Reviewerly/exHarmony huggingface dataset:
Fiat–Shamir via list-recoverable codes (or: parallel repetition of GMW is not zero-knowledge)

In a seminal work, Goldreich, Micali and Wigderson (CRYPTO '86) demonstrated the wide applicability of zero-knowledge proofs by constructing such a proof system for the NP-complete problem of graph 3-coloring. A long-standing open question has been whether parallel repetition of their protocol preserves zero knowledge. In this work, we answer this question in the negative, assuming a standard cryptographic assumption (i.e., the hardness of learning with errors (LWE)).
ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular Data

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and objectcentric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to lowlevel visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO [31] and Cityscapes [8] with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training. The code is available at https://github.com/janghyuncho/PiCIE.
Robust Instance Segmentation through Reasoning about Multi-Object Occlusion

Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://github.com/XD7479/Multi-Object-Occlusion.
Security Comparisons and Performance Analyses of Post-quantum Signature Algorithms

Quantum computing challenges the computational hardness assumptions anchoring the security of public-key ciphers, such as the prime factorization and the discrete logarithm problem. To prepare for the quantum era and withstand the attacks equipped with quantum computing, the security and cryptography communities are designing new quantum-resistant public-key ciphers. National Institute of Standards and Technology (NIST) is collecting and standardizing the post-quantum ciphers, similarly to its past involvements in establishing DES and AES as symmetric cipher standards. The NIST finalist algorithms for public-key signatures are Dilithium, Falcon, and Rainbow. Finding common ground to compare these algorithms can be difficult because of their design, the underlying computational hardness assumptions (lattice based vs. multivariate based), and the different metrics used for security strength analyses in the previous research (qubits vs. quantum gates). We overcome such challenges and compare the security and the performances of the finalist post-quantum ciphers of Dilithium, Falcon, and Rainbow. For security comparison analyses, we advance the prior literature by using the depth-width cost for quantum circuits (DW cost) to measure the security strengths and by analyzing the security in Universal Quantum Gate Model and with Quantum Annealing. For performance analyses, we compare the algorithms' computational loads in the execution time as well as the communication costs and implementation overheads when integrated with Transport Layer Security (TLS) and Transmission Control Protocol (TCP)/Internet Protocol (IP). Our work presents a security comparison and performance analysis as well as the trade-off analysis to inform the post-quantum cipher design and standardization to protect computing and networking in the post-quantum era.
One-for-All: An Efficient Variable Convolution Neural Network for In-Loop Filter of VVC

Recently, many researches on convolution neural network (CNN) based in-loop filters have been proposed to improve coding efficiency. However, most existing CNN based filters tend to train and deploy multiple networks for various quantization parameters (QP) and frame types (FT), which drastically increases resources in training these models and the memory burdens for video codec. In this paper, we propose a novel variable CNN (VCNN) based in-loop filter for VVC, which can effectively handle the compressed videos with different QPs and FTs via a single model. Specifically, an efficient and flexible attention module is developed to recalibrate features according to QPs or FTs. Then we embed the module into the residual block so that these informative features can be continuously utilized in the residual learning process. To minimize the information loss in the learning process of the entire network, we utilize a residual feature aggregation module (RFA) for more efficient feature extraction. Based on it, an efficient network architecture VCNN is designed that can not only effectively reduce compression artifacts, but also can be adaptive to various QPs and FTs. To address training data imbalance on various QPs and FTs and improve the robustness of the model, a focal mean square error loss function is employed to train the proposed network. Then we integrate the VCNN into VVC as an additional tool of in-loop filters after the deblocking filter. Extensive experimental results show that our VCNN approach obtains on average 3.63%, 4.36%, 4.23%, 3.56% under all intra, low-delay P, low-delay, and random access configurations, respectively, which is even better than QP-Separate models.
A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

Abstract Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the results, it can be seen that the designed model can help and improve the decision boundary by reducing the global loss function while maintaining both security and privacy.
Separating Skills and Concepts for Novel Visual Question Answering

Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question, such as objects and people. VQA methods should be able to compose skills and concepts in novel ways, regardless of whether the specific composition has been seen in training, yet we demonstrate that existing models have much to improve upon towards handling new compositions. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. We enforce these properties with a novel contrastive learning procedure that does not rely on external annotations and can be learned from unlabeled image-question pairs. Experiments demonstrate the effectiveness of our approach for improving compositional and grounding performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Promoting the Quality of Life of Elderly during the COVID-19 Pandemic

This study explored the technology learning model of the elderly in a senior learning center under the impact of the COVID-19 pandemic. Many senior learning centers were closed during the pandemic, and many of them adopted the mode of online education. However, problems such as decreased motivation and a lack of peer interaction still exist. To solve these problems, this study used the easy-to-implement calligraphy AR approach and E-book approach to conduct a quasi-experiment on the elderly of a calligraphy course offered by a senior learning center. The results show a higher learning motivation among the elderly who use calligraphy AR. The learning effectiveness and technology acceptance of the elderly in the E-book learning group were higher than those in the calligraphy AR group. The elderly mentioned that the E-book learning approach is more user-friendly. In general, in the face of the COVID-19 pandemic affecting the suspension of classes in senior centers, education through the development of technology has stimulated the growth of education in advanced learning centers. Through this kind of scientific and technological learning method, it will bring a whole new experience to the elderly. It can improve the stress relief methods, mental health, and quality of life of the elderly during the COVID-19 emergency shutdown, and provide a novel calligraphy technique learning experience for the elderly. Therefore, we believe that the calligraphy AR learning approach and the calligraphy E-book learning approach are practical and may promote quality of life and mental health of the elderly during the emergency closures due to COVID-19, providing elderly attendees with a novel calligraphy technology learning experience.
Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching

Subgraph query processing (also known as subgraph search) and subgraph matching are fundamental graph problems in many application domains. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we propose a new subgraph search algorithm using equivalences of vertices in order to reduce search space: (1) static equivalence of vertices in a query graph that leads to an efficient matching order of the vertices, and (2) dynamic equivalence of candidate vertices in a data graph, which enables us to capture and remove redundancies in search space. These techniques for subgraph search also lead to an improved algorithm for subgraph matching. Experiments show that our approach outperforms state-of-the-art subgraph search and subgraph matching algorithms by up to several orders of magnitude with respect to query processing time.
Unsupervised Monocular Depth Estimation Using Attention and Multi-Warp Reconstruction

Monocular depth estimation has become one of the most studied topics in computer vision. Most approaches treat depth prediction as a fully supervised regression problem requiring vast amounts of corresponding ground-truth depth and image pairs for training. Unsupervised monocular depth estimation has emerged as a promising alternative that eliminates dataset limitations. This paper proposes an end-to-end unsupervised deep learning framework integrating attention blocks and multi-warp loss for monocular depth estimation. In this framework, to explore more general contextual information among the feature volumes, an attention block that sequentially refines the feature maps along the channel and spatial dimensions is inserted after the first and last stages of the network encoder. Additionally, to further utilize the errors in the original disparity estimation from the network, a novel multi-warp reconstruction strategy is designed for the loss function. The experimental results evaluated on the KITTI, CityScapes and Make3D datasets demonstrate the state-of-the-art performance and satisfactory generalization ability of our proposed method.
Probing Toxic Content in Large Pre-Trained Language Models

Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems.We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates.The templates are prompted by a name of a social group followed by a cause-effect relation.We use PTLMs to predict masked tokens at the end of a sentence in order to examine how likely they enable toxicity towards specific communities.We shed the light on how such negative content can be triggered within unrelated and benign contexts based on evidence from a large-scale study, then we explain how to take advantage of our methodology to assess and mitigate the toxicity transmitted by PTLMs.
A Trust-Centric Privacy-Preserving Blockchain for Dynamic Spectrum Management in IoT Networks

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.
The Trustworthiness of Voice Assistants in the Context of Healthcare Investigating the Effect of Perceived Expertise on the Trustworthiness of Voice Assistants, Providers, Data Receivers, and Automatic Speech Recognition

As an emerging market for voice assistants (VA), the healthcare sector imposes increasing requirements on the users’ trust in the technological system. To encourage patients to reveal sensitive data requires patients to trust in the technological counterpart. In an experimental laboratory study, participants were presented a VA, which was introduced as either a “specialist” or a “generalist” tool for sexual health. In both conditions, the VA asked the exact same health-related questions. Afterwards, participants assessed the trustworthiness of the tool and further source layers (provider, platform provider, automatic speech recognition in general, data receiver) and reported individual characteristics (disposition to trust and disclose sexual information). Results revealed that perceiving the VA as a specialist resulted in higher trustworthiness of the VA and of the provider, the platform provider and automatic speech recognition in general. Furthermore, the provider’s trustworthiness affected the perceived trustworthiness of the VA. Presenting both a theoretical line of reasoning and empirical data, the study points out the importance of the users’ perspective on the assistant. In sum, this paper argues for further analyses of trustworthiness in voice-based systems and its effects on the usage behavior as well as the impact on responsible design of future technology.
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision

Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks. Obtaining such a dataset for any new domain can be very expensive and time-consuming. In addition, models trained on certain annotated categories do not generalize well to unseen objects. The goal of this paper is to propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation, through learning instance embeddings of masked regions. Leveraging rich relationship and hierarchical structure between objects in the images, we propose self-supervised losses for learning mask embeddings. Trained on COCO [34] dataset without additional annotations of the long-tail objects, our model is able to discover novel and more fine-grained objects than the common categories in COCO. We show that the model achieves competitive quantitative results on LVIS [17] as compared to the supervised and partially supervised methods.
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers

We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising future improvements from the advancements of Siamese trackers. The carefully designed architecture of the Siamese Natural Language Region Proposal Network (SNL-RPN), together with the Dynamic Aggregation of vision and language modalities, is introduced to perform the tracking by NL task. Empirical results over tracking benchmarks with NL annotations show that the proposed SNLT improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of speed. The proposed SNLT outperforms all NL trackers to-date and is competitive among state-of-the-art real-time trackers on LaSOT benchmarks while running at 50 frames per second on a single GPU. Code for this work is available at https://github.com/fredfung007/snlt.
A Petri Dish for Histopathology Image Analysis

With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists. However, challenges such as limited data, costly annotation, and processing high-resolution and variable-size images make it difficult to quickly iterate over model designs.Throughout scientific history, many significant research directions have leveraged small-scale experimental setups as petri dishes to efficiently evaluate exploratory ideas. In this paper, we introduce a minimalist histopathology image analysis dataset (MHIST), an analogous petri dish for histopathology image analysis. MHIST is a binary classification dataset of 3,152 fixed-size images of colorectal polyps, each with a gold-standard label determined by the majority vote of seven board-certified gastrointestinal pathologists and annotator agreement level. MHIST occupies less than 400 MB of disk space, and a ResNet-18 baseline can be trained to convergence on MHIST in just 6 min using 3.5 GB of memory on a NVIDIA RTX 3090. As example use cases, we use MHIST to study natural questions such as how dataset size, network depth, transfer learning, and high-disagreement examples affect model performance.By introducing MHIST, we hope to not only help facilitate the work of current histopathology imaging researchers, but also make the field more-accessible to the general community. Our dataset is available at https://bmirds.github.io/MHIST.