Reviewerly/exHarmony huggingface dataset:
Classification of Moduli Sets for Residue Number System With Special Diagonal Functions

The paper presents algorithms for the generation of Residue Number System (RNS) triples with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SQ=2^{k}-1$ </tex-math></inline-formula> and quadruples with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SQ=2^{k}$ </tex-math></inline-formula> for some k. Triples and quadruples allow us to design efficient hardware implementations of non-modular operations in RNS such as division, sign detection, comparison of numbers, reverse conversion with using of a diagonal function from requiring division with the remainder by the diagonal module SQ. Division with a remainder in the general case is the most complex arithmetic operation in computer technology. However, the consideration of special cases can significantly simplify this operation and increase the efficiency of hardware implementation. We show that there are 5573 good RNS triples (2301 even and 2372 odd) with elements less than 10 000, as the values of SQ vary from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{5}-1$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{27}-1$ </tex-math></inline-formula> . In contrast, RNS quadruples with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SQ=2^{k}$ </tex-math></inline-formula> seem to be quite rare. Restricting our search to sums of the elements in a quadruple less than 4000 we find that exactly 31 such quadruples exist. Their values of SQ vary between 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sup> with always even exponent. We suggest the measure of RNS balance and find perfectly balanced RNS among triples according to this measure. We demonstrate the advantages of more balanced quadruples by means of hardware implementation.
Video scene parsing: An overview of deep learning methods and datasets

Video scene parsing (VSP) has become a key problem in the field of computer vision in recent years due to its wide range of applications in numerous domains (e.g., autonomous driving). With the renaissance of deep learning (DL) techniques, various of VSP methods under this framework have demonstrated promising performance. However, no thorough review has been provided to comprehensively summarize the advantages and disadvantages of these methods, their datasets, or the directions for development. To remedy this, we provide an overview of the different DL methods applied to VSP in various scientific and engineering areas. Firstly, we describe several indispensable preliminaries of this field, defining essential background concepts as well as fundamental terminologies and differentiating between VSP and other similar problems. Then, according to their principles, contributions and importance, recent advanced DL methods for VSP are meticulously classified and thoroughly analyzed. Thirdly, we elaborate on the most frequently-used datasets and describe common evaluation metrics for VSP. Besides, extensive of experimental results for the aforementioned methods are presented to demonstrate their advantages and disadvantages. This is followed by further comparisons and discussions on the main challenges faced by researchers. Finally, we sum up the paper by drawing conclusions on the state-of-the-art methods for VSP and highlights potential research orientations as well as promising future work for DL techniques applied to VSP.
Consistent change propagation within models

Developers change models with clear intentions-e.g., for refactoring, defects removal, or evolution. However, in doing so, developers are often unaware of the consequences of their changes. Changes to one part of a model may affect other parts of the same model and/or even other models, possibly created and maintained by other developers. The consequences are incomplete changes and with it inconsistencies within or across models. Extensive works exist on detecting and repairing inconsistencies. However, the literature tends to focus on inconsistencies as errors in need of repairs rather than on incomplete changes in need of further propagation. Many changes are non-trivial and require a series of coordinated model changes. As developers start changing the model, intermittent inconsistencies arise with other parts of the model that developers have not yet changed. These inconsistencies are cues for incomplete change propagation. Resolving these inconsistencies should be done in a manner that is consistent with the original changes. We speak of consistent change propagation. This paper leverages classical inconsistency repair mechanisms to explore the vast search space of change propagation. Our approach not only suggests changes to repair a given inconsistency but also changes to repair inconsistencies caused by the aforementioned repair. In doing so, our approach follows the developer's intent where subsequent changes may not contradict or backtrack earlier changes. We argue that consistent change propagation is essential for effective model-driven engineering. Our approach and its tool implementation were empirically assessed on 18 case studies from industry, academia, and GitHub to demonstrate its feasibility and scalability. A comparison with two versioned models shows that our approach identifies actual repair sequences that developers had chosen. Furthermore, an experiment involving 22 participants shows that our change propagation approach meets the workflow of how developers handle changes by always computing the sequence of repairs resulting from the change propagation.
Application Research of Crawler and Data Analysis Based on Python

Abstract Combined with the actual situation, this paper explores how to develop a crawler method based on the specific framework for the complete interface of steam manufacturers and stores, which should be able to automatically and efficiently crawl the data of specific targets, analyze the dynamic pages, and complete the data cleaning, downloading, saving and other operations, explore the methods of general data analysis, and Analyze the downloaded data, extract useful information from it, analyze and summarize the specific crawler method and data analysis method through practical application.
Security-Aware Workflow Allocation Strategy for IaaS Cloud Environment

In last few decades, cloud computing has been established itself as a globally accepted platform even in security services needed in various domains having capacity to attain better performance. Security is emerging as an essential issue in IaaS cloud environment. The performance by reducing the number of failure tasks and average failure probability on cloud resources has been carried out in the domain of research to generate efficient schedule for virtual machines (VMs) allocation. Yet, the optimal schedule generation to process security-aware applications stands for a big challenge. So, many researchers have proposed various security-based models for the purpose and leaving scope for more models to be proposed because none of them is proven optimum in all respect. In this paper, a security-aware workflow allocation (SAWA) strategy has been proposed for workflow applications of tasks to machines mapping on cloud system by incorporating security overheads with aim of reduced number of failure tasks and average failure probability. It considers the security demand of tasks in the workflow and negotiating cipher suite between the tasks and machine in terms of security demand and trust level. The proposed strategy has been evaluated with comparative performance analysis to find the suitable place for it in the literature and established it better than its considered peer in the study.
Determinants of police officers' support for the public-private partnerships (PPPs) in policing cyberspace

Purpose This exploratory research examined law enforcement officers' attitudes toward the public-private partnerships (PPPs) in policing cyberspace. Particularly, by investigating the predictors of police officers' support for the PPPs, the authors aimed to offer practical implications for maintaining order and responding to illegal activities in the virtual environment. Design/methodology/approach A convenience sample of South Korean police officers was surveyed using a paper-and-pencil questionnaire. Findings The findings showed that officer perceptions of the seriousness and the frequency of property cybercrimes, computer proficiency and awareness of a lack of training were positively associated with the support for the PPPs. In addition, years of experience was negatively related to the support for the PPPs. Originality/value Law enforcement officers' perceptions toward public-private cooperation in combatting cybercrime have never been examined. This research fills the gap by exploring the predictors of officer support for cross-sectoral partnerships within the framework of nodal governance security in the cultural context of South Korea.
Revenue-Optimal Auction For Resource Allocation in Wireless Virtualization: A Deep Learning Approach

Wireless virtualization has become a key concept in future cellular networks which can provide multiple virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation is a main challenging issue in wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare (i.e., the sum of all valuations of winning bidders) due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues (i.e., received payments from auction winners). However, the revenue-optimal auction problem is much more complex since the payment price is unknown before calculation. In this paper, we aim to design a revenue-optimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity, deep learning techniques are applied. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users’ bids as the input and the allocation rule and conditional payment rule for the users as the output. The proposed auction mechanism possesses several desirable properties, e.g., individual rationality, incentive compatibility and budget constraint. Finally, simulation results demonstrate the effectiveness of the proposed scheme. Comparing with second-price auction and optimization-based schemes, the proposed scheme can increase the revenue by 10 and 30 percent on average, for single MVNO and multi-MVNO cases, respectively.
Quantum coherence to interstellar distances

Quantum coherence could be sustained up to interstellar distances. It is shown that the photon mean free path in certain regions of the electromagnetic spectrum, such as within the radio or x-ray ranges, could allow sustaining of the quantum state of a photon up to galactic distances. Therefore processes involving quantum entanglement, such as quantum teleportation, could be realized over very long distances in the Milky Way or other galaxies. This is of fundamental interest and offers a new direction in the role of quantum mechanics. Some limited applications of this observation are discussed.
Visualization Method Corresponding to Regression Problems and Its Application to Deep Learning-Based Gaze Estimation Model

The human gaze contains substantial personal information and can be extensively employed in several applications if its relevant factors can be accurately measured. Further, several fields could be substantially innovated if the gaze could be analyzed using popular and familiar smart devices. Deep learning-based methods are robust, making them crucial for gaze estimation on smart devices. However, because internal functions in deep learning are black boxes, deep learning systems often make estimations for unclear reasons. In this paper, we propose a visualization method corresponding to a regression problem to solve the black box problem of the deep learning-based gaze estimation model. The proposed visualization method can clarify which region of an image contributes to deep learning-based gaze estimation. We visualized the gaze estimation model proposed by a research group at the Massachusetts Institute of Technology. The accuracy of the estimation was low, even when the facial features important for gaze estimation were recognized correctly. The effectiveness of the proposed method was further determined through quantitative evaluation using the area over the MoRF perturbation curve (AOPC).
APLIKASI PENDAFTARAN DAN PENERIMAAN DATA PERSYARATAN CALON ANGGOTA LEGISLATIF SEMENTARA SECARA ONLINE PADA PARTAI KEADILAN SEJAHTERA DI KABUPATEN BENGKALIS

Currently, the process of registering and receiving data on the requirements for legislative candidates to the Bengkalis Regency Prosperous Justice Party is still being carried out conventionally. So that each prospective member of the PKS party must submit directly the required data in the form of sheets of paper or documents. To solve this problem, it is proposed that the Application for Registration and Acceptance of Data Requirements at the Sejatera Justice Party in Bengkalis Regency uses the programming language PHP and MySQL as a database using the waterfall method which can be computerized so that it becomes faster and more accurate. This application has two parts, namely the admin of the political party, who works in the section on the requirement data for each candidate for legislative member of the PKS Party, and each candidate for the legislative member itself to input the required data. This application has a print feature of data that has been input by each prospective member, and also party data that can be printed by the PKS party, namely Model B, Model B1, Model B2, and Model B3 data , while the data that can be printed by each prospective member are Model.BB1, Model.BB2, which are implemented in pdf format. In addition, in this application, prospective members of the PKS party can view data information that has not been completely filled in
Quantum computing and simulation

Abstract Quantum computers and simulators can have an extraordinary impact on our society. Despite the extraordinary progress they have made in recent years, there are still great challenges to be met and new opportunities to be discovered.
Recognition of multifont English electronic prescribing based on convolution neural network algorithm

Abstract The printed character recognition is an efficient and automatic method for inputting information to a computer nowadays that is used to translate the printed or handwritten images into an editable and readable text file. This paper aims to recognize a multifont and multisize of the English language printed word for a smart pharmacy purpose. The recognition system has been based on a convolution neural network (CNN) approach where line, word, and character are separately corrected, and then each of the separated characters is fed into the CNN algorithm for recognition purposes. The OpenCV open-source library has been used for preprocessing, which can segment English characters accurately and efficiently, and for recognition, the Keras library with the backend of TensorFlow has been used. The training and testing data sets have been designed to include 23 different fonts with six different sizes. The CNN algorithm achieves the highest accuracy of 96.6% comparing to the other state-of-the-art machine learning methods. The higher classification accuracy of the CNN approach shows that this type of algorithm is ideal for the English language printed word recognition. The highest error rate after testing the system using English electronic prescribing written with all proposed font-types is 0.23% in Georgia font.
A Novel Approach of Data Clustering Using An Improved Particle Swarm Optimization Based K–Means Clustering Algorithm

In this paper, a Modified Particle Swarm Optimization (MfPSO) based K-Means algorithm is presented to cluster multidimensional data. Poor selection of cluster centers in K-Means at the initial stage may affect the clustering result and it may get stuck at local minima. To get rid of these problems, the proposed MfPSO is employed to generate the cluster centers for a dataset. The inertia weight of PSO algorithm plays a vital role to balance the global search and local search in PSO. In the proposed algorithm, the inertia weight has been modified to improve the convergence velocity and better global search capability. The MfPSO generates the cluster centers and those derived cluster centers are then applied as the initial cluster centers in the K-Means algorithm. It has been proved quantitatively that the proposed algorithm produces better result and the local minima problem has been resolved. The proposed algorithm has been compared extensively with the conventional K-Means algorithm and chaotic descending inertia weight based PSO (CDIW PSO) on four well known dataset. The superiority of the proposed algorithm is visually and quantitatively established on the basis of two standard cluster evaluation criteria, computational time, mean, standard deviation of fitness and two other statistical significance test, called ANOVA test and t-test, best fitness curves and convergence curves for different levels of clustering.
Proposal for the Global Boundary Stratotype Section and Point (GSSP) for the Priabonian Stage (Eocene) at the Alano section (Italy)

The base of the Priabonian Stage is one of two stage boundaries in the Paleogene that remains to be formalized.The Alano section (NE Italy) was elected by consensus as a suitable candidate for the base of the Priabonian during the Priabonian Working Group meeting held in Alano di Piave in June 2012.Further detailed research on the section is now followed by a formal proposal, which identifies the base of a prominent crystal tuff layer, the Tiziano bed, at meter 63.57 of the Alano section, as a suitable candidate for the Priabonian Stage.The choice of the Tiziano bed is appropriate from the historical point of view and several bio-magnetostratigraphic events are available to approximate this chronostratigraphic boundary and guarantee a high degree of correlatability over wide geographic areas.Events which approximate the base of the Priabonian Stage in the Alano section are the successive extinction of large acarininids and Morozovelloides (planktonic foraminifera), the Base of common and continuous Cribrocentrum erbae and the Top of Chiasmolithus grandis (nannofossils), as well as the Base of Subchron C17n.2n and the Base of Chron C17n (magnetostratigraphy).Cyclostratigraphic analysis of the Bartonian-Priabonian transition of the Alano section as well as radioisotopic data of the Tiziano tuff layer provide an absolute age respectively) of this bed and, consequently, of the base of the Priabonian Stage.
Neural Network Heuristics for Classical Planning: A Study of Hyperparameter Space

Neural networks (NN) have been shown to be powerful state-value predictors in several complex games. Can similar suc- cesses be achieved in classical planning? Towards a systematic ex- ploration of that question, we contribute a study of hyperparameter space in the most canonical setup: input = state, feed-forward NN, supervised learning, generalization only over initial state. We inves tigate a broad range of hyperparameters pertaining to NN design and training. We evaluate these techniques through their use as heuristic functions in Fast Downward. The results on IPC benchmarks show that highly competitive heuristics can be learned, yielding substan tially smaller search spaces than standard techniques on some do mains. But the heuristic functions are costly to evaluate, and the range of domains where useful heuristics are learned is limited. Our study provides the basis for further research improving on current weaknesses.
Fake Video Detection With Certainty-Based Attention Network

DeepFake synthesizes realistic fake videos that could be used maliciously such as manipulation and harassment. In order to prevent such malicious usages, detecting fake videos is immediately needed. In this paper, we propose a novel fake video detection method by adopting predictive uncertainty in detection. We devise the certainty-based attention network which guides to focus certainty-key frames in detecting fake videos. In addition, certainty-based attention is proposed for refining the features with consideration for frame-level certainty. Experiments are performed to validate the effectiveness of the proposed method by comparing the existing methods on Celeb-DF, the latest DeepFake dataset.
Implementation Status and Development Thinking on “Cloud National Examination” in China under the situation of “Online Anti-COVID-19 Epidemic”

At the beginning of 2020, China was first hit by the COVID-19 epidemic. In order to effectively prevent the spread of the virus, the Chinese people work online, teach online, study online and shop online from home, the whole country rapidly entered the era of “Cloud Anti-COVID-19 Epidemic”. With the passage of time, the Chinese relevant national examinations such as postgraduate second round examination, the senior high school and college entrance examination gradually approach. In response, some regions have launched the “Cloud National Examination” model. Based on this background, through the actual situation commentary and case proof of adaptive mock test of the “Cloud National Examination” that has been carried out in some areas and schools, this article analyzes, discusses, summarizes and deeply reflects the epidemic prevention and control, policy formulation, education care, scientific and technological progress, and social problems hidden behind the hot phenomenon of “Cloud National Examination”, so as to offer advice and suggestions for online education in such a special period. Also, to provide reference for the rapid deployment, preparation and implementation of “Cloud National Examination” by relevant education administrative departments, schools, candidates and their families, and supply the evaluation viewpoint and theoretical contribution for similar global problems and phenomena.