Reviewerly/exHarmony huggingface dataset:
Symbolic uniform sampling with XOR circuits

Uniform sampling is an important method in statistics and has various applications in model counting, system verification, algorithm design, among others. Symbolic sampling in a Boolean space is a recently proposed technique that combines sampling and symbolic representation for effective Boolean reasoning. Under the framework of symbolic sampling, we propose a method to construct compact XOR circuits achieving uniform sampling in a given Boolean space. The method is further extended to biased sampling within a focused subspace of interest. Experimental results show the effectiveness of compact sampling circuit generation and its potential to facilitate Boolean reasoning.
Quantum codes from (1+βu)-constacyclic codes over F<sub>p</sub> <sup>m</sup>+uF<sub>p</sub> <sup>m</sup>

In this paper quantum codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m are constructed from self-orthogonal (1 + βu)-constacyclic codes of length N = np <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over R = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m , u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0, where p is an odd prime, (n, p) = 1, and β is a non-zero element of Fpm. To obtain such quantum codes, the algebraic structure of (1 + βu)-constacyclic codes of length N over R is studied. The structure of their duals is also determined. From the algebraic structures of the (1+βu)-constacyclic codes and their duals over R, self-orthogonal codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m are obtained as Gray images of self-orthogonal codes over R. Then applying the CalderbankShor-Steane (CSS) construction to these self-orthogonal codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> m, the parameters of the corresponding quantum codes are determined. An example is given to illustrate the results.
RETRACTED: To Reduce Error Rate and Improve Performance of Classification Algorithm

Abstract In this paper, an improved method is introduced to reduce the error rate of the standard SD in the context of a two-class classification problem. The learning procedure of the improved method consists of two stages. Initially a shorter learning periodic carried out to identify an important space where all the misclassified samples are located. And the discriminative optimal criterion is computationally intractable as it involves probabilities that are not known a priori. And we have presented an algorithmic framework for feature selection based on nonparametric Bayes error minimization and our proposed framework offers sound interpretations over the existing approaches and also provides principled building blocks for establishing new algorithms. For example when weighted features are used as the search strategy as the framework reveals that the Relief algorithm greedily attempts to minimize Bayes error estimated by classification estimator. The new interpretation of Relief insightfully explains the secret behind the heuristically margin. the lower the error rate of the classifier. Consequently, the proposed improved method by its capability of achieving higher classification accuracy.
Research on Job Scheduling Algorithms Based on Cloud Computing

With the rapid development of digital technology, from the application of traditional databases and scientific computing to the emerging cloud computing services, the analysis and processing of massive data has become the focus of society. Providing low-cost, scalable, and configurable shared cloud services to users on cloud service platforms is a new hotspot for the development of major cloud service providers. Job scheduling plays an important role in improving the overall system performance of cloud service capabilities. Simple job scheduling strategies (such as Fair and FIFO scheduling) do not consider job size and may degrade performance when jobs of different sizes arrive. This paper proposes the MQWAG (Multi-queue Load-Sensitive Greedy Scheduling Algorithm) job scheduling algorithm to reorder multi-queue jobs so that short jobs are executed preferentially in multiple queues. In our experiments, our algorithm shortened the average job completion time by about 26% compared with other algorithms.
Graph-Based Heuristic Search for Module Selection Procedure in Neural Module Network

Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules’ structures, and its modularized architecture enables it to solve logical problems more reasonably. However, because of the non-differentiable procedure of module selection, NMN is hard to be trained end-to-end. To overcome this problem, existing work either included ground-truth program into training data or applied reinforcement learning to explore the program. However, both of these methods still have weaknesses. In consideration of this, we proposed a new learning framework for NMN. Graph-based Heuristic Search is the algorithm we proposed to discover the optimal program through a heuristic search on the data structure named Program Graph. Our experiments on FigureQA and CLEVR dataset show that our methods can realize the training of NMN without ground-truth programs and achieve superior program exploring efficiency compared to existing reinforcement learning methods. (The code of this work is available at https://github.com/evan-ak/gbhs)
An Analytical Discretization Approach to Continuous-time System for Kalman Filter

This paper presents an analytical discretization approach for continuous-time systems. By using the matrix multiplying expression of the state transition matrix and the matrix quadratic form theory, the general discretized model of the system state equation in the time domain is derived. The design of the parameter N is used to ensure the accuracy and robustness of the algorithm. Compared with the traditional methods in deducing the discretization formulas, the proposed method has the advantages of generality and easiness. This method is propitious to the integrated realization of the Kalman filter process for the continuous-time system. Simulation results verify the validity and feasibility of the proposed method.
Multiple Facial Expressions Synthesis Driven by Editable Line Maps

Facial expression is an important facial semantics on visual aspect. The facial expressions synthesis has a wide range of applications in human-computer interaction and virtual reality. In recent years, image synthesis base on generative adversarial networks(GANs) is developing rapidly. In the image-to-image translation work, we propose a new facial expression generation method base on the idea of conditional GANs and realize the optimization of the generated results. The main work of this paper includes: Editable facial lines map is utilized as a constraint, combining with neutral face images as inputs of generator, so that a variety of facial expression images can be generated by editing the constraints. Correntropy loss of feature matching is added, which is used to measure the intermediate representation between the real images and the generated images by improving the adversarial loss. Consequently, the generated facial expressions can be more realistic. Base on the ideas above, the proposed method needs only one generator to generate different realistic facial images with various expressions.
WITHDRAWN: Distractor filtering by context aware models in MCQs

Assessment of knowledge in the usage of affix based words is very much essential in the context of learning any language. Designing such questions focusing on the morphological aspects in the automated way is a challenging one. Especially, generating the distractors w.r.t. MCQs automatically concerned with affix words in Tamil Language is focused in this paper. In this study, we mainly focused on distractor filtering through context aware language models by two approaches. First one, list is filtered through masked word prediction from pre-trained BERT model. Next, filtering is formulated as sentence classification which implicitly checks the sentence completion in terms of morphemes. Classification model is fine-tuned from pre-trained multilingual model. Experimental results show that masked language model outperforms the classification model due to low dataset used in fine-tuning process. Compared to trigram filtering approach context aware language model optimizes reliability in affix based distractor generation.
Discrete Logarithmic Improvement for ElGamal Cryptosystem Using Matrix Concepts

The ElGamal cryptosystem is an asymmetric cryptosystem that works on abelian groups of integers. This study examines the improvement of the discrete logarithm of the matrix in the ElGamal cryptosystem. This system improvement involves using a square matrix as the private key. This research modifies the key pair generation algorithm, encryption, and description so that the proposed system can work properly. The results show that this system can work in a non-abelian group, which is claimed to be more resistant to quantum algorithms attacks. Furthermore, the private matrix key will make it more difficult for hackers to attempt a brute force attack. The proposed ElGamal cryptosystem also works well and is relatively safe at relatively small prime numbers. The strength of the proposed ElGamal cryptosystem lies in selecting prime numbers and the square matrix size. The larger the matrix size, the more likely it is to brute force attacks so that hackers will find it more difficult to carry out attacks.
Candidate Set Formation Policy for Mining Pools

Cryptocurrencies like Bitcoin use the blockchain technology to record transactions in a distributed and secure way. Miners are distributed entities responsible for validating the transactions in the network and producing and verifying new blocks of confirmed transactions. In addition, they are responsible for keeping the protocol’s integrity, protecting it against the double-spending problem. Miners are rewarded when they add new blocks to the blockchain. The reward consists of fresh coins created after adding a new block as well as the fees collected from the transactions inside the added block. The amount of new coins generated with new blocks is diminishing by the protocol over time. As such, the significance of collected fees is increasing for the miners.A recent trend in large mining pools is to allow miners to select transactions in the block they aim to mine. Allowing miners to select transactions increases transparency via discretization and also helps to avoid conflict of interest with the mining pool managers. Assuming that forming blocks is in miners’ hands, miners should have a strategy to maintain transactions inside the block in a way to maximize their collected fees. The mining process is a random process that is "progress free". That is, a miner can update the transactions inside the block without any impact on its chance of succeeding in adding the block. Given that transactions with higher fees might arrive at any time in an online manner, it is profitable for a miner to "refresh" its block during the mining process. In this paper, we study the impact of refreshing blocks via an experimental analysis on real-world data from Bitcoin on a controlled environment that is carefully tuned to match the real world. Our results indicate that refreshing blocks is essential for increasing miners’ collected fees, and overlooking it will have a significant impact on miners’ revenues.
Evaluating a Cybersecurity Training Program for Non-Computing Major Undergraduate ROTC Students

There is a rapidly growing demand for individuals in cybersecurity and a deficit of persons able to fill those roles. To help meet this need, students not majoring in computing can be utilized to fulfill this demand by exposing them to data mining, cybersecurity practices, and application of these concepts in the field. This paper presents findings from a twenty-one-week program in which minority undergraduate college students, all members of the Reserve Officer Training Corps (ROTC), were taught computer programming, natural language processing, data visualization, and computer vision fundamentals. Midshipmen and cadets used their newly gained knowledge, teamwork, planning, and communication skills to develop a threat detection prototype using publicly available social media data. Results from pre and post python assessments and post-program interviews that recorded participant attitudes and self-efficacy are reported to highlight the program's effectiveness.
The system of development of the methodological-mathematical competence within future mathematics teachers to be (theoretical-methodological aspect)

The strategic guidelines in determining the theoretical and methodological principles of the system for the development of the methodological and mathematical competence of future Mathematics teachers is the implementation of the personality-oriented paradigm of education; creation of conditions for subject-subject interaction of participants in the pedagogical process; compliance with the provisions of the systemic, personality-active, integration-technological and competence approaches, the optimal combination of traditions and trends in the development of methods of mathematical education within the modeling of the content, forms and methods of organizing of the educational process. The main principles are highlighted – feasibility, integration, functional completeness, continuity, professional and pedagogical orientation, on which the pedagogical conditions for ensuring the effectiveness of the development of methodological and mathematical competence of a future teacher of mathematics are based. The use of the listed theoretical foundations in the professional training of future mathematics teachers has confirmed the effectiveness of the developed model.
Dense Pixel-Wise Micro-motion Estimation of Object Surface by Using Low Dimensional Embedding of Laser Speckle Pattern

This paper proposes a method of estimating micro-motion of an object at each pixel that is too small to detect under a common setup of camera and illumination. The method introduces an active-lighting approach to make the motion visually detectable. The approach is based on speckle pattern, which is produced by the mutual interference of laser light on object’s surface and continuously changes its appearance according to the out-of-plane motion of the surface. In addition, speckle pattern becomes uncorrelated with large motion. To compensate such micro- and large motion, the method estimates the motion parameters up to scale at each pixel by nonlinear embedding of the speckle pattern into low-dimensional space. The out-of-plane motion is calculated by making the motion parameters spatially consistent across the image. In the experiments, the proposed method is compared with other measuring devices to prove the effectiveness of the method.
Self-concept as a factor in the formation of professional identity of future psychologists

The article deals with the problem of forming the professional identity of future psychologists, a study of the self-concept as a factor in the formation of the professional identity of future psychologists was conducted.
Information Systems, Big Data and Knowledge Landscapes

This chapter describes technical approaches that aim to address the challenges involved in finding accurate and up-to-date information. We focus on two challenges: the challenge of finding all relevant information and the challenge of determining the reliability of this information. The chapter examines the potential contributions of current technical solutions for navigating available knowledge. The first challenge, the challenge of finding relevant information, can be addressed by natural language processing (NLP) technologies that process text automatically and extract information from them at a highly detailed level. This includes the topic of the text, what is being claimed about this topic, by whom and based on what evidence. We describe the system architecture of tools providing this information and report on the accuracy of the technologies involved according to the latest state of the art. The second challenge, the challenge of determining the transparency of the information found, can be addressed by representing the automatically extracted information using the Grounded Representation and Source Perspective (GRaSP) framework (Fokkens et al. Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities Associated with RANLP 2017, pp. 19–25, 2017). The chapter offers, therefore, some important practical steps that can be taken in navigating the knowledge landscape more assuredly by deploying the NLP approach. We illustrate how the technologies and representation format work and their current performance through the analysis of the online vaccination debate.
Probability logic: A model-theoretic perspective

Abstract This paper provides some model-theoretic analysis for probability (modal) logic ($PL$). It is known that this logic does not enjoy the compactness property. However, by passing into the sublogic of $PL$, namely basic probability logic ($BPL$), it is shown that this logic satisfies the compactness property. Furthermore, by drawing some special attention to some essential model-theoretic properties of $PL$, a version of Lindström characterization theorem is investigated. In fact, it is verified that probability logic has the maximal expressive power among those abstract logics extending $PL$ and satisfying both the filtration and disjoint unions properties. Finally, by alternating the semantics to the finitely additive probability models ($\mathcal{F}\mathcal{P}\mathcal{M}$) and introducing positive sublogic of $PL$ including $BPL$, it is proved that this sublogic possesses the compactness property with respect to $\mathcal{F}\mathcal{P}\mathcal{M}$.
Conceptual Design of the Artificial Intelligence based Tactical Command Decision Support System using the Functional Analysis Method

The research of the AI-based command decision support system was insufficient both quantitatively and qualitatively.In particular, in Korea, there was no research on concrete concept design at the current concept research level.This paper proposed the conceptual design of a tactical echelon command decision support system based on artificial intelligence(AI) according to the current army's doctrine of the operation process.The suggested conceptual design clarified the problem and proposed an appropriate process for design, and applied the function analysis method among rational techniques that enable conceptual design systematically.
Logic Expression and Structure Simplification for SFEP of Sensor System

The system consisting of numerous sensors can be abstracted as a network structure with topological properties. Therefore, the safety and reliability of sensor system can be studied based on the abstract topology. In the topology, the nodes represent the sensor and the lines represent the signals transmitted by these sensors. Aiming at the abstract topology structure of sensor system, the author proposes to use the system fault evolution process (SFEP) to describe the fault evolution process of sensor system, and uses the Space Fault Network (SFN) to describe and analysis. To make the SFN describe the SFEP with the uncertainty, diversity and evolution characteristics, the paper puts forward the SFEP logical expression and SFN structure simplification method. The safety of sensor system is studied on the system topology. Firstly, it introduces the research background and the basic theories involved. Using flexible logic (FL), ternary logic (TL), factor space (FS) and other theories, we have realized FL description of SFN, TL system and ternary division of SFEP, and structure simplification of SFN in SFN framework. SFN has the ability to describe the uncertainty, diversity and network structure of SFEP. It provides a theoretical basis for the intelligent research of SFEP, and also realizes the intelligent analysis of SFN. The proposed method can be used to represent and analyze the fault process of sensor system.
GPU Side-Channel Attacks are Everywhere: A Survey

GPU has been widely used in many fields ranging from multimedia to scientific applications as it outperforms CPUs owing to its massive parallel processing capability. While the CPU has received much attention regarding side-channel vulnerability on its microarchitecture, little attention is paid to GPU. In order to preserve security in GPU-based applications, we need to pay much attention on side-channel vulnerabilities in the GPU architecture. In this paper, we survey recently discovered GPU side-channel attacks targeting on memory bank, coalescing unit and internal caches. We also give comparison analysis on these attacks and present future research directions.
Unconditional Secure Topology-hiding Broadcast Via Shamir’s Secret Sharing

Secure Multi-Party Computation is a hot topic in recent years. However, though Secure Multi-Party Computation allows each participant to calculate without making their information known to other nodes, the underlying topology of their network is prone to exposure. Inspired by Tal Moran, Bo Mi et al. designed a broadcast protocol based on NTRUEcrypt, which realized topology-hiding but relying on a third party. In view of this shortcoming, we proposed a topology-hiding broadcast protocol based on Shamir's Secret Sharing scheme with self-adaptive paradigm. Security and performance analyses demonstrated that our scheme is unconditional secure and computationally light-weighted.
Remote sensing for geology-geophysics

Geophysical remote sensing is a novel discipline which strengthens geological application along with advance remote sensing methods and describes morpho-geological process. A collection of geo-scientific techniques is frequently used by field planner, analysis, researchers, scientist. This chapter follows the purport of introducing remote sensing techniques in geology. The emphasis is on mineral mapping, as it interfaces strongly with geology and remote sensing technique. The lists of topics covered are including mineral spectral character (hyperspectral) with special reference to iron ore and copper ore, mineral exploration technique through satellite data, mineral alteration mapping and lineament extraction methods. These approaches will reduce the field efforts for geologists who used conventional methods to explore field data. Furthermore, this chapter covers the various applications of multi and hyperspectral satellites data, such as Landsat, ASTER, and EO-1 Hyperion and their processing technique. The use of remote sensing techniques in geological application, by means of either spaceborne or airborne remote sensing sensor or ground-based sensor (Spectroradiometers) namely as ‘Geological remote sensing'.
Studies on backscattering noise in reflective coherent optical communication system

For the safety information transmission technology, here an improved high-security reflective coherent optical communication system is studied in this paper. The backscattering, mainly Rayleigh backscattering and stimulated Brillouin scattering, is analyzed to adversely affect the system performance. The results show that the sensitivity of the system can be as high as -36.3 dB m and the transmission distance is 83.2 km with a low incident power and a data rate of 10Gbps. In addition, under high incident power, the sensitivity of the system is -36.4 dB m and the transmission distance is 95.4 km. Therefore, we can reach the following conclusion that the reflective system can realize high-speed and long-distance communication. It can be applied to secure communication or some special fields.