Reviewerly/exHarmony huggingface dataset:
BiaScope: Visual Unfairness Diagnosis for Graph Embeddings

The issue of bias (i.e., systematic unfairness) in machine learning models has recently attracted the attention of both researchers and practitioners. For the graph mining community in particular, an important goal toward algorithmic fairness is to detect and mitigate bias incorporated into graph embeddings since they are commonly used in human-centered applications, e.g., social-media recommendations. However, simple analytical methods for detecting bias typically involve aggregate statistics which do not reveal the sources of unfairness. Instead, visual methods can provide a holistic fairness characterization of graph embeddings and help uncover the causes of observed bias. In this work, we present BIAS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COPE</inf> , an interactive visualization tool that supports end-to-end visual unfairness diagnosis for graph embeddings. The tool is the product of a design study in collaboration with domain experts. It allows the user to (i) visually compare two embeddings with respect to fairness, (ii) locate nodes or graph communities that are unfairly embedded, and (iii) understand the source of bias by interactively linking the relevant embedding subspace with the corresponding graph topology. Experts’ feedback confirms that our tool is effective at detecting and diagnosing unfairness. Thus, we envision our tool both as a companion for researchers in designing their algorithms as well as a guide for practitioners who use off-the-shelf graph embeddings.
Concrete analysis of approximate ideal-SIVP to decision ring-LWE reduction

&lt;p style='text-indent:20px;'&gt;A seminal 2013 paper by Lyubashevsky, Peikert, and Regev proposed basing post-quantum cryptography on ideal lattices and supported this proposal by giving a polynomial-time security reduction from the approximate Shortest Independent Vectors Problem (SIVP) to the Decision Learning With Errors (DLWE) problem in ideal lattices. We give a concrete analysis of this multi-step reduction. We find that the tightness gap in the reduction is so great as to vitiate any meaningful security guarantee, and we find reasons to doubt the feasibility in the foreseeable future of the quantum part of the reduction. In addition, when we make the reduction concrete it appears that the approximation factor in the SIVP problem is far larger than expected, a circumstance that causes the corresponding approximate-SIVP problem most likely not to be hard for proposed cryptosystem parameters. We also discuss implications for systems such as Kyber and SABER that are based on module-DLWE.&lt;/p&gt;
Defending Against Deepfakes with Ensemble Adversarial Perturbation

Maliciously manipulated images and videos, represented by prevalent deepfakes, can easily deceive human and mislead the public opinions. A great deal of effort was spent on detecting these fake images or videos. However, these detection methods always encounter various problems in practical applications. Do we have other ways to block the spread of fake image or videos? This motivates us to focus on an emerging interesting topic, disruption of deepfake generation. We propose the ensemble attacks of various types of deepfake models including facial attribute editing, face swapping and face reenactment models. With the help of hard model mining, we boost the attack success rate significantly comparing with the straightforward average ensemble. Extensive experiments demonstrate the proposed approach can successfully disrupt multiple deepfake models simultaneously under white-box or gray-box attack protocols.
Visualization of the Gap Between the Stances of Citizens and City Councilors on Political Issues

In local government, clarifying the gap between the political opinions of citizens and city councilors is important for reflecting the will of the people in politics. In this study, we focus on the difference between the stances (i.e., favor or against) of citizens and city councilors on political issues, and attempt to compare the arguments of both sides. Using a dataset of texts collected from citizen tweets and city council minutes, we performed a detailed analysis of the opinions expressed, based on the fine-tuning of pretrained language models. In particular, the model predicted the labels of four attributes: stance, usefulness, regional dependency, and relevance. In our experiments, we targeted two political issues: "eliminating nursery school waiting lists" and "attracting integrated resorts (IR)." However, the clues enabling the prediction of labels for the four attributes varied depending on the target. We, therefore, introduced a target-attention mechanism, which extracted helpful information from target sentences, to improve the prediction performance. In addition, we improved the prediction of attribute labels that considered relevance-related issues, by adopting multitask learning of relevance and other attributes. Our experimental results showed that the macro F1-scores for stance and regional-dependency attributes were improved by up to 1.0% and 3.7%, respectively, when using the target-attention mechanism, and by up to 7.2% for usefulness with multitask learning. Using the trained model to analyze real opinion gaps, we found that the citizens of Osaka City were relatively more supportive of attracting IR than the citizens of Yokohama City.
A Particle Bernoulli Filter Based on Gaussian Process Learning for Maneuvering Target Tracking

In this work, the Bayes-optimal Bernoulli filter (BF) is studied for the target tracking where the target is randomly present or absent in the view field of the sensor while the sensor may provide imperfect measurement which contains miss detection and false alarm. To solve the issue that the dynamic model of the target is switching in an unknown mode, we employ the Gaussian process (GP) regression tool, which is a data-driven approach for learning the motion model online, to approximate the transitional density in the formulation of the BF. To deal with the nonlinear measurement model, the proposed GP-based BF is implemented using particles. In the simulation experiment, the proposed approach is performed on a maneuvering target tracking scenario and compared with the Bernoulli particle filters utilizing the full or partial model changing information.
Faster Sounder Succinct Arguments and $$\textsf{IOP}$$s

Succinct arguments allow a prover to convince a verifier that a given statement is true, using an extremely short proof. A major bottleneck that has been the focus of a large body of work is in reducing the overhead incurred by the prover in order to prove correctness of the computation. By overhead we refer to the cost of proving correctness, divided by the cost of the original computation. In this work, for a large class of Boolean circuits $$C=C(x,w)$$ , we construct succinct arguments for the language $$\{ x : \exists w\; C(x,w)=1\}$$ , with $$2^{-\lambda }$$ soundness error, and with prover overhead $$\textrm{polylog}(\lambda )$$ . This result relies on the existence of (sub-exponentially secure) linear-size computable collision-resistant hash functions. The class of Boolean circuits that we can handle includes circuits with a repeated sub-structure, which arise in natural applications such as batch computation/verification, hashing and related block chain applications. The succinct argument is obtained by constructing interactive oracle proofs for the same class of languages, with $$\textrm{polylog}(\lambda )$$ prover overhead, and soundness error $$2^{-\lambda }$$ . Prior to our work, the best $$\textsf{IOP}$$ s for Boolean circuits either had prover overhead of $$\textrm{polylog}(|C|)$$ based on efficient $$\textsf{PCP}$$ s due to Ben Sasson et al. (STOC, 2013) or $$\textrm{poly}(\lambda )$$ due to Rothblum and Ron-Zewi (STOC, 2022).
Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release

We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns. We alleviate these concerns by sanitizing datasets in a two-stage process. First, we introduce a global decoupling stage for decomposing raw data into sensitive and non-sensitive latent representations. Secondly, we design a local sampling stage to synthetically generate sensitive information with differential privacy and merge it with non-sensitive latent features to create a useful representation while preserving the privacy. This newly formed latent information is a task-agnostic representation of the original dataset with anonymized sensitive information. While most algorithms sanitize data in a task-dependent manner, a few task-agnostic sanitization techniques sanitize data by censoring sensitive information. In this work, we show that a better privacy-utility trade-off is achieved if sensitive information can be synthesized privately. We validate sanitizer’s effectiveness by outperforming state-of-the-art baselines on the existing tasks and demonstrating tasks that are not possible using existing techniques. Our code and benchmark is available at https://github.com/splitlearning/sanitizer .
AvantGraph query processing engine

We demonstrate AvantGraph, a graph query processing engine developed by the Database group at TU Eindhoven. Designed for efficient processing of both subgraph matching and navigational graph queries, AvantGraph encompasses innovation in three key areas: the planner, the cardinality estimator, and the execution engine. We present demonstration scenarios covering a wide range of workloads across diverse domains which (1) provides deep insights into the core challenges of complex graph query processing and (2) showcases corresponding critical optimizations via "under-the-hood" operational insights of AvantGraph's key components.
Efficient Fairness Testing Through Hash-Based Sampling

There is a growing concern on algorithm fairness, according to wider adoption of machine learning techniques in our daily life. Testing of individual fairness is an approach to algorithm fairness concern. Verification Based Testing (VBT) is a state-of-the-art testing technique for individual fairness, that leverages verification techniques using constraint solving. In this paper, we develop a black-box individual fairness testing technique Vbt-X, which applies hash-based sampling techniques to the test case generation part of Vbt, aiming to improve its testing ability. Our evaluation by experiments confirms that Vbt-X improves the testing ability of Vbt by 2.92 times in average.
ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring

Video deblurring aims to restore sharp frames from blurry video sequences. Existing methods usually adopt optical flow to compensate misalignment between reference frame and each neighboring frame. However, inaccurate flow estimation caused by large displacements will lead to artifacts in the warped frames. In this work, we propose an equivalent receptive field deformable network (ERDN) to perform alignment at the feature level without estimating optical flow. The ERDN introduces a dual pyramid alignment module, in which a feature pyramid is constructed to align frames using deformable convolution in a cascaded manner. Specifically, we adopt dilated spatial pyramid blocks to predict offsets for deformable convolutions, so that the theoretical receptive field is equivalent for each feature pyramid layer. To restore the sharp frame, we propose a gradient guided fusion module, which incorporates structure priors into the restoration process. Experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods on multiple benchmark datasets. The code is made available at: https://github.com/TencentCloud/ERDN .
Self-attention Networks for Non-recurrent Handwritten Text Recognition

Handwritten text recognition is still an unsolved problem in the field of machine learning. Nevertheless, this technology has improved considerably in the last decade in part thanks to advancements in recurrent neural networks. Unfortunately, due to their sequential nature, recurrent models cannot be effectively parallelised during training. Meanwhile, in natural language processing research, the transformer has recently become the dominant architecture; replacing the recurrent networks that were once popular. These new models are far more efficient to train than their predecessors because their primary building block, the self-attention network, can process sequences entirely non-recurrently. This work demonstrates that self-attention networks can replace the recurrent networks of state-of-the-art handwriting recognition models and achieve competitive error rates, while reducing the time required to train and the number of parameters significantly.
Self-supervised Correlation Mining Network for Person Image Generation

Person image generation aims to perform non-rigid deformation on source images, which generally requires unaligned data pairs for training. Recently, self-supervised methods express great prospects in this task by merging the disentangled representations for self-reconstruction. However, such methods fail to exploit the spatial correlation between the disentangled features. In this paper, we propose a Self-supervised Correlation Mining Network (SCM-Net) to rearrange the source images in the feature space, in which two collaborative modules are integrated, Decomposed Style Encoder (DSE) and Correlation Mining Module (CMM). Specifically, the DSE first creates unaligned pairs at the feature level. Then, the CMM establishes the spatial correlation field for feature rearrangement. Eventually, a translation module transforms the rearranged features to realistic results. Meanwhile, for improving the fidelity of cross-scale pose transformation, we propose a graph based Body Structure Retaining Loss (BSR Loss) to preserve reasonable body structures on half body to full body generation. Extensive experiments conducted on DeepFashion dataset demonstrate the superiority of our method compared with other supervised and unsupervised approaches. Furthermore, satisfactory results on face generation show the versatility of our method in other deformation tasks.
Information-Theoretic Approaches to Blockchain Scalability

Blockchain systems fundamentally provide an environment of distributed trust in networks by creating individual copies of cryptographically secure ledgers of all transactions on the network at each node in the network. This redundant storage when combined with democratized transaction validation and the security from recording the ledgers as hash chains enable a self-sustainable system of distributed trust. However, the principal source of security and fairness of blockchain systems is from every participating node maintaining a local record of all transactions in the network. This in turn implies a significant amount of storage cost that scales prohibitively with larger block sizes, higher transaction volume, greater size of the network, and time in use. In this chapter, we will take a few blockchain applications as examples and highlight the storage and communication demands for maintaining a full node in the network. We then study some approaches with roots in coding theory that aim to reduce this cost and enable network scaling. Finally, we study some practical use cases in establishing distributed trust in computational systems using coding-theoretic methods.
Application of Augmented Reality for Remote Collaborative Work in Architecture, Engineering, and Construction – A Systematic Review

Augmented Reality (AR) enhances the user’s view of the existing world by adding a computer-generated model. AR technology is used in many domains to help users complete tasks more efficiently. This paper reports a systematic review of literature on where and how AR is used in the architecture, engineering, and construction industries for remote collaboration, identifying the devices adopted as well as their impact and challenges. The initial search resulted in a total of 695 studies, of which 12 were screened for final review. We found that integrating AR results in a significant reduction in time to complete the task, errors, and workload while at the same time improving the performance and accuracy of risk communication in construction projects. This review concludes with a discussion of the findings and the opportunities for future research
Semantic Image Collection Summarization With Frequent Subgraph Mining

Applications such as providing a preview of personal albums (e.g., Google Photos) or suggesting thematic collections based on user interests (e.g., Pinterest) require a semantically-enriched image representation, which should be more informative with respect to simple low-level visual features and image tags. To this aim, we propose an image collection summarization technique based on frequent subgraph mining. We represent images with a novel type of scene graphs including fine-grained relationship types between objects. These scene graphs are automatically derived by our method. The resulting summary consists of a set of frequent subgraphs describing the underlying patterns of the image dataset. Our results are interpretable and provide more powerful semantic information with respect to previous techniques, in which the summary is a subset of the collection in terms of images or image patches. The experimental evaluation shows that the proposed technique yields non-redundant summaries, with a high diversity of the discovered patterns.
Motion-Adjustable Neural Implicit Video Representation

Implicit neural representation (INR) has been successful in representing static images. Contemporary image-based INR, with the use of Fourier-based positional encoding, can be viewed as a mapping from sinusoidal patterns with different frequencies to image content. Inspired by that view, we hypothesize that it is possible to generate temporally varying content with a single image-based INR model by displacing its input sinusoidal patterns over time. By exploiting the relation between the phase information in sinusoidal functions and their displacements, we incorporate into the conventional image-based INR model a phase-varying positional encoding module, and couple it with a phase-shift generation module that determines the phase-shift values at each frame. The model is trained end-to-end on a video to jointly determine the phase-shift values at each time with the mapping from the phase-shifted sinusoidal functions to the corresponding frame, enabling an implicit video representation. Experiments on a wide range of videos suggest that such a model is capable of learning to interpret phase-varying positional embeddings into the corresponding time-varying content. More importantly, we found that the learned phase-shift vectors tend to capture meaningful temporal and motion information from the video. In particular, manipulating the phase-shift vectors induces meaningful changes in the temporal dynamics of the resulting video, enabling non-trivial temporal and motion editing effects such as temporal interpolation, motion magnification, motion smoothing, and video loop detection.
Does coding in Pythonic zen peak performance?

In the field of data science, and for academics in general, the Python programming language is a popular choice, mainly because of its libraries for storing, manipulating, and gaining insight from data. Evidence includes the versatile set of machine learning, data visualization, and manipulation packages used for the ever-growing size of available data. The Zen of Python is a set of guiding design principles that developers use to write acceptable and elegant Python code. Most principles revolve around simplicity. However, as the need to compute large amounts of data, performance has become a necessity for the Python programmer. The new idea in this paper is to confirm whether writing the Pythonic way peaks performance at scale. As a starting point, we conduct a set of preliminary experiments to evaluate nine Pythonic code examples by comparing the performance of both Pythonic and Non-Pythonic code snippets. Our results reveal that writing in Pythonic idioms may save memory and time. We show that incorporating list comprehension, generator expression, zip, and itertools.zip_longest idioms can save up to 7,000 MB and up to 32.25 seconds. The results open more questions on how they could be utilized in a real-world setting. The replication package includes all scripts, and the results are available at https://doi.org/10.5281/zenodo.5712349
Sea surface height super-resolution using high-resolution sea surface temperature with a subpixel convolutional residual network

Abstract The oceans have a very important role in climate regulation due to their massive heat storage capacity. Thus, for the past decades, oceans have been observed by satellites to better understand their dynamics. Satellites retrieve several data with various spatial resolutions. For instance, sea surface height (SSH) is a low-resolution data field where sea surface temperature (SST) can be retrieved in a much higher one. These two physical parameters are linked by a physical link that can be learned by a super-resolution machine-learning algorithm. In this work, we present a subpixel convolutional deep learning model that takes advantage of the higher resolution SST field to guide the downscaling of the SSH one. The data fields that we use are simulated by a physic-based ocean model at a higher sampling rate than the satellites provide. We compared our approach with a convolutional neural network model. Our architecture generalized well with validation performances of 3.94 cm root mean squared error (RMSE) and training performances of 2.65 cm RMSE.
Digital Image Watermarking using Self Inverting Permutations - A Comparative Study

In this work we present a comparative study of two watermarking techniques, both based on frequency domain transformations but differing on watermark construction. Many state-of-the-art approaches utilize binary images as watermarks. An alternative way of watermark construction, which incorporates error correction properties, proposes the transformation of an integer number w into a self-inverting permutation (SiP). SiP can be represented in a two dimensional (2D) object, since images are 2D structures, and that constitutes a watermark. Through this study, we highlight the importance of developing robust digital image watermarking systems through their operation in real attack scenarios. Moreover, through the conduction of a series of experiments required for the comparative study, there are provided further insights regarding the strengths and weaknesses of each approach. Several image sizes have been tested along with a range of evaluation metrics for imperceptibility evaluation. In addition, robustness of both of the techniques has also been confirmed by applying a variety of attacks on the resulting images.