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Pressure-dependent electronic,optical,and mechanical properties of antiperovskite X_(3)NP(X=Ca,Mg):A first-principles study

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Journal of Semiconductors 2023年第10期44卷 52-60页

作者： Chunbao Feng Changhe Wu Xin Luo Tao Hu Fanchuan Chen Shichang Li Shengnan Duan Wenjie Hou Dengfeng Li Gang Tang Gang Zhang School of Science Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute for Advanced Sciences Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Computer Science and Technology Northwestern Polytechnical UniversityXi’an 710129China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China Institute of High Performance Computing A*STAR138632Singapore

Hydrostatic pressure provides an efficient way to tune and optimize the properties of solid materials without chang-ing their *** this work,we investigate the electronic,optical,and mechanical properties of antiperovskite X_(3)NP(X^(2+)=Ca,Mg)upon compression by first-principles *** results reveal that the system is anisotropic,and the lat-tice constant a of X_(3)NP exhibits the fastest rate of decrease upon compression among the three directions,which is different from the typical Pnma phase of halide and chalcogenide ***,Ca_(3)NP has higher compressibility than Mg_(3)NP due to its small bulk *** electronic and optical properties of Mg_(3)NP show small fluctuations upon compression,but those of Ca_(3)NP are more sensitive to pressure due to its higher compressibility and lower unoccupied 3d orbital *** example,the band gap,lattice dielectric constant,and exciton binding energy of Ca_(3)NP decrease rapidly as the pressure *** addition,the increase in pressure significantly improves the optical absorption and theoretical conversion effi-ciency of Ca_(3)***,the mechanical properties of X_(3)NP are also increased upon compression due to the reduction in bond length,while inducing a brittle-to-ductile *** research provides theoretical guidance and insights for future experi-mental tuning of the physical properties of antiperovskite semiconductors by pressure.

关键词： antiperovskite hydrostatic pressure physical properties first-principles calculations

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Facial Depression Estimation via Multi-Cue Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6007-6020页

作者： Wang, Xinke Xu, Jingyuan Sun, Xiao Li, Mingzheng Hu, Bin Qian, Wei Guo, Dan Wang, Meng Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230031 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230094 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Vision-based depression estimation is an emerging yet impactful task, whose challenge lies in predicting the severity of depression from facial videos lasting at least several minutes. Existing methods primarily focus on fusing frame-level features to create comprehensive representations. However, they often overlook two crucial aspects: 1) inter- and intra-cue correlations, and 2) variations among samples. Hence, simply characterizing sample embeddings while ignoring to mine the relation among multiple cues leads to limitations. To address this problem, we propose a novel Multi-Cue Contrastive Learning (MCCL) framework to mine the relation among multiple cues for discriminative representation. Specifically, we first introduce a novel cross-characteristic attentive interaction module to model the relationship among multiple cues from four facial features (e.g., 3D landmarks, head poses, gazes, FAUs). Then, we propose a temporal segment attentive interaction module to capture the temporal relationships within each facial feature over time intervals. Moreover, we integrate contrastive learning to leverage the variations among samples by regarding the embeddings of inter-cue and intra-cue as positive pairs while considering embeddings from other samples as negative. In this way, the proposed MCCL framework leverages the relationships among the facial features and the variations among samples to enhance the process of multi-cue mining, thereby achieving more accurate facial depression estimation. Extensive experiments on public datasets, DAIC-WOZ, CMDC, and E-DAIC, demonstrate that our model not only outperforms the advanced depression methods but that the discriminative representations of facial behaviors provide potential insights about depression. Our code is available at:https://***/xkwangcn/***. © 1991-2012 IEEE.

关键词： Face recognition

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Mitigating Credit Card Fraud: A Machine Learning Perspective 2

Mitigating Credit Card Fraud: A Machine Learning Perspective

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2nd International Conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Bhushan, Shivam Gupta, Indrajeet Angayarkanni, S.A. Balakiruthiga, B. Pandimurugan, V. Rajaram, V. School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India Acharya Institute of Technology Computer Science and engineering Bangalore India

ISBN: (纸本)9798350366570

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape © 2024 IEEE.

关键词： Random forests

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Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

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science China(Technological sciences) 2024年第1期67卷 197-208页

作者： REN ZiLiang ZHANG QieShi CHENG Qin XU ZhenYu YUAN Shuai LUO DeLin School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Department of the mechanical engineering Erlangen-Nuremberg UniversityErlangen 91508Germany School of Aerospace Engineering Xiamen UniversityXiamen 361102China

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive ***,one of the main challenges is to effectively extract complementary features from different modalities for action *** this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action *** on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation *** evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and *** results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

关键词： action recognition segment frame difference aggregation supervised compensation learning ConvNets

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Secure communication routing and attack detection in UAV networks using Gannet Walruses optimization algorithm and Sheppard Convolutional Spinal Network

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Peer-to-Peer Networking and Applications 2024年第5期17卷 3269-3285页

作者： Renu, Yuvaraj Sarveshwaran, Velliangiri Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu India Department of Computer Science and Information Engineering National Chung Cheng University Minxiong Chiayi Taiwan

Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the Satellite-Air -Ground-Sea (SAGS) incorporated network. However, UAVs are affected by communication delays and malicious attacks. Therefore, an adequate and secure communication routing and attack detection model is necessary for UAV communication networks. This research described a novel approach for initiating secure communication in UAV networks namely Gannet Walruses Optimization Algorithm + Sheppard Convolutional Spinal Network (GWOA + ShCSpinalNet). Initially, the UAV network is simulated, and the data packets are transmitted among the nodes using optimal routing paths. An optimal routing path is computed using the Gannet Walruses Optimization Algorithm (GWOA) by considering some multi-objective functions through the Deep Recurrent Neural Network (DRNN). The developed GWAO integrates Gannet Optimization (GOA) and Walruses Optimization (WaOA). The data communication is done through monitoring agents. The newly devised Sheppard Convolutional Spinal Network (ShCSpinalNet) is utilized as a decision-making agent for malicious attack detection. The attributes considered for decision-making are round trip time, packet delivery ratio, the strength of the signal, the size of the packet, and the number of incoming packets. Once the SpinalNet categorizes the normal and attacked nodes the defense agent is implemented for attack migration. The ShCSpinalNet is devised by the combination of the Sheppard Convolutional Neural Network and Spinal Network. The GWOA + ShCSpinalNet accomplishes a diminished delay of 0.614 s, an increased detection rate of 0.930%, an energy of 0.439 J, and a Packet Delivery Ratio (PDR) of 0.749. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recurrent neural networks

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Leveling Up Together: Fostering Positive Growth and Safe Online Spaces for Teen Roblox Developers 25

Leveling Up Together: Fostering Positive Growth and Safe Onl...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Choi, Yubin Choi, Jeanne Seering, Joseph Computer Science Korea Advanced Institute of Science & Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science & Technology Daejeon Korea Republic of

ISBN: (纸本)9798400713941

Creating games together is both a playful and effective way to develop skills in computational thinking, collaboration, and more. However, game development can be challenging for younger developers who lack formal training. While teenage developers frequently turn to online communities for peer support, their experiences may vary. To better understand the benefits and challenges teens face within online developer communities, we conducted interviews with 18 teenagers who created games or elements in Roblox and received peer support from one or more online Roblox developer communities. Our findings show that developer communities provide teens with valuable resources for technical, social, and career growth. However, teenagers also struggle with inter-user conflicts and a lack of community structure, leading to difficulties in handling complex issues that may arise, such as financial scams. Based on these insights, we propose takeaways for creating positive and safe online spaces for teenage game creators. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis 3

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Roshan Ahmed, N. Shridevi, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Center for Advanced Data Science Chennai India

ISBN: (纸本)9798350372120

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis. © 2024 IEEE.

关键词： Embeddings

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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advanced 3D-printed antibacterial nano 58S bioglass/AgNPs/CeO2-based scaffolds for bone tissue engineering

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Ceramics International 2025年第15期51卷 20939-20953页

作者： Singh, Indrajeet Kong, Ing Viswakarma, Ashok Balani, Kantesh Advanced Polymer and Composite Materials Laboratory Department of Engineering School of Computing Engineering and Mathematical Sciences La Trobe University BendigoVIC3550 Australia Department of Materials Science and Engineering Indian Institute of Technology Kanpur208016 India Department of Mechanical Engineering Indian Institute of Technology Kanpur208016 India

58S bioglass (BG) has been considered an effective bioceramic in bone tissue engineering due to its good osteoinductive and osteoconductive properties;however, its poor antibacterial and printing properties remain a challenge in the development of ideal patient-specific, fully interconnected, bone-like scaffolds. To address this issue, this study presents novel 3D-printed BG/AgNPs/CeO2 (BGAC3D) composite scaffolds. These scaffolds exhibit a fully interconnected pore network (≈99 %) with a porosity of ≈64 % and optimally designed pore and strut sizes of 404 ± 15 μm and 394 ± 30 μm, respectively, making them suitable for bone healing applications. Adding AgNPs and ceria nanoparticles significantly increased the shear-thinning properties of Pluronic F-127/BG/AgNPs/CeO2-based ink, as compared to Pluronic F-127/BG-based ink. As a result, BGAC3D scaffold showed improved dimensional accuracy during printing relative to pristine BG. Nanoindentation studies revealed substantial increase in the mechanical properties of BGAC3D scaffolds, with elastic modulus, hardness, and compressive strength showing enhancement of 76 % (57.9 ± 8 GPa), 24 % (3.1 ± 1 GPa), and 275 % (1.5 ± 0.4 MPa), respectively. The antibacterial properties of BGAC3D scaffolds were investigated using disc diffusion and bacterial adhesion assays, which demonstrated an exceptional bactericidal efficacy of 99.3 % against Escherichia coli (E. coli) and 99.4 % against Staphylococcus aureus (S. aureus) compared to pristine BG. The cytocompatibility of the scaffolds was assessed using resazurin assays with mouse BMSCs and MTT assays with mouse fibroblasts, revealing enhanced cell viability for the BGAC3D composition compared to BG. Additionally, both BG and BGAC3D composites showed similar apatite mineralization on their surfaces, as observed under a scanning electron microscope. These findings indicate that BGAC3D scaffolds possess the necessary mechanical, antibacterial, and cytocompatibility properties, making th

关键词： Escherichia coli

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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