Reviewerly/exHarmony huggingface dataset:
On Non-commutative Cryptography with Cubical Multivariate Maps of Predictable Density

Multivariate cryptosystems are divided into public rules for which tools of encryption are open for users and systems of El Gamal type for which encryption function is not given in public and for its generation opponent has to solve discrete logarithm problem in affine Cremona group. Infinite families of transformations of free module Kn over finite commutative ring K such that the degrees of their members are not growing with iteration are called stable families of transformations. Such families are needed for practical implementations of multivariate cryptosystems of El Gamal type. New explicit constructions of such families and families of stable groups and semigroups of transformations of free modules are given. New methods of creation of cryptosystems which use stable transformation groups and semigroups, and homomorphisms between them are suggested. The security of these schemes is based on a complexity of decomposition problem for element of affine Cremona semigroup into product of given generators. Proposed schemes can be used for the exchange of semigroup messages in a form of elements of a free module and for a secure delivery of multivariate maps which could be encryption tools and instruments for digital signatures.
A New Arabic Dataset for Emotion Recognition

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and Naïve Bayes achieving the highest results in terms of accuracy, precision, recall, and F-measure.
Capture &amp; Analysis of Active Reading Behaviors for Interactive Articles on the Web

Abstract Journalists, educators, and technical writers are increasingly publishing interactive content on the web. However, popular analytics tools provide only coarse information about how readers interact with individual pages, and laboratory studies often fail to capture the variability of a real‐world audience. We contribute extensions to the Idyll markup language to automate the detailed instrumentation of interactive articles and corresponding visual analysis tools for inspecting reader behavior at both micro‐ and macro‐levels. We present three case studies of interactive articles that were instrumented, posted online, and promoted via social media to reach broad audiences, and share data from over 50,000 reader sessions. We demonstrate the use of our tools to characterize article‐specific interaction patterns, compare behavior across desktop and mobile devices, and reveal reading patterns common across articles. Our contributed findings, tools, and corpus of behavioral data can help advance and inform more comprehensive studies of narrative visualization.
Delivering comprehension features into source code editors through LSP

The maintenance of large, legacy software often results in higher development time and cost due to increasing size and complexity of the codebase and its documentation, their continuously eroding quality and fluctuation among developers. Code comprehension tools are designed to tackle this issue by providing various textual information, visualization views and source code metrics on multiple abstraction levels. These tools usually process not only the codebase, but also the build information, the version control repository and other available information sources. Meanwhile source code editors and integrated development environments (IDEs) are not performing well in the field of code comprehension as they are optimized for writing new code, not for effective browsing. This can easily result in frequent switching between environments during development, hindering effective programming and raising development cost. Language Server Protocol (LSP) is an open-source protocol to connect source code editors with servers that provide language-specific features. In this research we analyze how LSP can be utilized to improve the code comprehension experience inside code editors by integrating the features of such tools through remote procedure calls. As a prototype solution we showcase the integration of two open-source applications: Visual Studio Code and the Code Compass code comprehension tool.
Human-object Interaction Recognition Using Multitask Neural Network

Human behavior recognition is a popular research area in the field of computer vision and has been studied due to its important applications such as visual surveillance and video retrieval. In this paper, we propose a new approach for recognizing human-object interaction actions based on multitask 2D convolutional neural network, which combines human body motion, human hand motion and object recognition network. By using RGBD camera and digital gloves, refined recognition of human body and hand movements are collected and learned. In addition, a new object recognition network based on YOLOv3 is introduced which increases the accuracy of predicting human-object interaction labels. We designed eight representative actions and built our own data set containing body and accurate hand motions. In our experiment, the accuracy of recognizing interactive actions reached 93%, which shows the correctness and effectiveness of the multitasking framework we propose.
Skew multi-twisted codes over finite fields and their Galois duals

In this paper, we introduce a new class of linear codes over finite fields, viz. skew multi-twisted codes (or skew generalized quasi-twisted codes), which is a generalization of some well-known classes of linear codes such as cyclic codes and quasi-cyclic codes. We also study algebraic structures of skew multi-twisted codes and their Galois duals (i.e., orthogonal complements with respect to the Galois inner product). We also view skew multi-twisted codes as direct sums of certain concatenated codes, which gives rise to a method to construct these codes. We also determine a lower bound on their minimum Hamming distances using their multilevel concatenated structure. Apart from this, we determine the parity-check polynomial of each skew multi-twisted code, and obtain BCH type bounds on their minimum Hamming distances. We also determine generating sets of Galois duals of some skew multi-twisted codes from generating sets of these codes. We also derive necessary and sufficient conditions under which a skew multi-twisted code is (i) Galois self-dual, (ii) Galois self-orthogonal and (iii) Galois LCD (linear with complementary dual). As an application of this result, we also provide enumeration formulae for all Hermitian self-dual, Hermitian self-orthogonal, Hermitian LCD and Euclidean LCD multi-twisted codes over finite fields. We also obtain many linear codes with best known and optimal parameters from 1-generator skew multi-twisted codes over finite fields F8 and F9.
The Semantic Complex Event Processing Based on Metagraph Approach

In this paper, the features of semantic complex event processing (SCEP) approach based on the metagraph model is considered. The idea of event and event processing is one of the fundamental ideas in the fields of complex systems and software engineering. The definitions and descriptions of Complex Event Processing (CEP) and semantic complex event processing (SCEP) are reviewed and summed up. Semantic Web technologies are typically used for semantics description in SCEP approach. Complex event processing combines data from multiple sources. Complex event processing engine may include several processing levels, and semantic complex events may be enriched during processing. The goal of complex event processing is to identify meaningful events (such as opportunities or threats) in the form of the complex situation. Complex event processing engine may use global ontology and static and dynamic semantics. The RDF approach has limitations in describing complex situations, while the metagraph approach addresses RDF limitations in a natural way without emergence loss. The metagraph model is used as a unified model for semantic events description (static and dynamic semantics), complex situation description, global ontology description. Using the combination of the metagraph data model and metagraph agents model it is possible to construct the complex semantic event processing engine in the form of the hierarchy of dynamic metagraph agents.
Cardinality compensation method based on information-weighted consensus filter using data clustering for multi-target tracking

In this paper, a cardinality compensation method based on Information-weighted Consensus Filter (ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density (CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality (target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.
An off-center technique: Learning a feature transformation to improve the performance of clustering and classification

This paper proposes a feature transformation method to improve the performance of clustering and classification, which is named as weight-matrix learning (WML). A feed-forward neural network is particularly designed for WML, which aims to learn the optimal weights by minimizing an objective function similar to cross-entropy, and the training process is finished based on the technique of batch gradient descent or stochastic gradient descent. The proposed feature transformation is linear, which is a non-trivial extension of a previous technique named feature-weight learning (FWL). Essentially, WML can be considered as a learning technique of departing 0.5-similarity, since it can make the samples with similarity larger than 0.5 closer and the samples with similarity lower than 0.5 farther away. From this perspective, WML is identified as an off-center technique with the center of 0.5-similarity. Theoretically and experimentally, it is validated that WML can significantly improve the performance of some clustering algorithms like k-means, and enhance the performance of some classification algorithms like random weight neural network.
Filtering of systems with nonlinear measurements with an application to target tracking

Summary This paper studies the problem of recursive state estimation of stochastic linear systems with nonlinear measurements. The main idea is to rewrite the measurement map in a linear form by considering, as system output, a vector of “virtual” measurements. The result is a linear system with a non‐Gaussian and nonstationary output noise. State estimation is therefore obtained using a Kalman filter or, alternatively, a quadratic filter, suitably designed for non‐Gaussian systems. This work provides two sufficient conditions for the application of the virtual measurement approach and shows its effectiveness in the case of the maneuvering target tracking problem.
Stroboscopic approach to trapped-ion quantum information processing with squeezed phonons

In trapped-ion quantum information processing, interactions between spins (qubits) are mediated by collective modes of motion of an ion crystal. While there are many different experimental strategies to design such interactions, they all face both technical and fundamental limitations to the achievable coherent interaction strength. In general, obtaining strong interactions and fast gates is an ongoing challenge. Here, we extend previous work [Phys. Rev. Lett. 112, 030501 (2019)] and present a general strategy for enhancing the interaction strengths in trapped-ion systems via parametric amplification of the ions' motion. Specifically, we propose a stroboscopic protocol using alternating applications of parametric amplification and spin-motion coupling. In comparison with the previous work, we show that the current protocol can lead to larger enhancements in the coherent interaction that increase exponentially with the gate time.
Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment

Machine learning algorithms have been widely used in the field of client credit assessment. However, few of the algorithms have focused on and solved the problems of concept drift and class imbalance. Due to changes in the macroeconomic environment and markets, the relationship between client characteristics and credit assessment results may change over time, causing concept drift in client credit assessments. Moreover, client credit assessment data are naturally asymmetric and class imbalanced because of the screening of clients. Aiming at solving the joint research issue of concept drift and class imbalance in client credit assessments, in this paper, a novel sample-based online learning ensemble (SOLE) for client credit assessment is proposed. A novel multiple time scale ensemble classifier and a novel sample-based online class imbalance learning procedure are proposed to handle the potential concept drift and class imbalance in the client credit assessment data streams. The experiments are carried out on two real-world client credit assessment cases, which present a comprehensive comparison between the proposed SOLE and other state-of-the-art online learning algorithms. In addition, the base classifier preference and the computing resource consumption of all the comparative algorithms are tested. In general, SOLE achieves a better performance than other methods using fewer computing resources. In addition, the results of the credit scoring model and the Kolmogorov–Smirnov (KS) test also prove that SOLE has good practicality in actual client credit assessment applications.
How to Securely Record Logs based on ARM TrustZone

A number of logs are generated from IT devices. Since logs have important information regarding a system, they are used for finding the trace of an intrusion or obtaining important information through a big data analysis. Hence, the logs have become a major attack surface for attackers. To protect logs, IT devices require secure logging methods as a mandatory service. Secure logging can provide detection of malicious manipulation of logs and verification of their origin. In this paper, we propose a secure logging method satisfying forward and backward secrecy based on ARM TrustZone for embedded systems, which enables to efficiently generate secure logs through inter-process communication without modification of the existing system (Syslog). Also, we show that the proposed method does not require extra overhead compared with the existing logging method.
An efficient approach based on selective partitioning for maximal frequent itemsets mining

We present a maximal frequent itemset (MFI) mining algorithm based on selective partitioning called SelPMiner. It makes use of a novel data format named Itemset-count tree—a compact and optimized representation in the form of partition that reduces memory requirement. It also does selective partitioning of the database, which reduces runtime to scan database. As the algorithm progressively searches for longer frequent itemsets in a depth-first manner, it creates new partitions with even smaller sizes having less dimensions and unique data instances, which results in faster support counting. SelPMiner uses a number of optimizations to prune the search space. We also prove upper bounds on the amount of memory consumed by these partitions. Experimental comparisons of the SelPMiner algorithm with popular existing fastest MFI mining algorithms on different types of datasets show significant speedup in computation time for many cases. SelPMiner works especially well when the minimum support is low and consumes less memory.
Color Medical Image Encryption using 3D Chaotic Cat Map and NCA

In this paper, we propose a secure and reliable color medical image encryption algorithm based on NCA and a generalized 3D chaotic cat map. The NCA is an effective pseudorandom number generator (PRNG) which is a group cellular automata (CA) obtained by combining two nonlinear CA and a maximum length CA (MLCA) which has nonlinearity and expand key space. It can also change pixel values of a plain image to be unpredictable. And we use a generalized 3D chaotic cat map for the efficient shuffling of color medical image encryption. This map can change the positions of pixels in R, G and B channels of color image. The proposed method performs a thorough experiment test through detailed analysis to demonstrate the high security and reliability of the new color medical image encryption system.
A hierarchy of entanglement criteria for four-qubit symmetric Greenberger–Horne–Zeilinger diagonal states

With a two-step method of optimizing entanglement witness, we propose a set of necessary and sufficient entanglement criteria for four-qubit symmetric Greenberger–Horne–Zeilinger (GHZ) diagonal states. The criterion set contains four criteria. Two of them are linear with density matrix elements. The other two criteria are nonlinear with density matrix elements. The criterion set has a nest structure. A proper subset of the criteria is necessary and sufficient for entanglement of the corresponding subset of states. We illustrate the nest structure of criterion set with highly symmetric GHZ diagonal state set, its subset the general Werner state set and its superset the symmetric GHZ diagonal state set.
Novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance

Reinforcement learning (RL) problems often feature deceptive local optima, and methods that optimize purely for reward often fail to learn strategies for overcoming them [2]. Deep neuroevolution and novelty search have been proposed as effective alternatives to gradient-based methods for learning RL policies directly from pixels. We introduce and evaluate the use of novelty search over agent action sequences by Levenshtein distance as a means for promoting innovation. We also introduce a method for stagnation detection and population regeneration inspired by recent developments in the RL community [5], [1] that is derived from novelty search. Our methods extend a state-of-the-art method for deep neuroevolution using a simple genetic algorithm (GA) designed to efficiently learn deep RL policy network weights [6]. Results provide further evidence that GAs are competitive with gradient-based algorithms for deep RL in the Atari 2600 benchmark. Results also demonstrate that novelty search over agent action sequences can be effectively used as a secondary source of evolutionary selection pressure.
Versatile Linkage: a Family of Space-Conserving Strategies for Agglomerative Hierarchical Clustering

Agglomerative hierarchical clustering can be implemented with several strategies that differ in the way elements of a collection are grouped together to build a hierarchy of clusters. Here we introduce versatile linkage, a new infinite system of agglomerative hierarchical clustering strategies based on generalized means, which go from single linkage to complete linkage, passing through arithmetic average linkage and other clustering methods yet unexplored such as geometric linkage and harmonic linkage. We compare the different clustering strategies in terms of cophenetic correlation, mean absolute error, and also tree balance and space distortion, two new measures proposed to describe hierarchical trees. Unlike the $\beta$-flexible clustering system, we show that the versatile linkage family is space-conserving.
Medical faculty members’ perception of smartphones as an educational tool

The rapid adoption of modern technology has changed many aspects of our life and communication; it has the power to influence and change the way we teach, learn and practice different types of professions mainly teaching and health care providing. Smartphone applications are increasingly becoming popular and widespread. Generally, these applications are likely to play a significant role in supporting education, in general, and medical education, in particular. This study aims at investigating how medical faculty members are using smartphones in medical education and practice, and how they perceive them as an educational tool at university level.The researchers have distributed an online questionnaire - including three parts: a demographic part with five variables; a 15-item part of various applications of the smartphones; and a 14-item part measuring attitudes towards using these smartphones - among medical faculty members at two Palestinian universities.Medical faculty members working at two Palestinian universities. Data have been collected from 30 participants out of 72 representing a response rate of 41.6%.The average skills score with smartphones usage is (3.18) which tells that faculty members use smartphones to support their teaching practices. In general, faculty members are positive towards smartphones as a prospective teaching tool since the average attitude towards using smartphones is (3.60). The study results show no significant differences among faculty members based on the five demographic variables, i.e. university, title, department affiliation, gender, and years of experience.It seems that the majority of faculty members believe that smartphones would be a significant instrument as well as addition to their teaching practices.
A case study of developing gaze controlled interface for users with severe speech and motor impairment

BACKGROUND: People with severe speech and motor impairment (SSMI) often depend on electronic user interfaces for communication, learning and many other daily activities. However, these interfaces are often designed assuming the preference and ease of
The application of radial basis function interpolation in reactor core power distribution on-line monitoring

Reactor core power mapping can be regarded as an interpolation problem of scattered data which is common in many scientific and engineering fields, and the Radial Basis Function (RBF) method provides an excellent tool for interpolating multidimensional scattered data and thus an appropriate approach for power mapping. By using the dual form of the RBF interpolation expression, this study shows that the Ordinary Kriging (OK) method authors utilized before can be regarded as an instance of RBF framework. Moreover, Gaussian function, multiquadric function, inverse quadratic function and inverse multiquadric function have been implemented in RBF interpolation framework to carry out core power mapping. Measurements from DayaBay Unit 1 PWR are used to verify the accuracies of these four RBF functions, and comparisons are made among these four methods and the OK method. The reconstructed assembly power distribution results and the calculation speeds show that the RBF interpolation framework is suitable for on-line core power distribution monitoring.