#multilabels

[sir-dyke-genderpunk]

Maybe I like identifying as genderfuck. Maybe I like identifying as soft butch. Maybe I like identifying as t4t. Maybe I like being a fagdyke and a lesbian and genderfluid and transgender and a furry and posic and objectum. Maybe I like having multiple ways to describe my identity.

Multilabels and microlabels are so hated (especially by terfs I've noticed) by the queer community but personally I think we need More, even Weirder identities. Be a dog, or an asexual kinkster, be abrosexual and a dragon and an inanimate object all at once. Let's all get weirder and queerer, together <3

[imoga-pride]

Just because some identity is contradictory to you doesn't mean it can't make sense. Actually, nuanced identities are often contradictory when you know the basics of it, ignoring all details and complexity a multilabeled or combined identity can be.

If you think cisgender nonbinary people can't have a definition bc then it would describe someone who is not cis, then you're in the wrong bc just cause something describes you it doesn't mean it applies or include you bc the identity requires self-identification as well.

Same goes for bi lesbian, demisexual, or lesboy, you 'are technically lesboy' (/demi/lunian) but refuses to label yourself as such? Cool then you're not/don't experience that identity 🤯 and that's okay. But to imply anyone with such identity are not that thing bc it can't exist, then you're being a reality denier, bc ppl with such identities exist regardless if you recognize them or not. For instance, just bc someone uses ungrammatical conlangs doesn't mean that language can't exist. In fact, it's written already and it's on you if you're gonna cry or ignore.

Also transhet people are allowed to be nonbinary; binaryn't transfems/transmascs are allowed to be hetero due to their alignment, presentation, AGAB, or any other typical reason.

[bamthefox]

so the idea is that a hunted house was turned into a dark world with most stuff being based on horror tropes or monsters

we have two lightners who heard rumors of the hunted hunted house with no one daring to go there be-besides this two (their sharing half a brain cell). When getting there the house was just a dark abyss the two took a step into a dark and fell.

First area would be a spooky forest swamp area a fisherman based on Jason being a boss there. The two would meet their new partner here a rubber hose ghost based on sadako (haven’t made a design for her sorry)

Second area is very luigi mansion coated but it’s just a town a SPOOKY TOWN with you meeting the secret boss there

his a candy bucket that was left behind by some trick or treats who got (somehow) scared of the haunted house and ran out of the house.

he was left alone and was lost not knowing about where he is but a voice no multilabel voices wanting to guide him, his heavily based on frankenstein and bob velseb from spooky month.

he ties to act and be friendly but the darkners are scared of him. the voices in his head have a plan to trick the two lightner and have them be led away.

the catacombs a dark very dusty place (the shafts out of site for the people who go into the haunted house)

the main boss's EVIL LAB (the employees only room)

the lightners and the pumpkin go into a room where the pumpkin no longer wants to go along with the plan but the candy in his head take control and now he has to fight his new and only friends.

[blackbutlerfandomnerddomain]

My Stardust and Tearducts AU will be officially starting soon! My ask blog is up and ready for everything to come!

This is a post mostly to show this forum/poll list to help me with the romances of the AU since I am STILL struggling with the ships I want to have to the point I went to the original post and changed Zim's sexual/romantic identity for future ideas, I did write some fics related to the AU but aren't 100% set in the universe until further notice!

Now since I can't make multicable polls in one post and I don't want to make multilabel posts of polls for varies ships I'm debating with the cast, I have decided to make a form in Google Forms to log all the votes of possible shipping of the AU and will post the final results as the stories get posted. There will be a deadline but I haven't yet decided on it so you're free to do it until I close it/remove the link on this post until further notice

If there's a ship you want me to add in the form let me know on this post!

[beautifulpersonpeach]

potentially dumb question: you keep referring to bsh being the driver of all of this, but my understanding is that the multilabel system was propelled by pjw when he became ceo in 2021 when bsh stepped back. He’s chairman now and has like 30-something% shares, but why would the blame for the legal strategy and multilabel strategy be placed on bsh? I get that he’s probably personally influential within the company and on the BoD, but could we really say that he’s behind all of these decisions related to this case? He did actively step back, and seemingly it was his own choice. He clearly doesn’t like mhj and is influential but people are speaking like he is the ceo and I am confused

***

It’s because Bang Sihyuk bears the most responsibility in this situation. He hired Park Jiwon to be CEO, as Chairman and head of the Board of Directors, Park Jiwon answers to him. Bang Sihyuk has given media presentations where he takes credit for implementing the multi- label system. And as Min Heejin revealed during her press conference, he apparently holds tight rein on everything that happens in the company. Meaning in reality, he didn’t actually step back. Also, I initially thought Min Heejin’s dispute was with Kim Taeho (Belift CEO) with Bang Sihyuk as a bystander, but she’s clarified beyond all doubt that this mess started with Bang Sihyuk himself and Park Jiwon.

Bang Sihyuk is a man who likes control. Given he spent 15 years basically micromanaging in BigHit, I didn’t really buy he’d ’stepped back’ as much as was implied when it was announced but I assumed he’d at least focus solely on BigHit and producing for some time at least. But lol, I guess old habits die hard.

I keep saying Bang Sihyuk is singularly responsible for this situation, because that’s what I see to be true.

[orchid-merryweather]

Bi cis(afab)

Omni, ageosexual, cupiosexual, demisexual, myrromantic, gender fluid, gender apathetic, rlly just gender: ???. In irl id just say I'm multilabel or just say I'm bi because *gestures vaguely* all that

Everyone introduce yourself by what you thought your OG queer identity was and what you currently identify as

Ill go first: hi I was bisexual now I’m a pansexual aroace

[drmikewatts]

IEEE Transactions on Artificial Intelligence, Volume 5, Issue 9, September 2024

1) Editorial: From Explainable Artificial Intelligence (xAI) to Understandable Artificial Intelligence (uAI)

Author(s): Hussein Abbass, Keeley Crockett, Jonathan Garibaldi, Alexander Gegov, Uzay Kaymak, Joao Miguel C. Sousa

Pages: 4310 - 4314

2) Incomplete Graph Learning via Partial Graph Convolutional Network

Author(s): Ziyan Zhang, Bo Jiang, Jin Tang, Jinhui Tang, Bin Luo

Pages: 4315 - 4321

3) Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

Author(s): Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali, Mohamed Abdallah, Junaid Qadir, Dinh Thai Hoang, Dusit Niyato, Ala Al-Fuqaha

Pages: 4322 - 4343

4) A Distributed Conditional Wasserstein Deep Convolutional Relativistic Loss Generative Adversarial Network With Improved Convergence

Author(s): Arunava Roy, Dipankar Dasgupta

Pages: 4344 - 4353

5) A Similarity-Based Positional Attention-Aided Deep Learning Model for Copy–Move Forgery Detection

Author(s): Ayush Roy, Sk Mohiuddin, Ram Sarkar

Pages: 4354 - 4363

6) Enhancing Reinforcement Learning via Transformer-Based State Predictive Representations

Author(s): Minsong Liu, Yuanheng Zhu, Yaran Chen, Dongbin Zhao

Pages: 4364 - 4375

7) Bilateral-Head Region-Based Convolutional Neural Networks: A Unified Approach for Incremental Few-Shot Object Detection

Author(s): Yiting Li, Haiyue Zhu, Sichao Tian, Jun Ma, Cheng Xiang, Prahlad Vadakkepat

Pages: 4376 - 4390

8) Regional Ensemble for Improving Unsupervised Outlier Detectors

Author(s): Jiawei Yang, Sylwan Rahardja, Susanto Rahardja

Pages: 4391 - 4402

9) Progressively Select and Reject Pseudolabeled Samples for Open-Set Domain Adaptation

Author(s): Qian Wang, Fanlin Meng, Toby P. Breckon

Pages: 4403 - 4414

10) A Lightweight Multidendritic Pyramidal Neuron Model With Neural Plasticity on Image Recognition

Author(s): Yu Zhang, Pengxing Cai, Yanan Sun, Zhiming Zhang, Zhenyu Lei, Shangce Gao

Pages: 4415 - 4427

11) A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

Author(s): Emrah Hancer, Bing Xue, Mengjie Zhang

Pages: 4428 - 4442

12) A Nonparametric Split and Kernel-Merge Clustering Algorithm

Author(s): Khurram Khan, Atiq ur Rehman, Adnan Khan, Syed Rameez Naqvi, Samir Brahim Belhaouari, Amine Bermak

Pages: 4443 - 4457

13) Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function

Author(s): Sheng-Min Chiu, Yow-Shin Liou, Yi-Chung Chen, Chiang Lee, Rong-Kang Shang, Tzu-Yin Chang, Roger Zimmermann

Pages: 4458 - 4469

14) Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

Author(s): Rachel Selva Dhanaraj, M. Sridevi

Pages: 4470 - 4478

15) Retain and Adapt: Online Sequential EEG Classification With Subject Shift

Author(s): Tiehang Duan, Zhenyi Wang, Li Shen, Gianfranco Doretto, Donald A. Adjeroh, Fang Li, Cui Tao

Pages: 4479 - 4492

16) Prefetching-based Multiproposal Markov Chain Monte Carlo Algorithm

Author(s): Guifeng Ye, Shaowen Lu

Pages: 4493 - 4505

17) Shuffled Grouping Cross-Channel Attention-Based Bilateral-Filter-Interpolation Deformable ConvNet With Applications to Benthonic Organism Detection

Author(s): Tingkai Chen, Ning Wang

Pages: 4506 - 4518

18) An Intelligent Fingerprinting Technique for Low-Power Embedded IoT Devices

Author(s): Varun Kohli, Muhammad Naveed Aman, Biplab Sikdar

Pages: 4519 - 4534

19) A Novel Applicable Shadow Resistant Neural Network Model for High-Efficiency Grid-Level Pavement Crack Detection

Author(s): Handuo Yang, Ju Huyan, Tao Ma, Yitao Song, Chengjia Han

Pages: 4535 - 4549

20) Prioritized Local Matching Network for Cross-Category Few-Shot Anomaly Detection

Author(s): Huilin Deng, Hongchen Luo, Wei Zhai, Yanming Guo, Yang Cao, Yu Kang

Pages: 4550 - 4561

21) IOTM: Iterative Optimization Trigger Method—A Runtime Data-Free Backdoor Attacks on Deep Neural Networks

Author(s): Iram Arshad, Saeed Hamood Alsamhi, Yuansong Qiao, Brian Lee, Yuhang Ye

Pages: 4562 - 4573

22) An Unbiased Fuzzy Weighted Relative Error Support Vector Machine for Reverse Prediction of Concrete Components

Author(s): Zongwen Fan, Jin Gou, Shaoyuan Weng

Pages: 4574 - 4584

23) Stabilizing Diffusion Model for Robotic Control With Dynamic Programming and Transition Feasibility

Author(s): Haoran Li, Yaocheng Zhang, Haowei Wen, Yuanheng Zhu, Dongbin Zhao

Pages: 4585 - 4594

24) A Unified Conditional Diffusion Framework for Dual Protein Targets-Based Bioactive Molecule Generation

Author(s): Lei Huang, Zheng Yuan, Huihui Yan, Rong Sheng, Linjing Liu, Fuzhou Wang, Weidun Xie, Nanjun Chen, Fei Huang, Songfang Huang, Ka-Chun Wong, Yaoyun Zhang

Pages: 4595 - 4606

25) Learning Counterfactual Explanation of Graph Neural Networks via Generative Flow Network

Author(s): Kangjia He, Li Liu, Youmin Zhang, Ye Wang, Qun Liu, Guoyin Wang

Pages: 4607 - 4619

26) Linear Regression-Based Autonomous Intelligent Optimization for Constrained Multiobjective Problems

Author(s): Yan Wang, Xiaoyan Sun, Yong Zhang, Dunwei Gong, Hejuan Hu, Mingcheng Zuo

Pages: 4620 - 4634

27) Strategic Gradient Transmission With Targeted Privacy-Awareness in Model Training: A Stackelberg Game Analysis

Author(s): Hezhe Sun, Yufei Wang, Huiwen Yang, Kaixuan Huo, Yuzhe Li

Pages: 4635 - 4648

28) An Explainable Intellectual Property Protection Method for Deep Neural Networks Based on Intrinsic Features

Author(s): Mingfu Xue, Xin Wang, Yinghao Wu, Shifeng Ni, Leo Yu Zhang, Yushu Zhang, Weiqiang Liu

Pages: 4649 - 4659

29) Unsupervised Representation Learning for 3-D Magnetic Resonance Imaging Superresolution With Degradation Adaptation

Author(s): Jianan Liu, Hao Li, Tao Huang, Euijoon Ahn, Kang Han, Adeel Razi, Wei Xiang, Jinman Kim, David Dagan Feng

Pages: 4660 - 4674

30) A Causality-Informed Graph Intervention Model for Pancreatic Cancer Early Diagnosis

Author(s): Xinyue Li, Rui Guo, Hongzhang Zhu, Tao Chen, Xiaohua Qian

Pages: 4675 - 4685

31) Remaining Useful Life Prediction via Frequency Emphasizing Mix-Up and Masked Reconstruction

Author(s): Haoren Guo, Haiyue Zhu, Jiahui Wang, Vadakkepat Prahlad, Weng Khuen Ho, Clarence W. de Silva, Tong Heng Lee

Pages: 4686 - 4695

32) UPR-BP: Unsupervised Photoplethysmography Representation Learning for Noninvasive Blood Pressure Estimation

Author(s): Chenbin Ma, Peng Zhang, Fan Song, Zeyu Liu, Youdan Feng, Yufang He, Guanglei Zhang

Pages: 4696 - 4707

33) SBP-GCA: Social Behavior Prediction via Graph Contrastive Learning With Attention

Author(s): Yufei Liu, Jia Wu, Jie Cao

Pages: 4708 - 4722

34) Quadratic Neuron-Empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection

Author(s): Jing-Xiao Liao, Bo-Jian Hou, Hang-Cheng Dong, Hao Zhang, Xiaoge Zhang, Jinwei Sun, Shiping Zhang, Feng-Lei Fan

Pages: 4723 - 4737

35) Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network

Author(s): Le Zhang, Kevin Bronik, Stefan K. Piechnik, Joao A. C. Lima, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

Pages: 4738 - 4752

36) Variable Curvature Gabor Convolution and Multibranch Structures for Finger Vein Recognition

Author(s): Jun Li, Huabin Wang, Shicheng Wei, Jian Zhou, Yuankang Shen, Liang Tao

Pages: 4753 - 4764

37) ClassLIE: Structure- and Illumination-Adaptive Classification for Low-Light Image Enhancement

Author(s): Zixiang Wei, Yiting Wang, Lichao Sun, Athanasios V. Vasilakos, Lin Wang

Pages: 4765 - 4775

38) Improving Code Summarization With Tree Transformer Enhanced by Position-Related Syntax Complement

Author(s): Jie Song, Zexin Zhang, Zirui Tang, Shi Feng, Yu Gu

Pages: 4776 - 4786

39) Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

Author(s): Wahidur Rahman, Muhammad Minoar Hossain, Md. Mahedi Hasan, Md. Sadiq Iqbal, Mohammad Motiur Rahman, Khondokar Fida Hasan, Mohammad Ali Moni

Pages: 4787 - 4798

40) MTPret: Improving X-Ray Image Analytics With Multitask Pretraining

Author(s): Weibin Liao, Qingzhong Wang, Xuhong Li, Yi Liu, Zeyu Chen, Siyu Huang, Dejing Dou, Yanwu Xu, Haoyi Xiong

Pages: 4799 - 4812

41) Reinforced Reweighting for Self-Supervised Partial Domain Adaptation

Author(s): Keyu Wu, Shengkai Chen, Min Wu, Shili Xiang, Ruibing Jin, Yuecong Xu, Xiaoli Li, Zhenghua Chen

Pages: 4813 - 4822

42) Cross-Modality Calibration in Multi-Input Network for Axillary Lymph Node Metastasis Evaluation

Author(s): Michela Gravina, Domiziana Santucci, Ermanno Cordelli, Paolo Soda, Carlo Sansone

Pages: 4823 - 4836

43) A Novel Grades Prediction Method for Undergraduate Students by Learning Explicit Conditional Distribution

Author(s): Na Zhang, Ming Liu, Lin Wang, Shuangrong Liu, Runyuan Sun, Bo Yang, Shenghui Zhu, Chengdong Li, Cheng Yang, Yuhu Cheng

Pages: 4837 - 4848

[craigbrownphd-blog-blog]

If you did not already know

Deep Residual Hashing In this paper, we define an extension of the supersymmetric hyperbolic nonlinear sigma model introduced by Zirnbauer. We show that it arises as a weak joint limit of a time-changed version introduced by Sabot and Tarr\`es of the vertex-reinforced jump process. It describes the asymptotics of rescaled crossing numbers, rescaled fluctuations of local times, asymptotic local times on a logarithmic scale, endpoints of paths, and last exit trees. … EILearn We propose an algorithm for incremental learning of classifiers. The proposed method enables an ensemble of classifiers to learn incrementally by accommodating new training data. We use an effective mechanism to overcome the stability-plasticity dilemma. In incremental learning, the general convention is to use only the knowledge acquired in the previous phase but not the previously seen data. We follow this convention by retaining the previously acquired knowledge which is relevant and using it along with the current data. The performance of each classifier is monitored to eliminate the poorly performing classifiers in the subsequent phases. Experimental results show that the proposed approach outperforms the existing incremental learning approaches. … BiasedWalk Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-Search (DFS) samplings with the goal to capture homophily and role equivalence between the nodes in the network. We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks. The experiment results show that the proposed method outperforms the baseline ones in most of the tasks and datasets. … Cyclically Annealed Learning Rate (CALR) Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training,to compress the word embedding layer which represents the size bottleneck for most NLP models. Our models are trained, compressed and then further re-trained on the downstream task to recover accuracy while maintaining the reduced size. Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression. We also analyze the inference time and storage space for our method through FLOP calculations, showing that we can compress DNN models by a configurable ratio and regain accuracy loss without introducing additional latency compared to fixed point quantization. Finally, we introduce a novel learning rate schedule, the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate to outperform other popular adaptive learning rate algorithms on a sentence classification benchmark. … https://analytixon.com/2022/12/20/if-you-did-not-already-know-1915/?utm_source=dlvr.it&utm_medium=tumblr

[craigbrownphd]

If you did not already know

Deep Residual Hashing In this paper, we define an extension of the supersymmetric hyperbolic nonlinear sigma model introduced by Zirnbauer. We show that it arises as a weak joint limit of a time-changed version introduced by Sabot and Tarr\`es of the vertex-reinforced jump process. It describes the asymptotics of rescaled crossing numbers, rescaled fluctuations of local times, asymptotic local times on a logarithmic scale, endpoints of paths, and last exit trees. … EILearn We propose an algorithm for incremental learning of classifiers. The proposed method enables an ensemble of classifiers to learn incrementally by accommodating new training data. We use an effective mechanism to overcome the stability-plasticity dilemma. In incremental learning, the general convention is to use only the knowledge acquired in the previous phase but not the previously seen data. We follow this convention by retaining the previously acquired knowledge which is relevant and using it along with the current data. The performance of each classifier is monitored to eliminate the poorly performing classifiers in the subsequent phases. Experimental results show that the proposed approach outperforms the existing incremental learning approaches. … BiasedWalk Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-Search (DFS) samplings with the goal to capture homophily and role equivalence between the nodes in the network. We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks. The experiment results show that the proposed method outperforms the baseline ones in most of the tasks and datasets. … Cyclically Annealed Learning Rate (CALR) Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training,to compress the word embedding layer which represents the size bottleneck for most NLP models. Our models are trained, compressed and then further re-trained on the downstream task to recover accuracy while maintaining the reduced size. Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression. We also analyze the inference time and storage space for our method through FLOP calculations, showing that we can compress DNN models by a configurable ratio and regain accuracy loss without introducing additional latency compared to fixed point quantization. Finally, we introduce a novel learning rate schedule, the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate to outperform other popular adaptive learning rate algorithms on a sentence classification benchmark. … https://analytixon.com/2022/12/20/if-you-did-not-already-know-1915/?utm_source=dlvr.it&utm_medium=tumblr

[strategictech]

What to Avoid When Solving Multilabel Classification Problems

April Miller, a senior IT and cybersecurity writer for ReHack Magazine, suggests that If you are working with a model with a multilabel classification problem, there is a likely chance you will run into something in need of fixing. Here are a few common issues you may encounter and what to avoid when solving them.

[lovingitaromantic]

hey, if you’re aro and hoard labels like a dragon? I wanted to tell you that you’re so cool. Your identity is so vast and wonderful there’s all these words you can use! Having so many labels doesn���t make you a bad aro. the chemical components of the universe are vast and multiple, would you shame the universe for its complexity?

[sir-dyke-genderpunk]

person on tumblr: *had a perfectly balanced and nuanced, down to earth take*

Someone in the replies: You are objectively wrong and you must have a radical black and white view about everything

[imoga-pride]

Is there a term for when you use a lot of labels and are fluid between which ones you feel at a certain time ?

labelfluid/multilabel maybe?

[crystaljuice]

LITERALLY

This pride month lets discourse less and spend more time being uplifting to everyone, especially those who are frequently discoursed about

[analyticssteps]

Science and technology have significantly helped the human race to overcome most of its problems. From making people fly in the air to helping them in managing traffic on roads, science has been present everywhere.

[t4tstarvingdog]

and something i especially love and find joy in, is words like dyke and fag and transsexual!! sometimes binary, easy-box, checkmark labels don’t work for me! i’m queer because gay doesn’t have the range and obscurity my identity requires, i’m a fag because my gender and sexuality are all kinds of fucked up and fucked around!! i’m transsexual because it’s rad as hell ! there’s so much joy to be had in being unlabeled or multilabeled or to switch and swap ans be as vague as you want <3