multi-Scale Depthwise Separable Convolution for Semantic Segmentation in Street–Road Scenes

Vision is an important way for unmanned mobile platforms to understand surrounding environmental information. For an unmanned mobile platform, quickly and accurately obtaining environmental information is a basic requirement for its subsequent visual tasks. Based on this, a unique convolution module called Multi-Scale Depthwise Separable Convolution module is proposed for real-time semantic segmentation. This module mainly consists of concatenation pointwise convolution and multi-scale depthwise convolution. Not only does the concatenation pointwise convolution change the number of channels, but it also combines the spatial features from the multi-scale depthwise convolution operations to produce additional features. The Multi-Scale Depthwise Separable Convolution module can strengthen the non-linear relationship between input and output. Specifically, the multi-scale depthwise convolution module extracts multi-scale spatial features while remaining lightweight. This fully uses multi-scale information to describe objects despite their different sizes. Here, Mean Intersection over Union (MIoU), parameters, and inference speed were used to describe the performance of the proposed network. On the Camvid, KITTI, and Cityscapes datasets, the proposed algorithm compromised between accuracy and memory in comparison to widely used and cutting-edge algorithms. In particular, the proposed algorithm acquired 61.02 MIoU with 2.68 M parameters on the Camvid test dataset.
Vulnerability assessment of the eco-geo-environment of mining cities in arid and semi-arid areas: A case study from Zhungeer, China

Assessing the ecological and geological environment vulnerability (EGEV) of cities arid and semi-arid regions is important for urban planning, development, and ecological protection. This study investigated the ecological and geological environmental problems and possible potential risk factors in Zhungeer City and constructed an EGEV assessment index system covering the geological environment, human activities, coal mining, and hydrological conditions. We proposed a linear weighted model to calculate the comprehensive weight of each indicator using the Analytical Hierarchy Process (AHP) and Entropy method based on distance function optimization (AELW), and frequency ratio accuracy is then introduced to compare the remarkable classification performance between AELW and other models. The frequency ratio accuracy results show that AELW model has the highest accuracy for EGEV assessment (62.77 %), followed by Multiplicative Integrated Normalization Model (MINM, 61.46 %), AHP (60.98 %), Ideal Point Model (IPM, 60.30 %) and Entropy (53.55 %). The EGEV of Zhungeer was classified as I-V from lowest into highest, among which the EGEV of I-II-III accounted for 78.15 %, EGEV of IV accounted for 16.42 %, and the EGEV of V risk area accounted for 5.43 %, with a good overall ecological and geological environment quality. These results can provide tangible management suggestions for reducing EGEV in Zhungeer city, and the AELW model provides direction for EGEV assessment in other similar areas.
Bidirectional temporal feature for <scp>3D</scp> human pose and shape estimation from a video

Abstract 3D human pose and shape estimation is the foundation of analyzing human motion. However, estimating accurate and temporally consistent 3D human motion from a video remains a challenge. By now, most of the video‐based methods for estimating 3D human pose and shape rely on unidirectional temporal features and lack more comprehensive information. To solve this problem, we propose a novel model “bidirectional temporal feature for human motion recovery” (BTMR), which consists of a human motion generator and a discriminator. The transformer‐based generator effectively captures the forward and reverse temporal features to enhance the temporal correlation between frames and reduces the loss of spatial information. The motion discriminator based on Bi‐LSTM can distinguish whether the generated pose sequences are consistent with the realistic sequences of the AMASS dataset. In the process of continuous generation and discrimination, the model can learn more realistic and accurate poses. We evaluate our BTMR on 3DPW and MPI‐INF‐3DHP datasets. Without the training set of 3DPW, BTMR outperforms VIBE by 2.4 mm and 14.9 mm/s 2 in PA‐MPJPE and Accel metrics and outperforms TCMR by 1.7 mm in PA‐MPJPE metric on 3DPW. The results demonstrate that our BTMR produces better accurate and temporal consistent 3D human motion.
Counterfactual can be strong in medical question and answering

Medical question and answering is a crucial aspect of medical artificial intelligence, as it aims to enhance the efficiency of clinical diagnosis and improve treatment outcomes. Despite the numerous methods available for medical question and answering, they tend to overlook the data generation mechanism’s imbalance and the pseudo-correlation caused by the task’s text characteristics. This pseudo-correlation is due to the fact that many words in the question and answering task are irrelevant to the answer but carry significant weight. These words can affect the feature representation and establish a false correlation with the final answer. Furthermore, the data imbalance mechanism can cause the model to blindly follow a large number of classes, leading to bias in the final answer. Confounding factors, including the data imbalance mechanism, bias due to textual characteristics, and other unknown factors, may also mislead the model and limit its performance. In this study, we propose a new counterfactual-based approach that includes a feature encoder and a counterfactual decoder. The feature encoder utilizes ChatGPT and label resetting techniques to create counterfactual data, compensating for distributional differences in the dataset and alleviating data imbalance issues. Moreover, the sampling prior to label resetting also helps us alleviate the data imbalance issue. Subsequently, label resetting can yield better and more balanced counterfactual data. Additionally, the construction of counterfactual data aids the subsequent counterfactual classifier in better learning causal features. The counterfactual decoder uses counterfactual data compared with real data to optimize the model and help it acquire the causal characteristics that genuinely influence the label to generate the final answer. The proposed method was tested on PubMedQA, a medical dataset, using machine learning and deep learning models. The comprehensive experiments demonstrate that this method achieves state-of-the-art results and effectively reduces the false correlation caused by confounders.
Homomorphic Encryption based Federated Learning for Financial Data Security

Federated Learning is a distributed machine learning technique that enables on-device training without exchanging the sensitive data over the centralized server. In this paper, Federated Learning is used to train financial data models on-devices with the help of IoT applications in financial or business systems. This creates more advanced and secured financial business models. We have applied the mechanism of homomorphic encryption and cryptographic primitives, including masking and local model protection to prevent any kind of inferring in private financial or business data where multiple attackers usually find a way of model inversion or reconstruction attack. We used datasets of various financial sectors as the primary measure, rather than taking the size of datasets that is generally used in deep learning, to get the correct measurement of the rate of contribution in every session of the local model’s training to the training of the global model. If the calculated online number of clients exceeds a predetermined threshold, then the federated learning process will be continued with the dropout-tolerant plan. The security study demonstrates that the suggested solution fulfils data privacy requirements. The costs of computation and communication are also examined theoretically. According to research observations, the proposed approach achieved promising outcomes while assuring privacy preservation when compared to existing schemes.
Learning Orientation-Aware Distances for Oriented Object Detection

Oriented object detectors have suffered severely from the discontinuous boundary problem for a long time. In this work, we ingeniously avoid this problem by relating regression outputs to regression target orientations. The core idea of our method is to build a contour function which imports orientations and outputs the corresponding distance predictions. Inspired by Fourier transformations, we assume this function can be represented as a linear combination of trigonometric functions and Fourier series. We replace the final 4D layer in the regression branch of fully convolutional one-stage object detector (FCOS) with a Fourier Series Transformation (FST) module and term this new network FCOSF. By this unique design, the regression outputs in FCOSF can adaptively vary according to the regression target orientations. Thus, the discontinuous boundary has no impact on our FCOSF. More importantly, FCOSF avoids building complicated oriented box representations, which usually cause extra computations and ambiguities. With only flipping augmentation and single-scale training and testing, FCOSF with ResNet-50 achieves 73.64% mAP on the DOTA-v1.0 dataset with up to 23.6 FPS speed, surpassing all one-stage oriented object detectors. On the more challenging DOTA-v2.0 dataset, FCOSF also achieves the highest results of 51.75% mAP among one-stage detectors. More experiments on DIOR-R and HRSC2016 are also conducted to verify the robustness of FCOSF. Code and models will be available at https://github.com/DDGRCF/FCOSF.
Executive skills and reading comprehension: A guide for educators

"Executive skills and reading comprehension: A guide for educators." The Journal of Educational Research, ahead-of-print(ahead-of-print), pp. 1–3 AcknowledgmentsThe book reviewers wish to thank LPDP (Lembaga Pengelola Dana Pendidikan) for its support of this book review.
Rapid Analysis of Muscovites on a Lithium Pegmatite Prospect by Handheld LIBS

Laser-induced breakdown spectroscopy (LIBS) is a technology for compositional analysis that is particularly effective for light elements, particularly Li, which is a critical commodity for emerging green technologies. This study undertook analysis by handheld LIBS of muscovite from the drill core, outcrop, and soil on the Carolina Lithium Prospect (CLP) in Gaston County, North Carolina (USA), which lies within the Carolina Tin-Spodumene Belt (CTSB). Abundances of the alkali elements Li, K, and Rb were determined for more than 130 muscovites from the Li-rich pegmatites to track the degree of pegmatite fractionation as a pathfinder for spodumene mineralization. Across the CTSB and including the CLP, muscovite Li contents vary over an order of magnitude, ranging from 0.04 to 0.74 wt. %, with their K/Rb ratios varying between 63 and 8, features that together document the highly evolved character of pegmatites within the CTSB district. On average, muscovite Li contents are greater in spodumene-bearing pegmatites at 0.21 ± 0.12 wt. % than for common quartz-feldspar pegmatites at 0.14 ± 0.08 wt. %. Although overlapping substantially in the middle portions of their distributions, muscovite K/Rb ratios are biased toward low values for spodumene-bearing pegmatites (X- = 21 ± 6) compared to those for quartz-feldspar pegmatites (X- = 33 ± 9). This study provides a framework for the use of LIBS analysis of muscovite in outcrop, drill core, and soil samples as an analytical tool for in-field and on-site geochemical analysis during Li pegmatite exploration and prospect evaluation.
Generative AI

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Optimized deep learning-based cricket activity focused network and medium scale benchmark

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for automatic activity recognition methods due to its closely overlapped activities such as cover drive, and pull short, to name a few. Existing methods often rely on hand-crafted features as the limited availability of public data has restricted the scope of research to only the significant categories of cricket activities. To this end, we proposed a cricket activities dataset and an intuitive end-to-end deep learning model for cricket activity recognition. The data is collected from online sources and pre-processed through cleaning, resizing, and organizing. Similarly, an intuitive deep model is designed with a combination of time-distributed 2D CNN layers and LSTM cells for extracting and learning the spatiotemporal information from the input sequences. For benchmarking, we evaluated the model on our cricket datasets and four standard datasets namely UCF101, HMDB51, YouTube action, and Kinetics. The quantitative results show that the proposed model outperforms different variants of recurrent neural networks and achieved an accuracy of 92%, recall of 91%, and F1 score of 91%. Our code and dataset is publicly available for further research on https://drive.google.com/file/d/1c9qcAz4q00qvx4yFA3pSudWFczm1cWUL/view?usp=sharing.
Adding tactile feedback increases avatar ownership and makes virtual reality more effective at reducing pain in a randomized crossover study

Severe pain is a widespread health problem in need of novel treatment approaches. In the current study we used real water to give virtual objects (i.e., animated virtual water) more realistic physical properties (wet liquid qualities). Healthy volunteers aged 18-34 participated in a within-subject randomized study comparing participants' worst pain during brief thermal stimuli with (1) No Immersive Virtual Reality (VR), versus (2) during VR + no tactile feedback versus (3) VR + real water (with tactile feedback from co-located real objects). Tactile feedback significantly decreased pain intensity (VR analgesia, p < 0.01), compared to VR with no tactile feedback, and compared to No VR (baseline). Tactile feedback made the virtual water feel significantly more real, increased participant's sense of presence, and both VR conditions were distracting (significantly reduced accuracy on an attention demanding task). As a non-pharmacologic analgesic, mixed reality reduced pain by 35% in the current study, comparable to the analgesia from a moderate dose of hydromorphone in previous published experimental studies. Tactile feedback also significantly increased avatar embodiment, the participants illusion of ownership of the virtual hands, which has potential to improve the effectiveness of avatar therapy for chronic pain in future studies. Mixed reality should be tested as treatment in pain patients.
nmODE: neural memory ordinary differential equation

Abstract Brain neural networks are regarded as dynamical systems in neural science, in which memories are interpreted as attractors of the systems. Mathematically, ordinary differential equations (ODEs) can be utilized to describe dynamical systems. Any ODE that is employed to describe the dynamics of a neural network can be called a neuralODE. Inspired by rethinking the nonlinear representation ability of existing artificial neural networks together with the functions of columns in the neocortex, this paper proposes a theory of memory-based neuralODE, which is composed of two novel artificial neural network models: nmODE and $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -net, and two learning algorithms: nmLA and $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -LA. The nmODE (neural memory Ordinary Differential Equation) is designed with a special structure that separates learning neurons from memory neurons, making its dynamics clear. Given any external input, the nmODE possesses the global attractor property and is thus embedded with a memory mechanism. The nmODE establishes a nonlinear mapping from the external input to its associated attractor and does not have the problem of learning features homeomorphic to the input data space, as occurs frequently in most existing neuralODEs. The nmLA (neural memory Learning Algorithm) is developed by proposing an interesting three-dimensional inverse ODE (invODE) and has advantages in memory and parameter efficiency. The proposed $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -net is a discrete version of the nmODE, which is particularly feasible for digital computing. The proposed $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -LA ( $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> learning algorithm) requires no prior knowledge of the number of network layers. Both nmLA and $$\epsilon$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -LA have no problem with gradient vanishing. Experimental results show that the proposed theory is comparable to state-of-the-art methods.
Ethereum Smart Contract Vulnerability Detection Model Based on Triplet Loss and BiLSTM

The wide application of Ethereum smart contracts in the Internet of Things, finance, medical, and other fields is associated with security challenges. Traditional detection methods detect vulnerabilities by stacking hard rules, which are associated with the bottleneck of a high false-positive rate and low detection efficiency. To make up for the shortcomings of traditional methods, existing deep learning methods improve model performance by combining multiple models, resulting in complex structures. From the perspective of optimizing the model feature space, this study proposes a vulnerability detection scheme for Ethereum smart contracts based on metric learning and a bidirectional long short-term memory (BiLSTM) network. First, the source code of the Ethereum contract is preprocessed, and the word vector representation is used to extract features. Secondly, the representation is combined with metric learning and the BiLSTM model to optimize the feature space and realize the cohesion of similar contracts and the discreteness of heterogeneous contracts, improving the detection accuracy. In addition, an attention mechanism is introduced to screen key vulnerability features to enhance detection observability. The proposed method was evaluated on a large-scale dataset containing four types of vulnerabilities: arithmetic vulnerabilities, re-entrancy vulnerabilities, unchecked calls, and inconsistent access controls. The results show that the proposed scheme exhibits excellent detection performance. The accuracy rates reached 88.31%, 93.25%, 91.85%, and 90.59%, respectively.
A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP

Currently, worldwide attention to clean energy and sustainable energy has been expedited because of its many environmental benefits. In fact, wind and solar energies play a prime role in decarbonizing the energy market. However, finding the most suitable locations for wind/solar power plants is difficult because of the non-homogeneous distribution of these sources. This paper presents a novel method for selecting the optimal locations for wind and solar farms by mapping the space of the decision criteria to the site score. In addition, the multiple linear regression model was used, with the help of the combination of GIS and AHP methods, to model the siting of wind and solar power plants. The site scoring method used in this study is reliable and globally evaluated; therefore, the scores are accurate and effective. To reveal the ability of the proposed method, two study areas were investigated and researched. The results achieved based on the introduced method showed that, in case study 1, areas with an area of about 9, 4 and 7 km2 are suitable for the construction of wind, solar and wind/solar power plants, respectively. This paper also used fourteen existing wind/solar, wind and solar farms from five continents around the world. The results showed that the suggested model acts the same as the real data. In addition to the interest these results hold for the development of renewable energy in the study area, this novel approach may be applied elsewhere to select optimum sites for wind, solar, and combined wind and solar farms.