Reviewerly/exHarmony huggingface dataset:
Practical Algebraic Attack on DAGS

DAGS scheme is a key encapsulation mechanism (KEM) based on quasi-dyadic alternant codes that was submitted to NIST standardization process for a quantum resistant public key algorithm. Recently an algebraic attack was devised by Barelli and Couvreur (Asiacrypt 2018) that efficiently recovers the private key. It shows that DAGS can be totally cryptanalysed by solving a system of bilinear polynomial equations. However, some sets of DAGS parameters were not broken in practice. In this paper we improve the algebraic attack by showing that the original approach was not optimal in terms of the ratio of the number of equations to the number of variables. Contrary to the common belief that reducing at any cost the number of variables in a polynomial system is always beneficial, we actually observed that, provided that the ratio is increased and up to a threshold, the solving can be heavily improved by adding variables to the polynomial system. This enables us to recover the private keys in a few seconds. Furthermore, our experimentations also show that the maximum degree reached during the computation of the Gröbner basis is an important parameter that explains the efficiency of the attack. Finally, the authors of DAGS updated the parameters to take into account the algebraic cryptanalysis of Barelli and Couvreur. In the present article, we propose a hybrid approach that performs an exhaustive search on some variables and computes a Gröbner basis on the polynomial system involving the remaining variables. We then show that the updated set of parameters corresponding to 128-bit security can be broken with $$2^{83}$$ operations.
Improved Model for Detecting Fake Profiles in Online Social Network: A Case Study of Twitter

Online Social Network (OSN) is like a virtual community where people build social networks and relations with one another. The open access to the Internet has increased the growth of OSN which has attracted intruders to exploit the weaknesses of the Internet and OSN to their own gain. The rise in the usage of OSN has posed security threats to OSN users as they share personal and sensitive information online which could be exploited by these intruders by creating profiles to carry out a series of malicious activities on the social network. In fact, it is no gain saying that the intent of creating fake accounts has adverse effect and the Internet has made it quite easy to concede one’s identity; and this makes it difficult to detect fake accounts as they try to imitate real accounts. In this study, a model that can accurately identify fake profiles in OSN which uses Natural Language Processing Technique to eliminate or reduce the size of the dataset thereby improving the overall performance of the model was proposed. Principal Component Analysis was used for appropriate feature selection. After extraction, six attributes/features that influenced the classifier were found. Support Vector Machine (SVM), Naïve Bayes and Improved Support Vector Machine (ISVM) were used as Classifiers. ISVM introduced a penalty parameter to the standard SVM objective function to reduce the inequality constraints between the slack variables. This gave a better result of 90% than the SVM and Naïve Bayes which gave 77.4% and 77.3% respectively.
A High Result in Wavelet Watermarking Using Singular Value Decomposition

Internet that has developed into a good distribution tool for digital data, causing a large increase in digital data sharing, especially in the form of digital images, and causing problems that need attention. One of them is about copyright protection. Watermarking is one technique that aims to protect digital image copyright. In this study, watermarking was carried out using the Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) algorithms in digital images with different file extensions. Transformation of 2 levels of DWT and SVD on the host image and SVD transformation on the watermarked image that inserted in the LL2 sub-band of the host image. Watermarking with the proposed method produces good image quality with PSNR values exceeding 40 dB, SSIM reaching 0.99 and NCC reaching 1. This shows a robust and imperceptible watermarking image.
A data structure perspective to the RDD-based Apriori algorithm on Spark

During the recent years, a number of efficient and scalable frequent itemset mining algorithms for big data analytics have been proposed by many researchers. Initially, MapReduce-based frequent itemset mining algorithms on Hadoop cluster were proposed. Although, Hadoop has been developed as a cluster computing system for handling and processing big data, but the performance of Hadoop does not meet the expectation for the iterative algorithms of data mining, due to its high I/O, and writing and then reading intermediate results in the disk. Consequently, Spark has been developed as another cluster computing infrastructure which is much faster than Hadoop due to its in-memory computation. It is highly suitable for iterative algorithms and supports batch, interactive, iterative, and stream processing of data. Many frequent itemset mining algorithms have been re-designed on the Spark, and most of them are Apriori-based. All these Spark-based Apriori algorithms use Hash Tree as the underlying data structure. This paper investigates the efficiency of various data structures for the Spark-based Apriori. Although, the data structure perspective has been investigated previously, but for the MapReduce-based Apriori, and it must be re-investigated in the distributed computing environment of Spark. The considered underlying data structures are Hash Tree, Trie, and Hash Table Trie. The experimental results on the benchmark datasets show that the performance of Spark-based Apriori with Trie and Hash Table Trie are almost similar but both perform many times better than Hash Tree in the distributed computing environment of Spark.
Personalized Medication and Activity Planning in PDDL+

The emergence of planners capable of reasoning with continuous dynamics, as expressed in PDDL+, has increased the range of problems that fall within the capabilities of PDDL planners. One such problem is planning patients’ activities and medication regimes, considering non-linear medication pharmacokinetics. In this paper we explore the application of contemporary PDDL+ planners to this problem. To address their performance limitations, we present a linearize–validate cycle; tasks are solved by iterative refinement of a linear approximation of the domain, solved by a linear planner, then validated at each stage against the full non-linear semantics. In doing this we allow this domain to fall within the capabilities of current planners; and in our evaluation we use OPTIC to demonstrate this.
Worst-Case Optimal Querying of Very Expressive Description Logics with Path Expressions and Succinct Counting

Among the most expressive knowledge representation formalisms are the description logics of the Z family. For well-behaved fragments of ZOIQ, entailment of positive two-way regular path queries is well known to be 2EXPTIME-complete under the proviso of unary encoding of numbers in cardinality constraints. We show that this assumption can be dropped without an increase in complexity and EXPTIME-completeness can be achieved when bounding the number of query atoms, using a novel reduction from query entailment to knowledge base satisfiability. These findings allow to strengthen other results regarding query entailment and query containment problems in very expressive description logics. Our results also carry over to GC2, the two-variable guarded fragment of first-order logic with counting quantifiers, for which hitherto only conjunctive query entailment has been investigated.
Quantum optical interferometry via general photon-subtracted two-mode squeezed states

We investigate the sensitivity of phase estimation in a Mach–Zehnder interferometer with photon-subtracted two-mode squeezed vacuum states. Our results show that, for given initial squeezing parameter, both symmetric and asymmetric photon subtractions can further improve the quantum Cramér–Rao bound ( i.e. , the ultimate phase sensitivity), especially for single-mode photon subtraction. On the other hand, the quantum Cramér–Rao bound can be reached by parity detection for symmetric photon-subtracted two-mode squeezed vacuum states at particular values of the phase shift, but it is not valid for asymmetric photon-subtracted two-mode squeezed vacuum states. In addition, compared with the two-mode squeezed vacuum state, the phase sensitivity via parity detection with asymmetric photon-subtracted two-mode squeezed vacuum states will be getting worse. Thus, parity detection may not always be the optimal detection scheme for nonclassical states of light when they are considered as the interferometer states.
Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing

Deep supervised hashing has become an active topic in information retrieval. It generates hashing bits by the output neurons of a deep hashing network. During binary discretization, there often exists much redundancy between hashing bits that degenerates retrieval performance in terms of both storage and accuracy. This paper proposes a simple yet effective Neurons Merging Layer (NMLayer) for deep supervised hashing. A graph is constructed to represent the redundancy relationship between hashing bits that is used to guide the learning of a hashing network. Specifically, it is dynamically learned by a novel mechanism defined in our active and frozen phases. According to the learned relationship, the NMLayer merges the redundant neurons together to balance the importance of each output neuron. Moreover, multiple NMLayers are progressively trained for a deep hashing network to learn a more compact hashing code from a long redundant code. Extensive experiments on four datasets demonstrate that our proposed method outperforms state-of-the-art hashing methods.
Planning with Expectation Models

Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the state and action spaces are large. Expectation models, on the other hand, are relatively easier to learn due to their compactness and have also been widely used for deterministic environments. For stochastic environments, it is not obvious how expectation models can be used for planning as they only partially characterize a distribution. In this paper, we propose a sound way of using approximate expectation models for MBRL. In particular, we 1) show that planning with an expectation model is equivalent to planning with a distribution model if the state value function is linear in state features, 2) analyze two common parametrization choices for approximating the expectation: linear and non-linear expectation models, 3) propose a sound model-based policy evaluation algorithm and present its convergence results, and 4) empirically demonstrate the effectiveness of the proposed planning algorithm.
Three-quarter Sibling Regression for Denoising Observational Data

Many ecological studies and conservation policies are based on field observations of species, which can be affected by systematic variability introduced by the observation process. A recently introduced causal modeling technique called 'half-sibling regression' can detect and correct for systematic errors in measurements of multiple independent random variables. However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes. We present a technique called 'three-quarter sibling regression' to partially overcome this limitation. It can filter the effect of systematic noise when the latent variables have observed common causes. We provide theoretical justification of this approach, demonstrate its effectiveness on synthetic data, and show that it reduces systematic detection variability due to moon brightness in moth surveys.
Answer Set Programming for Judgment Aggregation

Judgment aggregation (JA) studies how to aggregate truth valuations on logically related issues. Computing the outcome of aggregation procedures is notoriously computationally hard, which is the likely reason that no implementation of them exists as of yet. However, even hard problems sometimes need to be solved. The worst-case computational complexity of answer set programming (ASP) matches that of most problems in judgment aggregation. We take advantage of this and propose a natural and modular encoding of various judgment aggregation procedures and related problems in JA into ASP. With these encodings, we achieve two results: (1) paving the way towards constructing a wide range of new benchmark instances (from JA) for answer set solving algorithms; and (2) providing an automated tool for researchers in the area of judgment aggregation.
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of information for this task. The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees. One potential problem with this approach is it might prevent the models from capturing important context information beyond syntactic structures and cause the poor cross-domain generalization. This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations. We also propose a new mechanism to control the information flow in the model based on the input entity mentions. Our extensive experiments on benchmark datasets show that the proposed model outperforms the existing methods for RE significantly.
Deep Covariance Estimation Hashing

Deep hashing, the combination of advanced convolutional neural networks and efficient hashing, has recently achieved impressive performance for image retrieval.However, state-of-the-art deep hashing methods mainly focus on constructing hash function, loss function and training strategies to preserve semantic similarity.For the fundamental image characteristics, they depend heavily on the first-order convolutional feature statistics, failing to take their global structure into consideration.To address this problem, we present a deep covariance estimation hashing (DCEH) method with robust covariance form to improve hash code quality.The core of DCEH involves covariance pooling as deep hashing representation, performing global pairwise feature interactions.The covariance pooling can capture richer statistic information of deep convolutional features and produce more informative global representations.Due to convolutional features are usually high dimension and small sample size, we estimate robust covariance by shrinking its eigenvalues using power normalization, forming an independent structural layer.Then the structural layer is embedded into deep hashing paradigm in an end-to-end learning manner.Extensive experiments on three benchmarks show that the proposed DCEH outperforms its counterparts and achieves superior performance.
An Analysis System for Mobile Applications MVC Software Architectures

Mobile applications are software systems that are highly used by all modern people; a vast majority of those are intricate systems. Due to their increase in complexity, the architectural pattern used plays a significant role in their lifecycle. Architectural patterns can not be enforced on a codebase without the aid of an external tool; with this idea in mind, the current paper describes a novel technique for an automatically analysis of Model View Controller mobile application codebases from an architectural point of view. The analysis takes into account the constraints imposed by this layered architecture and offers insightful metrics regarding the architectural health of the codebase, while also highlighting the architectural issues. Both open source and private codebases have been analysed by the proposed approach and the results indicate an average accuracy of 89.6% of the evaluation process.
Adaptive Augmented Reality User Interfaces Using Face Recognition for Smart Home Control

Augmented Reality (AR) offers the possibility to present information depending on the context/location: pointing at a given appliance will automatically present relevant interfaces. In this work, we explore Pervasive AR in the context of the Smart Home: users may select the control of a given appliance by pointing at it, simplifying the interaction process. This idea is then extended to consider user profile in the loop: face recognition is used to recognize a given user of and adapt the interface not only to location but also to specific users needs. A preliminary study was conducted to evaluate and validate the concept.
Trajectory Generation Using Semidefinite Programming For Multi-Rotors

Trajectory generation is one of the most popular techniques for multi-rotors to achieve autonomous navigation capabilities. In this paper, we present an optimization-based approach for generating smooth, feasible and collision-free trajectories. Our method does not require a prior proper time allocation but attempts to optimize time allocation automatically. This method first optimizes trajectories subjected to dynamic constraints and 3D corridor constraints using a quadratic program. Then time allocation is optimized as a semidefinite program by leveraging semidefinite relaxation. A feasible solution is obtained using the rank-one approximation and used to update previous time allocation. This method run iteratively until a feasible trajectory is obtained. Our approach is validated with simulation results. Kinematic simulation results show the proposed method ensures to generate feasible trajectory from collision-free path in clutter environments.
Model Predictive Control Design for Overtaking Maneuvers for Multi-Vehicle Scenarios

The paper proposes the design of an MPC-based method for overtaking maneuvers of autonomous vehicles. The strategy incorporates a graph-based optimization algorithm, in which the probability of collisions with the surrounding vehicles is incorporated. The purpose of the graph search is to determine the road and velocity profile of the autonomous vehicle, which is guaranteed by the proposed MPC control. The novel algorithm is able to consider the multi-vehicle scenarios during the overtaking maneuver. The effectiveness of the method is presented in CarMaker simulation scenarios.
Hybrid Chaotic Encryption Algorithm for Securing DICOM Systems

The DICOM system contains patients' personal privacy and may present a risk of information leakage in telemedicine diagnostics.In order to solve the security problem of the DICOM system, we propose a medical image encryption method based on the cross-diffusion of the logistic map and the Chebyshev map.We first parse the DICOM image and read the data elements, and then we use the logistic map and the Chebyshev map to generate a key.Then, we cross-use these two sequences for further image encryption diffusion.Finally, we conduct in-depth experiments and comparisons with existing algorithms to validate the characteristics of high security and low correlation of adjacent pixels of the proposed encryption algorithm.
MORI coefficients as indicators of a ‘real’ cluster structure

The present study examines some methods for identifying a 'real' cluster structure in situations where objects are classified according to their value profile in a set of variables.The authors investigate the usefulness of various coefficients used mainly in cluster analysis for evaluating the quality of a cluster solution with an emphasis on the newly developed MORI coefficient.Three types of data sets are analysed: a bivariate normal data set and two different data sets where the underlying classification structure is perfect but includes errors of measurement.The findings indicate that the MORI coefficient is a useful tool for detecting the existence of a real classification structure; they also indicate the degree of correspondence between the underlying theoretical classification structure and the empirical cluster structure found in the analyses.
Evo-Devo Algorithms: Gene-Regulation for Digital Architecture

The majority of current visual-algorithmic architecture is constricted to specific parameters that are gradient related, keeping their parts' relation fixed within the algorithm, far away from a truly parametric modeling with a flexible topology. Recent findings around genetics and certain genes capable of shape conditioning (development) have succeeded in recovering the science of embryology as a valid field that connects and affects the evolutionary ecosystem, showing the existence of universal mechanisms that are present in living species, thus describing powerful strategies for generation and emergence. Therefore, a new dual discipline is justified: Evolutionary developmental biology science. Authors propose the convergence of genetics algorithms and simulated features from evolutionary developmental biology into a single data-flow that will prove itself capable of generating great diversity through a simple and flexible structure of data, commands, and polygonal geometry. For that matter, a case study through visual-algorithmic software deals with the hypothesis that for obtaining a greater emergence and design space, a simpler and more flexible approach might only be required, prioritizing hierarchical levels over complex and detailed operations.
E-KOMPEN (ELEKTRONIK-KOMIK PENDEK) sebagai solusi cerdas dalam meningkatkan minat baca masyarakat Indonesia di era digital

In this journal, the author describes the idea of E-KOMPEN (Short Electronics-Comics) as a smart solution in increasing reading interest in Indonesian society in the digital era. With this solution, it is hoped that it can help the community to increase their reading interest in books, journals, and other sources. Comics are illustrated stories. This idea was motivated by the problem of reading interest in Indonesia. The reading culture in Indonesia is ranked the lowest with a value of 0.001. That is, from around one thousand Indonesian residents, only one has a high reading culture. This condition is much different when compared to America which has an index reading of 0.45 and Singapore 0.55. This solution is expected to be able to answer the challenges of increasing the reading interest of the Indonesian people to improve the quality of Indonesia's human resources. With the increasing quality of Indonesia's human resources, Indonesia's natural resources will be managed well by the Indonesian people themselves without having to make massive imports. Also, Indonesia will be able to use the demographic bonus as well as possible so that the demographic bonus can be a big advantage for Indonesia. Keywords: Comic, reading, library.