Reviewerly/exHarmony huggingface dataset:
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection

Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set. The coexistence of in-distribution and out-of-distribution samples will exacerbate the model overfitting when no distinction is made. To address this problem, we propose a novel uncertainty-aware optimal transport scheme. Our scheme consists of an energy-based transport (ET) mechanism that estimates the fluctuating cost of uncertainty to promote the assignment of semantic-agnostic representation, and an inter-cluster extension strategy that enhances the discrimination of semantic property among different clusters by widening the corresponding margin distance. Furthermore, a T-energy score is presented to mitigate the magnitude gap between the parallel transport and classifier branches. Extensive experiments on two standard SCOOD benchmarks demonstrate the above-par OOD detection performance, outperforming the state-of-the-art methods by a margin of 27.69% and 34.4% on FPR@95, respectively. Code is available at https://github.com/LuFan3/IET-OOD.
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360°

Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360° with diverse appearance and detailed geometry using only in-the-wild unstructured images for training. At its core, we lift up the representation power of recent 3D GANs and bridge the data alignment gap when training from in-the-wild images with widely distributed views. Specifically, we propose a novel two-stage self-adaptive image alignment for robust 3D GAN training. We further introduce a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement rooted in the widely-adopted tri-plane formulation. Our method instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds. Benefiting from these designs, our method significantly outperforms previous 3D GANs, generating high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses. Furthermore, we show that our system can reconstruct full 3D heads from single input images for personalized realistic 3D avatars.
Hint-Aug: Drawing Hints from Foundation Vision Transformers towards Boosted Few-shot Parameter-Efficient Tuning

Despite the growing demand for tuningfoundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tuning) remains a challenge due to FViTs' data-hungry nature. Common data augmentation techniques fall short in this context due to the limited features contained in the few-shot tuning data. To tackle this challenge, we first identify an opportunity for FViTs in few-shot tuning: pretrained FViTs themselves have already learned highly representative features from large-scale pretraining data, which are fully preserved during widely used parameter-efficient tuning. We thus hypothesize that leveraging those learned features to augment the tuning data can boost the effectiveness of few-shot FViT tuning. To this end, we propose a framework called Hint-based Data Augmentation (Hint-Aug), which aims to boost FViT in few-shot tuning byaugmenting the over-fitted parts of tuning samples with the learned features of pretrained FViTs. Specifically, Hint-Aug integrates two key enablers: (1) an Attentive Over-fitting Detector (AOD) to detect over-confident patches offoundation ViTs for potentially alleviating their over-fitting on the few-shot tuning data and (2) a Confusion-based Feature Infusion (CFI) module to infuse easy-to-confuse features from the pretrained FViTs with the over-confident patches detected by the above AOD in order to enhance the feature diversity during tuning. Extensive experiments and ablation studies on five datasets and three parameter-efficient tuning techniques consistently validate Hint-Aug's effectiveness: 0.04% ~ 32.91 % higher accuracy over the state-of-the-art (SOTA) data augmentation method under various low-shot settings. For example, on the Pet dataset, Hint-Aug achieves a 2.22% higher accuracy with 50% less training data over SOTA data augmentation methods.
Neural Dependencies Emerging from Learning Massive Categories

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call neural dependency. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code to reproduce the results in this paper is available at https://github.com/RuiLiFengiNeural-Dependencies.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time

Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and less than ideal exposure setting. As a result, computational methods that jointly perform video frame interpolation and deblurring begin to emerge with the unrealistic assumption that the exposure time is known and fixed. In this work, we aim ambitiously for a more realistic and challenging task - joint video multi-frame interpolation and deblurring under unknown exposure time. Toward this goal, we first adopt a variant of supervised contrastive learning to construct an exposure-aware representation from input blurred frames. We then train two U-Nets for intramotion and inter-motion analysis, respectively, adapting to the learned exposure representation via gain tuning. We finally build our video reconstruction network upon the exposure and motion representation by progressive exposureadaptive convolution and motion refinement. Extensive experiments on both simulated and real-world datasets show that our optimized method achieves notable performance gains over the state-of-the-art on the joint video ×8 interpolation and deblurring task. Moreover, on the seemingly implausible ×16 interpolation task, our method outperforms existing methods by more than 1.5 dB in terms of PSNR.
Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach. Codes are provided in https://github.com/qpc1611094/FPL.
How You Feelin’? Learning Emotions and Mental States in Movie Scenes

Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [72], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted state-of-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx's self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.
Exploring the Design Space of Three Criteria Decision Making

This article presents different interface designs of sliders to support decision-making problems with three criteria. We present an exploration of the design space through an iterative development process with eight prototypes and the results of several evaluation studies with visualization experts and nonexperts. Our findings show three candidates for consideration: a standard ternary triangular slider, a novel circular slider, and a standard basic slider displayed three times. All three were considered intuitive and easy to use. The triangular slider is best for exploration with vague user intuition, the circular slider performs best for preference comparisons, and the parallel slider is best for direct preference setting.
Comparing the Performance of Google Translate and SYSTRAN on Arabic Lexical Ambiguity

Machine translation systems face many challenges when dealing with Arabic lexical ambiguity, which affects the quality and accuracy of their translations. This study investigates how two popular MT systems, Google Translate and SYSTRAN, handle three problematic linguistic features of the Arabic language: homonyms, heteronyms, and polysemes. A test suite was designed to include sentences that contain these features in different contexts and domains. The translations produced by the two MT systems were evaluated by four independent evaluators for intelligibility and accuracy using a four-point scale. Results showed that both MT systems struggled with the three chosen linguistic features, with average scores below 40%. Google Translate outperformed SYSTRAN in almost every sentence in the test suite. Both systems scored better in intelligibility than in accuracy. Heteronyms proved to be the most challenging for both MT systems due to the unique design of Arabic discretization, which is not yet recognized by translation systems. This study contributes to the field of machine translation by providing a comprehensive analysis of Arabic lexical ambiguity and its impact on MT quality, as well as suggesting possible ways to improve MT systems for Arabic-English translation.
THE EFFECTIVENESS OF USING BOARD GAMES TO IMPROVE EIGHTH GRADE STUDENTS’ SPEAKING FLUENCY

Speaking is the most important skill to acquire when learning a language. In fact, fluency is recognized as the natural ability to speak arguably spontaneously, quickly, and thoroughly when accuracy focuses on the phonological, grammatical, and discourse elements of speech output. The work is about improving her 8th grade fluency through the use of board games at SMP Al Ma’arif Bantul. The purpose of this study was to determine whether board games were effective in improving her 8th grade fluency. The study design used is based on quantitative quasi-experimental studies. Based on research results analyzed in SPSS 21, the result showed the different significance before and after treatment with data analysis criteria of t-test that H0 (null hypothesis) was rejected and Ha (alternative hypothesis) was accepted if the value was probably (p) lower than alpha (&lt;) 0,05. The conclusion of the study showed the effectiveness of using Board Game for speaking fluency
KLASIFIKASI BEBAN LISTRIK DENGAN MACHINE LEARNING MENGGUNAKAN METODE K-NEAREST NEIGHBOR

ABSTRAKSistem pengenalan beban listrik merupakan peran yang penting dalam melakukan manajemen dan penghematan listrik. Sistem pengenalan beban listrik memiliki keandalan dalam memperoleh informasi yang relevan dari setiap beban listrik. Pada penelitian ini, akan dilakukan analisis karakteristik berbagai fitur beban listrik secara independen dan hubungan pencocokan antara fitur yang berbeda dan algoritma klasifikasi yang berbeda. Yaitu dengan melakukan perbandingan antara dua algoritma klasifikasi K-Nearest Neighbours dan Regresi Logistik Multinomial dari segi akurasi dan kecepatan proses dalam analisis. Sistem klasifikasi akan mengidentifikasikan jenis, model, dan prasyarat yang tidak diketahui dari beban listrik dan mengelompokannya. Karakteristik kelistrikan dari beban listrik yang akan diteliti antaralain besarnya tegangan dan arus root mean square, gelombang harmonisa, daya dan faktor daya dari variasi sample beban listrik yang berbeda. Hasil penelitian pada metode k-Nearest Neighbours didapatkan akurasi sebesar 99.619% sedangkan dengan metode Regresi Logistik Multinomial didapatkan akurasi sebesar 91.125% Kata Kunci : Beban Listrik, Klasifikasi, K-Nearest Neighbours, Regresi Logistik MultinomialABSTRACTThe electrical load recognition system plays an important role in managing and saving electricity. In this study, the caharacteristic of various electrical load features in independent condition and the matching relationship between different features and different classification algorithm will be analyzed by doing a comparison between two classification algorithms, k-Nearest Neighbours and Multinomial Logistic Regression in terms of accuracy and speed of analysis process. The classification system will be identify unknown types, models and prerequisites of different electrical loads and classify them. The characteristics of the electrical load that will be analyzed include the magnitude of root mean square voltage and current, harmonic waves, power series, and power factor from variety of different electrical load samples. The results of the research on the k-Nearest Neighbors method obtained an accuracy of 99.619% while the Multinomial Logistics Regression method obtained an accuracy of 91.125%.Keywords: Electrical Load, Classification, Machine Learning, k-Nearest Neighbours, Multinomial Logistic Regression
Communication Planning Corporate Social Responsibility (CSR) Program of Pt Trisensa Mineral Utama

A communication planning for a CSR program is essential to be conveyed appropriately, enabling it to genuinely empower the targeted community, while ensuring good execution, compliance with applicable laws and regulations, long-term sustainability, and consideration of the interests of the community and stakeholders. Often, CSR programs lack long-term orientation and merely spend budgets without providing proper development or guidance. PT Trisensa Mineral Utama, a coal mining company located in Kutai Kartanegara Regency, East Kalimantan, plays a role in implementing such CSR activities, in line with the Ministry of Energy and Mineral Resources Decree No. 1824 of 2018 on Guidelines for Community Development and Empowerment Implementation. The aim of this research is to understand how CSR programs or Community Empowerment Programs (PPM) are planned within a company. The methodology used is qualitative, collecting data through interviews with informants, field research observation, and literature studies. This study finds that PT Trisensa Mineral Utama aligns with Philip Lesly's communication planning model, covering analysis and research, policy formulation, implementation program planning, communication activities, and feedback and evaluation.
Maneuvering Target Tracking Based on LSTM for Radar Application

Aiming at the problem of model mismatch in traditional model-based target tracking methods when the target undergoes maneuvers, an adaptive tracking method based on Long Short-Term Memory (LSTM) is proposed. The algorithm replaces traditional filtering algorithms with LSTM and introduces the error between the measurement and the predicted value during training. This enables the network to simultaneously acquire information about the target position and measurement error, and during prediction, use the measurement error to directly modify the network's prediction results to obtain the final tracking results. Simulation results demonstrate that this method effectively solves the problem of describing the motion state of maneuvering targets, and has higher tracking accuracy and stronger adaptability compared to traditional filtering algorithms.
Blockchain architecture for social opinion vote-cast system

Elections play a crucial role for a democratic country. However, there is wariness among the people of these democratic countries over their electronic voting systems which further is a major issue for these citizens. Rigging votes, EVM hacking, vote manipulation are some of the major concerns among the people with the electronic voting systems. These shortcomings need to be addressed in order to achieve widespread adoption. Blockchain technology can be used to address these problems. Blockchain offers decentralized nodes that can be used for electronic voting and hence provide a complete framework for voting because of its end on verification benefits. With the blockchain technology, voting solutions can be created which are distributed, non-repudiated, and security protected. This chapter gives an insight to the existing vote-cast system using blockchain technology. The current state-of-the-art of blockchain architecture for voting-cast systems are presented. The blockchain architecture fundamental concepts and its characteristics are discussed with respect to the social opinion vote-cast system.
Implementation of Faceted Vocabularies: An Introduction

AbstractThis special issue on the “Implementation of Faceted Vocabularies: An Introduction” focuses on strategies and methods for implementing faceted vocabularies in MARC and non-MARC environments in library related settings. The following introduction provides a brief description of each article in the issue. The articles are grouped around three themes: Introduction to Faceted Vocabularies, Faceted Application of Subject Terminology (FAST), and Genre Terms and Other Faceted Vocabularies.Keywords: Faceted vocabulariesFaceted Application of Subject Terminology (FAST)genre/form termsdigital repositories
Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition

Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feedforward layers, with the latter containing a vast number of parameters that significantly contribute to the model’s memory footprint. Consequently, it becomes pertinent to consider pruning these layers to reduce the model’s size. In this article, our primary focus is on the feedforward layers. We conduct a comprehensive analysis of their parameter count and distribution. Specifically, we examine the weight distribution within each layer and observe how the weight values progress across the transformer model’s blocks. Our findings demonstrate a correlation between the depth of the feedforward layers and the magnitude of their weights. Consequently, layers with higher weight values require less pruning. Building upon this insight, we propose a novel pruning algorithm based on variable rates. This approach sets the pruning rate according to the significance and location of each feedforward layer within the network. To evaluate our new pruning method, we conduct experiments on various datasets. The results reveal its superiority over conventional pruning techniques, such as local pruning and global pruning.
Texture image classification method based on improved bilinear model

Image recognition methods based on deep learning are widely used in various fields of scientific research and industry, but the lack of targeted structure for subtle texture features in images leads to the tendency to ignore their texture characteristics during the recognition process. Traditional bilinear neural networks are prone to overfitting risk due to excessive parameters, resulting in low recognition efficiency. Combining the effectiveness of the bilinear structure for texture feature information extraction, a deep learning network based on an improved bilinear model is proposed. The model is able to extract fine textures on the surface of objects as discriminative features in the absence of object solid boundaries, spatial information and color information in images, while avoiding the risk of overfitting caused by small sample texture image datasets. Firstly, SE-ResNet18 network is constructed based on SE channel attention to increase the effective information channel weights while avoiding the risk of overfitting due to the large parameter size. Then, SE-ResNet18 network is used as the feature extraction sub-network of the bilinear model to obtain the unordered texture features in the images as discriminative features to obtain the optimal classification results. The classification performance of the algorithm is evaluated on three public texture datasets, KTH-TIPS, KTH-TIPS-2b and UIUC, and 99.82%, 99.41% and 99.52% accuracies are obtained, respectively, indicating the generally efficient recognition performance of the proposed recognition method.
UNIVERSAL JURISDICTION FOR TRANSNATIONAL CYBERCRIMES?

Transnational cybercrimes (TNCCs) are cybercrimes occurring across several jurisdictions. The advancement of technology has brought about an increase in the sophistication, severity and comprehensiveness of incidents of cybercrimes such that cybercrimes can now be effortlessly transnational. In the main, however, the various legal responses to TNCCs have shown States consistently applying traditional territorially based rules to online activities by enacting laws that do not adequately address the borderless nature of the Internet. This paper examines the jurisdictional challenges of transnational cybercrimes as well as the inadequacies of present legal responses to transnational cybercrime. With the aid of the doctrinal research methodology (legal analysis), it finds that purely domestic legal responses to cybercrimes, no matter how advanced, are inadequate as a fragmented approach cannot effectively eradicate the problem created by the presence of safe havens. It concludes that a holistic approach is needed and recommends the adoption of a global instrument with international recognition of universal jurisdiction for serious categories of TNCCs capable of compromising international Peace and security, such as cyber terrorism, hacking and the creation and dissemination of malicious codes targeting critical infrastructures or leading to the denial of essential services.