Reviewerly/exHarmony huggingface dataset:
Efficient in-situ image and video compression through probabilistic image representation

Authors:
raw_author_name: Rongjie Liu
raw_affiliation_string: Department of Statistics, Florida State University, Tallahassee, FL, USA
raw_author_name: Meng Li
raw_affiliation_string: Department of Statistics, Rice University, Houston, TX, USA
raw_author_name: Li Ma
raw_affiliation_string: Department of Statistical Science, Duke University, Durham, NC, USA

Abstract: Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method for multi-dimensional image and video compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. Furthermore, it adopts a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. The properties of our proposed method include high reconstruction quality at a wide range of compression rates while preserving key local details, applicability to a variety of different image/video types and of different dimensions, computational scalability, progressive transmission and ease of tuning. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP compares favorably to—and often uniformly outperforms—a wide range of popular image/video compression approaches, including JPEG, JPEG2000, AVI, BPG, MPEG4, HEVC, AV1, and three neural network-based methods.
Prototypical Graph Contrastive Learning

Authors:
raw_author_name: Shuai Lin
raw_affiliation_string: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
raw_author_name: Chen Liu
raw_affiliation_string: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
raw_author_name: Pan Zhou
raw_affiliation_string: Sea AI Laboratory, Fusionopolis Place, Singapore
raw_author_name: Zi-Yuan Hu
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Ruihui Zhao
raw_affiliation_string: Tencent Jarvis Lab, Shenzhen, China
raw_author_name: Yefeng Zheng
raw_affiliation_string: Tencent Jarvis Lab, Shenzhen, China
raw_author_name: Liang Lin
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Eric Xing
raw_affiliation_string: School of Computer Science, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
raw_author_name: Xiaodan Liang
raw_affiliation_string: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China

Abstract: Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generally very expensive and time-consuming. To address this issue, graph contrastive learning constructs instance discrimination task which pulls together positive pairs (augmentation pairs of the same graph) and pushes away negative pairs (augmentation pairs of different graphs) for unsupervised representation learning. However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i.e., the negatives likely having the same semantic structure with the query, leading to performance degradation. To mitigate this sampling bias issue, in this paper, we propose a Prototypical Graph Contrastive Learning (PGCL) approach. Specifically, PGCL models the underlying semantic structure of the graph data via clustering semantically similar graphs into the same group, and simultaneously encourages the clustering consistency for different augmentations of the same graph. Then given a query, it performs negative sampling via drawing the graphs from those clusters that differ from the cluster of query, which ensures the semantic difference between query and its negative samples. Moreover, for a query, PGCL further reweights its negative samples based on the distance between their prototypes (cluster centroids) and the query prototype such that those negatives having moderate prototype distance enjoy relatively large weights. This reweighting strategy is proved to be more effective than uniform sampling. Experimental results on various graph benchmarks testify the advantages of our PGCL over state-of-the-art methods. Code is publicly available at https://github.com/ha-lins/PGCL.
The right complexity measure in locally private estimation: It is not the Fisher information

Authors:
raw_author_name: John C. Duchi
raw_affiliation_string: Department of Statistics, Stanford University
raw_author_name: Feng Ruan
raw_affiliation_string: Department of Statistics and Data Science, Northwestern University

Abstract: We identify fundamental tradeoffs between statistical utility and privacy under local models of privacy in which data is kept private even from the statistician, providing instance-specific bounds for private estimation and learning problems by developing the local minimax risk. In contrast to approaches based on worst-case (minimax) error, which are conservative, this allows us to evaluate the difficulty of individual problem instances and delineate the possibilities for adaptation in private estimation and inference. Our main results show that the local modulus of continuity of the estimand with respect to the variation distance—as opposed to the Hellinger distance central to classical statistics—characterizes rates of convergence under locally private estimation for many notions of privacy, including differential privacy and its relaxations. As consequences of these results, we identify an alternative to the Fisher information for private estimation, giving a more nuanced understanding of the challenges of adaptivity and optimality.
Understanding Pooling in Graph Neural Networks

Authors:
raw_author_name: Daniele Grattarola
raw_affiliation_string: Faculty of Informatics, Universit&#x00E0; della Svizzera italiana (USI), Lugano, Switzerland
raw_author_name: Daniele Zambon
raw_affiliation_string: Faculty of Informatics, Universit&#x00E0; della Svizzera italiana (USI), Lugano, Switzerland
raw_author_name: Filippo Maria Bianchi
raw_affiliation_string: Department of Mathematics and Statistics, UiT the Arctic University of Norway, Troms&#x00F8;, Norway
raw_author_name: Cesare Alippi
raw_affiliation_string: Faculty of Informatics, Universit&#x00E0; della Svizzera italiana (USI), Lugano, Switzerland

Abstract: Many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. In this article, we present an operational framework to unify this vast and diverse literature by describing pooling operators as the combination of three functions: selection, reduction, and connection (SRC). We then introduce a taxonomy of pooling operators, based on some of their key characteristics and implementation differences under the SRC framework. Finally, we propose three criteria to evaluate the performance of pooling operators and use them to investigate the behavior of different operators on a variety of tasks.
Ideal-Theoretic Explanation of Capacity-Achieving Decoding

Authors:
raw_author_name: Siddharth Bhandari
raw_affiliation_string: Tata Institute of Fundamental Research, Mumbai, India
raw_author_name: Prahladh Harsha
raw_affiliation_string: Tata Institute of Fundamental Research, Mumbai, India
raw_author_name: Mrinal Kumar
raw_affiliation_string: Tata Institute of Fundamental Research, Mumbai, India
raw_author_name: Madhu Sudan
raw_affiliation_string: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

Abstract: In this work, we present an abstract framework for some algebraic error-correcting codes with the aim of capturing codes that are list-decodable to capacity, along with their decoding algorithms. In the polynomial ideal framework, a code is specified by some ideals in a polynomial ring, messages are polynomials and the encoding of a message polynomial is the collection of residues of that polynomial modulo the ideals. We present an alternate way of viewing this class of codes in terms of linear operators, and show that this alternate view makes their algorithmic list-decodability amenable to analysis. Our framework leads to a new class of codes that we call affine Folded Reed-Solomon codes (which are themselves a special case of the broader class we explore). These codes are common generalizations of the well-studied Folded Reed-Solomon codes and Univariate Multiplicity codes as well as the less-studied Additive Folded Reed-Solomon codes, and lead to a large family of codes that were not previously known/studied. More significantly our framework also captures the algorithmic list-decodability of the constituent codes. Specifically, we present a unified view of the decoding algorithm for ideal-theoretic codes and show that the decodability reduces to the analysis of the distance of some related codes. We show that a good bound on this distance leads to a capacity-achieving performance of the underlying code, providing a unifying explanation of known capacity-achieving results. In the specific case of affine Folded Reed-Solomon codes, our framework shows that they are efficiently list-decodable up to capacity (for appropriate setting of the parameters), thereby unifying the previous results for Folded Reed-Solomon, Multiplicity and Additive Folded Reed-Solomon codes.
QUBO formulations for a system of linear equations

Authors:
raw_author_name: Kyungtaek Jun
raw_author_name: Kwangmin Yu

Abstract: With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in modern science and engineering. The Harrow Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on gate model quantum computers, was invented and several advanced variations have been developed. The algorithm was difficult to apply to general linear equations because it required various conditions for the matrix. In this paper, we introduce a new algorithm that can be applied to all linear systems. For a given general square matrix A∈Rn×n and a vector b→∈Rn, we will find quadratic unconstrained binary optimization (QUBO) models for a vector x→∈Rn that satisfies Ax→=b→. To formulate QUBO models for solving linear systems, we made use of a linear least-square problem with binary representation of the solution. We validated those QUBO models on the D-Wave system and discussed the results. For a simple system, we provide a Python code to calculate the matrix characterizing the relationship between the variables, and to print the test code that can be used directly in the D-Wave system.
Monte Carlo DropBlock for modeling uncertainty in object detection

Authors:
raw_author_name: P. K. Srijith
raw_affiliation_string: Indian Institute of Technology Hyderabad, Telangana, India
raw_author_name: C. Krishna Mohan
raw_affiliation_string: Indian Institute of Technology Hyderabad, Telangana, India

Abstract: With the advancements made in deep learning, computer vision problems have seen a great improvement in performance. However, in many real-world applications such as autonomous driving vehicles, the risk associated with incorrect predictions of objects or segmentation of images is very high. Standard deep learning models for object detection and segmentation such as YOLO models are often overconfident in their predictions and do not take into account the uncertainty in predictions on out-of-distribution data. In this work, we propose an efficient and effective approach, Monte-Carlo DropBlock (MC-DropBlock), to model uncertainty in YOLO and convolutional vision Transformers for object detection. The proposed approach applies drop-block during training time and testing time on the convolutional layer of the deep learning models such as YOLO and convolutional transformer. We theoretically show that this leads to a Bayesian convolutional neural network capable of capturing the epistemic uncertainty in the model. Additionally, we capture the aleatoric uncertainty in the data using a Gaussian likelihood. We demonstrate the effectiveness of the proposed approach on modeling uncertainty in object detection and segmentation tasks using out-of-distribution experiments. Experimental results show that MC-DropBlock improves the generalization, calibration, and uncertainty modeling capabilities of YOLO models and convolutional Transformer models for object detection and segmentation.
Generating novel scene compositions from single images and videos

Authors:
raw_author_name: Vadim Sushko
raw_affiliation_string: Bosch Center for Artificial Intelligence, Renningen, 71272, Germany
raw_author_name: Dan Zhang
raw_affiliation_string: Bosch Center for Artificial Intelligence, Renningen, 71272, Germany; University of Tübingen, Tübingen, 72070, Germany
raw_author_name: Juergen Gall
raw_affiliation_string: Lamarr Institute for Machine Learning and Artificial Intelligence, Germany; University of Bonn, Bonn, 53115, Germany
raw_author_name: Anna Khoreva
raw_affiliation_string: Bosch Center for Artificial Intelligence, Renningen, 71272, Germany; University of Tübingen, Tübingen, 72070, Germany

Abstract: Given a large dataset for training, generative adversarial networks (GANs) can achieve remarkable performance for the image synthesis task. However, training GANs in extremely low data regimes remains a challenge as overfitting often occurs, leading to memorization or training divergence. In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip. We propose a two-branch discriminator architecture with content and layout branches that are designed to judge internal content and scene layout realism separately from each other. This discriminator design enables the synthesis of visually plausible, novel compositions of a scene with varying content and layout while preserving the context of the original sample. Compared to previous single-image GANs, our model generates more diverse images of higher quality while not being restricted to a single image setting. We further introduce a new challenging task of learning from a few frames of a single video. In this training setup the training images are highly similar to each other, which makes it difficult for prior GAN models to achieve a synthesis of both high quality and diversity.
Work in Progress: STEM Students’ Experiences with Educational Technology Tools

Authors:
raw_author_name: Saira Anwar
raw_affiliation_string: Department of Engineering Education, Univer-sity of Florida.; Purdue University at West Lafayette; School of Engineering Education, Purdue University.; University of Florida
raw_author_name: Muhsin Menekse
raw_affiliation_string: Department of Engineering Education, Univer-sity of Florida.; Purdue University at West Lafayette; School of Engineering Education, Purdue University.

Abstract: Dr. Anwar has over 12
Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution

Authors:
raw_author_name: Chuanguang Yang
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhulin An
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
raw_author_name: Linhang Cai
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yongjun Xu
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Abstract: Knowledge distillation (KD) is an effective framework that aims to transfer meaningful information from a large teacher to a smaller student. Generally, KD often involves how to define and transfer knowledge. Previous KD methods often focus on mining various forms of knowledge, for example, feature maps and refined information. However, the knowledge is derived from the primary supervised task, and thus, is highly task-specific. Motivated by the recent success of self-supervised representation learning, we propose an auxiliary self-supervision augmented task to guide networks to learn more meaningful features. Therefore, we can derive soft self-supervision augmented distributions as richer dark knowledge from this task for KD. Unlike previous knowledge, this distribution encodes joint knowledge from supervised and self-supervised feature learning. Beyond knowledge exploration, we propose to append several auxiliary branches at various hidden layers, to fully take advantage of hierarchical feature maps. Each auxiliary branch is guided to learn self-supervision augmented tasks and distill this distribution from teacher to student. Overall, we call our KD method a hierarchical self-supervision augmented KD (HSSAKD). Experiments on standard image classification show that both offline and online HSSAKD achieves state-of-the-art performance in the field of KD. Further transfer experiments on object detection further verify that HSSAKD can guide the network to learn better features. The code is available at https://github.com/winycg/HSAKD.
Speech recognition datasets for low-resource Congolese languages

Authors:
raw_author_name: Ussen Kimanuka
raw_affiliation_string: Department of Electrical Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya
raw_author_name: Ciira wa Maina
raw_affiliation_string: Centre for Data Science and Artificial Intelligence (DSAIL), Nyeri, Kenya; Department of Electrical Engineering, Dedan Kimathi University of Technology, Nyeri, Kenya
raw_author_name: Osman Büyük
raw_affiliation_string: Department of Electrical and Electronics Engineering, Izmir Demokrasi University, Izmir, Turkey

Abstract: Large pre-trained Automatic Speech Recognition (ASR) models have shown improved performance in low-resource languages due to the increased availability of benchmark corpora and the advantages of transfer learning. However, only a limited number of languages possess ample resources to fully leverage transfer learning. In such contexts, benchmark corpora become crucial for advancing methods. In this article, we introduce two new benchmark corpora designed for low-resource languages spoken in the Democratic Republic of the Congo: the Lingala Read Speech Corpus, with 4 h of labelled audio, and the Congolese Speech Radio Corpus, which offers 741 h of unlabelled audio spanning four significant low-resource languages of the region. During data collection, Lingala Read Speech recordings of thirty-two distinct adult speakers, each with a unique context under various settings with different accents, were recorded. Concurrently, Congolese Speech Radio raw data were taken from the archive of broadcast station, followed by a designed curation process. During data preparation, numerous strategies have been utilised for pre-processing the data. The datasets, which have been made freely accessible to all researchers, serve as a valuable resource for not only investigating and developing monolingual methods and approaches that employ linguistically distant languages but also multilingual approaches with linguistically similar languages. Using techniques such as supervised learning and self-supervised learning, they are able to develop inaugural benchmarking of speech recognition systems for Lingala and mark the first instance of a multilingual model tailored for four Congolese languages spoken by an aggregated population of 95 million. Moreover, two models were applied to this dataset. The first is supervised learning modelling and the second is for self-supervised pre-training.
Objective Dimensions on Fishermen's Quality of Life

Authors:
raw_author_name: Antonia Junianty Laratmase

Abstract: The purpose of this study was to find out fishermen’s quality of life construct validity used survey by involving 44 fishermen in Cimalaya, Karawang. Data analyzed by correlations test, cronbach’s alpha and factor analysis (CFA). The Study results revealed that there were four objective dimensions of fishermen’s quality of life that can be seen from the indicators: (1) income with value of 0.455; (2) Marital status with value of 0.545; (3) Health status with value of 0.691; and (4) Social relationship with value of 0.638. Those values were good since they were above 0.4 or had little tolerance to above 0.25. For this reason, the objective dimensions of quality of life can be measured in terms of harmony and ability which is the integration of quality of life.
EMF-Net: An edge-guided multi-feature fusion network for text manipulation detection

Authors:
raw_author_name: Ruyong Ren
raw_affiliation_string: Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China; Department of Computer Science, College of Staten Island, City University of New York, Staten Island, NY 10314, USA
raw_author_name: Qixian Hao
raw_affiliation_string: Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Feng Gu
raw_affiliation_string: Department of Computer Science, College of Staten Island, City University of New York, Staten Island, NY 10314, USA
raw_author_name: Shaozhang Niu
raw_affiliation_string: Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China; Southeast Digital Economy Development Institute, Quzhou, Zhejiang 324000, China
raw_author_name: Jiwei Zhang
raw_affiliation_string: Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Maosen Wang
raw_affiliation_string: Southeast Digital Economy Development Institute, Quzhou, Zhejiang 324000, China

Abstract: Text images have a more uniform background than natural images, making it difficult to capture tampering traces. Thus, it poses significant challenges to the detection and localization of tampered text images. To effectively detect tampered text images, we propose the edge-guided multi-feature fusion network (EMF-Net) including four different modules, the difference semantic discriminator (DSD), the edge-guidance feature aggregation module (EFAM), the edge supervised module (ESM), and the multi-branch attention-induced fusion module (MAFM). The proposed network combines RGB images and learned noise-sensitive features to deeply excavate the hidden tampered region features to improve the detection accuracy due to the insufficient feature information. Meanwhile, the multi-branch attention-induced fusion and semantic discriminator methods are integrated to reduce false alarms by shielding the feature interference in non-tampered regions and diminish the external interference. Furthermore, we craft a dataset of 12,000 text images and their tempered versions with three tempering operations including copy-move, splicing and inpainting. Extensive experiments have shown that the proposed network can improve the generalization performance and achieve the higher detection accuracy compared to current other state-of-the-art methods.