Reviewerly/exHarmony huggingface dataset:
Turbo-Sim: a generalised generative model with a physical latent space

We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. By maximising the mutual information between the input and the output of both the encoder and the decoder, we are able to rediscover the loss terms usually found in adversarial autoencoders and generative adversarial networks, as well as various more sophisticated related models. Our generalised framework makes these models mathematically interpretable and allows for a diversity of new ones by setting the weight of each loss term separately. The framework is also independent of the intrinsic architecture of the encoder and the decoder thus leaving a wide choice for the building blocks of the whole network. We apply Turbo-Sim to a collider physics generation problem: the transformation of the properties of several particles from a theory space, right after the collision, to an observation space, right after the detection in an experiment.
VaultDB: A Real-World Pilot of Secure Multi-Party Computation within a Clinical Research Network

Electronic health records represent a rich and growing source of clinical data for research. Privacy, regulatory, and institutional concerns limit the speed and ease of sharing this data. VaultDB is a framework for securely computing SQL queries over private data from two or more sources. It evaluates queries using secure multiparty computation: cryptographic protocols that evaluate a function such that the only information revealed from running it is the query answer. We describe the development of a HIPAA-compliant version of VaultDB on the Chicago Area Patient Centered Outcomes Research Network (CAPriCORN). This multi-institutional clinical research network spans the electronic health records of nearly 13M patients over hundreds of clinics and hospitals in the Chicago metropolitan area. Our results from deploying at three health systems within this network show its efficiency and scalability for distributed clinical research analyses without moving patient records from their site of origin.
Text2LIVE: Text-Driven Layered Image and Video Editing

We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with visual effects (e.g., smoke, fire) in a semantically meaningful manner. We train a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. We demonstrate localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes.
Passage Retrieval on Structured Documents Using Graph Attention Networks

Passage Retrieval systems aim at retrieving and ranking small text units according to their estimated relevance to a query. A usual practice is to consider the context a passage appears in (its containing document, neighbour passages, etc.) to improve its relevance estimation. In this work, we study the use of Graph Attention Networks (GATs), a graph node embedding method, to perform passage contextualization. More precisely, we first propose a document graph representation based on several inter- and intra-document relations. Then, we investigate two ways of leveraging the use of GATs on this representation in order to incorporate contextual information for passage retrieval. We evaluate our approach on a Passage Retrieval task for structured documents: CLEF-IP2013. Our results show that our document graph representation coupled with the expressive power of GATs allows for a better context representation leading to improved performances.
Forward-reverse fractional and fuzzy logic augmented partial differential equation-based enhancement and thresholding for degraded document images

This paper presents a thresholding algorithm using an alternate forward-reverse flow-based partial differential equation (PDE)-based formulation. Additionally, revised models are augmented with fuzzy intensification and fuzzy rule-based structure tensor edge terms for handling degraded document images. Furthermore, an effective technique for enhancement of document images with extreme bleed-through is proposed to further improve binarization results. Comparisons with state-of-the-art PDE thresholding/binarization approaches show superior results of proposed methods based on visual and objective analysis.
Generalized Visual Quality Assessment of GAN-Generated Face Images

Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scenarios. However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model. Herein, we make the first attempt to study the subjective and objective quality towards generalized quality assessment of GFIs. More specifically, we establish a large-scale database consisting of GFIs from four GAN algorithms, the pseudo labels from image quality assessment (IQA) measures, as well as the human opinion scores via subjective testing. Subsequently, we develop a quality assessment model that is able to deliver accurate quality predictions for GFIs from both available and unseen GAN algorithms based on meta-learning. In particular, to learn shared knowledge from GFIs pairs that are born of limited GAN algorithms, we develop the convolutional block attention (CBA) and facial attributes-based analysis (ABA) modules, ensuring that the learned knowledge tends to be consistent with human visual perception. Extensive experiments exhibit that the proposed model achieves better performance compared with the state-of-the-art IQA models, and is capable of retaining the effectiveness when evaluating GFIs from the unseen GAN algorithms.
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension

Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step.Comprehending PMDs and inducing their representations for the downstream reasoning tasks is designated as Procedural MultiModal Machine Comprehension (M 3 C).In this study, we approach Procedural M 3 C at a fine-grained level (compared with existing explorations at a document or sentence level), that is, entity.With delicate consideration, we model entity both in its temporal and cross-modal relation and propose a novel Temporal-Modal Entity Graph (TMEG).Specifically, a heterogeneous graph structure is formulated to capture textual and visual entities and trace their temporal-modal evolution.In addition, a graph aggregation module is introduced to conduct graph encoding and reasoning.Comprehensive experiments across three Procedural M 3 C tasks are conducted on a traditional dataset RecipeQA and our new dataset CraftQA, which can better evaluate the generalization of TMEG.
A Utility to Transform CSV Data into EMF

With the era of data evolution, enterprises increasingly depend on data utilization tools to import or export data from various data sources. Traditionally, enterprises archive such data into row formats, commonly in CSV files. The flat representation of these files has become an excessive burden to opt the right approach for developing and designing applications that structurally meet business needs. CASE (Computer-Aided Software Environment) tools have been praised by domain experts to build applications by describing their domains in a high abstracted level and automatically generating the appropri-ate implementations. However, these tools lack the appropriate facilities to support efficient and generic bulk data import. In this paper, we present a generic CSV data parser based on EMF (Eclipse Modeling Framework) to automatically map row data into platform-specific models. We define a mapping model which defines the mapping between the CSV files and the target metamodels, and an auxiliary Python script to retrieve the corresponding elements. The experimental evaluation of our parser demonstrates its efficiency to import large CSV files into EMF. In this sense, we aim to increase the adoption of model-based approaches for data-driven use cases by executing bulk and row data import into EMF in an agnostic manner.
Power Signature for Multimedia Forensics

Abstract There has been an increasing amount of work surrounding the Electric Network Frequency (ENF) signal, an environmental signature captured by audio and video recordings made in locations where there is electrical activity. ENF is the frequency of power distribution networks, 60 Hz in most of the Americas and 50 Hz in most other parts of the world. The ubiquity of this power signature and the appearance of its traces in media recordings motivated its early application toward time–location authentication of audio recordings. Since then, more work has been done toward utilizing this signature for other forensic applications, such as inferring the grid in which a recording was made, as well as applications beyond forensics, such as temporally synchronizing media pieces. The goal of this chapter is to provide an overview of the research work that has been done on the ENF signal and to provide an outlook for the future.
Thickness determination of laminate core layers from microscopy images

Abstract In sandwich laminates made of metal–polymer–metal (MPM) layers under forming conditions, the thickness changes along the thin‐layered specimens are of particular interest. To measure this distribution, it is common to take microscopic images and determine manually the thicknesses at particular discrete points. We compare and provide several methods determining the thickness distribution from microscopic images. First, an interpolation scheme is chosen to obtain a continuous function of the interfaces between the layers instead of the discrete pixel data. Afterwards, the methods—ruled surface approach, orthogonal projection, embedded circle scheme, and a dull but simple method—are applied and compared with each other. For very smooth data, most of the schemes show equivalent results. However, the “brute force”—called the dull approach—turns out to be the most robust scheme, particularly, if noisy data is studied. The Gaussian error propagation concept is applied to study the uncertainties resulting from noisy pixel data. The schemes are adapted to finite element simulation results as well so that a direct comparison of experimental and numerical data would be possible.
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions

Autonomous driving has achieved significant milestones in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate reliably without human intervention. However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, the AI systems of autonomous vehicles also need to explain how their decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds comprehensive light on the development of explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the state-of-the-art studies on XAI for autonomous driving. We then propose an XAI framework that considers the societal and legal requirements for the explainability of autonomous driving systems. Finally, as future research directions, we provide several XAI approaches that can improve operational safety and transparency to support public approval of autonomous driving technology by regulators and engaged stakeholders.
ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation

Recent advancements in computer vision have seen a rise in the prominence of applications using neural networks to understand human poses. However, while accuracy has been steadily increasing on State-of-the-Art datasets, these datasets often do not address the challenges seen in real-world applications. These challenges are dealing with people distant from the camera, people in crowds, and heavily occluded people. As a result, many real-world applications have trained on data that does not reflect the data present in deployment, leading to significant underperformance. This article presents ADG-Pose, a method for automatically generating datasets for real-world human pose estimation. ADG-Pose utilizes top-down pose estimation for extracting human keypoints from unlabeled data. These datasets can be customized to determine person distances, crowdedness, and occlusion distributions. Models trained with our method are able to perform in the presence of these challenges where those trained on other datasets fail. Using ADG-Pose, end-to-end accuracy for real-world skeleton-based action recognition sees a 20% increase on scenes with moderate distance and occlusion levels, and a 4 $$\times $$ increase on distant scenes where other models failed to perform better than random.
Adaptive Risk-Limiting Ballot Comparison Audits

Risk-limiting audits (RLAs) are rigorous statistical procedures meant to detect invalid election results. RLAs examine paper ballots cast during the election to statistically assess the possibility of a disagreement between the winner determined by the ballots and the winner reported by tabulation. The most ballot efficient approaches proceed by "ballot comparison." However, ballot comparison requires an untrusted declaration of the contents of each cast ballot, rather than a simple tabulation of vote totals. This "cast-vote record table" (CVR) is then spot-checked against ballots for consistency. In many practical settings, the cost of generating a suitable CVR dominates the cost of conducting the audit, preventing widespread adoption of these sample-efficient techniques. We introduce a new RLA procedure: an "adaptive ballot comparison" audit. In this audit, a global CVR is never produced; instead, a three-stage procedure is iterated: 1) a batch is selected, 2) a CVR is produced for that batch, and 3) a ballot within the batch is sampled, inspected by auditors, and compared with the CVR. We prove that such an audit can achieve risk commensurate with standard comparison audits while generating a fraction of the CVR. We present three main contributions: 1) a formal adversarial model for RLAs; 2) definition and analysis of an adaptive audit procedure with rigorous risk limits and an associated correctness analysis accounting for the incidental errors arising in typical audits; and 3) an analysis of practical efficiency. This method can be organized in rounds (as is typical for comparison audits) where sampled CVRs are produced in parallel. Using data from Florida's 2020 presidential election with 5% risk and 1% margin, only 22% of the CVR is generated; at 10% margin, only 2% is generated.
Big Data Analysis of Personalized Recommendation in E-Commerce

The personalized recommendation analysis for users' behaviors in e-commerce platform by big data analysis.Study on what kind of factors will affect on personal recommendations for goods on Amazon more effective by using Principal Component Analysis (PCA), Association Analysis and SVM (Super Vector Machine) Model.Using RFM Model to increase platform sales by analysis those data.This model aims to improve the accuracy and effectiveness of personalized recommendation, therefore improving the sales of goods on platform and the satisfaction of users.
Realization for Finger Character Recognition Method by Similarity Measure of Finger Features

In this paper, we present a novel finger character recognition method in sign language using dimension reduction finger character feature knowledge base for similarity measure. A sign language communication is crucial method for deaf or hearing-impaired people. One of the most important problems is that very few people can understand a sign language. Essentially, there is not enough image data set for finger character learning. In addition to aligning a corpus of images of finger character, it is necessary to realize an automatic recognition system for finger characters in a sign language. We construct a knowledge base for finger character features and apply it to realize a novel finger character recognition. Our method enables finger character recognition by similarity measure between the input finger character features and a knowledge base. The experimental results show that our approach efficiently utilizes the knowledge base generated from a small amount of finger character images. We also present our prototype system and experimental evaluation.
DataMUX: Data Multiplexing for Neural Networks

In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation. DataMUX demonstrates that neural networks are capable of generating accurate predictions over mixtures of inputs, resulting in increased throughput with minimal extra memory requirements. Our approach uses two key components -- 1) a multiplexing layer that performs a fixed linear transformation to each input before combining them to create a mixed representation of the same size as a single input, which is then processed by the base network, and 2) a demultiplexing layer that converts the base network's output back into independent representations before producing predictions for each input. We show the viability of DataMUX for different architectures (Transformers, and to a lesser extent MLPs and CNNs) across six different tasks spanning sentence classification, named entity recognition and image classification. For instance, DataMUX for Transformers can multiplex up to $20$x/$40$x inputs, achieving $11$x/$18$x increase in throughput with minimal absolute performance drops of $<2\%$ and $<4\%$ respectively on MNLI, a natural language inference task. We also provide a theoretical construction for multiplexing in self-attention networks and analyze the effect of various design elements in DataMUX.
Digitally topological groups

&lt;abstract&gt;&lt;p&gt;The purpose of the paper is to study digital topological versions of typical topological groups. In relation to this work, given a digital image $ (X, k), X \subset {\mathbb Z}^n $, we are strongly required to establish the most suitable adjacency relation in a digital product $ X \times X $, say $ G_{k^\ast} $, that supports both $ G_{k^\ast} $-connectedness of $ X \times X $ and $ (G_{k^\ast}, k) $-continuity of the multiplication $ \alpha: (X \times X, G_{k^\ast}) \to (X, k) $ for formulating a digitally topological $ k $-group (or $ DT $-$ k $-group for brevity). Thus the present paper studies two kinds of adjacency relations in a digital product such as a $ C_{k^\ast} $- and $ G_{k^\ast} $-adjacency. In particular, the $ G_{k^\ast} $-adjacency relation is a new adjacency relation in $ X \times X\subset {\mathbb Z}^{2n} $ derived from $ (X, k) $. Next, the paper initially develops two types of continuities related to the above multiplication $ \alpha $, e.g., the $ (C_{k^\ast}, k) $- and $ (G_{k^\ast}, k) $-continuity. Besides, we prove that while the $ (C_{k^\ast}, k) $-continuity implies the $ (G_{k^\ast}, k) $-continuity, the converse does not hold. Taking this approach, we define a $ DT $-$ k $-group and prove that the pair $ (SC_k^{n, l}, \ast) $ is a $ DT $-$ k $-group with a certain group operation $ \ast $, where $ SC_k^{n, l} $ is a simple closed $ k $-curve with $ l $ elements in $ {\mathbb Z}^{n} $. Also, the $ n $-dimensional digital space $ ({\mathbb Z}^n, 2n, +) $ with the usual group operation "$ + $" on $ {\mathbb Z}^n $ is a $ DT $-$ 2n $-group. Finally, the paper corrects some errors related to the earlier works in the literature.&lt;/p&gt;&lt;/abstract&gt;
Preserving Location-Privacy in Vehicular Networks via Reinforcement Learning

In vehicular networks, the benefits of jointly-optimizing data prefetching and caching with broadcast transmission scheduling and rate adaptation strategies at road-side units (RSUs) have already been demonstrated in the literature. Nevertheless, the effectiveness of the solution depends greatly on the accurate knowledge of vehicular trajectories. In practice, as specified in the standards, this is at odds with the important issue of privacy. One of the main contributions of this work is to address this issue by providing a scheme that jointly optimizes the throughput of broadcast-transmission scheduling from RSUs to vehicles, and the privacy of vehicles’ locations, by enabling them to disseminate obfuscated locations to the server from time to time. We formulate this problem as a reinforcement learning (RL) problem, where the vehicles learn when to report obfuscated locations, in order to maximize a utility that encompasses both the network capacity (related to vehicles’ throughput) and the level of privacy achieved. The proposed scheme is shown to consistently outperform alternative randomized schemes considered in past work, and in particular it proves to be robust against prediction errors of the future locations of the vehicles.
Retail Central Bank Digital Currencies (CBDC), Disintermediation and Financial Privacy: The Case of the Bahamian Sand Dollar

The fast-growing, market-driven demand for cryptocurrencies worries central banks, as their monetary policy could be completely undermined. Central bank digital currencies (CBDCs) could offer a solution, yet our understanding of their design and consequences is in its infancy. This non-technical paper examines how The Bahamas has designed the Sand Dollar, the first real-world instance of a retail CBDC. It contrasts the Sand Dollar with definition-based specifications. I then develop a scenario analysis to illustrate commercial bank risks. In this process, the central bank becomes a deposit monopolist, leading to high funding risks, disintermediation risks, and solvency risks for the com-mercial banking sector. I argue that restrictions and caps will be the new specifications of a regulatory framework for CBDCs if disintermediation in the banking sector is to be prevented. I identify the anonymity of CBDCs as a comparative disadvantage that will affect their adoption. These findings provide insight into governance problems facing central banks, and coherently lead to the design of the Sand Dollar. I conclude by suggesting that combating cryptocurrencies is a task that cannot be solved by a CBDC.