Reviewerly/exHarmony huggingface dataset:
Remote Data Checking With a Designated Verifier in Cloud Storage

Remote data possession checking (RDPC) supplies an efficient manner to verify the integrity of the files stored in cloud storage. Public verification allows anyone to check the integrity of remote data so that it has a wider application in public cloud storage. Private verification just allows the data owner to verify the data integrity, which is mainly applied for the verification of secret data. However, in many real applications, the data owner expects a specific user to check the files in cloud storage, whereas others cannot execute such work. It is obvious that neither public verification nor private verification can satisfy such a requirement. To solve this issue, Ren et al. provided a designated-verifier provable data possession (DV-PDP) protocol. Unfortunately, the DV-PDP is insecure against replay attack launched by the malicious cloud server. To overcome this shortcoming, we present a new RDPC scheme with the designated verifier, in which the data owner specifies a unique verifier to check the data integrity. Based on the computational Diffie-Hellman assumption, we prove the security for our RDPC scheme in a random oracle model. The theoretical analysis and experiment results indicate that our scheme has less communication, storage, and computation overhead while achieving high error detection probability.
Credit data generators for data reuse

To promote effective sharing, we must create an enduring link between the people who generate data and its future uses, urge Heather H. Pierce and colleagues. To promote effective sharing, we must create an enduring link between the people who generate data and its future uses, urge Heather H. Pierce and colleagues.
Understanding Emerging Design Practices for Avatar Systems in the Commercial Social VR Ecology

In the emerging ecology of commercial social VR, avatars that serve to represent individuals within these multi-user virtual worlds are at the heart of the embodied social experience. Current industry approaches to avatars in social VR applications vary widely, and the (sometimes tacit) design knowledge acquired by those who created these platforms has much to offer research in HCI. In this paper, we describe current design practices, and reflect on the design approaches that characterize avatars and avatar systems in this emerging commercial sector. To investigate design approaches to avatar systems and their impact on communication and interaction with people within this medium, we interviewed industry experts associated with a range of platforms including Rec Room, AltspaceVR, High Fidelity, VRChat, Anyland, and Mozilla Hubs. In analyzing the ways that design choices shape embodied experience, we map design approaches to avatar systems in this evolving landscape and make preliminary claims about the impact of these varying design approaches.
Dimensions of digital literacy based on five models of development / Dimensiones de la alfabetización digital a partir de cinco modelos de desarrollo

Digital literacy has been an emerging and increasing priority in many governments' educational and assessment policies since the first decade of the twenty-first century because it is a priority in citizenship training. The difficulty with understanding and developing it comes from its changing, transversal and flexible nature; for this reason, the fundamental objective of this research is to offer an exhaustive study of the concept by analysing the main models of digital literacy in the international context in order to offer a holistic model that incorporates the contributions from all of these initiatives. The theoretical study presented herein provides an objective, rigorous perspective on the main models of development of digital literacy: the DIGCOMP Project (European Commission), the Krumsvik model (Norway), the TPACK model (USA), the JISC model (UK), the ISTE Standards (USA) and the P21 model (USA). The conclusion offers an innovative proposal of four dimensions that should define digital literacy today, taking into account the five initiatives studied, which could be very worthwhile for researchers, educators and policymakers.
Secure and Differentially Private Logistic Regression for Horizontally Distributed Data

Scientific collaborations benefit from sharing information and data from distributed sources, but protecting privacy is a major concern. Researchers, funders, and the public in general are getting increasingly worried about the potential leakage of private data. Advanced security methods have been developed to protect the storage and computation of sensitive data in a distributed setting. However, they do not protect against information leakage from the outcomes of data analyses. To address this aspect, studies on differential privacy (a state-of-the-art privacy protection framework) demonstrated encouraging results, but most of them do not apply to distributed scenarios. Combining security and privacy methodologies is a natural way to tackle the problem, but naive solutions may lead to poor analytical performance. In this paper, we introduce a novel strategy that combines differential privacy methods and homomorphic encryption techniques to achieve the best of both worlds. Using logistic regression (a popular model in biomedicine), we demonstrated the practicability of building secure and privacy-preserving models with high efficiency (less than 3 min) and good accuracy [<;1% of difference in the area under the receiver operating characteristic curve (AUC) against the global model] using a few real-world datasets.
Locomotion in Place in Virtual Reality: A Comparative Evaluation of Joystick, Teleport, and Leaning

Recent VR head-mounted displays for consumers feature 3-DOF or 6-DOF head tracking. However, position tracking (when available) is limited to a small area. Moreover, in small or cluttered physical spaces, users can safely experience VR only by staying in place, standing or seated. Different locomotion techniques have been proposed to allow users to explore virtual environments by staying in place. Two in-place locomotion techniques, frequently employed in the literature and in consumer applications, are based on joystick and teleport. Some authors explored leaning with the aim of proposing a more natural in-place locomotion technique. However, more research is needed to understand the effects of the three techniques, since no user study thoroughly compared them all together on a variety of fundamental aspects. Therefore, this paper presents a comparative evaluation with 75 users, assessing the effects of the three techniques on performance, sickness, presence, usability, and different aspects of comfort. Performance of teleport was better than the other techniques, and performance of leaning was better than joystick. Teleport also caused less nausea than the other techniques. Unexpectedly, no significant differences were found for presence. Teleport received a higher usability score than the other techniques. Finally, the techniques had different effects on comfort that we discuss in detail.
Performance of Johnson-Lindenstrauss transform for <i>k</i> -means and <i>k</i> -medians clustering

Consider an instance of Euclidean k-means or k-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of (1+ε) under a projection onto a random O(log(k /ε) / ε2)-dimensional subspace. Further, the cost of every clustering is preserved within (1+ε). More generally, our result applies to any dimension reduction map satisfying a mild sub-Gaussian-tail condition. Our bound on the dimension is nearly optimal. Additionally, our result applies to Euclidean k-clustering with the distances raised to the p-th power for any constant p.
Weighted Guided Image Filtering With Steering Kernel

Due to its local property, guided image filter (GIF) generally suffers from halo artifacts near edges. To make up for the deficiency, a weighted guided image filter (WGIF) was proposed recently by incorporating an edge-aware weighting into the filtering process. It takes the advantages of local and global operations, and achieves better performance in edge-preserving. However, edge direction, a vital property of the guidance image, is not considered fully in these guided filters. In order to overcome the drawback, we propose a novel version of GIF, which can leverage the edge direction more sufficiently. In particular, we utilize the steering kernel to adaptively learn the direction and incorporate the learning results into the filtering process to improve the filter's behavior. Theoretical analysis shows that the proposed method can get more powerful performance with preserving edges and reducing halo artifacts effectively. Similar conclusions are also reached through the thorough experiments including edge-aware smoothing, detail enhancement, denoising, and dehazing.
Uldor: A Universal Lesion Detector For Ct Scans With Pseudo Masks And Hard Negative Example Mining

Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis, which is still very challenging due to similar appearances (e.g., in-tensity and texture) between lesions and other tissues. Instead of developing a specific-type lesion detector, this work builds a Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect all different kinds of lesions from whole body parts. As a state-of-the-art object detector, Mask R-CNN adds a branch for predicting segmentation masks on each Region of Interest (RoI) to improve the detection performance. However, it is almost impossible to manually annotate a large-scale dataset with pixel-level lesion masks to train the Mask R-CNN for lesion detection. To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection. On the other hand, this work proposes a hard negative example mining strategy to reduce the false positives for improving the detection performance. Experimental results on the NIH DeepLesion dataset demonstrate that the ULDor is enhanced using pseudo masks and the proposed hard negative example mining strategy and achieves a sensitivity of 86.21% with five false positives per image.
Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning

One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of “blockchain” as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.
Seeded graph matching

Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs. The seeded graph matching problem is the graph matching problem when we are first given a partial alignment that we are tasked with completing. In this article, we modify the state-of-the-art approximate graph matching algorithm "FAQ" of Vogelstein et al. (2015) to make it a fast approximate seeded graph matching algorithm, adapt its applicability to include graphs with differently sized vertex sets, and extend the algorithm so as to provide, for each individual vertex, a nomination list of likely matches. We demonstrate the effectiveness of our algorithm via simulation and real data experiments; indeed, knowledge of even a few seeds can be extremely effective when our seeded graph matching algorithm is used to recover a naturally existing alignment that is only partially observed.
Active Fairness in Algorithmic Decision Making

Society increasingly relies on machine learning models for automated decision making. Yet, efficiency gains from automation have come paired with concern for algorithmic discrimination that can systematize inequality. Recent work has proposed optimal post-processing methods that randomize classification decisions for a fraction of individuals, in order to achieve fairness measures related to parity in errors and calibration. These methods, however, have raised concern due to the information inefficiency, intra-group unfairness, and Pareto sub-optimality they entail. The present work proposes an alternativeactive framework for fair classification, where, in deployment, a decision-maker adaptively acquires information according to the needs of different groups or individuals, towards balancing disparities in classification performance. We propose two such methods, where information collection is adapted to group- and individual-level needs respectively. We show on real-world datasets that these can achieve: 1) calibration and single error parity (e.g.,equal opportunity ); and 2) parity in both false positive and false negative rates (i.e.,equal odds ). Moreover, we show that by leveraging their additional degree of freedom,active approaches can substantially outperform randomization-based classifiers previously considered optimal, while avoiding limitations such as intra-group unfairness.
Networked fusion estimation with multiple uncertainties and time-correlated channel noise

This paper is concerned with the fusion filtering and fixed-point smoothing problems for a class of networked systems with multiple random uncertainties in both the sensor outputs and the transmission connections. To deal with this kind of systems, random parameter matrices are considered in the mathematical models of both the sensor measurements and the data available after transmission. The additive noise in the transmission channel from each sensor is assumed to be sequentially time-correlated. By using the time-differencing approach, the available measurements are transformed into an equivalent set of observations that do not depend on the time-correlated noise. The innovation approach is then applied to obtain recursive distributed and centralized fusion estimation algorithms for the filtering and fixed-point smoothing estimators of the signal based on the transformed measurements, which are equal to the estimators based on the original ones. The derivation of the algorithms does not require the knowledge of the signal evolution model, but only the mean and covariance functions of the processes involved (covariance information). A simulation example illustrates the utility and effectiveness of the proposed fusion estimation algorithms, as well as the applicability of the current model to deal with different network-induced random phenomena.
Proof-of-Learning: A Blockchain Consensus Mechanism Based on Machine Learning Competitions

This article presents WekaCoin, a peer-to-peer cryptocurrency based on a new distributed consensus protocol called Proof-of-Learning. Proof-of-learning achieves distributed consensus by ranking machine learning systems for a given task. The aim of this protocol is to alleviate the computational waste involved in hashing-based puzzles and to create a public distributed and verifiable database of state-of-the-art machine learning models and experiments.
Machine learning based video coding optimizations: A survey

Video data has become the largest source of data consumed globally. Due to the rapid growth of video applications and boosting demands for higher quality video services, video data volume has been increasing explosively worldwide, which has been the most severe challenge for multimedia computing, transmission and storage. Video coding by compressing videos into a much smaller size is one of the key solutions; however, its development has become saturated to some extent while the compression ratio continuously grows in the last three decades. Machine leaning algorithms, especially those employing deep learning, which are capable of discovering knowledge from unstructured massive data and providing data-driven predictions, provide new opportunities for further upgrading video coding technologies. In this article, we present a review on machine learning based video encoding optimization, aiming to provide researchers with a strong foundation and inspire future developments for data-driven video coding. Firstly, we analyze the representations and redundancies of video data. Secondly, we review the development of video coding standards and key requirements. Subsequently, we present a systemic survey on the recent advances and challenges associated with the machine learning based video coding optimizations from three key aspects, including high efficiency, low complexity and high visual quality. Their workflows, representative schemes, performances, advantages and disadvantages are analyzed in detail. Finally, the challenges and opportunities are identified, which may provide the academic and industrial communities with groundwork and potential directions for future research.
The optimal combination of feature selection and data discretization: An empirical study

Feature selection and data discretization are two important data pre-processing steps in data mining, with the focus in the former being on filtering out unrepresentative features and in the latter on transferring continuous attributes into discrete ones. In the literature, these two domain problems have often been studied, individually. However, the combination of these two steps has not been fully explored, although both feature selection and discretization may be required for some real-world datasets. In this paper, two different combination orders of feature selection and discretization are examined in terms of their classification accuracies and computational times. Specifically, filter, wrapper, and embedded feature selection methods are employed, which are PCA, GA, and C4.5, respectively. For discretization, both supervised and unsupervised learning based discretizers are used, specifically MDLP, ChiMerge, equal frequency binning, and equal width binning. The experimental results, based on 10 UCI datasets, show that, for the SVM classifier performing MDLP first and C4.5 second outperforms the other combinations. Not only is less computational time required but this also provides the highest rate of classification accuracy. For the decision tree classifier, performing C4.5 first and MDLP second is recommended.
High‐Precision Cd Isotope Measurements of Soil and Rock Reference Materials by MC‐ICP‐MS with Double Spike Correction

This study presents a high‐precision Cd isotope measurement method for soil and rock reference materials using MC‐ICP‐MS with double spike correction. The effects of molecular interferences (e.g ., 109 Ag 1 H + , 94 Zr 16 O + , 94 Mo 16 O + and 70 Zn 40 Ar + ) and isobaric interferences (e.g ., Pd, In and Sn) to Cd isotope measurements were quantitatively evaluated. When the measured solution has Ag/Cd ≤ 5, Zn/Cd ≤ 0.02, Mo/Cd ≤ 0.4, Zr/Cd ≤ 0.001, Pd/Cd ≤ 5 × 10 −5 and In/Cd ≤ 10 −3 , the measured Cd isotope data were not significantly affected. The intermediate measurement precision of pure Cd solutions (BAM I012 Cd, Münster Cd and AAS Cd) was better than ± 0.05‰ (2 s ) for δ 114/110 Cd. The δ 114/110 Cd values of soil reference materials (NIST SRM 2709, 2709a, 2710, 2710a, 2711, 2711a and GSS‐1) relative to NIST SRM 3108 were in the range of −0.251 to 0.632‰, the δ 114/110 Cd values of rock reference materials (BCR‐2, BIR‐1, BHVO‐2, W‐2, AGV‐2, GSP‐2 and COQ‐1) varied from −0.196‰ to 0.098‰, and that of the manganese nodule (NOD‐P‐1) was 0.163 ± 0.040‰ (2 s , n = 8). The large variation in Cd isotopes in soils and igneous rocks indicates that they can be more widely used to study magmatic and supergene processes.
Instrument mix for energy transition: A method for policy formulation

The present paper illustrates a methodological strategy to address the first phases of the policy cycle – namely agenda setting, impact analysis, and policy formulation – with the aim of fostering the development of an innovation niche. Through a combination of fuzzy cognitive maps and social network analysis, our methodology allows firstly assessing the needs of the niche and then, the identification of the most effective instrument mix and policy brokers to overcome the niche's criticalities. We investigate the case of the Italian liquid biofuel niche, which offers a near-ideal laboratory for testing our methodological approach due to its recent slump. Our simulations show that the persistence of negative context conditions in the niche under investigation would be detrimental for all three niche development mechanisms – i.e., networking, learning, and convergence of expectations – and especially for the last one, thus providing a clear priority in setting the policy agenda. Additionally, the impact analysis highlights that the combination of different policy instruments better supports the niche mechanisms than the implementation of single instruments. Finally, our methodology enables identification of the most effective instrument mix by considering interactions and feedback effects among instruments.
Deep learning: A philosophical introduction

Abstract Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science.