Reviewerly/exHarmony huggingface dataset:
Avoiding Risk of Disputes by Re-Engineering Telecommunication Services With Blockchain Technologies

Blockchain is a technology used to immutably and transparently store information that has gained wide popularity due to the use with cryptocurrency, but it is suitable for many other business scenarios. In this paper, the authors deal with carriers providing voice services by exchanging calls with each other. These companies need to transparently store call detail records (CDR) in order to avoid billing discrepancies which can lead to disputes and risk of interruption of services with heavy consequences from the legal point of view. In this paper, the authors present a solution to this problem by using hyperledger fabric to develop smart contracts, which are invoked to store information about each CDR generated. The proposed solution initially stores CDRs before inputting these to the blockchain network. The paper presents experiments with thorough testing on the blockchain network and also some performance improvements. Results show the effectiveness of avoiding disputes by guaranteeing that CDRs are exchanged effectively and immutably without room for ambiguities or misinterpretation.
Textual Analysis of News for Stock Market Prediction

Stock market prediction constitutes an important factor in business. There are a large number of different mathematical models for predicting stock price movements. One of the alternative approaches is application of methods based on Natural Language Processing (NLP).Though NLP tasks are getting popular, they remain complex and voluminous. In the digital age almost, all information has been transferred to digital records that is good achievement. That is a good achievement. But on the other side because of the ease in creating new information, information search in Internet becomes complicated. This problem becomes more relevant, and scientists continue to search ways to structure a huge amount of information.There are many methods of traditional representations of words based on statistics. But these methods don’t give representation about contexts and semantics of text document. In this paper, we will consider approaches that help to get semantics from news. To evaluate our methods, we will use them for predicting direction of S&P 500 Index. In other words, we will compare our approaches with a stock market prediction problem based on news.
Compounding in Old Italian

Abstract This paper provides a corpus-based analysis of compounding in Old Italian. The semantic and formal properties of compound words attested in Old Italian are described and discussed through the theoretical tools provided by Construction Morphology. The analysis confirms that compounding is exploited since the earliest attestations of the language. It reveals that Old Italian compounds are mostly right-headed endocentric or exocentric: particularly, endocentric [ADV-Y] Y , [A-N] N|A and exocentric [V-N] N are the most productive schemas. Moreover, this study highlights a significant influence of Latin on Italian compounding, whereby many Old Italian compounds are Latin loanwords and calques which served as a model for the creation of new native compounds.
Geochemical (REE and Trace elements) characteristics and tectonic setting discrimination of Permo-Carboniferous sandstone from Sikkim Lesser Himalaya, NE India

The geochemical (REE & traces elements) characteristics of Permo-Carboniferous sandstone has been taken into account to decipher tectonic setting and provenance of the Rangit Pebble Slate Formation of Sikkim Lesser Himalaya.The chondrite normalized REE pattern with Eu negative anomaly and a bivariate plot (Th/Co-La/Sc) clearly indicates that studied sediments were likely derived from upper crust felsic source.The average elemental ratio of traces elements La/Sc (∼ 3.85), Th/Sc (∼ 2.57), Cr/Th (∼ 6.64), Th/Co (∼ 2.52), La/Co (∼ 3.74), and Eu/Eu* (∼ 0.32) also shows close affinities with Upper Continental Crust.The trivariate plot (La-Th-Sc plot, Th-Sc-Zr/10 plot, & Th-Co-Zr/10 plot) and a bivariate plot (Ti/Zr-La/Sc) plotted on the field of passive tectonic region for the Rangit Pebble Slate Formation sandstone.A binary plot between the ratio of Th/Sc-Zr/Sc and Th/U-Th reflects the enrichment of zircon and weathering trend during sedimentary recycling.
Neural Blockmodeling for Multilayer Networks

Networks have a rich history of capturing interactions and are widely used in fields ranging from medicine to sociology to computer science. A network where links between nodes may exist on different relations - called a multilayer network - is a flexible and increasingly common type of network since it allows for describing more complex interactions between nodes. Modeling such networks is thus an important task as it allows machines to reason with them intelligently and perform tasks such a link prediction, node classification, and community detection. Two common approaches to modeling multilayer networks are blockmodeling and embedding models. In this paper, we propose a model which fuses these approaches in a neural architecture, allowing it to leverage their respective strengths. To do this, we follow the standard blockmodeling approach of assigning nodes to communities and extending community interactions to node interactions. Community assignment is inspired by embedding models such that each node has an embedding which generates its community assignment. We account for network multilayeredness by mixing a relation embedding with the community interactions vector which acts as a relational modifier on community interactions. We evaluate our model on the three aforementioned tasks using real-world datasets and compare against state-of-the-art models. We find that performance by state-of-the-art models is highly dataset dependent and that our model achieves comparable results. We conclude that blockmodels may be successfully fused with embedding models for network modeling using a neural architecture and that simply mixing a relation embedding with community interactions is a viable way of handling for network multilayeredness.
‘Phoenix men’

‘Phoenix man’ is an internet and mass media term popular in contemporary China. This chapter examines the rise and genealogy of this catchphrase in Chinese popular culture. The chapter presents textual analyses of several related television series and novels: Life (1982), New Age of Marriage (2006–2007), Double-Sided Adhesive Tape (2007) and In the Name of the People (2017). Through this analysis and a survey of the ‘phoenix man’ discourse on the internet, the chapter explores the urban–rural division, class inequality and gender issues embodied in the term.
Fast Reinforcement Learning with Incremental Gaussian Mixture Models

This work presents a novel algorithm that integrates a data-efficient function approximator with reinforcement learning in continuous state spaces. An online and incremental algorithm capable of learning from a single pass through data, called Incremental Gaussian Mixture Network (IGMN), was employed as a sample-efficient function approximator for the joint state and Q-values space, all in a single model, resulting in a concise and data-efficient algorithm, i.e., a reinforcement learning algorithm that learns from very few interactions with the environment. Results are analyzed to explain the properties of the obtained algorithm, and it is observed that the use of the IGMN function approximator brings some important advantages to reinforcement learning in relation to conventional neural networks trained by gradient descent methods.
q-Divergence Regularization of Bezdek-Type Fuzzy Clustering for Categorical Multivariate Data

In this paper, the q-divergence-regularized Bezdek-type fuzzy clustering approach is proposed for categorical multivariate data. Because the approach proposed here reduces to the conventional methods via appropriate control of the fuzzification parameters, it is considered as a generalization. Further, numerical experiments were conducted to show that the proposed method outperformed the conventional method in terms of clustering accuracy.
An Obstacle recognition algorithm Based on Modified SSD

In order to solve the problems of the traditional target detection algorithm in the process of obstacle recognition, such as slow speed, low precision and easy to be affected by complex background. This paper proposes an obstacle recognition method based on improved SSD (Single Shot MultiBox Detector). Firstly, the network structure is optimized. Soft-NMS(softening non-maximum suppression) is used to obtain anchor boxes, providing better initial values for bounding box's prediction. SPP(spatial pyramid pooling) method is used to keep the image scale unchanged and reduce the overfitting. At the same time, batch processing normalization BatchNorm is used to randomly initialize the training model to obtain a stable and predictable gradient, which can stabilize the detector with random initialization training while maintaining good performance independent of the network architecture. Finally, four kinds of obstacles collected in the natural background were identified. The test results showed that the recognition speed of obstacles was the fastest based on the improved SSD, and the average recognition accuracy mAP could reach 79.95%, which was 5.35% higher than that of ssd-300. The SSD method proposed in this paper can effectively detect and recognize obstacles.
TO THE QUESTION OF THE PROCESS OF FORMING THE WRITTEN COMPETENCE OF A JUNIOR SCHOOL STUDENT

The article presents theoretical generalizations of the process of forming the written competence of a junior schoolchild by means of calligraphic skills. The relevance of the issue raised in the article is associated with identifying the problems of deteriorating handwriting of a primary school student and the task facing an elementary school teacher, increasing the percentage of children who have a calligraphically accurate writing skill. The primary rules for mastering a calligraphic skill are the rules of landing, the position of the notebook, and then the rules related to the writing process, such as the tilt and position of letters on a line. The formation of written competence through the calligraphic skill of a junior schoolchild is the most important task of an elementary school teacher. This article is devoted to the issue of studying the written competence of younger students through the selection of diagnostics to determine the level of formation of calligraphic skills in younger students. When identifying the level of formation of calligraphic skills among first graders, the article describes the criteria and indicators of assessment: graphic literacy; calligraphic clarity and stability; connectivity; writing speed. On the basis of the selected criteria and indicators of the formation of calligraphic skills among first graders, the characteristics of the levels are also presented: high, medium and low. In accordance with the criteria and indicators of the formation of calligraphic skills, diagnostic tasks were selected for the first graders.
On the Cardinality of Relator Sets of Groups F/ ∏ [Ni, Nj]

The present paper generalizes the results of our paper “On the finite presentability of the group F/[M, N]” to an arbitrary family of normal subgroups {Ni | i ∈ I} in a free group F. We obtain conditions for finite presentability of the quotient group F/ ∏ [Ni, Nj]. In both papers, the proof of the main result is based on the properties of verbal wreath products introduced by Alfred Lvovich Shmelkin.
Research on Relevant Problems concerning the Integration of Information Technology and Physical Education

The present era witness’s rapid development of information technology (IT). So far, IT has been widely applied to school teaching. As a crucial part of the curriculum system, naturally, the physical education (PE) class is greatly affected by IT. At the information age, the effective integration of IT and PE teaching is an inevitable step to promote the quality development of PE teaching. This paper, in the first place, elaborates the important value and development status of the integration of IT and PE teaching, and then emphatically analyzes the primary methods and crucial guarantee for the integration.
Operation of a Discrete Communication System

Abstract In this chapter, based on definitions of signal synthesizers and analysers, a general and generic scheme of a discrete communication system is developed in order to deduce practical systems as its special cases. The synthesizer is transferred into a discrete transmitter, and the analyser is used as a correlation receiver followed by an optimum detector. The system structure is presented in terms of mathematical operators and supported by exact mathematical expressions based on the theory of discrete-time stochastic processes. The likelihood function is derived, and the maximum likelihood rule is applied to specify the decision process and construct the optimum detector. A multilevel system and a quadrature phase-shift keying system are deduced as special cases, and the bit error probability expression is derived. For the sake of continuity and completeness in presenting communication systems theory, a generic digital communication system is developed and related to its discrete counterpart.
A Study on the Reconstruction of Chinese Folk Dance Culture’s Education Path in Colleges Based on Information Technology

Purpose: This paper is To explore the best education path of Chinese folk dance in the wave of the information and technology revolution. This thesis adopts the methods of literature review and information research to study the promote methods of Chinese folk dance culture in colleges. The study finds that conventional teaching in physical education classes cannot achieve the efficient and rapid promotion of Chinese folk dance, and cultural education is even harder to achieve. Suggestions: it is suggested to break through the offline and online teaching barriers; improve the abilities of relevant teachers, and enrich teaching methods with the help of network live broadcast platforms such as Bilibili, Dingding, and Tencent Conference, etc. It is also suggested to deepen the cultural connotation of Chinese folk dance, enrich and authorize the contents in the text-book, and build a channel for students to promote this culture. With the help of network platforms and several dimensions can we reconstruct the cultural education path of Chinese folk dance, and realize the unique national cultural inheritance and educational value of Chinese folk dance.
Adaptive Texture Distillation Network for Image Hybrid Super-Resolution

To save the transmission bandwidth of high-resolution (HR) images, we can send down-sampled low-resolution (LR) images and reconstruct them using super-resolution (SR) technology at the receiving end. However, image down-sampling by a large factor results in the loss of many spatial details. Instead, we use a combination of spatial down-sampling by a small factor and gray-level quantization to obtain the low hybrid-resolution images. Although the small down-sampling factor makes images retain more spatial details and real textures, the gray-level quantization introduces fake textures. Obviously, the real textures should be enhanced, and the fake textures should be eliminated. To address this issue, we propose a lightweight Adaptive Texture Distillation Network (ATDN) for image hybrid super-resolution. Our model uses the texture enhancement block (TEB) and the texture smoothing block (TSB) to handle real and fake textures in different ways. Considering that the mixing proportions of two kinds of textures in low hybrid-resolution images vary with regions, we specifically use a cascaded weight branch to adaptively adjust the weights of real and fake textures. Experiments reveal that our model can effectively deal with the mixing problem of real and fake textures, and our method can achieve superior performance to other lightweight methods.
The Effect of False Positives: Why Fuzzy Message Detection Leads to Fuzzy Privacy Guarantees?

Fuzzy Message Detection (FMD) is a recent cryptographic primitive invented by Beck et al. (CCS’21) where an untrusted server performs coarse message filtering for its clients in a recipient-anonymous way. In FMD—besides the true positive messages—the clients download from the server their cover messages determined by their false-positive detection rates. What is more, within FMD, the server cannot distinguish between genuine and cover traffic. In this paper, we formally analyze the privacy guarantees of FMD from three different angles. First, we analyze three privacy provisions offered by FMD: recipient unlinkability, relationship anonymity, and temporal detection ambiguity. Second, we perform a differential privacy analysis and coin a relaxed definition to capture the privacy guarantees FMD yields. Finally, we simulate FMD on real-world communication data. Our theoretical and empirical results assist FMD users in adequately selecting their false-positive detection rates for various applications with given privacy requirements.
Designing a Self-Payment Cashier For Bakeries Using YOLO V4

This paper introduces an automation project for bakery stores, using the YOLOv4 object detector. A self-checkout cashier was developed where the client puts a tray with the products underneath a webcam, and the system returns the total price of the purchase. This project focuses on the software and aims to build a low-cost, high accuracy, fast response, and easy to use application. To achieve this goal, it was developed some process and protocols, a user interface, a segmentation algorithm, and a server to integrate it all. The menu defined for this research is composed by 10 items - coke, guaraná, coxinha, banana, apple, ice tea, choux cream, eclair, cheese bread, and bread - and the mAP obtained for our test set is 91.52% and 96.25%, when all requisites were respected. Despite the high precision obtained, two important problems were found during the validation process: the system has some problems recognizing products when they are too close and when an unknown object is put on the trail. Throughout the text, it is provided how this project was developed, the system requirements, and operating protocols.
RA: A Static Analysis Tool for Analyzing Re-Entrancy Attacks in Ethereum Smart Contracts

Ethereum smart contracts are programs that are deployed and executed in a consensus-based blockchain managed by a peer-to-peer network. Several re-entrancy attacks that aim to steal Ether, the cryptocurrency used in Ethereum, stored in deployed smart contracts have been found in the recent years. A countermeasure to such attacks is based on dynamic analysis that executes the smart contracts themselves, but it requires the spending of Ether and knowledge of attack patterns for analysis in advance. In this paper, we present a static analysis tool named RA (Re-entrancy Analyzer), a combination of symbolic execution and equivalence checking by a satisfiability modulo theories solver to analyze vulnerability of smart contracts to re-entrancy attacks. In contrast to existing tools, RA supports analysis of inter-contract behaviors by using only the Ethereum Virtual Machine bytecodes of target smart contracts, i.e., even without prior knowledge of attack patterns and without spending Ether. Furthermore, RA can verify existence of vulnerability to re-entrancy attacks without execution of smart contracts and it does not provide false positives and false negatives. We also present an implementation of RA to evaluate its performance in analyzing the vulnerability of deployed smart contracts to re-entrancy attacks and show that RA can precisely determine which smart contracts are vulnerable.
Research on Face Degraded Image Generation Algorithm for Practical Application Scenes

In general face super-resolution (FSR) networks, the degraded model usually adopts a simple bicubic down-sampling, without considering more complex degradation conditions and actual scenes. Although this can verify the basic effectiveness of the model, it makes the model lack of generalization. Therefore, in order to solve the problem of poor generalization ability, this paper constructs a Degraded Generative Adversarial Network (DGAN), which replaces the method of bicubic down-sampling to realize implicit modeling of degradation process, and generates degraded images in real scene. Combining DGAN and face super-resolution networks DIC/DICGAN, a new FSR network is trained to recognize the low- resolution (LR) face of real scene. The face recognition test under FSR reconstruction is carried out using SCFace dataset constructed by actual monitoring camera. It is proved that DGAN can effectively simulate the degradation conditions of actual scenes, and improve the generalization of face reconstruction model, so that the SR algorithm can better adapt to face recognition in actual scenes.
Area Optimized Hardware Architecture of Piccolo-80 Lightweight Block Cipher

Lightweight cryptography is a field of cryptography where implementation for resource constrained devices are in big demand. Piccolo is an ultra-lightweight block cipher having block size of 64-bit and key size of 80-bit and 128-bit. Compact implementations becomes more important when they can provide sufficient security without compromise in performance. In this paper an efficient architecture for Piccolo-80 cipher have been proposed. This cipher requires extreamely low area in hardware implementation, and hence it is useful in RFID applications. Piccolo with its proposed architecture has been implemented on various platforms of FPGA.The proposed work shows 112% improvement in terms of area and 17.65% better results in terms of efficiency. Different devices of Virtex and Spartan have been used to get the results.
Engineering experiment as an underlying tool for training research engineers

Abstract The paper analyzes the contribution of the engineering experiment to professional skills of research engineers. The discussion is based on the educational programs and methodology of the disciplines taught at the Engineering Institute of Kazan Federal University.
Reward Shaping with Dynamic Trajectory Aggregation

Reinforcement learning, which acquires a policy maximizing long-term rewards, has been actively studied. Unfortunately, this learning type is too slow and difficult to use in practical situations because the state-action space becomes huge in real environments. The essential factor for learning efficiency is rewards. Potential-based reward shaping is a basic method for enriching rewards. This method is required to define a specific real-value function called a “potential function” for every domain. It is often difficult to represent the potential function directly. SARSA-RS learns the potential function and acquires it. However, SARSA-RS can only be applied to the simple environment. The bottleneck of this method is the aggregation of states to make abstract states since it is almost impossible for designers to build an aggregation function for all states. We propose a trajectory aggregation that uses subgoal series. This method dynamically aggregates states in an episode during trial and error with only the subgoal series and subgoal identification function. It makes designer effort minimal and the application to environments with high-dimensional observations possible. We obtained subgoal series from participants for experiments. We conducted the experiments in three domains, four-rooms(discrete states and discrete actions), pinball(continuous and discrete), and picking(both continuous). We compared our method with a baseline reinforcement learning algorithm and other subgoal-based methods, including random subgoal and naive subgoal-based reward shaping. As a result, our reward shaping outperformed all other methods in learning efficiency.