Reviewerly/exHarmony huggingface dataset:
Multi-qubit Quantum Rabi Model and Multi-partite Entangled States in a Circuit QED System

Abstract Multi-qubit quantum Rabi model, which is a fundamental model describing light-matter interaction, plays an important role in various physical systems. In this paper, we propose a theoretical method to simulate multi-qubit quantum Rabi model in a circuit quantum electrodynamics system. By means of external transversal and longitudinal driving fields, an effective Hamiltonian describing the multi-qubit quantum Rabi model is derived. The effective frequency of the resonator and the effective splitting of the qubits depend on the external driving fields. By adjusting the frequencies and the amplitudes of the driving fields, the stronger coupling regimes could be reached. The numerical simulation shows that our proposal works well in a wide range of parameter space. Moreover, our scheme can be utilized to generate two-qubit gate, Schrödinger states, and multi-qubit GHZ states. The maximum displacement of the Schrödinger cat states can be enhanced by increasing the number of the qubits and the relative coupling strength. It should be mention that we can obtain high fidelity Schrödinger cat states and multi-qubit GHZ states even the system suffering dissipation. The presented proposal may open a way to study the stronger coupling regimes whose coupling strength is far away from ultrastrong coupling regimes.
Coded Distributed Computing with Heterogeneous Function Assignments

Coded distributed computing (CDC) introduced by Li et. al. is an effective technique to trade computation load for communication load in a MapReduce framework. CDC achieves an optimal trade-off by duplicating map computations at $r$ computing nodes to yield multicasting opportunities such that $r$ nodes are served simultaneously in the Shuffle phase. However, in general, the state-of-the-art CDC scheme is mainly designed only for homogeneous networks, where the computing nodes are assumed to have the same storage, computation and communication capabilities. In this work, we explore two novel approaches of heterogeneous CDC design. First, we study CDC schemes which operate on multiple, collaborating homogeneous computing networks. Second, we allow heterogeneous function assignment in the CDC design, where nodes are assigned a varying number of reduce functions. Finally, we propose an expandable heterogeneous CDC scheme where $r-1$ nodes are served simultaneously in the Shuffle phase. In comparison to the state-of-the-art homogeneous CDC scheme with an equivalent computation load, we find our newly proposed heterogeneous CDC scheme has a smaller communication load in some cases.
Matrix Adaptation Evolution Strategy with Multi-Objective Optimization for Multimodal Optimization

The standard covariance matrix adaptation evolution strategy (CMA-ES) is highly effective at locating a single global optimum. However, it shows unsatisfactory performance for solving multimodal optimization problems (MMOPs). In this paper, an improved algorithm based on the MA-ES, which is called the matrix adaptation evolution strategy with multi-objective optimization algorithm, is proposed to solve multimodal optimization problems (MA-ESN-MO). Taking advantage of the multi-objective optimization in maintaining population diversity, MA-ESN-MO transforms an MMOP into a bi-objective optimization problem. The archive is employed to save better solutions for improving the convergence of the algorithm. Moreover, the peaks found by the algorithm can be maintained until the end of the run. Multiple subpopulations are used to explore and exploit in parallel to find multiple optimal solutions for the given problem. Experimental results on CEC2013 test problems show that the covariance matrix adaptation with Niching and the multi-objective optimization algorithm (CMA-NMO), CMA Niching with the Mahalanobis Metric and the multi-objective optimization algorithm (CMA-NMM-MO), and matrix adaptation evolution strategy Niching with the multi-objective optimization algorithm (MA-ESN-MO) have overall better performance compared with the covariance matrix adaptation evolution strategy (CMA-ES), matrix adaptation evolution strategy (MA-ES), CMA Niching (CMA-N), CMA-ES Niching with Mahalanobis Metric (CMA-NMM), and MA-ES-Niching (MA-ESN).
Learning where to look: Semantic-Guided Multi-Attention Localization for Zero-Shot Learning.

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominantly focus on learning the proper mapping function for visual-semantic embedding, while neglecting the effect of learning discriminative visual features. In this paper, we study the significance of the discriminative region localization. We propose a semantic-guided multi-attention localization model, which automatically discovers the most discriminative parts of objects for zero-shot learning without any human annotations. Our model jointly learns cooperative global and local features from the whole object as well as the detected parts to categorize objects based on semantic descriptions. Moreover, with the joint supervision of embedding softmax loss and class-center triplet loss, the model is encouraged to learn features with high inter-class dispersion and intra-class compactness. Through comprehensive experiments on three widely used zero-shot learning benchmarks, we show the efficacy of the multi-attention localization and our proposed approach improves the state-of-the-art results by a considerable margin.
Toward Scalable Superconducting Quantum Computer Implementation

In this paper, we briefly review the concept of superconducting quantum computers and discuss their hardware architecture. We also describe the necessary technologies for the development of a medium-scale quantum computer with more than tens of thousands of quantum bits.
An Efficient Multicore Algorithm for Minimal Length Addition Chains

A minimal length addition chain for a positive integer m is a finite sequence of positive integers such that (1) the first and last elements in the sequence are 1 and m, respectively, (2) any element greater than 1 in the sequence is the addition of two earlier elements (not necessarily distinct), and (3) the length of the sequence is minimal. Generating the minimal length addition chain for m is challenging due to the running time, which increases with the size of m and particularly with the number of 1s in the binary representation of m. In this paper, we introduce a new parallel algorithm to find the minimal length addition chain for m. The experimental studies on multicore systems show that the running time of the proposed algorithm is faster than the sequential algorithm. Moreover, the maximum speedup obtained by the proposed algorithm is 2.5 times the best known sequential algorithm.
Modelling the distribution of arsenic and mercury in urine using chemometric tools

Among the heavy metals and other chemicals that contaminate the immediate environment of all gold mining communities, Arsenic and Mercury compounds are some of the most prevalent, exposing residents to their health risks. This menace is the result of the mining activities, particularly, Artisanal and Small-scale gold mining, in these communities. Among the mining areas in Ghana, Artisanal and small-scale gold mining is a recent activity only in the Eastern Region, particularly in the Aduasena, Afosu, Intronang, Hweakwae, New Abirem, Mamano and Hamlets communities. The objective of this research is to investigate the levels of Arsenic and Mercury in the residents of these seven communities using spectroscopic analysis of their urine samples and to identify which gender and at what age does any resident in a typical Ghanaian mining community stand most endangered by the levels of these contaminants. Samples of first urine void of residents upon waking up in the morning were taken, digested with nitric acid and analysed for the concentrations of Arsenic and Mercury using the Perkin Elmer PinAAcle 900T Graphite Furnace AAS. The spectroscopic analysis of the urine samples showed that more than 20% of the residents had concentrations of Arsenic higher than the (normal) recommended level, with some reaching as high as 221 μg/L for Arsenic and 3.90 μg/L for mercury. Statistical analysis of the results showed that the levels of both Arsenic and Mercury seem to increase with increasing age, despite some extremely high concentrations for arsenic in the Aduasena youth between 31 and 40 years. This trend indicates that males were seen to be more at risk from these contaminants, with the youth between 11 and 20 years standing most endangered.
Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming

Robotic catching of thrown objects is one of the common robotic tasks, which is explored in a number of papers.This task includes subtask of tracking and forecasting the trajectory of the thrown object.Here we propose an algorithm for estimating future trajectory based on video signal from two cameras.Most of existing implementations use deterministic trajectory prediction and several are based on machine learning.We propose a combined forecasting algorithm where the deterministic motion model for each trajectory is generated via the genetic programming algorithm.Object trajectory is extracted from video sequence by the image processing algorithm, which include Canny edge detection, Random Sample Consensus circle recognition and stereo triangulation.After that rajectory is forecasted using proposed method.Numerical experiments with real trajectories of the thrown tennis ball show that the algorithm is able to forecast the trajectory accurately.
PPM: Privacy Protection Method for Outsourcing Data Entry

In China, manual approaches are still widely used in outsourcing data entry because of the poor recognition of OCR for Chinese. However, manual entries from images may cause leakage of users' privacy. In this paper, we propose a privacy protection method to reduce privacy disclosure. First, we use the EAST algorithm to segment whole images into sub-images. Second, we propose a privacy association separation algorithm to protect users' complete information which could be derived from privacy associations between attributes. Finally, we propose a sub-image allocation algorithm that varies for different relations between attributes, sub-sets, and data-entry clerks. We use AHP to model and optimize sub-contractor selection as well. The experimental results on OutsourcingData and U.S. Census Adult dataset show the advantages of our proposed method in terms of both the attribute privacy protection rate and the member privacy protection rate.
Some Techniques to Compute Multiplicative Inverses for Advanced Encryption Standard

This paper gives some techniques to compute the set of multiplicative inverses, which uses in the Advanced Encryption Standard (AES).&#x0D;
Cognitive Trend in Implementing the Third Mission of University

The concept of “cognitive trend” applied in the article summarizes the specific theoretical approaches and changes in the management practice of modern regional University. The effect of synergy in relation to University third mission, which is further specified as a valuecompetence synergy, is approved as a key goal that led to the emergence of a cognitive trend. The phenomenon of knowledge is proposed to be considered as the main motivational resource to achieve this goal, which makes it possible to associate the author’s position with a wide area of research in the modern science of education called “cognitive management”. The motivational resource of the knowledge phenomenon is substantiated in the context of the four-level model of University organizational culture. An analysis of the four-level model gives a basis to assert its exceptional relevance to the idea of the University and the objectives of its third mission. The axiological content of the University organizational culture is revealed through the reflexive analysis of the phenomena of scientific truth, scientific knowledge, and evidence as a criterion of science. The possibility of extrapolation of the value content of the University organizational culture in the regional environment is justified from the standpoint of G. Kelly’s cognitive psychology. As a source of the fundamental principles of the communicative space of the University, the authors propose the social and philosophical concepts of communication by M. M. Bakhtin, J. Habermas and K.-O. Apel.
PEDAGOGICAL ASSISTANCE TO STUDENTS OF THE UNIVERSITY’S TECHNICAL DIRECTIONS IN ENHANCING THE SUCCESS OF EDUCATIONAL ACTIVITIES

The task of assisting students in mastering the educational program is relevant, as it is shown by the analysis of psychological and pedagogical literature of this issue and the educational practice of higher school. The problem of increasing the success of educational activities becomes especially important for technical training areas. The diagnostics of the student’s difficulties was made to develop measures for pedagogical assistance to students in improving the success of educational activities. A questionnaire method was used, supplemented by the method of clarifying conversations with students and teachers. The study involved students of 1–3 grade of five full-time technical faculties and teachers of the Polytechnic Institute. Based on the results a system of measures for pedagogical assistance to students-bachelors of technical directions was developed, which is aimed at increasing the success of educational activities. The target component of this system assumes a focus on the formation of sustainable motives for educational and professional activities, self-organization and professional selfdevelopment. The content component implies the selection of the content of education in close connection with the partner enterprises and the level differentiation of the content of education, dynamic unity of traditional training and preparation of the advanced level. The procedural component is based on the technology of contextual and project training. The material and technical base of a dozen scientific and educational centers and large scientific laboratories equipped with unique equipment is being used. The portal “Electronic SUSU” is used for informational support, counseling of students and distance pedagogical support.
Smokestack: thwarting DOP attacks with runtime stack layout randomization

Memory corruption vulnerabilities in type-unsafe languages are often exploited to perform a control-flow hijacking attack, in which an attacker uses vulnerabilities to corrupt control data in the program to eventually gain control over the execution of the program. However, widespread adoption of control-flow attack defenses such as Control-flow Integrity(CFI) has led attackers to exploit memory errors to corrupt non-control data that can not be detected by these defenses. Non-control data attacks can be used to corrupt security critical data or leak sensitive information. Moreover, recent attacks such as data-oriented programming (DOP) have generalized non-control data attacks to achieve Turing-complete computation capabilities within the programmer-specified control-flow graph, leaving previously proposed control-flow protections unable to stop these attacks. In this paper, we present a stack-layout randomization scheme that can effectively thwart DOP attacks. Our approach, called Smokestack, provides each function invocation with a randomly permuted ordering of the local stack organization. In addition, we utilize true-random value sources combined with disclosure-resistant pseudo-random number generation to ensure that an adversary cannot anticipate a function’s invocation permutation of automatic variables. Our evaluation on SPEC benchmarks and various real-world applications shows that Smokestack can stop DOP attacks with minimal overhead.
CREATING NARRATIVES AND REVISION CIRCLE: LITERACY DIMENSION LEARNING STRATEGIES USING DIDACTIC VALUES OF M.KASIM’S FUNNY SHORT STORIES

Creating narratives and revision circle are the collaboration of two learning strategies of literacy dimensions. In literacy learning, students can develop a narration as a skill in literacy, particularly basic literacy in form of reading and writing. The present research aims at describing 2 strategies of literacy dimension learning through the analysis of didactic values from funny short stories written by M. Kasim. The funny short stories provided a variety of specific didactic values. As one of the literature learning materials, the didactic values were in line with the national education goals. The present research employed qualitative approach with a descriptive method. Specifically, the data were analyzed by the application of literary work didactic study framework. The funny short stories to analyze entitled: 1) “Pendikar bukan Pendekar; 2) “Podato yang Gagal”; 3) “Ada Seorang Raja…”, and 4) “Kue yang Nakal”. The result denoted the funny short stories contained various didactic values which could be regarded as learning materials in literacy development. The didactic values consisted of well-mannered behavior, dignified personality, independence, creativity, being democratic, responsibility, self-control, intelligence, and skill. Those values are considered excellent to implement by the collaboration of creating narratives and revision circle.
Multi-feature based Foggy Image Classification

Classifying the density of foggy images is an important step in further processing for foggy images. In this paper, we propose a fog density estimation algorithm from the multifeature perspective. Based on the analysis of the characteristics of foggy images with different levels of density, three features of color, edge gradient and transmittance are adopted to construct the feature-pool. During the process of feature constructing, the type of color space is firstly determined via the linear discriminant analysis (LDA). Then the histograms of the three features are extracted to form the whole feature. Finally, classification is completed by training a support vector machine classifier. Experimental results show that the method can accurately classify the density of foggy images, which has certain theoretical and practical significance.
Combining Interactive Hierarchy Visualizations in a Web-based Application

In this paper we describe a web-based tool combining several hierarchy visualization techniques. Those run in a browser and support the communication of hierarchy data that is omnipresent in many application fields like biology, software engineering, sports, or in algorithmic approaches like hierarchical clustering. To this end we provide node-link diagrams, Pythagoras trees, circular, as well as 3D treemaps also called 3D step-trees to give several visual perspectives on the same data and to improve data exploration tasks. The visualizations are interactive and linked, while the tool is available online, making it easily accessible for people all around the world without installing extra software or relying on additional libraries and frameworks. Hierarchy datasets can be uploaded to a server and shared with others. The visualizations were primarily implemented using JavaScript, and more specifically, rendered using the D3.js library. We illustrate the usefulness of the interactive visualization by applying them to the NCBI taxonomy and the Influenza dataset.
VARIABLE-SEPARATION BASED ITERATIVE ENSEMBLE SMOOTHER FOR BAYESIAN INVERSE PROBLEMS IN ANOMALOUS DIFFUSION REACTION MODELS

The iterative ensemble smoother (IES) has been widely used to estimate parameters and states of dynamic models where the data are collected at all observation steps simultaneously. A large number of IES ensemble samples may be required in the estimation. This implies that we need to repeatedly compute the forward model corresponding to the ensemble samples. This leads to slow efficiency for large-scale and strongly nonlinear models. To accelerate the posterior inference in the estimation, a low rank approximation using a variable-separation (VS) method is presented to reduce the cost of computing the forward model. It will be efficient to construct a surrogate model based on the low rank approximation, which gives a separated representation of the solution for the stochastic partial differential equations (SPDEs). The separated representation is the product of deterministic basis functions and stochastic basis functions. For the anomalous diffusion reaction equations, the solution of the next moment depends on all of the previous moments, and this causes expensive computation for the Bayesian inverse problem. The presented VS can avoid this process through a few deterministic basis functions. The surrogate model can work well as the iteration moves on because the stochastic basis becomes more accurate when the uncertainty of random parameters decreases. To enhance the applicability in Bayesian inverse problems, we apply the VS-based IES method to complex structure patterns, which can be parameterized by discrete cosine transform (DCT). The post-processing technique based on a regularization method is employed after the iterations to improve the connectivity of the main features. In the paper, we focus on the time fractional diffusion reaction models in porous media and investigate their Bayesian inverse problems using the VS-based IES. A few numerical examples are presented to show the performance of the proposed IES method by taking account of structure inversion in permeability fields, parameters in permeability and reaction fields, and source functions.
Statistical Analysis of the Spatial and Functional Localization of Education Subsystems in Russian Regions

The paper considers the specifics of the spatial and functional localization of regional education subsystems in the Russian Federation. Such subsystems include pre-school education, school education, secondary vocational education, and higher education. A set of indicators is suggested that can be used to assess the development stage of these subsystems. Cluster analysis made it possible to group the country’s regions according to the parameters of the education system, and determine the major characteristics of these groups (virtual clusters). Development tendencies of the education subsystems in the regions of each virtual cluster from 2010 to 2016 were analysed to identify the strong and the weak points of each group.
Differential Fault Attacks on KLEIN

This paper proposes two Differential Fault Attacks on the lightweight block cipher KLEIN. Variant one targets the intermediate state of the cipher. Using at least five faulty ciphertexts, the attacker is able to determine the last round key. The second variant, which works only on KLEIN-64, injects a byte-fault in the key schedule and requires at least four faulty ciphertexts in order to determine the whole key. Furthermore, we demonstrate the efficiency of both attack methods by simulation.
Effective Marker and IMU Based Calibration for Head Movement Compensation of Wearable Gaze Tracking

In this paper, the effective marker and inertial measurement unit (IMU) based calibration technique is developed for head movement compensation of wearable gaze tracker. Firstly, the location of the pupil center is estimated, and the initial calibration process is done by the affine transform based gaze mapping. When the head of user is moved after the initial calibration is finished, the wearable gaze tracker reduces gaze tracking errors effectively by the proposed marker and IMU based head movement compensation. When the user’s head changes from the initial calibration position (i.e. 40cm) to the 60cm position, the proposed design reduces from 33% to 64% gaze tracking errors at the gaze tracking mode than the design by only the initial calibration without any head compensation scheme. The proposed technique can be applied to the wearable consumer devices, which include the user-friendly gaze tracking function.