Reviewerly/exHarmony huggingface dataset:
Constraining and Watermarking PRFs from Milder Assumptions

Constrained pseudorandom functions (C-PRFs) let the possessor of a secret key delegate the ability to evaluate the function on certain authorized inputs, while keeping the remaining function values pseudorandom. A constraint-hiding constrained PRF (CHC-PRF) additionally conceals the predicate that determines which inputs are authorized. These primitives have a wealth of applications, including watermarking schemes, symmetric deniable encryption, and updatable garbled circuits. Recent works have constructed (CH)C-PRFs from rather aggressive parameterizations of Learning With Errors (LWE) with subexponential modulus-noise ratios, even for relatively simple “puncturing” or $$\text {NC}^{1}$$ circuit constraints. This corresponds to strong lattice assumptions and inefficient constructions, and stands in contrast to LWE-based unconstrained PRFs and fully homomorphic encryption schemes, which can be based on quasi-polynomial or even (nearly) polynomial modulus-noise ratios. In this work we considerably improve the LWE assumptions needed for building (constraint-hiding) constrained PRFs and watermarking schemes. In particular, for CHC-PRFs and related watermarking schemes we improve the modulus-noise ratio to $$\lambda ^{O((d+\log \lambda ) \log \lambda )}$$ for depth-d circuit constraints, which is merely quasi-polynomial for $$\text {NC}^{1}$$ circuits and closely related watermarking schemes. For (constraint-revealing) C-PRFs for $$\text {NC}^{1}$$ we do even better, obtaining a nearly polynomial $$\lambda ^{\omega (1)}$$ ratio. These improvements are partly enabled by slightly modifying the definition of C-PRFs, in a way that is still compatible with many of their applications. Finally, as a contribution of independent interest we build CHC-PRFs for special constraint classes from generic, weaker assumptions: we obtain bit-fixing constraints based on the minimal assumption of one-way functions, and hyperplane-membership constraints based on key-homomorphic PRFs.
Weakly-Supervised 3D Pose Estimation from a Single Image using Multi-View Consistency

We present a novel data-driven regularizer for weakly-supervised learning of 3Dhuman pose estimation that eliminates the drift problem that affects existing approaches.We do this by moving the stereo reconstruction problem into the loss of the networkitself. This avoids the need to reconstruct 3D data prior to training and unlike previoussemi-supervised approaches, avoids the need for a warm-up period of supervised training.The conceptual and implementational simplicity of our approach is fundamental to itsappeal. Not only is it straightforward to augment many weakly-supervised approacheswith our additional re-projection based loss, but it is obvious how it shapes reconstructionsand prevents drift. As such we believe it will be a valuable tool for any researcher workingin weakly-supervised 3D reconstruction. Evaluating on Panoptic, the largest multi-cameraand markerless dataset available, we obtain an accuracy that is essentially indistinguishablefrom a strongly-supervised approach making full use of 3D groundtruth in training.
Unified integration scheme using an N × N active switch for efficient generation of a multi-photon parallel state

A source to efficiently generate multiple indistinguishable single photons in different spatial modes in parallel (multi-photon parallel state) is indispensable for realizing large-scale photonic quantum circuits. “A naive scheme” may be to use a heralding single photon source with an on–off detector set at each of parallel modes and to select the cases where each mode contains one photon at the same time. However, it is also necessary to suppress the probability of generating more than two photons from a single-photon source. For this requirement, serial–parallel conversion and a multiplexed heralded single photon source (HSPS) have been proposed and demonstrated. In this paper, we propose and demonstrate a novel method to produce a multi-photon parallel state efficiently using multiple HSPSs and an N × N active optical switch. As an advantage over the simple combination of a spatial multiplexed HSPS and a serial–parallel converter, our method, called the “unified integration scheme,” can generate a multi-photon parallel state with minimized optical losses in the switch. Using a 2 × 2 active optical switch and a fixed delay, we achieve an enhancement factor of 1.59 ± 0.14, compared with a naive scheme using two HSPSs, and better than the factor of 1.46 using the simple combination scheme. Furthermore, we confirm the reduction of multi-photon events to 62 % of that of the naive scheme.
Topology-Hiding Computation for Networks with Unknown Delays

Topology-Hiding Computation (THC) allows a set of parties to securely compute a function over an incomplete network without revealing information on the network topology. Since its introduction in TCC’15 by Moran et al., the research on THC has focused on reducing the communication complexity, allowing larger graph classes, and tolerating stronger corruption types. All of these results consider a fully synchronous model with a known upper bound on the maximal delay of all communication channels. Unfortunately, in any realistic setting this bound has to be extremely large, which makes all fully synchronous protocols inefficient. In the literature on multi-party computation, this is solved by considering the fully asynchronous model. However, THC is unachievable in this model (and even hard to define), leaving even the definition of a meaningful model as an open problem. The contributions of this paper are threefold. First, we introduce a meaningful model of unknown and random communication delays for which THC is both definable and achievable. The probability distributions of the delays can be arbitrary for each channel, but one needs to make the (necessary) assumption that the delays are independent. The existing fully-synchronous THC protocols do not work in this setting and would, in particular, leak information about the topology. Second, in the model with trusted stateless hardware boxes introduced at Eurocrypt’18 by Ball et al., we present a THC protocol that works for any graph class. Third, we explore what is achievable in the standard model without trusted hardware and present a THC protocol for specific graph types (cycles and trees) secure under the DDH assumption. The speed of all protocols scales with the actual (unknown) delay times, in contrast to all previously known THC protocols whose speed is determined by the assumed upper bound on the network delay.
Correlation and Regression Analysis of the Relation between Ethereum Price and Both Its Volume and Bitcoin Price

This article deals with the theory and practical analysis of the relationship between bitcoin and ethereum cryptocurrencies. The existing developments on this issue are investigated by generalizing the literature on the basic characteristics and relationship of bitcoin and ethereum prices. Based on the problems and existing research on the topic, the analysis of the relationship between ethereum and bitcoin prices is defined as the goal of the article. A mathematical model of the cryptocurrency relationship analysis is presented, which helps to establish that the relationship is quite close. The main result is the conclusion that a connection exists between the prices of bitcoin and ethereum, and this connection can be used for diversification and risk insurance when trading on the cryptocurrency exchange. The purpose of this article is to identify a tool that can describe the relationship between the price of ethereum and both its volume and the price of bitcoin and can predict their changes over time. This article analyzes the price of ethereum as dependent on the price of bitcoin. <b>TOPICS:</b>Currency, performance-measurement, portfolio construction, statistical methods <b>Key Findings</b> • The relationship between the price of ethereum and its volume and the price of bitcoin, as well as the ability to predict their change over time is analyzed. • The revealed multiple regression equation can be used to create operational analytical programs capable of real-time forecasting the price movement of ethereum.
A generalization of the ElGamal public-key cryptosystem

The ElGamal cryptosystem is one of the most widely used public-key cryptosystems that depends on the difficulty of computing the discrete logarithms over finite fields. Over the years, the original system has been modified and altered in order to achieve a higher security and efficiency. In this paper, a generalization for the original ElGamal system is proposed which also relies on the discrete logarithm problem. The encryption process of the scheme is improved such that it depends on the prime factorization of the plaintext. Modular exponentiation is taken twice during the encryption; once with the number of distinct prime factors of the plaintext and then with the secret encryption key. If the plaintext consists of only one distinct prime factor, then the new method is similar to that of the basic ElGamal algorithm. The proposed system preserves the immunity against the Chosen Plaintext Attack (CPA).
Comparison Between Traditional and CNN Based Stereo Matching Algorithms

CNN has been widely used in computer vision. In particular, CNN has been applied in stereo matching, which involves finding pixels corresponding to each other in a pair of images that capture a scene at the same time. This paper focuses on analyzing stereo matching methods based on CNN, and compares them with a traditional stereo matching method. CNN-based methods produced good matching results for ill-posed areas, including textureless areas, repeated patterns and highly reflective surfaces. By contrast, traditional stereo matching methods could not produce good matching results for such ill-posed regions. Therefore, in this paper, we demonstrate how good CNN-based methods could produce matching results for both indoor and outdoor images.
Evoking Pseudo-Haptics of Resistance Force by Viewpoint Displacement

In conventional methods, pseudo-haptics is evoked by displacing a user’s hand in a virtual space. However, it is hard to apply this procedure in a mixed reality space, which is based on real space. In this study, we examined the pseudo-haptics evoked by dynamically displacing the viewpoint of a user in a video see-through environment. We simulated the proposed system in a virtual space, and conducted experiments to evaluate the effectiveness of the proposed method.
Invariance vs. Robustness Trade-Off in Neural Networks

We study the performance of neural network models on random geometric transformations and adversarial perturbations. Invariance means that the model's prediction remains unchanged when a geometric transformation is applied to an input. Adversarial robustness means that the model's prediction remains unchanged after small adversarial perturbations of an input. In this paper, we show a quantitative trade-off between rotation invariance and robustness. We empirically study the following two cases: (a) change in adversarial robustness as we improve only the invariance of equivariant models via training augmentation, (b) change in invariance as we improve only the adversarial robustness using adversarial training. We observe that the rotation invariance of equivariant models (StdCNNs and GCNNs) improves by training augmentation with progressively larger random rotations but while doing so, their adversarial robustness drops progressively, and very significantly on MNIST. We take adversarially trained LeNet and ResNet models which have good $L_\infty$ adversarial robustness on MNIST and CIFAR-10, respectively, and observe that adversarial training with progressively larger perturbations results in a progressive drop in their rotation invariance profiles. Similar to the trade-off between accuracy and robustness known in previous work, we give a theoretical justification for the invariance vs. robustness trade-off observed in our experiments. We also give additional empirical evidence for claim (a). We observe that the average distance of the test points to the decision boundary reduces when models are trained with larger rotations.
Vaccine prediction system using ARIMA method

Indonesia is a country that runs health programs in the form of primary compulsory immunizations for children aged 0-11 months. According to the Health Law, Number 36 of 2009 states that every child has the right to receive primary immunization by the provisions to prevent the occurrence of diseases that can be avoided through immunization. The Indonesian government are also obliged to provide complete immunization to every baby and child by the implementation of immunization contained in the Minister of Health Regulation Number 42 of 2013. The purpose of this study is to predict vaccine stock for immunization needs, and the government can use the application to determine vaccine stock requirements for each clinic so that there is no shortage or excess stock. This prediction can ensure that immunization coverage is well distributed. We can help parties who organize primary immunization activities by making predictions and forecasting results based on the R application. In addition, the application can provide information in the form of predictive analysis. The method used in measuring predictions is ARIMA (Auto-Regressive Integrated Moving Average) to calculate the prediction of immunization.
A Phrase Topic Model Based on Distributed Representation

Traditional topic models have been widely used for analyzing semantic topics from electronic documents. However, the obvious defects of topic words acquired by them are poor in readability and consistency. Only the domain... | Find, read and cite all the research you need on Tech Science Press
Modern Software Engineering

We stumble upon software in our everyday life, from handheld mobile devices to intelligent components of a smart home all the way to big machines we employ for regular daily tasks. As a software engineer, you get to see your work being used in every aspect of life. As interesting and energizing as it might sound, it has its own problems that one needs to tackle. The same products that help millions “achieve more” could someday cease to function due to a bug. Even worse, that bug might be an attempt to hijack the system for illegal activity. A lot of the software deployed to production follows poor design architecture, gets published in non-tested environments, or is being used by customers who are unaware of the socially engineered risks of software exploitation. Software packages are no longer just executable files sent to a customer upon receipt of payment. Software packages have become more Internet-based, agile, adaptable, accessible, and intelligent.
Aspects of Cyber-Security in Higher Education Institutions

The experience of recent years has brought to the forefront a problem that can affect our everyday life: cybercrime. In higher education institutions, this problem is quite acute due to the increase in the number and types of equipment that can be connected to data networks. This problem is also influenced by the nature of educational and research activities involving a large number of teaching staff, auxiliary or non-teaching staff and students as well as complex computing and research systems in various fields. Members of higher education institutions are may be victims of cyber-attacks of all kinds (social engineering, DDOS attacks, Trojans, viruses and worms), or the need to find win–win variants as easily as possible can bring some of these into cybercrime. Finding a restricted access variant with different security barriers is essential for the proper functioning of the education and research process in this type of institution. In this context, the research objective is to identify and classify the main threats and attacks on the data network, the various information and research currently faced by higher education institutions. These issues need to be treated with utmost seriousness because, in general, the annihilation of cyber-attacks is done after they have been done, thus completely or partially losing certain data, and the creation of security barriers could limit access by attackers to confidential information of the institution. The results serve to open a new research direction that leads to the development of a security management model tailored to academic activity.
Representing Etymology in the LiLa Knowledge Base of Linguistic Resources for Latin.

In this paper we describe the process of inclusion of etymological information in a knowledge base of interoperable Latin linguistic resources developed in the context of the LiLa: Linking Latin project. Interoperability is obtained by applying the Linked Open Data principles. Particularly, an extensive collection of Latin lemmas is used to link the (distributed) resources. For the etymology, we rely on the Ontolex-lemon ontology and the lemonEty extension to model the information, while the source data are taken from a recent etymological dictionary of Latin. As a result, the collection of lemmas LiLa is built around now includes 1,465 Proto-Italic and 1,393 Proto-Indo-European reconstructed forms that are used to explain the history of 1,400 Latin words. We discuss the motivation, methodology and modeling strategies of the work, as well as its possible applications and potential future developments.
Evolutionary Approach for Automatic Generation of Multi-Objective Morphological Filters for Depth Images in Embedded Navigation Systems

The efforts spent on the development of assistive technologies has led researches to explore many existing techniques as computer vision, image processing, etc. and apply them as embedded solutions to help people with several types of disabilities, including visual impairment. Embedded navigation systems for visually impaired people (VIP) often use RGB-D cameras to retrieve depth information from surroundings and present them as gray images with depth represented by gray level or black pixels if depth couldn't be estimated, which can be fixed by mathematical morphology. Morphological filters must be efficient to solve the problem and fast to avoid impact on performance. This paper presents an approach for automatic generation and optimization of low complexity and low error morphological filters to fix depth image's unknown distances based on NSGA-II and Cartesian Genetic Programming. Experiments were performed using two different error metrics and results showed that the presented approach managed to generate and optimize feasible morphological filters that fit within embedded navigation systems for VIP.
Generation of Visual Patterns from BoVW for Image Retrieval using modified Similarity Score Fusion

The Bag of Visual Words (BoVW) turns up to be an efficient method to represent images for Content Based Image Retrieval (CBIR). Despite their significant usage, the traditional BoVW me ...
Optical Neural Networks

Light-based computers inspired by the human brain could transform machine learning—if they can be scaled up.
A New Optimization Method application to Agricultural Plant Protection UAV Scheduling

Unmanned Aerial Vehicle (UAV) can be used to do agricultural plant protection work such as watering, sowing, pesticide spraying, which can greatly reduce manpower by creating an autonomous manufacturing system. Consequently, a reasonable scheduling method is one of the most essential component to be focused on; yet to the best of our knowledge, there are few research on practical Agricultural Plant Protection UAV scheduling. This work proposes a new optimization method to solve the Agricultural Plant Protection UAV scheduling problem in practice. During the execution of the algorithm, the tasks are put into a queue then each task is assigned to the UAV that can complete it in the shortest amount of time. This proposed method is tested on datasets generated based on a real flight demonstration in the agricultural plant protection environment. Compared with traditional optimization methods, our method can improve the efficiency compared with traditional while ensuring good performance.
A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

This paper introduces a neurodynamics optimization model to compute the solution of mathematical programming with equilibrium constraints (MPEC). A smoothing method based on NPC-function is used to obtain a relaxed optimization problem. The optimal solution of the global optimization problem is estimated using a new neurodynamic system, which, in finite time, is convergent with its equilibrium point. Compared to existing models, the proposed model has a simple structure, with low complexity. The new dynamical system is investigated theoretically, and it is proved that the steady state of the proposed neural network is asymptotic stable and global convergence to the optimal solution of MPEC. Numerical simulations of several examples of MPEC are presented, all of which confirm the agreement between the theoretical and numerical aspects of the problem and show the effectiveness of the proposed model. Moreover, an application to resource allocation problem shows that the new method is a simple, but efficient, and practical algorithm for the solution of real-world MPEC problems.
Application of make/use platform for sustainable fashion design

Abstract The article explores the challenges of the clothing industry by focusing more on textile and the clothing industry interactions to reduce industry fabric waste and promote sustainability. The main research focus is to gather and analyze various possibilities for fashion designers to achieve zero waste designs in nowadays context. The introduction gives insights on the problems of the fashion industry and formulates the purpose and tasks of the research work, identifies and unpacks problems and also justifying the relevance of this study. Attention is drawn to the analysis of pre-consumer waste generation in the clothing production processes and the ways to drive the industry towards sustainability. The rich source of zero waste design ideas can be found in the ethnographic costumes’ solutions. Integrating historical experience with the methods and approaches defined by the 21 st century designers can significantly reduce or even avoid fabric pre-consumer waste. The paper will reflect the collected principles of achieving zero waste fashion by using Make/Use platform, an open source system for zero waste user, modifiable fashion practice. The methods of Make/Use platform are described, and its practical application is illustrated through the zero-waste coat design. Premeditate changes are based on the fashion designer’s thinking encouraging minimizing the waste in the early stages of the costume idea development, providing a sensible use of the fabric with its silhouette and design specificity.