Reviewerly/exHarmony huggingface dataset:
A Traceable Blockchain-Based Vaccination Record Storage and Sharing System

Blockchain technology is essentially a decentralized database maintained by relevant parties and has been widely used in various scenarios such as logistics and finance. In terms of applications in the medical field, it is becoming more and more important because the patient's symptoms may be related to a certain vaccine. Whether the patient has been vaccinated with this vaccine will lead to different diagnostic results by the doctor. However, in the current vaccination environment of many regions, the vaccination record (VR) can only be kept in the patient's vaccination booklet, which is easy to lose or destroy. Therefore, the doctor needs to retrieve the patient's VR through a centralized database maintained by the government, which is time-consuming and will increase the medical risk. This study proposes a traceable blockchain-based vaccination record storage and sharing system. In the proposed system, the patient gets the vaccination at any legal clinic and the VR can be saved accompanied by the signature into the blockchain center, which ensures traceability. When the patient visits the hospital for treatment, the doctor can obtain the detail of the VR from the blockchain center and then make a diagnosis. The security of the proposed system will be protected by the programmed smart contracts. Through mutual authentication, our system can also provide and guarantee data integrity and nonrepudiation. Moreover, the proposed system has resistance to replay and man-in-the-middle attacks, and the performance is good.
Combining Real-World Constraints on User Behavior with Deep Neural Networks for Virtual Reality (VR) Biometrics

Deep networks have demonstrated enormous potential for identification and authentication using behavioral biometrics in virtual reality (VR). However, existing VR behavioral biometrics datasets have small sample sizes which can make it challenging for deep networks to automatically learn features that characterize real-world user behavior and that may enable high success, e.g., high-level spatial relationships between headset and hand controller devices and underlying smoothness of trajectories despite noise. We provide an approach to perform behavioral biometrics using deep networks while incorporating spatial and smoothing constraints on input data to represent real-world behavior. We represent the input data to neural networks as a combination of scale- and translation-invariant device-centric position and orientation features, and displacement vectors representing spatial relationships between device pairs. We assess identification and authentication by including spatial relationships and by performing Gaussian smoothing of the position features. We evaluate our approach against baseline methods that use the raw data directly and that perform a global normalization of the data. By using displacement vectors, our work shows higher success over baseline methods in 36 out of 42 cases of analysis done by varying user sets and pairings of VR systems and sessions.
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities

Whether to retrieve, answer, translate, or reason, multimodality opens up new challenges and perspectives. In this context, we are interested in answering questions about named entities grounded in a visual context using a Knowledge Base (KB). To benchmark this task, called KVQAE (Knowledge-based Visual Question Answering about named Entities), we provide ViQuAE, a dataset of 3.7K questions paired with images. This is the first KVQAE dataset to cover a wide range of entity types (e.g. persons, landmarks, and products). The dataset is annotated using a semi-automatic method. We also propose a KB composed of 1.5M Wikipedia articles paired with images. To set a baseline on the benchmark, we address KVQAE as a two-stage problem: Information Retrieval and Reading Comprehension, with both zero- and few-shot learning methods. The experiments empirically demonstrate the difficulty of the task, especially when questions are not about persons. This work paves the way for better multimodal entity representations and question answering. The dataset, KB, code, and semi-automatic annotation pipeline are freely available at https://github.com/PaulLerner/ViQuAE.
i-LaTeX : Manipulating Transitional Representations between LaTeX Code and Generated Documents

Document description languages such as LaTeX are used extensively to author scientific and technical documents, but editing them is cumbersome: code-based editors only provide generic features, while WYSIWYG interfaces only support a subset of the language. Our interviews with 11 LaTeX users highlighted their difficulties dealing with textually-encoded abstractions and with the mappings between source code and document output. To address some of these issues, we introduce Transitional Representations for document description languages, which enable the visualisation and manipulation of fragments of code in relation to their generated output. We present i-LaTeX, a LaTeX editor equipped with Transitional Representations of formulae, tables, images, and grid layouts. A 16-participant experiment shows that Transitional Representations let them complete common editing tasks significantly faster, with fewer compilations, and with a lower workload. We discuss how Transitional Representations affect editing strategies and conclude with directions for future work.
MultiFingerBubble: A 3D Bubble Cursor Variation for Dense Environments

In this work, we propose MultiFingerBubble, a new variation of the 3D Bubble Cursor. The 3D Bubble Cursor is sensitive to distractors in dense environments: the volume selection resizes to snap-to nearby targets. To prevent the cursor to constantly re-snap to neighboring targets, MultiFingerBubble includes multiple targets in the volume selection, and hence increases the targets effective width. Each target in the volume selection is associated with a specific finger. Users can then select a target by flexing its corresponding finger. We report on a controlled in-lab experiment to explore various design options regarding the number of fingers to use, and the target-to-finger mapping and its visualization. Our study results suggest that MultiFingerBubble is best used with three fingers and colored lines to reveal the mapping between targets and fingers.
Setting AI in Context: A Case Study on Defining the Context and Operational Design Domain for Automated Driving

[Context and motivation] For automated driving systems, the operational context needs to be known in order to state guarantees on performance and safety. The operational design domain (ODD) is an abstraction of the operational context, and its definition is an integral part of the system development process. [Question/problem] There are still major uncertainties in how to clearly define and document the operational context in a diverse and distributed development environment such as the automotive industry. This case study investigates the challenges with context definitions for the development of perception functions that use machine learning for automated driving. [Principal ideas/results] Based on qualitative analysis of data from semi-structured interviews, the case study shows that there is a lack of standardisation for context definitions across the industry, ambiguities in the processes that lead to deriving the ODD, missing documentation of assumptions about the operational context, and a lack of involvement of function developers in the context definition. [Contribution] The results outline challenges experienced by an automotive supplier company when defining the operational context for systems using machine learning. Furthermore, the study collected ideas for potential solutions from the perspective of practitioners.
Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck

Recent advances in sophisticated synthetic speech generated from text-to-speech (TTS) or voice conversion (VC) systems cause threats to the existing automatic speaker verification (ASV) systems.Since such synthetic speech is generated from diverse algorithms, generalization ability with using limited training data is indispensable for a robust anti-spoofing system.In this work, we propose a transfer learning scheme based on the wav2vec 2.0 pretrained model with variational information bottleneck (VIB) for speech anti-spoofing task.Evaluation on the ASVspoof 2019 logical access (LA) database shows that our method improves the performance of distinguishing unseen spoofed and genuine speech, outperforming current stateof-the-art anti-spoofing systems.Furthermore, we show that the proposed system improves performance in low-resource and cross-dataset settings of anti-spoofing task significantly, demonstrating that our system is also robust in terms of data size and data distribution.
Optimal synthesis into fixed XX interactions

We describe an optimal procedure, as well as its efficient software implementation, for exact and approximate synthesis of two-qubit unitary operations into any prescribed discrete family of XX-type interactions and local gates. This arises from the analysis and manipulation of certain polyhedral subsets of the space of canonical gates. Using this, we analyze which small sets of XX-type interactions cause the greatest improvement in expected infidelity under experimentally-motivated error models. For the exact circuit synthesis of Haar-randomly selected two-qubit operations, we find an improvement in estimated infidelity by 31.4% when including alongside CX its square- and cube-roots, near to the optimal limit of 36.9% obtained by including all fractional applications of CX.
Self-Organizing Interval Type-2 Fuzzy Neural Network Using Information Aggregation Method

Interval type-2 fuzzy neural networks (IT2FNNs) usually stack adequate fuzzy rules to identify nonlinear systems with high-dimensional inputs, which may result in an explosion of fuzzy rules. To cope with this problem, a self-organizing IT2FNN, based on the information aggregation method (IA-SOIT2FNN), is developed to avoid the explosion of fuzzy rules in this article. First, a relation-aware strategy is proposed to construct rotatable type-2 fuzzy rules (RT2FRs). This strategy uses the individual RT2FR, instead of multiple standard fuzzy rules, to interpret interactive features of high-dimensional inputs. Second, a comprehensive information evaluation mechanism, associated with the interval information and rotation information of RT2FR, is developed to direct the structural adjustment of IA-SOIT2FNN. This mechanism can achieve a compact structure of IA-SOIT2FNN by growing and pruning RT2FRs. Third, a multicriteria-based optimization algorithm is designed to optimize the parameters of IA-SOIT2FNN. The algorithm can simultaneously update the rotatable parameters and the conventional parameters of RT2FR, and further maintain the accuracy of IA-SOIT2FNN. Finally, the experiments showcase that the proposed IA-SOIT2FNN can compete with the state-of-the-art approaches in terms of identification performance.
Boosting Social Spam Detection via Attention Mechanisms on Twitter

Twitter is one of the largest social networking platforms, which allows users to make friends, read the latest news, share personal ideas, and discuss social issues. The huge popularity of Twitter mean it attracts a lot of online spammers. Traditional spam detection approaches have shown the effectiveness for identifying Twitter spammers by extracting handcrafted features and training machine learning models. However, such models need knowledge from domain experts. Moreover, the behaviors of spammers can change according to the defense strategies of Twitter. These result in the ineffectiveness of the traditional feature-based approaches. Although deep-learning-based approaches have been proposed for detecting Twitter spammers, they all treat each tweet equally, and ignore the differences among them. To solve these issues, in this paper, we propose a new attention-based deep learning model to detect social spammers in Twitter. In particular, we first introduce the state-of-the-art pretraining model BERTweet for learning the representation of each tweet, and then use the proposed novel attention-based mechanism to learn the user representations by distinguishing the differences among tweets posted by each user. Moreover, we take social interactions into consideration and propose that a graph attention network is used to update the learned user representations, to further improve the accuracy of identifying spammers. Experiments on a publicly available, real-world Twitter dataset show the effectiveness of the proposed model, which is able to significantly enhance the performance.
Lumos: Increasing Awareness of Analytic Behavior during Visual Data Analysis

Visual data analysis tools provide people with the agency and flexibility to explore data using a variety of interactive functionalities. However, this flexibility may introduce potential consequences in situations where users unknowingly overemphasize or underemphasize specific subsets of the data or attribute space they are analyzing. For example, users may overemphasize specific attributes and/or their values (e.g., Gender is always encoded on the X axis), underemphasize others (e.g., Religion is never encoded), ignore a subset of the data (e.g., older people are filtered out), etc. In response, we present Lumos, a visual data analysis tool that captures and shows the interaction history with data to increase awareness of such analytic behaviors. Using in-situ (at the place of interaction) and ex-situ (in an external view) visualization techniques, Lumos provides real-time feedback to users for them to reflect on their activities. For example, Lumos highlights datapoints that have been previously examined in the same visualization (in-situ) and also overlays them on the underlying data distribution (i.e., baseline distribution) in a separate visualization (ex-situ). Through a user study with 24 participants, we investigate how Lumos helps users' data exploration and decision-making processes. We found that Lumos increases users' awareness of visual data analysis practices in real-time, promoting reflection upon and acknowledgement of their intentions and potentially influencing subsequent interactions.
Ad-datasets: A Meta-collection of Data Sets for Autonomous Driving

Autonomous driving is among the largest domains in which deep learning has been fundamental for progress within the last years. The rise of datasets went hand in hand with this development. All the more striking is the fact that researchers do not have a tool available that provides a quick, comprehensive and up-to-date overview of data sets and their features in the domain of autonomous driving. In this paper, we present ad-datasets, an online tool that provides such an overview for more than 150 data sets. The tool enables users to sort and filter the data sets according to currently 16 different categories. ad-datasets is an open-source project with community contributions. It is in constant development, ensuring that the content stays up-to-date.
Network Gradient Descent Algorithm for Decentralized Federated Learning

AbstractAbstractWe study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed network structure without a central master. This greatly enhances the reliability of the entire algorithm. Those nice properties inspire us to carefully study the NGD method both theoretically and numerically. Theoretically, we start with a classical linear regression model. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator's statistical efficiency. The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is weakly balanced, even if the data are distributed heterogeneously. Those interesting findings are then extended to general models and loss functions. Extensive numerical studies are presented to corroborate our theoretical findings. Classical deep learning models are also presented for illustration purpose.Keywords: Decentralized federated learningDeep learningDistributed systemGradient descent Supplementary MaterialsSupplementary_Material.pdf:This document provides the extensions of the proposed method, the proofs of the theoretical results in the main text, and some additional simulation results. Appendix A provides for technical lemmas which are useful to prove the results in the main text. Appendix B contains the detailed proofs of the main theorems and corollaries developed in the main text. Appendix C reports some extensions and discussions of the proposed method.Code.zip:This file is the python code for the proposed method. Please see the "README.md" in the file for using the code.Additional informationFundingDanyang Huang's research is partially supported National Natural Science Foundation of China (No. 12071477, 71873137); fund for building world-class universities (disciplines) of Renmin University of China; Public Computing Cloud, Renmin University of China. Hansheng Wang's research is partially supported by National Natural Science Foundation of China (No. 11831008) and also partially supported by the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (KLATASDS-MOE-ECNU-KLATASDS2101).
NEAR: Named entity and attribute recognition of clinical concepts

Named Entity Recognition (NER) or the extraction of concepts from clinical text is the task of identifying entities in text and slotting them into categories such as problems, treatments, tests, clinical departments, occurrences (such as admission and discharge) and others. NER forms a critical component of processing and leveraging unstructured data from Electronic Health Records (EHR). While identifying the spans and categories of concepts is itself a challenging task, these entities could also have attributes such as negation that pivot their meanings implied to the consumers of the named entities. There has been little research dedicated to identifying the entities and their qualifying attributes together. This research hopes to contribute to the area of detecting entities and their corresponding attributes by modelling the NER task as a supervised, multi-label tagging problem with each of the attributes assigned tagging sequence labels. In this paper, we propose 3 architectures to achieve this multi-label entity tagging: BiLSTM n-CRF, BiLSTM-CRF-Smax-TF and BiLSTM n-CRF-TF. We evaluate these methods on the 2010 i2b2/VA and the i2b2 2012 shared task datasets. Our different models obtain best NER scores of 0.903 and 0.808 on the i2b2 2010/VA and i2b2 2012 respectively. The highest span based micro-averaged F1 polarity scores obtained were 0.832 and 0.836 on the i2b2 2010/VA and i2b2 2012 datasets respectively, and the highest macro-averaged F1 polarity scores obtained were 0.924 and 0.888 respectively. The modality studies conducted on i2b2 2012 dataset revealed high scores of 0.818 and 0.501 for span based micro-averaged F1 and macro-averaged F1 respectively.
Binary Horse Optimization Algorithm for Feature Selection

The bio-inspired research field has evolved greatly in the last few years due to the large number of novel proposed algorithms and their applications. The sources of inspiration for these novel bio-inspired algorithms are various, ranging from the behavior of groups of animals to the properties of various plants. One problem is the lack of one bio-inspired algorithm which can produce the best global solution for all types of optimization problems. The presented solution considers the proposal of a novel approach for feature selection in classification problems, which is based on a binary version of a novel bio-inspired algorithm. The principal contributions of this article are: (1) the presentation of the main steps of the original Horse Optimization Algorithm (HOA), (2) the adaptation of the HOA to a binary version called the Binary Horse Optimization Algorithm (BHOA), (3) the application of the BHOA in feature selection using nine state-of-the-art datasets from the UCI machine learning repository and the classifiers Random Forest (RF), Support Vector Machines (SVM), Gradient Boosted Trees (GBT), Logistic Regression (LR), K-Nearest Neighbors (K-NN), and Naïve Bayes (NB), and (4) the comparison of the results with the ones obtained using the Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Crow Search Algorithm (BCSA). The experiments show that the BHOA is effective and robust, as it returned the best mean accuracy value and the best accuracy value for four and seven datasets, respectively, compared to BGWO, BPSO, and BCSA, which returned the best mean accuracy value for four, two, and two datasets, respectively, and the best accuracy value for eight, seven, and five datasets, respectively.
Immune Logics

This article is concerned with an exploration of a family of systems—called immune logics—that arise from certain dualizations of the well-known family of infectious logics. The distinctive feature of the semantic of infectious logics is the presence of a certain “infectious” semantic value, by which two different though equivalent things are meant. On the one hand, it is meant that these values are zero elements for all the operations in the underlying algebraic structure. On the other hand, it is meant that these values behave in a value-in-value-out fashion for all the operations in the underlying algebraic structure. Thus, in a rather informal manner, we will refer to immune logics as those systems whose underlying semantics count with a certain “immune” semantic value behaving in a way that is somewhat dual to that of the infectious values. In a more formal manner, carrying out this dualization will prove to be not as straightforward as one could imagine, since the two characterizations of infectiousness discussed above lead to two different outcomes when one tries to conduct them. We explore these alternatives and provide technical results regarding them, and the various logical systems defined using such semantics.
DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing

Modern video conferencing technologies provide state-of-the-art end-to-end encryption models but do not verify the authenticity of the media broadcast, where the verification of the media is left to the end-users. A perpetrator can forge the video or audio streams using replay attacks or deepfake attacks to manipulate the real-time perception of transcribed events. Leveraging Electrical Network Frequency (ENF) signals as an environmental fingerprint, this paper proposes a distributed consensus network-based audio authentication scheme named DeFake - Decentralized ENF-consensus based deepFake detection, which detects multimedia manipulations in real-time. Since the fluctuations in an ENF signal are of a distributed and random nature, a novel Proof-of-ENF (PoENF) algorithm can guarantee byzantine resistant deepfakes detection on audio streams with minimal computational resources. By utilizing audio conferencing or audio editing applications as the frontend software service, the DeFake solution can effectively and efficiently verify the authenticity of the recorded video clip.