Reviewerly/exHarmony huggingface dataset:
Robust Overlays Meet Blockchains

Blockchains have become ubiquitous in the world of robust decentralized applications. A crucial requirement for implementing a blockchain is a reliable "overlay network" providing robust communication among the participants. In this work, we provide communication-efficient and churn-optimal (barring log factors) Byzantine-resilient algorithms for maintaining blockchain networks. Our approach utilizes an interesting "cross-layer optimization" wherein the overlay network relies on the blockchain that is built on top of it. An important contribution is a tight "half-life" analysis on the amount of churn that can be tolerated, where peers have bandwidth restrictions. Moreover, by leveraging synergies between the blockchain and the overlay network, we can provide non-trivial recovery guarantees from unexpected catastrophic failures, which include a large class of connectivity issues such as denial-of-service, or exponentially unlikely lucky streaks for Byzantine peers, etc.
Synergistic Knowledge

In formal epistemology, group knowledge is often modelled as the knowledge that the group would have if the agents shared all their individual knowledge. However, this interpretation does not account for relations between agents. In this work, we propose the notion of synergistic knowledge, which makes it possible to model those relationships. As examples, we investigate the use of consensus objects and the problem of the dining cryptographers. Moreover, we show that our logic can also be used to model certain aspects of information flow in networks.
Artificial Intelligence and Deep Learning in Malaria Diagnosis

Microscopic examination is the gold standard for malaria diagnosis. However, microscopic diagnosis of malaria relies heavily on pathologists or technicians with specialized knowledge and expertise, and manual microscopic screening is an unreliable method in nonexpert situations. In addition, diagnosing malaria requires considerable time and effort, overwhelming experts when too many cases need to be tested, and for the same test, different results can occur due to different testers. The same test can yield different results for different testers. Therefore, automated image analysis systems can break the dependence of accurate Plasmodium detection on specialized technicians, improve the accuracy and efficiency of detection and reduce the cost of Plasmodium detection. Traditional methods use image-based algorithms for extracting features (e.g., color, shape, texture, and gradient changes) for image detection, image segmentation, feature extraction, and classification. However, the accuracy rate of traditional methods is 80–90%, which cannot meet clinical requirements. With the re-emergence of artificial neural networks, deep learning algorithms based on convolutional neural networks and other deep learning algorithms driven by massive data have gained tremendous development in the direction of computer vision, such as image classification, target localization, target detection, and image segmentation, which has led to an increasing interest in the applicability of deep learning methods based on convolutional neural networks in medical image analysis. After applying the deep learning algorithm, compared with the traditional algorithm, the accuracy can be increased by 10–20%, which has reached the level of human doctors. It has become a reality that the detection of malaria parasites can be replaced by machines in the clinic.
Privacy in Population Protocols with Probabilistic Scheduling

The population protocol model [3] offers a theoretical framework for designing and analyzing distributed algorithms among limited-resource mobile agents. While the original population protocol model considers the concept of anonymity, the issue of privacy is not investigated thoroughly. However, there is a need for time- and space-efficient privacy-preserving techniques in the population protocol model if these algorithms are to be implemented in settings handling sensitive data, such as sensor networks, IoT devices, and drones. In this work, we introduce several formal definitions of privacy, ranging from assuring only plausible deniability of the population input vector to having a full information-theoretic guarantee that knowledge beyond an agent's input and output bear no influence on the probability of a particular input vector. We then apply these definitions to both existing and novel protocols. We show that the Remainder-computing protocol from [10] (which is proven to satisfy output independent privacy under adversarial scheduling) is not information-theoretically private under probabilistic scheduling. In contrast, we provide a new algorithm and demonstrate that it correctly and information-theoretically privately computes Remainder under probabilistic scheduling.
Do Not Trust in Numbers: Practical Distributed Cryptography with General Trust

In distributed cryptography independent parties jointly perform some cryptographic task. In the last decade distributed cryptography has been receiving more attention than ever. Distributed systems power almost all applications, blockchains are becoming prominent, and, consequently, numerous practical and efficient distributed cryptographic primitives are being deployed. The failure models of current distributed cryptographic systems, however, lack expressibility. Assumptions are only stated through numbers of parties, thus reducing this to threshold cryptography, where all parties are treated as identical and correlations cannot be described. Distributed cryptography does not have to be threshold-based. With general distributed cryptography the authorized sets, the sets of parties that are sufficient to perform some task, can be arbitrary, and are usually modeled by the abstract notion of a general access structure. Although the necessity for general distributed cryptography has been recognized long ago and many schemes have been explored in theory, relevant practical aspects remain opaque. It is unclear how the user specifies a trust structure efficiently or how this is encoded within a scheme, for example. More importantly, implementations and benchmarks do not exist, hence the efficiency of the schemes is not known. Our work fills this gap. We show how an administrator can intuitively describe the access structure as a Boolean formula. This is then converted into encodings suitable for cryptographic primitives, specifically, into a tree data structure and a monotone span program. We focus on three general distributed cryptographic schemes: verifiable secret sharing, common coin, and distributed signatures. For each one we give the appropriate formalization and security definition in the general-trust setting. We implement the schemes and assess their efficiency against their threshold counterparts. Our results suggest that the general distributed schemes can offer richer expressibility at no or insignificant extra cost. Thus, they are appropriate and ready for practical deployment.
Where Are the Constants? New Insights on the Role of Round Constant Addition in the SymSum Distinguisher

The current work makes a systematic attempt to describe the effect of the relative order of round constant (RCon) addition in the round function of an SPN cipher on its algebraic structure. The observations are applied to the SymSum distinguisher, introduced by Saha et al. in FSE 2017 which is one of the best distinguishers on the SHA3 hash function reported in literature. Results show that certain ordering (referred to as Type-LCN) of RCon makes the distinguisher less effective but it still works with some limitations. Results in the form of new SymSum distinguishers are reported on concrete Type-LCN constructions - NIST LWC competition finalist Xoodyak-Hash and its internal permutation Xoodoo. New linear structures are also reported on Xoodoo that augment the distinguisher to penetrate more rounds. Final results include SymSum distinguishers on 7 rounds of Xoodoo and 5 rounds of Xoodyak-Hash with complexity $$ 2^{128} $$ and $$ 2^{32} $$ , respectively. All practical distinguishers have been verified. The characterization encompassing the algebraic structure and effect of RCon provided by the current work improves the understanding of SymSum in general and constitutes one of the first such result on Xoodyak-Hash and Xoodoo.
Post-quantum Secure Stateful Deterministic Wallet from Code-Based Signature Featuring Uniquely Rerandomized Keys

The deterministic wallet is a promising cryptographic primitive used in cryptocurrencies to protect users' wealth where a key derivation process enables the creation of any number of derived keys from a master key. A general architecture of a deterministic wallet using a signature with rerandomizable keys was introduced by Das et al. in CCS'19. A signature scheme with rerandomizable keys allows independently but consistently rerandomizing the master private key and the master public key. The deterministic wallet from rerandomizable signatures by Das et al. was instantiated from the signature scheme of Boneh-Lynn-Shacham (BLS) and Elliptic Curve Digital Signature Algorithm (ECDSA) which do not provide security against quantum computers. Designing a deterministic wallet is a difficult task and there are only a limited number of deterministic wallet constructions. In this paper, we focus on designing a post-quantum secure code-based deterministic wallet as code-based public key cryptosystem is a promising alternative in the post-quantum era. We first develop a post-quantum secure signing technique with key rerandomizability property that relies on the hardness of Restricted Decision General Decoding problem (RDGDP), Decisional Syndrome Decoding Problem (DSDP), General Decision Syndrome Decoding problem (GDSDP) and Syndrome Decoding Problem (SDP). In addition, our scheme exhibits the feature of uniquely rerandomizable keys. We present a thorough security proof and show that our design is secure against existential unforgeability under chosen-message attacks using honestly rerandomized keys. Finally, we employ the property of uniquely rerandomizable keys of our construction to develop a deterministic wallet that achieves security against wallet unlinkability and wallet unforgeability under the hardness of RDGDP, DSDP, GDSDP and SDP problems. We support the conjectured security of our deterministic wallet with regional security analysis.
Using Django ORM, Click, and Rich to create useful command-line python apps

This is a post to document the setup that I use when I want quickly to create a great functional command-line python application with ORM database support. &lt;strong&gt;Django ORM&lt;/strong&gt; The Django ORM is an incredibly powerful database tool that lets you design models in python and then persist them to the database. Django, though, is primarily a web framework.
What if downloads are not actually non-rivalrous?

One of the basic tenets of digital philosophy is that downloads are non-rivalrous. When I download something it remains accessible to other users to download. But what if this isn’t actually true and what if there is a strong disutility premise at work? Bitcoin has not done much for the world, but it does provide a useful reference point for thinking about this. The annual energy consumption of Bitcoin is enormous.
Headless PDF printing in Chrome: when the standard timeout isn't enough

tl;dr: use the node.js module html-pdf-chrome to print programmatically, not Chrome’s built-in virtual-time-budget. See my print.js file for an example. My CV is generated automatically from Birkbeck’s online repository. It uses a system that basically generates a paginated version of the CV in the browser, using CSS regions technologies, then does a headless print via Chrome. All the source code is available for this.
Several classes of permutation pentanomials with the form <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msup><mml:mi>h</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup><mml:mo linebreak="badbreak" linebreakstyle="after">−</…

In this paper, we study the permutation property of pentanomials with the form xrh(xpm−1) over Fp2m, where p∈{2,3}. More precisely, based on some seventh-degree and eighth-degree irreducible pentanomials over F2, we present eight classes of permutation pentanomials over F22m by determining the solutions of some equations with low degrees. In addition, based on the investigation of algebraic curves associated with fractional polynomials over finite fields, eight classes of permutation pentanomials over F32m are discovered by choosing some seventh-degree irreducible pentanomials over F3. Finally, several classes of permutation pentanomials and heptanomials over F22m and F32m are derived from known permutation polynomials on μ2m+1 and μ3m+1, respectively, where μd is the set of d-th roots of unity.
Application of Dual-Tree Complex Wavelet Packet Transform for Generating Synthetic Multivariate Nonstationary Non-Gaussian Thunderstorm Wind Records

The available thunderstorm wind records with subsecond sampling intervals is scarce for a given site; stochastic models that can be used to sample multivariate nonstationary non-Gaussian thunderstorm winds at multiple points or tricomponent thunderstorm winds at a point are lacking. We propose the use of the dual-tree complex wavelet packet transform (DT-CWPT) within the framework of the iterative power and amplitude correction (IPAC) algorithm to generate multivariate nonstationary non-Gaussian thunderstorm wind records. This is a data-driven or seed-record-based approach, and the use of the IPAC algorithm ensures the matching of the marginal cumulative probability distribution function. The DT-CWPT is used to gain computational efficiency because it is a redundant transform with a low redundancy factor, and it provides phase information. The statistics of the time-frequency power spectral density of the sampled records and the seed record were compared to show the adequacy and effectiveness of the proposed approach. The results also show that the use of the DT-CWPT instead of the (discretized) continuous wavelet transform and S-transform significantly reduces the computational time.
Spatio-temporal hierarchical feature transformer for UAV object tracking

Most existing Siamese network based trackers calculate the similarity between template and search region through a simple correlation operation, and these trackers classify and regress the target object based on the similarity maps obtained by the correlation operation. However, the correlation operation itself is a local linear matching process, if these Siamese network based trackers decide only on feature maps obtained by the correlation operation, using only a single similarity feature map will reduce position accuracy in challenging environments, using multiple similarity feature maps separately will introduce complicated computations. Thus, we propose an efficient spatio-temporal hierarchical feature transformer (STHFT) for UAV tracking. The spatio-temporal hierarchical similarity maps generated by the multi-level convolutional layers are sent to the transformer to achieve the interactive fusion of shallow spatio-temporal and deep semantic information. Therefore, our STHFT not only enhances the learning ability for global contextual information, but also effectively learns the interdependencies among multi-level features. Moreover, STHFT treats object tracking as a straightforward bounding box prediction task, only using a simple fully convolutional neural network to directly estimate the corners of the target and localize it. We have conducted extensive experiments on the UAV123 and UAV20L benchmarks, STHFT outperforms other traditional trackers. Specifically, compared to the conventional tracker DaSiamRPN the success and accuracy scores of STHFT improved by 7.3% and 6.5% on the UAV123 dataset and 10.9% and 10.4% on UAV20L, which show that STHFT is robust to tracking challenges.
Global and direct solar irradiance estimation using deep learning and selected spectral satellite images

To fully exploit the spectral information of modern geostationary satellites, this work proposes a deep learning framework using convolutional neural networks (CNNs) and attention mechanism for 5-min ground-level global horizontal irradiance (GHI) and direct normal irradiance (DNI) estimations. The inputs are spectral satellite images with the target ground station in the center, and the labels are irradiance measurements normalized by their clear-sky estimations. The use of CNNs and attention mechanism aims to better extract the spatial information for estimating ground-level solar irradiance. To improve the modeling efficiency, only a subset of spectral bands is selected based on correlation analysis, which has comparable performance with the usage of all satellite bands. The results show that the proposed method produces GHI estimation with a normalized root mean squared error (nRMSE) of 20.57% and a normalized mean bias error (nMBE) of −2.04%, and the DNI estimation has an nRMSE of 23.63% and the nMBE is 0.36%. Compared with the national solar radiation database (NSRDB), GHI and DNI estimations of the proposed method has the nRMSE reduction of 5.15% and 13.77%, respectively. Meanwhile, the proposed models generally yield better GHI and DNI estimations under different intervals of clear-sky index than NSRDB. The combination of deep learning and remote sensing shows potential in better extracting the cloud information via multispectral satellite images, which can better support solar resource assessment, especially for cloudy conditions.
Analysis of multiple overlapping paths algorithms for secure key exchange in large-scale quantum networks

Quantum networks open the way to an unprecedented level of communication security. However, due to physical limitations on the distances of quantum links, current implementations of quantum networks are unavoidably equipped with trusted nodes. Consequently, the quantum key distribution can be performed only on the links. Due to this, some new authentication and key exchange schemes must be considered to fully benefit from the unconditional security of the links. One such approach uses Multiple Non-Overlapping Paths (MNOPs) for key exchange to mitigate the risk of an attack on a trusted node. The scope of the article is to perform a security analysis of this scheme for the case of both uncorrelated attacks and correlated attacks with finite resources. Furthermore, our analysis is extended to the case of Multiple Overlapping Paths (MOPs). We prove that introducing overlapping paths allows one to increase the security of the protocol, compared to the non-overlapping case with the same number of additional links added. This result may find application in optimising architectures of large-scale (hybrid) quantum networks.
Supervised spectral feature learning for fine-grained classification in small data set

Fine-grained image classification is a challenging task due to the small inter-class variance, the large intra-class difference, and the small training data. Traditional methods typically rely on large-scale training samples with annotated part annotations, making them costly and severely limiting their application area. In this paper, we propose an effective and weakly supervised fine-grained classification framework. In this framework, a discriminative class-specific spectral feature is learned by intra-class spectral coupling and inter-class spectral decoupling under the weak supervision of image-level category labels, and then the new input images are classified based on the learned class-specific spectral feature. Different from existing strong supervised methods, the proposed technique creatively combines weak supervision of the image-level category labels with unsupervised spectral graph decomposition, not relying on large-scale training samples with dense part annotations, which are heavily labor-consuming. The performance of the proposed methods has been verified on four kinds of typical datasets: the JAFFE dataset, the Yale database, the UCI-CMU face database, and the neural foramina dataset. The satisfactory classification results have been achieved by the proposed method in expression recognition on the JAFFE dataset (with a mean accuracy of 95.31%), face recognition on the Yale database (with a mean accuracy of 98.79%), object recognition on the UCI-CMU face database (with a mean accuracy of 96.96%), and disease grading on the neural foramina dataset (with a mean accuracy of 92.09%). Compared with most state-of-the-art methods, the proposed method has superior classification performance in the small data set.
Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach

Determining the right number of clusters without any prior information about their numbers is a core problem in cluster analysis. In this paper, we propose a nonparametric clustering method based on different weighted spatial rank (WSR) functions. The main idea behind WSR is to define a dissimilarity measure locally based on a localized version of multivariate ranks. We consider a nonparametric Gaussian kernel weights function. We compare the performance of the method with other standard techniques and assess its misclassification rate. The method is completely data-driven, robust against distributional assumptions, and accurate for the purpose of intuitive visualization and can be used both to determine the number of clusters and assign each observation to its cluster.
Effects of Augmented Reality in Primary Education: A Literature Review

Augmented reality can provide an interactive learning environment. This leads to an exploration of new possibilities in the educational domain. In this work, a systematic literature review of AR applications developed for preschool and primary education is presented. To conduct this review, the Google Scholar database was used. The articles were screened using a set of five inclusion-exclusion criteria for suitability. In total, seventy-one articles published between 2012 to 2021 were selected. Of those, forty-five are conference papers, one is a book chapter, and the remaining twenty-five are journals. These works were analyzed by nine research questions proposed by us. We identified enjoyment, engagement, and learning gains as the key achievements of AR-based applications. Besides this, improvement in motivation, learning interest, participation, understanding, and self-learning are also found important in our review. Although, there are challenges—usability issues, technical problems, acceptance, cognitive overload, and health issues; however, due to their numerous benefits, AR-based applications may be another mode of teaching-learning method in the near future.
Unsupervised Portrait Drawing Generation for Free Styles

Artistic portrait drawing (APDrawing) generation has seen progress in recent years. However, due to the naturally high scarcity and artistry, it is difficult to collect large-scale labeled and paired data and generally divide drawing styles into several specific recognized categories. Existing works suffer from the limited labeled data and naive manual division of drawing styles according to the corresponding artists. They cannot adapt to the actual situations, for example, a single artist might have multiple drawing styles and APDrawings from different artists might share similar styles. In this paper, we propose to use unlabeled and unpaired data and perform the task in an unsupervised manner. Without manual division of drawing styles, we take each portrait drawing as a unique style and introduce self-supervised feature learning to learn free styles for unlabeled portrait drawings. Besides, we devise a style bank and a decoupled cycle structure to take over two main considerations in the task: generation quality and style control. Extensive experiments show that our model is more adaptable to different style inputs than state-of-the-art methods.