Reviewerly/exHarmony huggingface dataset:
A Thorough Treatment of Highly-Efficient NTRU Instantiations

Cryptography based on the hardness of lattice problems over polynomial rings currently provides the most practical solution for public key encryption in the quantum era. Indeed, three of the four schemes chosen by NIST in the recently-concluded post-quantum standardization effort for encryption and signature schemes are based on the hardness of these problems. While the first encryption scheme utilizing properties of polynomial rings was NTRU (ANTS ’98), the scheme that NIST chose for public key encryption (CRYSTALS-Kyber) is based on the hardness of the somewhat-related Module-LWE problem. One of the reasons for Kyber’s selection was the fact that it is noticeably faster than NTRU and a little more compact. And indeed, the practical NTRU encryption schemes in the literature generally lag their Ring/Module-LWE counterparts in either compactness or speed, or both. In this paper, we put the efficiency of NTRU-based schemes on equal (even slightly better, actually) footing with their Ring/Module-LWE counterparts. We provide several instantiations and transformations, with security given in the ROM and the QROM, that are on par, compactness-wise, with their counterparts based on Ring/Module-LWE. Performance-wise, the NTRU schemes instantiated in this paper over NTT-friendly rings of the form $$\mathbb {Z}_q[X]/(X^d-X^{d/2}+1)$$ are the fastest of all public key encryption schemes, whether quantum-safe or not. When compared to the NIST finalist NTRU-HRSS-701, our scheme is $$15\%$$ more compact and has a 15X improvement in the round-trip time of ephemeral key exchange, with key generation being 35X faster, encapsulation being 6X faster, and decapsulation enjoying a 9X speedup.
Neural networks in Fréchet spaces

We propose a neural network architecture in infinite dimensional spaces for which we can show the universal approximation property. Indeed, we derive approximation results for continuous functions from a Fréchet space $\mathfrak {X}$ into a Banach space $\mathfrak {Y}$ . The approximation results are generalising the well known universal approximation theorem for continuous functions from $\mathbb {R}^{n}$ to $\mathbb {R}$ , where approximation is done with (multilayer) neural networks Cybenko (1989) Math. Cont. Signals Syst. 2, 303–314 and Hornik et al. (1989) Neural Netw., 2, 359–366 and Funahashi (1989) Neural Netw., 2, 183–192 and Leshno (1993) Neural Netw., 6, 861–867. Our infinite dimensional networks are constructed using activation functions being nonlinear operators and affine transforms. Several examples are given of such activation functions. We show furthermore that our neural networks on infinite dimensional spaces can be projected down to finite dimensional subspaces with any desirable accuracy, thus obtaining approximating networks that are easy to implement and allow for fast computation and fitting. The resulting neural network architecture is therefore applicable for prediction tasks based on functional data.
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning

Deep neural networks (DNNs) have been widely used recently while their hardware deployment optimizations are very time-consuming and the historical deployment knowledge is not utilized efficiently. In this paper, to accelerate the optimization process and find better deployment configurations, we propose a novel transfer learning method based on deep Gaussian processes (DGPs). Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge. Secondly, to transfer knowledge to a new task, a tuning set is sampled for the new task under the guidance of the DGP model. Then DGP is tuned according to the tuning set via maximum-a-posteriori (MAP) estimation to accommodate for the new task and finally used to guide the deployments of the task. The experiments show that our method achieves the best inference latencies of convolutions while accelerating the optimization process significantly, compared with previous arts.
Underwater Image Enhancement on Low-Cost Hardware Platform

In the recent years several cost effective and versatile remote operated vehicles (ROV) have been developed. However, these drones are equipped with low powerful hardware platforms. In this work we present an underwater image enhancement solution that works effectively on such hardware platforms. The approach is built on the underwater optical model and estimates locally the backscattered light component. In order to compute an optimal image patch size, we estimate two complementary values of the local backscattered light (an estimate for a large patch size and one for a small patch size) that are averaged in an optimal value. The transmission map is computed based on the well-known dark-channel prior (DCP) [1]. Finally, the results are yielded by inverting the simplified optical model using the estimated values of the local backscattered light an the trans-mission. The method was implemented and tested on Raspberry Pi. Our extensive experiments show that the proposed technique is computationally effective but also competitive compared to several specialized techniques.
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training

Thanks to their ability to learn data distributions without requiring paired data, Generative Adversarial Networks (GANs) have become an integral part of many computer vision methods, including those developed for medical image segmentation. These methods jointly train a segmentor and an adversarial mask discriminator, which provides a data-driven shape prior. At inference, the discriminator is discarded, and only the segmentor is used to predict label maps on test images. But should we discard the discriminator? Here, we argue that the life cycle of adversarial discriminators should not end after training. On the contrary, training stable GANs produces powerful shape priors that we can use to correct segmentor mistakes at inference. To achieve this, we develop stable mask discriminators that do not overfit or catastrophically forget. At test time, we fine-tune the segmentor on each individual test instance until it satisfies the learned shape prior. Our method is simple to implement and increases model performance. Moreover, it opens new directions for re-using mask discriminators at inference. We release the code used for the experiments at https://vios-s.github.io/adversarial-test-time-training.
Preprocessing Pipelines including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks

Artificial neural networks have become the go-to solution for computer vision tasks, including problems of the security domain. One such example comes in the form of reidentification, where deep learning can be part of the surveillance pipeline. The use case necessitates considering an adversarial setting-and neural networks have been shown to be vulnerable to a range of attacks. In this paper, the preprocessing defences against adversarial attacks are evaluated, including block-matching convolutional neural network for image denoising used as an adversarial defence. The benefit of using preprocessing defences comes from the fact that it does not require the effort of retraining the classifier, which, in computer vision problems, is a computationally heavy task. The defences are tested in a real-life-like scenario of using a pre-trained, widely available neural network architecture adapted to a specific task with the use of transfer learning. Multiple preprocessing pipelines are tested and the results are promising.
Geo-DRS: Geometric Dynamic Range Search on Spatial Data with Backward and Content Privacy

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R\(^{+}\)tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. Our evaluation shows the practicality of our schemes, due to (a) the minimal round-trip between the client and the server, and (b) low overhead in the client side in terms of computation and storage.
RotatHS: Rotation Embedding on the Hyperplane with Soft Constraints for Link Prediction on Knowledge Graph

Embedding vertices and relations is the main direction and poses many challenges for the research community in link prediction on the knowledge graph. Among the state-of-the-art approaches, the approach based on the geometric transformations has the strong point of good intuition representation. Besides, the relations in the knowledge graph is diverse, so the embedding needs to be flexible. For that reason, we focus on analyzing the characteristics of the relations and the suitability for each transformation. As a result, we proposed a new method of embedding to capture better symmetric and composed relations. Furthermore, the self-adversarial sampling scheme is applied to reduce the influence of negative samples that do not carry meaningful information to the learning process. In addition, we also optimized the loss function by using the unlimited loss function. Experiments reflect the suggestions increasing the accuracy of the original model on the standard datasets.
Exploiting Language Model For Efficient Linguistic Steganalysis

Recent advances in linguistic steganalysis have successively applied CNN, RNN, GNN and other efficient deep models for detecting secret information in generative texts. These methods tend to seek stronger feature extractors to achieve higher steganalysis effects. However, we have found through experiments that there actually exists significant difference between automatically generated stego texts and carrier texts in terms of the conditional probability distribution of individual words. Such kind of difference can be naturally captured by the language model used for generating stego texts. Through further experiments, we conclude that this ability can be transplanted to a text classifier by pre-training and fine-tuning to improve the detection performance. Motivated by this insight, we propose two methods for efficient linguistic steganalysis. One is to pre-train a language model based on RNN, and the other is to pre-train a sequence autoencoder. The results indicate that the two methods have different degrees of performance gain compared to the randomly initialized RNN, and the convergence speed is significantly accelerated. Moreover, our methods achieved the best performance compared to related works, while providing a solution for real-world scenario where there are more cover texts than stego texts.
Virtual Private Mobile Network with Multiple Gateways for B5G Location Privacy

In a beyond-5G (B5G) scenario, we consider a virtual private mobile network (VPMN), i.e., a set of user equipments (UEs) directly communicating in a device-to-device (D2D) fashion, and connected to the cellular network by multiple gateways. The purpose of the VPMN is to hide the position of the VPMN UEs to the mobile network operator (MNO). We investigate the design and performance of packet routing inside the VPMN. First, we note that the routing that maximizes the rate between the VPMN and the cellular network leads to an unbalanced use of the gateways by each UE. In turn, this reveals information on the location of the VPMN UEs. Therefore, we derive a routing algorithm that maximizes the VPMN rate, while imposing for each UE the same data rate at each gateway, thus hiding the location of the UE. We compare the performance of the resulting solution, assessing the location privacy achieved by the VPMN, and considering both the case of single hop and multihop in the transmissions from the UEs to the gateways.
HQ-Trans: A High-Quality Screening Based Image Translation Framework for Unsupervised Cross-Domain Pedestrian Detection

Pedestrian detection plays an important role in the research of computer vision, which has been used in many areas. Pedestrian detection is mainly to classify and locate pedestrians in a given input image, whose performance greatly depends on the number of annotations. When faced with unlabeled scenes in the target domain, the performance of a detector is severely degraded. Based on the above, an unsupervised image translation framework is adopted to generate an intermediate domain between the source and the target domains, which can effectively improve the cross-domain pedestrian detection performance. However, due to the instability of the translation network, some unsatisfactory images might be generated in the newly generated intermediate domain. Therefore, we propose a new method to process these unsatisfactory images. First, a blind image quality assessment framework is adopted on the original dataset, which aims to select relatively high-quality images as the training set for the translation framework and remove relatively low-quality images. Second, the image quality assessment framework is also adopted on the newly generated domain, which retain relatively high-quality generated images and replace the low-quality images with the corresponding images in the source domain. Finally, a new mixed domain, which is obtained from the above process, is applied to cross-domain pedestrian detection. The experimental results show that the proposed method can help to achieve good performance. Compared with some latest works, the proposed method can also achieve state-of-the-art performance under miss rate metrics.
Evidence Management System Using Blockchain and Distributed File System (IPFS)

Evidence gathering is at the core of every analysis process. The ability to verify the results and have appropriate paperwork, especially if a case lasts for several years, is vital. In later periods, information gathered at the outset of a prosecution may become crucial. If the documentation is handled by a system, the judicial authority can find the important facts in the appropriate time quicker. This system provides knowledge about electronic evidence collecting, processing, transportation and handling. Maintaining documents based on paper may be a tedious job that is exposed to human interference by mistakes and modifications. Security issues emerge from stored electronic records in a centralized consolidated archive. Any evidence obtained five or six years ago is very difficult to preserve in a paper-based evidence storage system. We propose a secure evidence management system to store evidence in a secure, distributed peer-to-peer (p2p) file storage network(IPFS) using blockchain technology. The system is designed with a custom transaction family on Hyperledger Sawtooth to document every transaction from the moment the evidence is collected, ensuring that only approved individuals can access or possess evidence. Our proposed framework provides a safe compromise between different stakeholders such as law enforcement agencies, attorneys, and forensic professionals that protects the integrity and permissibility of evidence.
Perspective Sharing in Culture Group Games

This work investigates how to digitally mediate perspective sharing and face-to-face conversations within groups of museum visitors. We use the case of a storytelling group game and explore the potential of mobile technology as a tool to facilitate perspective sharing activities. Using information asymmetry, a "Social Mediator" role is introduced: players' choices get displayed only to a single group member, who thus undertakes a leading role. The findings of an experience prototyping user study with playtesting in situ show that participants effectively and naturally carried out the pertinent dialogue facilitation activities. We identify behavioral patterns and discuss the insights they offer, showcasing how they shaped the mobile design for the particular use-case. We conclude that the distribution of the Social Mediator role between participants could successfully facilitate perspective sharing within groups and reflect on its potential and limitations in different collocated game experiences.
The extended Recursive Noisy OR model: Static and dynamic considerations

Many engineering problems rely on causal reasoning to analyze the interactions among the numerous causes/variables. Probabilistic graphical models (e.g., Bayesian networks) are widely used for such analyses, but the conditional probability tables (CPTs) grow exponentially with respect to the number of variables (nodes). Several canonical models have been developed to reduce the amount of information needed to complete the CPTs. In this paper, the Extended Recursive Noisy OR (ERNOR) is developed. It is an improved version of the long-established RNOR, in that it resolves the asymmetry problem when the number of causes exceeds three. Like RNOR, it is not restricted to the independence of causal influence, thus synergy can be considered. The dynamic form of the ERNOR is also developed, which provides a continuous causal influence function. This function is dependent on cumulative distributions that represent the presence level of each time-dependent cause, useful for models that do not rely on CPTs. The derivation of the ERNOR is presented, and it is contrasted with its predecessor, the RNOR.
The Implementation of the Ontological Approach to Control of the Processes of Designing Integrated Expert Systems Based on the Problem-Oriented Methodology

This work focuses on the experimental studies of a new technology for the development of integrated expert systems (IES) based on the further development of the problem-oriented methodology and intelligent software environment of the AT-TECHNOLOGY workbench through the integration of an ontological approach to software design for applied IES and methods of intelligent planning and management of IES development processes with different architectural typology.The description of prototyping methods for applied IES is based on the use of the basic components of the model of an intelligent software environment with an emphasis on expanding one of the components, the technological knowledge base, through the development of an ontology of typical IES architectures and the implementation of interaction with the intelligent planner.
Towards Robustness of Deep Neural Networks via Regularization

Recent studies have demonstrated the vulnerability of deep neural networks against adversarial examples. In-spired by the observation that adversarial examples often lie outside the natural image data manifold and the intrinsic dimension of image data is much smaller than its pixel space dimension, we propose to embed high-dimensional input images into a low-dimensional space and apply regularization on the embedding space to push the adversarial examples back to the manifold. The proposed framework is called Embedding Regularized Classifier (ER-Classifier), which improves the adversarial robustness of the classifier through embedding regularization. Besides improving classification accuracy against adversarial examples, the framework can be combined with detection methods to detect adversarial examples. Experimental results on several benchmark datasets show that, our proposed framework achieves good performance against strong adversarial at-tack methods.
CCO-VOXEL: Chance Constrained Optimization over Uncertain Voxel-Grid Representation for Safe Trajectory Planning

We present CCO-VOXEL: the very first chance-constrained optimization (CCO) algorithm that can compute trajectory plans with probabilistic safety guarantees in real-time directly on the voxel-grid representation of the world. CCO-VOXEL maps the distribution over the distance to the closest obstacle to a distribution over collision-constraint violation and computes an optimal trajectory that minimizes the violation probability. Importantly, unlike existing works, we never assume the nature of the sensor uncertainty or the probability distribution of the resulting collision-constraint violations. We leverage the notion of Hilbert Space embedding of distributions and Maximum Mean Discrepancy (MMD) to compute a tractable surrogate for the original chance-constrained optimization problem and employ a combination of A* based graph-search and Cross-Entropy Method for obtaining its minimum. We show tangible performance gain in terms of collision avoidance and trajectory smoothness as a consequence of our probabilistic formulation vis a vis state-of-the-art planning methods that do not account for such non-parametric noise. Finally, we also show how a combination of low-dimensional feature embedding and pre-caching of Kernel Matrices of MMD allow us to achieve real-time performance in simulations as well as in implementations on on-board commodity hardware that controls the quadrotor flight.
A Single-run Recognition of Nested Named Entities with Transformers

In the article we present a single-run approach to recognizing nested named entities using neural networks with transformers. The main advantage of this approach is that a single model is trained to recognize all entity types. The model can identify all entities in a single pass. Our main contribution is the simplified representation of nested named entities on the token level, and evaluation of the presented approach on three languages — Polish (PolEval 2018 dataset), German (GermEval 2014 dataset), and Czech (CNEC 2.0 dataset). For each dataset we obtained state-of-the-art results. For Polish and German we obtained a significant improvement — 1.4 pp and 3.0 pp respectively. For Czech we obtained an improvement of 0.5 pp.
“Who is protecting us? No one!” Vulnerabilities Experienced by Low-Income Indian Merchants Using Digital Payments

Low-income merchants in India, who conduct business via makeshift shops and handcarts, are increasingly using digital payment systems for business operations. Although these merchants are a key stakeholder in digital payment ecosystems, they have not yet received much attention from the research community. We present a qualitative study consisting of observations and interviews with 24 low-income merchants and 10 agents that explores the vulnerabilities merchants experience as they adopt and use digital payments. Using the notion of vulnerability as a lens, we show how socio-technical interactions between merchants and agents contribute to at least four different types of vulnerabilities: access-based, identity-based, financial, and informational vulnerabilities. We discuss how agents, customers, and fraudsters take advantage of merchants' vulnerabilities to commit different types of fraud that lead to serious harm for merchants. We show how merchants developed strategies to combat fraud that lead to more work and extra burdens for merchants. Our research suggests a cyclic model of vulnerability that exposes the cumulative effects of vulnerabilities, frauds, and harms experienced by merchants. We end by providing practical recommendations for digital payment companies to break this cycle and better serve low-income merchants.