Reviewerly/exHarmony huggingface dataset:
Practical Exchange for Unique Digital Goods

Smart contracts can be used for the fair exchange of digital goods. A smart contract can escrow the exchange where the receiver deposits the payment, and the sender claims it by providing the goods. In the case of misbehavior, the parties provide proof on whether the received goods match the pre-agreed description or not. In general, the description is assumed to be the hash of the goods, and it is publicly known. However, without trusting the description provided by the sender, this assumption is not plausible for the scenarios where the goods are uniquely created for a specific receiver. To overcome the trust issue, sampling-based exchange protocols have been introduced where the parties use a sample of the goods as the description. Yet, the existing sampling-based proposals suffer from high on- and off-chain computational and storage costs. In this paper, we present FairDEx: an efficient sampling- based protocol that is suitable for the exchange of unique goods. Our description protocol allows us to achieve low on- and off-chain costs, which are independent of the size of the goods. The off-chain part of the protocol only utilizes highly efficient algorithms, namely hashing and symmetric key encryption. To illustrate the feasibility of FairDEx, we evaluate a research prototype on the Ethereum test network. Our results show that the cost of running FairDEx is around 0.6M gas for reasonably large sample sets, which is 30% cheaper than the state-of-the-art Ethereum-based proposals.
Unweighted Bipartite Matching For Robust Vehicle Counting

Intelligent Transportation System (ITS) plays an essential role in smart cities. Through ITS, local authorities could handle enormous traffic flows with minimal effort and solve traffic-related problems such as traffic congestion or traffic regulation violating behaviours. In this work, we designed a system that has the ability to count vehicles moving in specific directions on the road. Such automated systems also have to deal with the diverse weather and instabilities in captured media, making current tracking algorithms become prone to errors. This problem is even more challenging in Vietnam and other developing countries, where traffic on the road is much more complex with the presence of small vehicles such as bicycles and motorbikes, thus tracking algorithms would be more likely to fail. Our proposed method for Track Joining was built on top of deepSORT, incorporating Taylor Expansion and Unweighted Bipartite Maximum Matching to predict missing movements or identify duplicated vehicle tracks, then attempt to merge them. In HCMC AI City Challenge 2020 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , our whole system outperforms other approaches by achieving the lowest overall RMSE score: an average of 1.39 fails per video segment on a benchmark dataset.
Low-power AES S-box design using dual-basis tower field extension method for cyber security applications

Abstract In cryptography, one among several investigated areas is the implementation of AES S-boxes. In this paper, a substitution-box is designed which follows combined data path using dual-basis tower field extension with Golod–Shafarevich theorem fed in immune genetic algorithm for optimization purpose for each and every block. The role of enhanced immune genetic algorithm is as follows: at first, chaotic system generates S-boxes population, these S-boxes which perform excellently are then optimized by a sequence of operators such as extraction of anti-agent and immune selection. Few criteria of S-boxes such as differential uniformity, nonlinear degree, and strict avalanche effect are analyzed. The obtained results are analyzed with CMOS 35 nm and 15 nm technologies to measure the performance of the proposed designs and was observed that the proposed one outperforms in power and area. The optimized S-box can be effectively applied for securing information. The proposed Golod–Shafarevich feeder Immune Genetic Algorithm S-box (GSIGA-Sbox) is compared with two baseline methods such as Reversed Genetic Algorithm S-box (RGA-Sbox) and Discrete Space Chaotic S-box (DSC-Sbox). As a result the proposed GSIGA-Sbox achieves encryption speed of 61 MHZ, decryption speed of 55 MHZ with 24% of power consumption for 35 nm CMOS technology and 57 MHZ encryption speed, 51 MHZ decryption speed with 28% of power consumption for 15 nm CMOS technology.
Composition of rainfall in the coastal zone of the Kaliningrad region of the Russian Federation (based on data from 2019)

Abstract The paper presents the results of a study conducted in 2019 in which 29 rainfall water samples were collected and analyzed. The concentration of insoluble particles in rainwater was determined, and analysis of the material composition was performed by scanning electron microscopy. The content of Al, Mn, Ni, Cu, Zn, Cr, Co, As, Sr, Cd, Mo, Ba, Be, V, and Pb was determined by inductively coupled plasma mass spectrometry on a quadrupole spectrometer Agilent7500a at the P.P. Shirshov Institute of Oceanology of RAS. The highest concentrations of insoluble particles (6.5–12.2 mg/L) with the domination of the anthropogenic components in the rainwater were revealed in the samples of the summer-autumn period, while the lowest ones (1.1–3.9 mg/L), with a predominance of biogenic and mineral components were detected in the spring period. A group of heavy metals (Pb, Cd, Zn, Cu, and Ni) of anthropogenic origin was identified by calculating the enrichment factor (EF).
New Systems Solution for Resolving Nonlinear Dynamics Based on Systems Thinking

This study describes a new solution for resolving nonlinear dynamics.Surprisingly, it has been resolved and completed by non-physicists on behalf of physicists in 2021.It is a revolutionary solution like the Copernican Theory, which is perfectly different from the existing chaos theory.In the past, nonlinear dynamics has been analyzed using logical solutions, such as chaos theory, based on logical thinking.However, it is not perfect systematic solution, hence; the new solution has been analyzed and resolved by systematic analytical tool in other sciences.Then, the result is more perfect and precise than the old chaos theory.Regrettably, most physicists do not welcome this advancement, because they have primitive solutions such as chaos theory.If the new solution is true, it is very disadvantageous to them like Galileo's heliocentric theory.Therefore, they do not welcome it and deny and reject it.Hence, they wish it to fail; moreover, they want to remain in safe zone.Unfortunately, they became outsiders because they have no ability to review new solutions.Unfortunately, we have no obligation to follow physicists.If so, non-physicists, bypassing physicists, must study independently nonlinear dynamics based on systems thinking, and have to share the findings other scientists.It means that the new solution would be replaced the chaos theory in traditional physics; moreover, it would be resolved many unsolved nonlinear dynamics in the future.
GPENs: Graph Data Learning With Graph Propagation-Embedding Networks

Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured data representation and learning tasks, such as graph semi-supervised learning, clustering, and low-dimensional embedding. In this article, we present graph propagation-embedding networks (GPENs), a new model for graph-structured data representation and learning problem. GPENs are mainly motivated by 1) revisiting of traditional graph propagation techniques for graph node context-aware feature representation and 2) recent studies on deeply graph embedding and neural network architecture. GPENs integrate both feature propagation on graph and low-dimensional embedding simultaneously into a unified network using a novel propagation-embedding architecture. GPENs have two main advantages. First, GPENs can be well-motivated and explained from feature propagation and deeply learning architecture. Second, the equilibrium representation of the propagation-embedding operation in GPENs has both exact and approximate formulations, both of which have simple closed-form solutions. This guarantees the compactivity and efficiency of GPENs. Third, GPENs can be naturally extended to multiple GPENs (M-GPENs) to address the data with multiple graph structures. Experiments on various semi-supervised learning tasks on several benchmark datasets demonstrate the effectiveness and benefits of the proposed GPENs and M-GPENs.
Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California

Geoscience and seismology have utilized the most advanced technologies and equipment to monitor seismic events globally from the past few decades. With the enormous amount of data, modern GPU-powered deep learning presents a promising approach to analyze data and discover patterns. In recent years, there are plenty of successful deep learning models for picking seismic waves. However, forecasting extreme earthquakes, which can cause disasters, is still an underdeveloped topic in history. Relevant research in spatiotemporal dynamics mining and forecasting has revealed some successful predictions, a crucial topic in many scientific research fields. Most studies of them have many successful applications of using deep neural networks. In Geology and Earth science studies, earthquake prediction is one of the world’s most challenging problems, about which cutting-edge deep learning technologies may help discover some valuable patterns. In this project, we propose a deep learning modeling approach, namely EQPred, to mine spatiotemporal patterns from data to nowcast extreme earthquakes by discovering visual dynamics in regional coarse-grained spatial grids over time. In this modeling approach, we use synthetic deep learning neural networks with domain knowledge in geoscience and seismology to exploit earthquake patterns for prediction using convolutional long short-term memory neural networks. Our experiments show a strong correlation between location prediction and magnitude prediction for earthquakes in Southern California. Ablation studies and visualization validate the effectiveness of the proposed modeling method.
Relational Computation of Sets of Relations

We present a technique for the relational computation of sets \(\mathcal {R}\) of relations. It is based on a specification of a relation R to belong to \(\mathcal {R}\) by means of an inclusion \(\mathfrak {s}\,\subseteq \,\mathfrak {t}\), where \(\mathfrak {s}\) and \(\mathfrak {t}\) are relation-algebraic expressions constructed from a vector model of R in a specific way. To get the inclusion, we apply properties of a mapping that transforms relations into their vectors models and, if necessary, point-wise reasoning. The desired computation of \(\mathcal {R}\) via a relation-algebraic expression \(\mathfrak {r}\) is then immediately obtained from \(\mathfrak {s}\,\subseteq \,\mathfrak {t}\) using a result of []. Compared with a direct development of \(\mathfrak {r}\) from a logical specification of R to belong to \(\mathcal {R}\), the proposed technique is much more simple. We demonstrate its use by some classes of specific relations and also show some applications.
Major types and occurrences of platinum-group minerals in the Jinchuan Ni-Cu-(PGE) sulfide deposit: Insights for PGE enrichment during hydrothermal alteration

金川铜镍硫化物矿床是我国最主要的铂族元素（PGE）资源产地，其矿石受热液蚀变作用影响明显，并产出多种铂族矿物（PGM）。岩浆演化和热液蚀变过程中PGE的迁移富集机制和PGM的成因，一直是研究PGE地球化学行为非常关注的问题。本文对金川铜镍硫化物矿床中PGM的研究发现，其主要类型包括含PGE的硫砷化物（硫砷铱矿）和砷化物（砷铂矿），Pd的铋化物、碲化物和硒化物，以及少量其他铂族矿物。其中，硫砷铱矿可包裹于各种贱金属硫化物（镍黄铁矿、磁黄铁矿和黄铜矿）中，表明硫砷铱矿可能结晶于早期的含As硫化物熔体，随后被包裹于硫化物熔体冷凝分异产生的单硫化物固溶体（MSS）和中间硫化物固溶体（ISS）中。硫化物熔体中的As可能主要通过地壳混染作用加入幔源岩浆。大量铋钯矿（PdBi）呈微细乳滴状包裹于黄铜矿中，为晚期ISS冷凝形成黄铜矿过程中出溶的产物。少量铋钯矿（PdBi<sub>2</sub>）呈不规则状充填于矿物裂隙，与次生磁铁矿脉紧密共生，并随矿石的蚀变程度增加，铋钯矿的化学成分由PdBi逐渐向PdBi<sub>2</sub>转变，表明这部分铋钯矿为后期热液蚀变产物。铋碲钯矿和钯的硒化物则主要产出于镍黄铁矿裂隙且与次生磁铁矿紧密共生，指示明显的热液成因。钯的硒化物的出现表明，岩浆期后酸性、高盐度、高氧逸度的富Cl<sup>-</sup>流体对金川铜镍硫化物矿床中Pd的迁移和富集起到了关键控制作用。;The Jinchuan Ni-Cu-(PGE) sulfide deposit hosts the major resource of platinum group elements (PGE) in China. Most Ni-Cu sulfide ores of the deposit have been altered by hydrothermal fluids and contain diverse types of platinum group minerals (PGM). The formation of PGM and enrichment of PGE are likely controlled by the partitioning behaviors of PGE during magmatic processes and late-stage hydrothermal overprints. In this study, main PGM types discovered in the ores include PGE-bearing arsenide (irarsite and sperrylite) and Pd-bearing bismuthide, telluride and selenide, as well as a few other types of PGM. Irarsite (IrAsS) is enclosed within pentlandite, pyrrhotite and chalcopyrite, indicating that irarsite may have crystallized earlier from the As-rich sulfide melt. During the fractionation of sulfide melts, the irarsite can be trapped in the initial monosulfide solid solution (MSS) or residual intermediate solid solution (ISS). Arsenic was likely incorporated into the mantle-derived mafic magmas by crustal contamination and then concentrated in sulfide melts. Most of the Pd bismuthide (PdBi) grains enclosed within chalcopyrite show obvious emulsion texture, indicating that they were exsolved from the ISS during subsolidus cooling. Some irregular Pd bismuthide (PdBi<sub>2</sub>) grains are associated with secondary magnetite veins along micro-fractures in the ores. The Pd bismuthides have chemical compositions varying from PdBi to PdBi<sub>2</sub> with increasing degrees of hydrothermal alteration, indicating that they were likely produced by late hydrothermal alteration. Michenerite (PdBiTe) and Pd selenides grains mainly occur in the fractures throughout pentlandite and are associated with secondary magnetite, indicating a hydrothermal origin. Abundant Pd selenides in the ores indicates that the fluids are of acidic, saline and highly oxidized, which may play a key role in the PGE enrichment of the altered ores in the Jinchuan Ni-Cu sulfide deposit.
Robust Security With Strong Authentication in Mobile Cloud Computing Based on Trefoil Congruity Framework

In the mobile cloud computing era, the sharing of secured large-scale data which have major challenges. From an existing quantum based security mechanism randomly chosen the photon detector which creates small length of qubits so it cannot provide much security in MCC also data storage in the cloud server doesn’t guarantees the lossless back up and data recovery as well attains more computation complex during secure access of stored data. Therefore to solve those issues a unique combination of the Trefoil Congruity framework is proposed which consist Quantum Key Fibo Privacy Approach (QKFPA) performing the quantum key generation for encrypt and decrypt the data with the aid of Fibonacci chain-slanting matrix. Based on that quantum key data is uploaded, then secured data should be stored, ultra-widely distributed data transfer mechanism does the scrambling with sorting the stored data by implementing novel HS-DRT technique that improves the lossless backup and recovery of data storage.
Ask me in your own words: paraphrasing for multitask question answering

Multitask learning has led to significant advances in Natural Language Processing, including the decaNLP benchmark where question answering is used to frame 10 natural language understanding tasks in a single model. In this work we show how models trained to solve decaNLP fail with simple paraphrasing of the question. We contribute a crowd-sourced corpus of paraphrased questions (PQ-decaNLP), annotated with paraphrase phenomena. This enables analysis of how transformations such as swapping the class labels and changing the sentence modality lead to a large performance degradation. Training both MQAN and the newer T5 model using PQ-decaNLP improves their robustness and for some tasks improves the performance on the original questions, demonstrating the benefits of a model which is more robust to paraphrasing. Additionally, we explore how paraphrasing knowledge is transferred between tasks, with the aim of exploiting the multitask property to improve the robustness of the models. We explore the addition of paraphrase detection and paraphrase generation tasks, and find that while both models are able to learn these new tasks, knowledge about paraphrasing does not transfer to other decaNLP tasks.
Research and Application of Blockchain Technology in the Distribution of Unbalanced Funds in the Spot Market

In the third power adjustment operation and trial settlement in May 2020, the spot pilot project in Shandong Province generated nearly 100 million yuan of “unbalanced funds” in just four days, which caused high attention in the “power transformation circle”. In October, Shandong Provincial Energy Bureau issued the Notice on Improving the Settlement Work of the Third Electric Power Spot Market Settlement Trial Operation in Shandong Province, announced the allocation results of unbalanced funds of the third power adjustment operation and trial settlement in May 2020, and the unbalanced funds of external electricity allocation accounted for 53.42%. This paper plans to carry out the following research contents: the first is to research the causes of the unbalanced funds, analyze the difficulties existing in the coordination between the “non-market units” and the spot market in Shandong Province, and put forward solutions. The second is to study the application requirements and technical route of block chain technology in the unbalanced capital allocation in the spot market, and build the unbalanced capital allocation algorithm model of a specific power generation enterprise, so as to provide technical endorsement for the unbalanced capital allocation of power generation enterprises. The third is to design a set of unbalanced fund allocation mechanism suitable for the spot market of Shandong Province, and put forward specific measures to promote and apply.
Provable-Security Analysis of Authenticated Encryption Based on Lesamnta-LW in the Ideal Cipher Model

Hirose, Kuwakado and Yoshida proposed a nonce-based authenticated encryption scheme Lae0 based on Lesamnta-LW in 2019. Lesamnta-LW is a block-cipher-based iterated hash function included in the ISO/IEC 29192-5 lightweight hash-function standard. They also showed that Lae0 satisfies both privacy and authenticity if the underlying block cipher is a pseudorandom permutation. Unfortunately, their result implies only about 64-bit security for instantiation with the dedicated block cipher of Lesamnta-LW. In this paper, we analyze the security of Lae0 in the ideal cipher model. Our result implies about 120-bit security for instantiation with the block cipher of Lesamnta-LW.
Using a Creative Approach to Teach Russian as a Foreign Language to International Students Majoring in “Music Education”

The study proves that the optimization of the educational process relies on the key characteristics of the creative approach taking into consideration the creative nature of the tasks assigned and the future professional activity of students. A set of exercises aimed at the formation and development of professional knowledge among international students majoring in “Music Education” is presented in the paper. The educational materials are presented step by step: from Elementary level to Certification level I (A1-B1). The tasks are getting more and more complicated with students’ acquiring new vocabulary and grammar. The nature of the tasks also undergoes changes: from receptive to productive. The students were offered to keep a thematic glossary “The Russian language and my profession” in two formats: in electronic and handwritten versions. The electronic format allows one to display photographs, save links to sources and, what seems to be important, work with a Russian keyboard. Handwritten format, in turn, involves improving writing skills. Based on the analysis of the students’ academic results, the influence of the activity approach on the productive skills and professionally oriented knowledge of international students is studied. The proposed approach allows to solve the urgent pedagogical problem of the formation and development of a student as an active subject of the educational process, capable of self-organization, self-improvement, and self-realization in the future profession.
Optimization in the construction of cardinal and symmetric wavelets on the line

We present an optimization approach to wavelet architecture that hinges on the Zak transform to formulate the construction as a minimization problem. The transform warrants parametrization of the quadrature mirror filter in terms of the possible integer sample values of the scaling function and the associated wavelet. The parameters are predicated to satisfy constraints derived from the conditions of regularity, compact support and orthonormality. This approach allows for the construction of nearly cardinal scaling functions when an objective function that measures deviation from cardinality is minimized. A similar objective function based on a measure of symmetry is also proposed to facilitate the construction of nearly symmetric scaling functions on the line.
Application of Artificial Intelligence System in Music Education

The modern music industry for a country for the development of cultural industries and emerging strategic culture industry development has the vital role, for the music industry of modernization, how best to release its creation vigor, build a comprehensive industrial system, further strengthen the integration with other industries development is the goal of the industry's future development needs to be clear. Such a development goal requires the integration of modern intelligent technology. The integration of artificial intelligence technology and modern information technology has opened a new portal for the development of modern music industry. Especially with the emergence of “online education”, “online class” and other teaching methods, the modern music education mode began to undergo great changes. In recent years, the development of music education industry has always been unable to get rid of the core word “intelligent”. The integrated application of artificial functional technology in the field of music education can better reshape music education. This paper discusses the integration of artificial intelligence and music education with practical cases.