Reviewerly/exHarmony huggingface dataset:
Cryptographic multilinear maps using pro-<i>p</i> groups

<p style='text-indent:20px;'>In [<xref ref-type="bibr" rid="b18">18</xref>], the authors show how, to any nilpotent group of class <inline-formula><tex-math id="M2">\begin{document}$ n $\end{document}</tex-math></inline-formula>, one can associate a non-interactive key exchange protocol between <inline-formula><tex-math id="M3">\begin{document}$ n+1 $\end{document}</tex-math></inline-formula> users. The <i>multilinear</i> commutator maps associated to nilpotent groups play a key role in this protocol. In the present paper, we explore some alternative platforms, such as pro-<inline-formula><tex-math id="M4">\begin{document}$ p $\end{document}</tex-math></inline-formula> groups.
PointCNN-Hand: 3D Hand Joints Estimate by PointCNN From Hand Point Cloud

This paper provides a novel method called "PointCNN-Hand" for 3D hand joints estimation based on PointCNN. To use the depth image effectively, we transfer the hand depth image into the 3D hand cloud point and implement end-to-end training by PointCNN-Hand for hand joint estimation. We then perform error analysis on MSRA, NYU, and ICVL datasets to compare with the state-of-the-art methods. The experiments show that the proposed method has desired results, and the model parameters are relatively smaller than those of other methods. To be specific, the parameters of the proposed PointCNN-Hand network are reduced to only 3 Mega Byte (MB) with Floating Point Operations (FLOPs) less than 232.05M.
Properties of an Algebraic Model of a Distributed Intelligent System with Fuzzy Rules

Algebraic methods represent an effective means of formal construction and research of models of information systems of a wide range, including intellectual. This statement fully applies to the well-known logical systems of the production type, which represent the theoretical basis of an important direction in the artificial intelligence discipline. This article discusses an algebraic structure that simulates production type distributed intelligent systems with fuzzy rules (FDLP-structure). A class of equations was introduced that serves as a theoretical basis for a new method of backward logical inference that reduces the number of requests to external sources of information (relevant FDLP-inference). Backward inference strategies are proposed using several relevance parameters. Another possible application of the apparatus of production-logical equations is the verification of intelligent system knowledge bases. The article formulates a methodology and a general algorithm for applying equations to test fuzzy distributed knowledge bases in terms of identifying contradictions and redundancy.
Method for Extracting Cases Relevant to Social Issues from Web Articles to Facilitate Public Debates

Recently, social issues that threaten sustainability have become increasingly prevalent at both domestic and global levels. To address these problems, citizens need to actively participate in public debates and work together to devise solutions. However they often lack sufficient background information about the problems needed to participate in debates. In this study, we introduce the mechanism we have developed for automatically collecting information related to social issues and solutions from Japanese web articles. This mechanism enables citizens to refer to cases from all over Japan. We propose a method for extracting cases related to social issues and solutions from Japanese web articles, and we designed a framework based on the Web Annotation Ontology to share metadata about social issues on the web as open data. Our proposed method extracts cases relevant to social issues and solutions from Japanese web articles by using our corpus as training data.
A General Method for Designing n-Qubits Entanglement Creating Circuits and its Application in Different 4-qubits Entanglement Creating Circuits

Abstract One of the most important aspects of quantum computation is quantum entanglement, it is an interesting feature of quantum physics, and has many important applications such as quantum teleportation, cryptography, quantum computation, and dense coding. It is an essential task in quantum computing to design circuits that create an entangled system, and many circuits were implied in the quantum computers that generate the desired entangled state of a system. In this paper, we suggest a general method to design different quantum circuits that produce an entangled system for any number of qubits in different manners. Also, we apply the suggested method in order to design different 4-qubits entanglement creating circuits. The results show that it is possible to design different entanglement creating circuits for any number of qubits by following certain method that will be discussed in the paper, and in the case of 4-qubits system we are going to look only at two circuits, the first creates a “GHZ” like state and the second creates a four-basis entanglement state.
Augmenting integrity and scalability in mobile payment applications using blockchain

The transactional information from the mobile wallets is offloaded from the mobile device to the mobile transaction server. The transaction involves various communication standards, confidential transaction information to ensure flawless transaction of data. There exist several encryption techniques to preserve confidentiality, hashing schemes to prove the integrity, signature schemes to prove the identity in the mobile transaction using mobile wallet applications. Even though mobile wallet possesses secure algorithms, the transactions are facing security issues such as double spending, lack of dispute redressal issue, lack of forward secrecy, lack of anonymity in the transaction and security. Therefore, Blockchain based Mobile transaction Scheme is proposed to solve the security issues including integrity, double spending and improve scalability. This paper presents a strategy which implements blockchain framework by using irreversible keys for mobile wallet applications. The proposed scheme proves to be secure against the security attacks and enhances integrity and scalability compared to the existing schemes.
Research on an Improved MB-LBP 3D Face Recognition Method

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.
A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks

Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS.
Enhancing Neural Architecture Search by Upgrading Weak Components

Neural Architecture Search (NAS) becomes increasingly popular due to the ability to liberate the researchers from the complicated model design. However, typical NAS methods search for high-quality networks at a substantial cost of time and resources. This work enhances the neural architecture search by upgrading the weak components of promising candidate networks. First, we define a search space stacked with repeated components, such as cells and blocks. Second, we propose an attention-wise component evaluation metric to score the components. Third, we exploit a novel search policy to discover weak and strong components in promising candidate networks. Lastly, we use strong components to replace weak components. Besides, to achieve higher accuracy with less time cost, we integrate two optimization schemes: knowledge transfer and self-ensemble. The knowledge transfer accelerates model training by integrating an attentive regularizer into the objective goal. And the self-ensemble method ensembles promising candidate networks as the teacher network to teach the student network. The experiment results demonstrate our method can achieve state-of-the-art results(97.48% accuracy on CIFAR-10 and 84.38% accuracy on CIFAR-100).
Learning to Assist Agents by Observing Them

The ability of an AI agent to assist other agents, such as humans, is an important and challenging goal, which requires the assisting agent to reason about the behavior and infer the goals of the assisted agent. Training such an ability by using reinforcement learning usually requires large amounts of online training, which is difficult and costly. On the other hand, offline data about the behavior of the assisted agent might be available, but is non-trivial to take advantage of by methods such as offline reinforcement learning. We introduce methods where the capability to create a representation of the behavior is first pre-trained with offline data, after which only a small amount of interaction data is needed to learn an assisting policy. We test the setting in a gridworld where the helper agent has the capability to manipulate the environment of the assisted artificial agents, and introduce three different scenarios where the assistance considerably improves the performance of the assisted agents.
Multimodal Topic Detection in Social Networks with Graph Fusion

Social networks have become a popular way for Internet users to express their thoughts and exchange real-time information. The increasing number of topic-oriented resources in social networks has drawn more and more attention, leading to the development of topic detection. Topic detection of pure texts originates from text mining and document clustering, aiming to automatically identify topics from massive data in an unsupervised manner. With the development of mobile Internet, user-generated content in social networks usually contains multimodal data, such as images, videos, etc. Multimodal topic detection poses a new challenge of fusing and aligning heterogeneous features from different modalities, which has received limited attention in existing research studies. To address this problem, we adopt a Graph Fusion Network (GFN) based encoder and a multilayer perceptron (MLP) decoder to hierarchically fuse information from images and texts. The proposed method regards multimodal features as vertices and models the interactions between modalities with edges layer by layer. Therefore, the fused representations contain rich semantic information and explicit multimodal dynamics, which are beneficial to improve the performance of multimodal topic detection. Experimental results on the real-world multimodal topic detection dataset demonstrate that our model performs favorably against all the baseline methods.
An Empirical Analysis on The Evaluation of English Teachers' Educational Competence in MOOC Environment

In this paper, the user views contained in the course reviews are used as the mining object, and the user comments are extracted automatically from the massive user reviews, which is used to construct a set of teaching comprehensive evaluation index system combined with the existing online teaching theory or MOOC evaluation theory. The weight of each evaluation index is obtained by AHP and a set of scientific and fair index weight reflecting the views of experts and students can be acquired, based on the scoring data of experts and students. Through such methods, a course can be scored objectively. Finally, by the analysis of specific cases, it can not only provide some valuable reference for the improvement of English MOOC courses, but also provide examples for evaluating other MOOC courses, so that the evaluation index system in this paper can be applied to practice more conveniently.
An Algebra of Reversible Quantum Computing

We extend the algebra of reversible computation to support quantum computing. Since the algebra is based on true concurrency, it is reversible for quantum computing and it has a sound and complete theory.
Argument-based generation and explanation of recommendations

In the recommender systems literature, it has been shown that, in addition to improving system effectiveness, explaining recommendations may increase user satisfaction, trust, persuasion and loyalty. In general, explanations focus on the filtering algorithms or the users and items involved in the generation of recommendations. However, on certain domains that are rich on user-generated textual content, it would be valuable to provide justifications of recommendations according to arguments that are explicit, underlying or related with the data used by the systems, e.g., the reasons for customers’ opinions in reviews of e-commerce sites, and the requests and claims in citizens’ proposals and debates of e-participation platforms. In this context, there is a need and challenging task to automatically extract and exploit the arguments given for and against evaluated items. We thus advocate to focus not only on user preferences and item features, but also on associated arguments. In other words, we propose to not only consider what is said about items, but also why it is said. Hence, arguments would not only be part of the recommendation explanations, but could also be used by the recommendation algorithms themselves. To this end, in this thesis, we propose to use argument mining techniques and tools that allow retrieving and relating argumentative information from textual content, and investigate recommendation methods that exploit that information before, during and after their filtering processes.
Perancangan Sistem Informasi Penjualan Berbasis Website pada Coffee Singa

At Coffee Singa, recording and processing data on goods, quantities and prices of goods, as well as sales transaction data is still done using handwriting. The difficulty of recording and estimating the number of types of goods, the number of goods, and also the amount of the price, resulted in the data obtained being inaccurate. To improve the accuracy of the data, it is necessary to develop a computerized sales information system. The Web-based Sales Information System application at Coffee Singa is made using the Php programming language, Adobe XD software as the main interface maker and MySQL as the database. This program allows users to enter data, edit data, delete data, search data, filter data and print data to paper media. The data entered is in the form of data on goods, data on types of goods and sales of goods. While the output produced by this information system is in the form of data reports about goods and sales of goods with a Thermal printer to print sales transactions directly. This sales application can be a solution that can be used to make it easier for sellers to process data on sales of goods, and make reports on goods data, reports on purchases and sales of goods, selling goods becomes easier, faster and more efficient. The method used for system development uses the Waterfall method.
Noise Space Optimization for GANs

Traditional Generative Adversarial Networks (GANs) sample from a continuous stochastic noise distribution, and then treating that sample as a constant, optimize the parameters of a generator network on the average loss over the sample. In other words, the generator must take samples from a continuous input distribution, and on average, match a non-continuous target distribution (the training data). The result is that, as the generator transitions from one realistic image to another, some regions of the noise space correspond to relatively high quality images and other regions correspond to relatively low quality images. To avoid these relatively low quality areas, and allow the generator to optimize the shape of its noise distribution in a data-aware way, we present a novel sampling procedure: alternating (a) the traditional approach of optimizing the generator while holding the noise space sample constant, but then also (b) moving points in the noise space to improve the loss, using the direction of the gradient, while holding the generator parameters constant. We demonstrate that this procedure, which we call noise space optimization, improves the overall quality of samples obtained from an identical model without it on a wide array of canonical datasets and training paradigms.
Cellblockistry in serous effusion cytology- A systematic review with recent concepts

Serous effusion cytology being a minimally invasive, readily accessible and inexpensive diagnostic procedure. Nevertheless, the accuracy of SEC could vary widely due to the multitude factors including the level of experience of the cytologists Conventional smear has its own limitations with varied efficacy and hence warranting ancillary studies. Cell block has emerged as a paramount and robust platform for sample processing techniques in cytology. Research studies have proved that the efficiency of cytological diagnosis increases by significant margin of 15-20 percent when it is done in conjunction with cell block techniques especially in cases of exudative fluids in picking up crucial cases and based on this, we intended with a novel aim to analyse the accuracy of Serous effusion cytology by combining conventional smear and cell block technique with an attempt to assess the effectiveness of the cell block by our indigenous Modified Bouin’s method. Cell block method prepared by our indigenous Modified Bouin’s preparation with formalin fixative proved to show high quality significance and hence it could be followed in routine practice across laboratories. Cell block technique is quantitatively superior both standalone as well as in conjunction with conventional smear by improving the effective diagnosis of SEC. Diligent use of cell block technique eliminates the suspicious of malignant category on CS and thereby giving more definite diagnosis and thus it is mandated that combined approach of CB in conjunction with CS should be practiced as binary protocol which proved to have obvious influence on patient management.
Conceptual Approach to Express Tacit Knowledge by Human–Machine Interactions

The paper analyzes the problem of expressing tacit knowledge by participants in social processes interacting through ICT. This unsolved problem prevents organizing the human–machine interaction, which with due completeness and efficiency would take into account the vast social experience of people. The research question is how we can extract individual tacit knowledge and so formalize it? Answered this question, we could process information with ICT more meaningfully especially when humans need to team working. To solve the problem of expressing tacit knowledge by participants in social processes, we propose that any human within ICT would express his tacit knowledge by means of natural language. In this way, we preserve its essential role in social communication. However, the tacit knowledge should assume not the traditional form of a text stream, but structural view obtained by using special visual linking mechanism (VLM) associating natural language utterances. The paper examines the foundations of the introduction and structure of the VLM. How a person can apply the mechanism is explained through an example of the expression of tacit knowledge, functioning as part of human common ideas.
Local partition subdivision algorithm for approximate nearest neighbor query

This paper solves the problem of approximate nearest neighbor queries on high-dimensional large data sets. The two most representative methods to solve the approximate nearest neighbor query problem on high-dimensional and large-scale data sets are based on inverted multi-index and based on inverted index. The query algorithm based on the inverted multi-index requires frequent access operations to the memory, which causes more waste of resources than the query operation based on the distance order. Traditional query algorithms based on inverted indexes need to divide the entire feature space into a large number of regions to ensure the accuracy of query results when solving the problem of high-dimensional large data sets, so a large amount of space resources are required. Based on the inverted index, a query algorithm is proposed. Through the subdivision process of Voronoi Cells that are initially divided into part of the original data set, the purpose is to significantly improve the high-dimensional data set without dividing a large number of regions. On the query performance, and avoid frequent access operations to the random access memory. Theoretical analysis and experimental results show that the proposed method not only effectively improves the query processing efficiency, but also ensures the accuracy of the query.
GPU Prices and Cryptocurrency Returns

We look at the association between the price of a cryptocurrency and the secondary market prices of the hardware used to mine it. We find the prices of the most efficient Graphical Processing Units (GPUs) for Ethereum mining are significantly positively correlated with the daily price returns to that cryptocurrency.
Real time sensing method of grounding resistance state in distribution network based on time series clustering algorithm

In order to ensure the operation safety of distribution network grounding resistance, it is necessary to detect and modify the node resistance fault value in time. A real-time sensing method of distribution network grounding resistance state based on time series clustering algorithm is proposed. Firstly, combining with the principle of feature acquisition, the admittance value of distribution network is collected and regulated. After removing the data interference information, the voltage sag value of distribution network grounding resistance is modified to ensure the real-time perception effect of distribution network grounding resistance state, realize the accurate collection of difference data, and complete the real-time perception of distribution network grounding resistance state. Finally, the experiment shows that the real-time sensing method based on time series clustering algorithm has higher accuracy in the practical application process and fully meets the research requirements.