Reviewerly/exHarmony huggingface dataset:
Protecting Sensitive Attributes by Adversarial Training Through Class-Overlapping Techniques

In recent years, machine learning as a service (MLaaS) has brought considerable convenience to our daily lives. However, these services raise the issue of leaking users’ sensitive attributes, such as race, when provided through the cloud. The present work overcomes this issue by proposing an innovative privacy-preserving approach called privacy-preserving class overlap (PPCO), which incorporates both a Wasserstein generative adversarial network and the idea of class overlapping to obfuscate data for better resilience against the leakage of attribute-inference attacks(i.e., malicious inference on users’ sensitive attributes). Experiments show that the proposed method can be employed to enhance current state-of-the-art works and achieve superior privacy–utility trade-off. Furthermore, the proposed method is shown to be less susceptible to the influence of imbalanced classes in training data. Finally, we provide a theoretical analysis of the performance of our proposed method to give a flavour of the gap between theoretical and empirical performances.
On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach

The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.
Analysis of the Digital Educational Scenario in Italian High Schools during the Pandemic: Challenges and Emerging Tools

During the COVID-19 pandemic, educational institutions around the world were forced to move from face-to-face lessons to distance learning. The application of distance learning fostered the use of new tools and applications that impacted the school system and produced several challenges to be addressed. This paper provides an analysis of distance learning tools that have been used during COVID-19 in Italian schools and the related emerging needs and challenges. A quantitative survey was carried out by using a standardized online questionnaire that involved 420 Italian teachers of different ages, gender, and teaching areas. This survey collected information about experiences, opportunities, and challenges of distance learning, used tools, and students’ inclusion and involvement. In addition, this work analyses emerging technologies and how they can be integrated into distance learning tools to overcome the identified challenges.
The Construction of NFT Digital Collection Trading Platform in the Perspective of Metauniverse

It is difficult to trace the origin of traditional cultural games, and there are many limitations.If NFT blockchain technology is introduced into cultural games, cultural games will become more popular and convenient.According to the investigation of the cultural game trading market, we analyzed the users' core needs in the traditional cultural game trading scenario, tried to intervene in the traditional cultural game industry with NFT blockchain technology in the perspective of the universe, established a 3D exhibition hall to broaden the appreciation of cultural games, built a digital platform to optimize the appreciation and trading channels of cultural games, and built a digital trading platform of cultural games in the universe, The new era culture of cultural relics can provide energy for the transformation of traditional cultural relics by using blockchain technology from the perspective of the meta-universe, and accelerate the process of the development of contemporary art towards digitalization.
A High Dynamic Range Imaging Method for Short Exposure Multiview Images

The restoration and enhancement of multiview low dynamic range (MVLDR) images captured in low lighting conditions is a great challenge. The disparity maps are hardly reliable in practical, real-world scenarios and suffers from holes and artifacts due to large baseline and angle deviation among multiple cameras in low lighting conditions. Furthermore, multiple images with some additional information (e.g., ISO/exposure time, etc.) are required for the radiance map and poses the additional challenges of deghosting to encounter motion artifacts. In this paper, we proposed a method to reconstruct multiview high dynamic range (MVHDR) images from MVLDR images without relying on disparity maps. We detect and accurately match the feature points among the involved input views and gather the brightness information from the neighboring viewpoints to optimize an image restoration function based on input exposure gain to finally generate MVHDR images. Our method is very reliable and suitable for a wide baseline among sparse cameras. The proposed method requires only one image per viewpoint without any additional information and outperforms others.
Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration

Abstract Background The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. Methods Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. Results Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for &gt; 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. Conclusions We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.
Self-Supervised and Interpretable Data Cleaning with Sequence Generative Adversarial Networks

We study the problem of self-supervised and interpretable data cleaning, which automatically extracts interpretable data repair rules from dirty data. In this paper, we propose a novel framework, namely Garf, based on sequence generative adversarial networks (SeqGAN). One key information Garf tries to capture is data repair rules (for example, if the city is "Dothan", then the county should be "Houston"). Garf employs a SeqGAN consisting of a generator G and a discriminator D that trains G to learn the dependency relationships ( e.g. , given a city value "Dothan" as input, the county can be determined as "Houston"). After training, the generator G can be used to generate data repair rules, but may contain both trusted and untrusted rules, especially when learning from dirty data. To mitigate this problem, Garf further updates the learned relationships with another discriminator D' to iteratively improve the quality of both rules and data. Garf takes advantages of both logical and learning-based methods, which allow cleaning dirty data with high interpretability and have no requirements for prior knowledge and training data. Extensive experiments on real-world and synthetic datasets demonstrate the effectiveness of Garf. Garf achieves new state-of-the-art data cleaning result with high accuracy, through learning from dirty datasets without human supervision.
Using Corrected and Imputed Polarity Measurements to Improve Focal Mechanisms in a Regional Earthquake Catalog Near the Mt. Lewis Fault Zone, California

Abstract We utilized relative polarity measurements and machine learning techniques to better resolve focal mechanisms and stress orientations considering a catalog of ∼29,000 relocated earthquakes that occurred during 1984–2021 in the southeastern San Francisco Bay Area. Earthquake focal mechanisms are commonly produced using P wave first motion polarities, which traditionally requires events to be well‐recorded across a seismic network with good focal sphere coverage. We adapted recently developed approaches that are less dependent on high signal‐to‐noise records and exploit similar waveforms to produce relative polarity and amplitude measurements between earthquake pairs. These techniques were previously only applied on localized earthquake sequences, and we further developed these approaches so that they can be utilized for regional catalogs. We validated or corrected manually identified polarities by performing polarity consensuses using earthquake pairs. Missing and unreliable polarity measurements were imputed using iterative random forests, an unsupervised ensemble machine learning method. Relative P and S wave amplitude measurements were made between earthquakes, constraining S / P ratios for low signal‐to‐noise waveforms. Using these techniques, we were able to reduce focal mechanism uncertainties by an average of ∼13° and produced well‐constrained focal mechanisms for ∼6 times as many earthquakes than those produced using only the traditionally derived polarities. We performed stress inversions using the focal mechanisms by grouping the focal mechanism results into a quadtree structure. Our stress results are consistent with previous work, albeit at a higher spatial resolution, and demonstrate these techniques can aid our understanding of fault structures and kinematics in more detail than was previously possible.
A Principle-based Account of Self-attacking Arguments in Gradual Semantics

Abstract The issue of how a semantics should deal with self-attacking arguments was always a subject of debate among argumentation scholars. A consensus exists for extension-based semantics because those arguments are always rejected (as soon as the semantics in question respects conflict-freeness). In case of gradual semantics, the question is more complex, since other criteria are taken into account. In this paper, we check the impact of those arguments by using a principle-based approach. Principles like self-contradiction and strong self-contradiction prescribe how to deal with self-attacking arguments. We show that they are incompatible with the well-known equivalence principle (which is satisfied by almost all the existing gradual semantics), as well as with some other principles (e.g. counting). This incompatibility was not studied until now and the class of semantics satisfying self-contradiction is under-explored. In the present paper, we explore that class of semantics. We show links and incompatibilities between several principles. We define a new general oriented argumentation semantics that satisfies (strong) self-contradiction and a maximal number of compatible principles. We introduce an iterative algorithm to calculate our semantics and prove that it always converges. We also provide a characterization of our semantics. Finally, we experimentally show that our semantics is computationally efficient.
MIML-GAN: A GAN-Based Algorithm for Multi-Instance Multi-Label Learning on Overlapping Signal Waveform Recognition

Existing studies for automatic waveform recognition of overlapping signals have mostly been conducted in a supervised manner. Although demonstrating superior performance in recent years, supervised methods rely heavily on sufficient labeled samples, but the acquisition of annotated data is expensive, time-consuming, and sometimes infeasible. This shortage drives the need for semi-supervised learning methods, where the unlabeled samples can be fully exploited in the training stage. In addition, multi-instance multi-label (MIML) learning is essentially another weakly supervised learning protocol, and precisely fits the form of the time-frequency images TFIs obtained from the transformation of overlapping signals. In this paper, delving into the MIML learning problem, we leverage the advantage of adversarial training to formulate an effective algorithm MIML-GAN, which is tailored to the MIML problem of overlapping signal waveform recognition. After feeding the TFIs into the network, MIML-GAN approximates the distribution of the training data using the adversarial learning principle. Subsequently, the bag-level prediction can be derived from the instance-level prediction upon the MIML discriminator through adaptive threshold calibration. Specifically, we elaborately studied the global optimality of the MIML-GAN objective function, and extensive simulations are carried out with overlapping signal dataset, validating the ascendancy of the proposed method. Comparative experiments demonstrate that the proposed algorithm possesses promising feature representation capability, and outperforms the existing semi-supervised and supervised signal waveform recognition approaches.
AttTrack: Online Deep Attention Transfer for Multi-object Tracking

Multi-object tracking (MOT) is a vital component of intelligent video analytics applications such as surveillance and autonomous driving. The time and storage complexity required to execute deep learning models for visual object tracking hinder their adoption on embedded devices with limited computing power. In this paper, we aim to accelerate MOT by transferring the knowledge from high-level features of a complex network (teacher) to a lightweight network (student) at both training and inference times. The proposed AttTrack framework has three key components: 1) cross-model feature learning to align intermediate representations from the teacher and student models, 2) interleaving the execution of the two models at inference time, and 3) incorporating the updated predictions from the teacher model as prior knowledge to assist the student model. Experiments on pedestrian tracking tasks are conducted on the MOT17 and MOT15 datasets using two different object detection backbones YOLOv5 and DLA34 show that AttTrack can significantly improve student model tracking performance while sacrificing only minor degradation of tracking speed.
Self-improving Multiplane-to-layer Images for Novel View Synthesis

We present a new method for lightweight novel-view synthesis that generalizes to an arbitrary forward-facing scene. Recent approaches are computationally expensive, require per-scene optimization, or produce a memory-expensive representation. We start by representing the scene with a set of fronto-parallel semitransparent planes and afterwards convert them to deformable layers in an end-to-end manner. Additionally, we employ a feed-forward refinement procedure that corrects the estimated representation by aggregating information from input views. Our method does not require any fine-tuning when a new scene is processed and can handle an arbitrary number of views without any restrictions. Experimental results show that our approach surpasses recent models in terms of both common metrics and human evaluation, with the noticeable advantage in inference speed and compactness of the inferred layered geometry.
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection

3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics. However, most existing studies focus on single point cloud frames without harnessing the temporal information in point cloud sequences. In this paper, we design TransPillars, a novel transformer-based feature aggregation technique that exploits temporal features of consecutive point cloud frames for multi-frame 3D object detection. TransPillars aggregates spatial-temporal point cloud features from two perspectives. First, it fuses voxel-level features directly from multi-frame feature maps instead of pooled instance features to preserve instance details with contextual information that are essential to accurate object localization. Second, it introduces a hierarchical coarse-to-fine strategy to fuse multi-scale features progressively to effectively capture the motion of moving objects and guide the aggregation of fine features. Besides, a variant of deformable transformer is introduced to improve the effectiveness of cross-frame feature matching. Extensive experiments show that our proposed TransPillars achieves state-of-art performance as compared to existing multi-frame detection approaches.
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation

We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only relies on independent image features and optical flows, which can be obtained using off-the-shelf self-supervised methods. It scales with the length of the sequence with no need for superpixels or sparsification, and it generalizes to different datasets without any specific training. This objective function can actually be derived from a form of spectral clustering applied to the entire video. Our method achieves on-par performance with the state of the art on standard bench-marks (DAVIS2016, SegTrack-v2, FBMS59), while being conceptually and practically much simpler.
Real-world K-Anonymity applications: The KGen approach and its evaluation in fraudulent transactions

K-Anonymity is a property for the measurement, management, and governance of the data anonymization. Many implementations of k-anonymity have been described in state of the art, but most of them are not practically usable over a large number of attributes in a “Big” dataset, i.e., a dataset drawing from Big Data. To address this significant shortcoming, we introduce and evaluate KGen, an approach to K-anonymity featuring meta-heuristics, specifically, Genetic Algorithms to compute a permutation of the dataset which is both K-anonymized and still usable for further processing, e.g., for private-by-design analytics. KGen promotes such a meta-heuristic approach since it can solve the problem by finding a pseudo-optimal solution in a reasonable time over a considerable load of input. KGen allows the data manager to guarantee a high anonymity level while preserving the usability and preventing loss of information entropy over the data. Differently from other approaches that provide optimal global solutions compatible with smaller datasets, KGen works properly also over Big datasets while still providing a good-enough K-anonymized but still processable dataset. Evaluation results show how our approach can still work efficiently on a real world dataset, provided by Dutch Tax Authority, with 47 attributes (i.e., the columns of the dataset to be anonymized) and over 1.5K+ observations (i.e., the rows of that dataset), as well as on a dataset with 97 attributes and over 3942 observations.
CLSA: A Contrastive Learning Framework With Selective Aggregation for Video Rescaling

Video rescaling has recently drawn extensive attention for its practical applications such as video compression. Compared to video super-resolution, which focuses on upscaling bicubic-downscaled videos, video rescaling methods jointly optimize a downscaler and a upscaler. However, the inevitable loss of information during downscaling makes the upscaling procedure still ill-posed. Furthermore, the network architecture of previous methods mostly relies on convolution to aggregate information within local regions, which cannot effectively capture the relationship between distant locations. To address the above two issues, we propose a unified video rescaling framework by introducing the following designs. First, we propose to regularize the information of the downscaled videos via a contrastive learning framework, where, particularly, hard negative samples for learning are synthesized online. With this auxiliary contrastive learning objective, the downscaler tends to retain more information that benefits the upscaler. Second, we present a selective global aggregation module (SGAM) to efficiently capture long-range redundancy in high-resolution videos, where only a few representative locations are adaptively selected to participate in the computationally-heavy self-attention (SA) operations. SGAM enjoys the efficiency of the sparse modeling scheme while preserving the global modeling capability of SA. We refer to the proposed framework as Contrastive Learning framework with Selective Aggregation (CLSA) for video rescaling. Comprehensive experimental results show that CLSA outperforms video rescaling and rescaling-based video compression methods on five datasets, achieving state-of-the-art performance.
Elliptic Curve Cryptography for the Security of Insecure Internet of Things

Increasing multimedia devices are also inviting numerous threats to the shared data. This shared data include confidential information regarding business, banking, personal data, and health related information. Insecurity of such important data should be controlled by applying cryptographic techniques. Several researchers contributed in the field by presenting different solutions. The contribution of the current paper is to provide a comprehensive study on the existing cryptographic algorithms like Elliptic Curve Cryptography (ECC). It also compares differences between hardware and software perspectives of the ECC. The paper also proposes a novel scheme for the security of Internet of Things (IoT), based on the ECC. According to study, ECC has proved better authentication that aspires for designing a new security algorithm.