Reviewerly/exHarmony huggingface dataset:
An improvement on the chaotic behavior of the Gauss Map for cryptography purposes using the Circle Map combination

Abstract Chaos based cryptography has becoming an interesting topic lately, as it utilizes chaotic systems properties for secure key concealment. Many chaotic functions are discovered, constructed, and used time over time for this purpose, which will be our main aim here. Two well known maps that has been known for exhibiting chaotic behaviors are the Gauss Map and the Circle Map, where the Circle Map has unlimited chaos potential, while the Gauss Map’s is much weaker and limited. In this paper, we investigate computationally using Python whether the Gauss Map can be improved by combining it with the Circle Map, allowing exploitation of greater chaotic behaviors. For this purpose, an improved version of the Gauss map is constructed, from which, we plot its bifurcation diagrams and Lyapunov exponents graphics, and show that it has a good potential to be a random number generator (RNG) using the NIST test, as these are the three main aspects of chaotic maps utilized in chaos based cryptography. The results obtained from this observation shows that composing the Circle Map into the Gauss Map, along with several manipulations, generates a significantly improved version of the Gauss Map, as it has a bifurcation diagram with much higher density, much higher Lyapunov exponents, and mostly better P-Values from the NIST tests, although it is still not fully suitable for a RNG. The manipulations done here, which aims to conserve the maps ranges to stay within the chaotic intervals and position the Circle Map to be the “variable” of the Gauss Map, allows the chaotic behaviors from the original maps to be bequeathed and strengthened in the new map.
Evolutionary Product Description Generation

Conventional models on Neural Text Generation (NTG) determine the output distribution by applying maximum likelihood estimation on training corpora. However, as user preference for generated content can be constantly changing, an optimized text generator needs to assimilate such non-static nature into the outcome adaptively. In this paper, our goal is to generate product descriptions on e-commerce platforms, and we explore this classic task from a novel perspective that allows the optimal output text to vary with ever-changing user preference. Specifically, we propose an evolutionary NTG model to enable its interactive environment to fine-tune the pre-trained generative policy via Reinforcement Learning (RL). To this end, a dynamic context of textual fitness is established based on the user click behavior associated with previously generated content to estimate reward/penalty signals for each output text. Our motivation is to leverage the click-through rate as a kind of user-centric measurement on text quality, by which we can assess how likely a product description attracts people's attention and follows shopping trends. Extensive experiments on a real e-commerce website demonstrate that the proposed approach achieves a significant superiority over two statically RL-based variants and four state-of-the-art NTG solutions.
Regular ω-languages with an informative right congruence

A regular language is almost fully characterized by its right congruence relation. The same does not hold for regular ω-languages. The right congruence of a regular ω-language may not be informative enough; many regular ω-languages have a trivial right congruence, and in general it is not always possible to define an ω-automaton recognizing a given language that is isomorphic to its right congruence. The weak regular ω-languages do have fully informative right congruences. That is, any weak regular ω-language can always be recognized by a deterministic Bu¨chi automaton that is isomorphic to its right congruence. Weak regular ω-languages reside in the lower levels of the expressiveness hierarchy of regular ω-languages. Are there more expressive sub-classes of regular ω-languages that have fully informative right congruences? Can we characterize the class of languages that have trivial right congruences? In this paper we try to place some additional pieces of this big puzzle.
Robust Homography Estimation via Dual Principal Component Pursuit

We revisit robust estimation of homographies over point correspondences between two or three views, a fundamental problem in geometric vision. The analysis serves as a platform to support a rigorous investigation of Dual Principal Component Pursuit (DPCP) as a valid and powerful alternative to RANSAC for robust model fitting in multiple-view geometry. Homography fitting is cast as a robust nullspace estimation problem over either homographic or epipolar/trifocal embeddings. We prove that the nullspace of epipolar or trifocal embeddings in the homographic scenario, of dimension 3 and 6 for two and three views respectively, is defined by unique, computable homographies. Experiments show that DPCP performs on par with USAC with local optimization, while requiring an order of magnitude less computing time, and it also outperforms a recent deep learning implementation for homography estimation.
Personalized Query Suggestions

With the exponential growth of information on the internet, users have been relying on search engines for finding the precise documents. However, user queries are often short. The inherent ambiguity of short queries imposes great challenges for search engines to understand user intent. Query suggestion is one key technique for search engines to augment user queries so that they can better understand user intent. In the past, query suggestions have been relying on either term-frequency--based methods with little semantic understanding of the query, or word-embedding--based methods with little personalization efforts. Here, we present a sequence-to-sequence-model--based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally. This capability opens up the opportunity to better understand query semantics and user intent at the same time. As the largest professional network, LinkedIn has the advantage of utilizing a rich amount of accurate member profile information to personalize query suggestions. We applied this framework in the LinkedIn production traffic and showed that personalized query suggestions significantly improved member search experience as measured by key business metrics at LinkedIn.
ElasticSDK: A Monitoring Software Development Kit for enabling Data-driven Management and Control in 5G

5G networks generate massive (quasi-) real-time data streams that different network apps can exploit to implement sophisticated single- or cross-domain control and management logic. This paper presents ElasticSDK, a Software Development Kit specially designed to abstract the development and chaining of such agile 5G monitoring apps for the control, management, and coordination of the underlying 5G network heterogeneous modules. Custom apps can collect, incrementally process and further expose flows in a flexible Pub/Sub fashion via appropriate SDK API calls, thus sharing both raw and complex data flows among themselves. Furthermore, the design of ElasticSDK allows respecting typical 5G data ownership and privacy models, as desired by the different 5G stakeholders ranging from physical infrastructure providers up to service providers over slicing. Finally, we provide two important contributions to the 5G open-source research community: (i) a RAN monitoring prototype implementation over the ElasticSearch and FlexRAN platforms that allows to demonstrate ElasticSDK app development and capturing hierarchical control features of typical SDN-enabled 5G architectures, and (ii) a first-ever publicly available dataset of realistic 5G RAN monitoring traces.
From Coarse to Fine (FC2F): A New Scheme of Colorizing Thermal Infrared Images

Colorization of the thermal infrared image is an unsolved problem because usually there is no one-to-one relationship between an object's color and its temperature.In this paper, we propose a new colorization scheme to address this problem.We first create a coarse colorization image that maintains crosscorrelation between its RGB channels.Then the matrix of shift, rotation, and scaling between the coarse colorization image and a source natural color image is constructed.Finally, the transformations act on the coarse colorization image to obtain the resultant image, which has the appearance of the source image.Experiments demonstrate the effectiveness of the new scheme and show its superiority to other state-of-theart methods.The new scheme can be easily extended to the colorization of low illumination images and near-infrared images.
Application of Dijkstra Algorithm in Path Planning for Geomagnetic Navigation

Path planning is one of the key technologies to realize the hidden navigation of underwater vehicles during long-haul. Path planning efficiency and accuracy are at the core of submarine track planning. Combining the navigation task with the geomagnetic map adaptability, the optimal path between the starting point and the target point is searched in the target space. The underwater geomagnetic navigation path planning model is established, and the principle and implementation method of Dijkstra algorithm are analyzed. An underwater geomagnetic navigation path planning model is established, and the Dijkstra algorithm is used for underwater geomagnetic navigation path planning. Combining different local windows in the adaptation area, the path planning calculation time and track cost are optimized. The simulation analyzes the influence of different local windows on the path planning in the adaptation area. The experiment results demonstrate that the Dijkstra algorithm can effectively find the optimal path that satisfies the constraints.
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks

.We study the transfer learning process between two linear regression problems. An important and timely special case is when the regressors are overparameterized and perfectly interpolate their training data. We examine a parameter transfer mechanism whereby a subset of the parameters of the target task solution are constrained to the values learned for a related source task. We analytically characterize the generalization error of the target task in terms of the salient factors in the transfer learning architecture, i.e., the number of examples available, the number of (free) parameters in each of the tasks, the number of parameters transferred from the source to target task, and the relation between the two tasks. Our nonasymptotic analysis shows that the generalization error of the target task follows a two-dimensional double descent trend (with respect to the number of free parameters in each of the tasks) that is controlled by the transfer learning factors. Our analysis points to specific cases where the transfer of parameters is beneficial as a substitute for extra overparameterization (i.e., additional free parameters in the target task). Specifically, we show that the usefulness of a transfer learning setting is fragile and depends on a delicate interplay among the set of transferred parameters, the relation between the tasks, and the true solution. We also demonstrate that overparameterized transfer learning is not necessarily more beneficial when the source task is closer or identical to the target task.Keywordsoverparameterized learninglinear regressiontransfer learningdouble descentMSC codes62J0568Q32
A generalized Bayes framework for probabilistic clustering

Summary Loss-based clustering methods, such as k-means clustering and its variants, are standard tools for finding groups in data. However, the lack of quantification of uncertainty in the estimated clusters is a disadvantage. Model-based clustering based on mixture models provides an alternative approach, but such methods face computational problems and are highly sensitive to the choice of kernel. In this article we propose a generalized Bayes framework that bridges between these paradigms through the use of Gibbs posteriors. In conducting Bayesian updating, the loglikelihood is replaced by a loss function for clustering, leading to a rich family of clustering methods. The Gibbs posterior represents a coherent updating of Bayesian beliefs without needing to specify a likelihood for the data, and can be used for characterizing uncertainty in clustering. We consider losses based on Bregman divergence and pairwise similarities, and develop efficient deterministic algorithms for point estimation along with sampling algorithms for uncertainty quantification. Several existing clustering algorithms, including k-means, can be interpreted as generalized Bayes estimators in our framework, and thus we provide a method of uncertainty quantification for these approaches, allowing, for example, calculation of the probability that a data point is well clustered.
Digital Manipulation and the Future of Electoral Democracy in the U.S.

This article examines the problem of unreliable information on the Web and its implications for the integrity of elections, and representative democracy, in the U.S. Effects of digital disinformation campaigns on voters are hard to quantify because understanding voter behavior is notoriously difficult for scholars and politicians alike. But false digital information does seem to influence voter behavior by amplifying negative attitudes and heightening fears. One rational response is to advocate for standards of veracity for digital information, but the challenges to imposing new standards are at least twofold. First, “useful fictions” have been found to sometimes engender beneficial effects; so flatly banning digital falsehoods would seem undesirable. Second, emerging “deepfake” technologies may worsen the problem of digital dissembling. One consolation is the persistent uncertainty about whether video and other forms of digital manipulation influence voters in predictable ways, reducing the perceived value of the practice. The surest remedy may be a “technological fix,” inserted into public digital platforms, which can identify instances of maliciously false information, and either quickly destroy or isolate them. Yet no single actor-neither governments, nor “Big Tech” corporations, nor individual citizens or their voluntary associations-can introduce and maintain such a “fix.” Partnerships are needed in order to sustain robust remedies. Until then, the influence on voter behavior of unreliable digital information must be better monitored and documented, analyzed, and understood.
A smart mobile robot commands predictor using recursive neural network

Autonomous navigation of mobile robot via classic neural network (NN) models are no more valid in terms of efficiency and accuracy due to the development of new advanced techniques. However, the necessity of finding an implementable Recursive Neural Network (RNN) model to predict the motor control of the robot with both speed and accuracy constraints still remains stagnant because of the nonlinearity and complexity of the trajectories. To provide new solutions for smart navigation problems, this paper proposes a new implementable recursive neural network controller (RNNC) predictor that calculates the Pulse Width Modulation (PMW) signals of the motors. Such proposed Multi-input Multi-output (MIMO) Controller succeeded to solve the problem of speed and accuracy of autonomous navigation. The Smart RNNC model design is illustrated with its architecture in details. Due to the complexity and the non-efficiency of the training process in real-world, a 3D Simulator was developed to create all possible scenarios. The machine learning and navigation predictions processes for designing the new RNNC model are presented together in details. In addition, the motor commands generation speed and accuracy as well as their efficiency are theoretically and practically proven. Moreover, numerical studies, 3D scenarios of trajectory tracking and obstacle avoidance prove the effectiveness and robustness of the proposed technique.
Homomorphic extensions of CRT-based secret sharing

In this work, we explore the homomorphic aspect of CRT-based secret sharing schemes. Secret sharing homomorphism is the notion of operating on multiple secrets by direct computation on shares. There are schemes based on polynomial interpolation which have partially homomorphic properties, whereas CRT-based secret sharing homomorphism is an open area. Because of the disparate structure of the CRT-based schemes, we introduce advanced security notion and analyze the existing schemes. We formulate homomorphic inadequacy caused by the overflow problem and present sufficient and necessary homomorphism conditions. Then, we show the impossibility of homomorphic and secure threshold Asmuth–Bloom scheme while keeping the original structure. Accordingly, we propose possible homomorphic extensions to the Asmuth–Bloom SSS. Our first extension, additively homomorphic ramp scheme, can attain arbitrarily large information rate. Besides, it is the only CRT-based scheme possessing perfect secrecy. The second scheme allows homomorphic addition and also multiplication to some point. The bound on addition operations is correlated with the share size of the scheme, whereas multiplication bound is inversely proportional to the secrecy threshold. We give detailed analyses of the extensions and their security proofs as well as their properties like information rate, security characteristic, and homomorphic capabilities.
An Image Compression Framework with Learning-based Filter

In this paper, a coding framework VIP-ICT-Codec is introduced. Our method is based on the VTM (Versatile Video Coding Test Model). First, we propose a color space conversion from RGB to YUV domain by using a PCA-like operation. A method for the PCA mean calculation is proposed to de-correlate the residual components of YUV channels. Besides, the correlation of UV components is compensated considering that they share the same coding tree in VVC. We also learn a residual mapping to alleviate the over-filtered and under-filtered problem of specific images. Finally, we regard the rate control as an unconstraint Lagrangian problem to reach the target bpp. The results show that we achieve 32.625dB at the validation phase.
Is Hardware More Secure Than Software?

Computer hardware is usually perceived as more secure than software. However, recent trends lead us to reexamine this belief. We draw attention to the "firmwarization" of hardware and argue for revisiting the role of hardware and software in systems security.
Robust Bidirectional Generative Network For Generalized Zero-Shot Learning

In this work, we propose a novel generative approach named Robust Bidirectional Generative Network (RBGN) based on Conditional Generative Adversarial Network (CGAN) for Generalized Zero-shot Learning (GZSL). RBGN employs the adversarial attack to train a more rigorous discriminator, thus enhancing the generalizability and robustness of the feature generator under minimax strategy. Moreover, RBGN decodes the generated visual features back to their semantic representations to further improve the representational ability of generated visual features and alleviate the hubness problem. The experimental results of GZSL on four datasets, i.e. CUB, SUN, AWA1, AWA2, demonstrate that our model achieves competitive performance compared to state-of-the-art approaches and owns better generalizability to the unseen classes over conventional generative GZSL models. Further robustness analysis also validates the strong robustness of our model to the different types of semantic disturbance.
A system engineering perspective on quantum radar

We compute the minimum cost for an optimal quantum radar, and we compare it with the cost of actual real world classical radars as a function of range. Our calculations show that the minimum cost quantum radar at X-Band is many orders of magnitude more expensive than the corresponding classical radar, even assuming the most optimistic wideband phased array radar architecture. We also assume that the quantum radar is optimal; that is, the effective signal-to-noise ratio is 6 dB better than for a classical radar with the same transmit power and bandwidth at low photon flux per mode.
Local differential privacy for unbalanced multivariate nominal attributes

Abstract Data with unbalanced multivariate nominal attributes collected from a large number of users provide a wealth of knowledge for our society. However, it also poses an unprecedented privacy threat to participants. Local differential privacy, a variant of differential privacy, is proposed to eliminate the privacy concern by aggregating only randomized values from each user, with the provision of plausible deniability. However, traditional local differential privacy algorithms usually assign the same privacy budget to attributes with different dimensions, leading to large data utility loss and high communication costs. To obtain highly accurate results while satisfying local differential privacy, the aggregator needs a reasonable privacy budget allocation scheme. In this paper, the Lagrange multiplier (LM) algorithm was used to transform the privacy budget allocation problem into a problem of calculating the minimum value from unconditionally constrained convex functions. The solution to the nonlinear equation obtained by the Cardano formula (CF) and Newton-Raphson (NS) methods was used as the optimal privacy budget allocation scheme. Then, we improved two popular local differential privacy mechanisms by taking advantage of the proposed privacy budget allocation techniques. Extension simulations on two different data sets with multivariate nominal attributes demonstrated that the scheme proposed in this paper can significantly reduce the estimation error under the premise of satisfying local differential privacy.
Local Non-Rigid Structure-From-Motion From Diffeomorphic Mappings

We propose a new formulation to non-rigid structure-from-motion that only requires the deforming surface to preserve its differential structure. This is a much weaker assumption than the traditional ones of isometry or conformality. We show that it is nevertheless sufficient to establish local correspondences between the surface in two different images and therefore to perform point-wise reconstruction using only first-order derivatives. To this end, we formulate differential constraints and solve them algebraically using the theory of resultants. We will demonstrate that our approach is more widely applicable, more stable in noisy and sparse imaging conditions and much faster than earlier ones, while delivering similar accuracy. The code is available at https://github.com/cvlab-epfl/diff-nrsfm/.
Context-Free Path Querying with Single-Path Semantics by Matrix Multiplication

A recent study showed that the applicability of context-free path querying (CFPQ) algorithms with relational query semantics integrated with graph databases is limited because of low performance and high memory consumption of existing solutions. In this work, we implement a matrix-based CFPQ algorithm by using appropriate high-performance libraries for linear algebra and integrate it with RedisGraph graph database. Also, we introduce a new CFPQ algorithm with single-path query semantics that allows us to extract one found path for each pair of nodes. Finally, we provide the evaluation of our algorithms for both semantics which shows that matrix-based CFPQ implementation for Redis-Graph database is performant enough for real-world data analysis.