Reviewerly/exHarmony huggingface dataset:
The publications on printing industry of the latter half of the 19th century

The author reviews the typographer textbooks published in St. Petersburg on the cusp of the 19 th –20 th centuries. She substantiates the need for integrated study of the problem of bibliographic references in textbooks and learning aids. The publications were examined through the method of detailed retrospective analysis of technical and learning literature in print trade. The list of publications reveals the character of gradual increasing book size and improving quality of the textbooks; the methods of production and design, as well as the content characteristics are specified. Not only the titles but also the authors’ names are introduced into scientific use. The study is based on observation, com-parison and measurement methods, as well as the proprietary methodology of content analysis. The author emphasizes that the textbooks demonstrated the experience of their authors as the qualified typographers and were intended for vocational schools. The requirements for apprentices are cited, e. g. age, literacy level, aptitude for learning. The need for professional library comprising textbooks and learning literature on book publishing is substantiated.
Winning strategies: the emergence of base 2 in the game of nim

Many players know that the secret of winning the game of nim (and other “impartial” combinatorial games) is to write the sizes of the game’s piles in base 2 and then add them together without carry. The proof of this well-known procedure (described below) is both straightforward and convincing. Nonetheless, the procedure still appears magical, as though a rabbit has been pulled out of a hat. Astute students (and frustrated professors) often ask why the winning strategy for such games involves base 2, and not some other base. After all, nothing about the game of nim itself – the game rules, the configuration of the tokens, etc. – provides any hints about the origin of base 2 in this setting. Minimal insight is offered by most published proofs, which themselves tend to either appear almost wizardly in nature (i.e. assume the base-2 method and show that it miraculously solves the problem) or employ combinatorial arguments that supply little abstract intuition (at least to the authors of this article).
Design of Optimization Algorithm for Configuration of Amateur Sports Training Equipment in Smart City Community

Community amateur sports training equipment is necessary to ensure the development of national fitness activities. It is found that the design concept of community public sports space in China is not perfect, the structural layout is not reasonable, the stock space is not optimized, and the resource allocation is not balanced. In particular, there is a lack of indoor fitness facilities and sports venues in the community, and the people's "fitness where to go" is still a difficult problem. In the smart city construction, the resource integration and optimization of community amateur sports training facilities have also been further developed. Considering various influencing factors, this paper establishes a location model of community amateur sports training facilities with the least total cost and convenience. Aiming at the practical optimization problem with high complexity, an improved adaptive weight multiobjective particle swarm optimization (PSO) model is proposed. These parameters in the algorithm were adjusted dynamically, which balanced the overall search ability and partial improvement ability of the PSO algorithm and completed the optimal scheduling of community amateur sports training equipment configuration while ensuring the optimal global solution. Experimental results show that the algorithm's efficiency and searching ability have been further promoted. It also has an excellent performance in solving the complex location problem of community amateur sports training facilities.
Abnormality Detection in Kidney Ultrasound Images by Various Classifiers with FPGA

Ultrasound scanning has been used as the preliminary diagnosis tool all over the world. The ultrasound data are being analyzed by tele-radiologists. It lacks online availability. The drawbacks of tele-radiology have been overcome by using computer-aided diagnosis. As an aid to this, the Random Forest Classifier has been used here for detecting kidney abnormalities. The initial pre-processing stage filters the speckle noise existing in the input kidney image. Then feature extraction has been performed. Image categorization as normal, cyst and stone has been done with Random Forest Classifier. Then the performance is evaluated by comparing it with K-nearest neighbor classifier and support vector machine. From experimentation, it is observed that the accuracy and F-measure values of Random Forest Classifier range high when compared with other classifiers due to the continuous split of the trees until accurate categorization is done. Simulation and Implementation have been done using Modelsim 6.4a and Xilinx Spartan-6 FPGA board.
Application of the Patron Module in Koha Software for Library User Database Management System

The use of information and technology has introduced modernism and automation to many aspects of library work and has eased the workload of library professionals. The management of library users and the providing of their effective library services are crucial components of the library system. When a new visitor first arrives at the library, he or she must first become a library member in order to use the library. Currently, the entire process of the library’s membership system is done using the patron module of Koha software. In the present study, all the features of patron module in Koha have been highlighted so that the library staff can perform patron management work quickly. As a result, this study is helpful for library and information professionals to improve their technical skills and expertise while dealing with library users.
Learning to Prune: General and Efficient Approximate Nearest Neighbor Search with Direction Navigating Graph

The approximate k-Nearest Neighbor (kNN) graph has been proved extremely useful in approximate nearest neighbor search (ANNS). However, under the high-dimensional and large-scale circumstance, the performance of nearest neighbor search is challenging due to its high costs of distance calculation. To solve the problem, most approaches focus on pruning the edges during the phase of graph construction. In our observation, searching a given query in an even well pruned approximate kNN graph, a large proportion of edges can be cut-off during the search procedure. In this paper we propose the concept of Direction Navigating Graph (DNG) to offer an online pruning strategy combined with a heuristic search algorithm. To build the DNG we develop a neural network that project queries and vertices into a low-dimensional direction vector space. With the DNG, we can apply online edge pruning with almost all those state- of-the-art graph-based approaches. In a number of experiments on public datasets we demonstrate that our approach outperforms those original methods by a significant margin. In addition, the approach has been integrated into Alibaba's (Alibaba Group) billion-scale search applications for its superior performance.
An Improved Anonymous Identifier

Privacy is an integral part of today’s digital transformation era and there is a paradigm shift toward anonymity in communication. This paper proposes an algorithm for assigning anonymous IDs to every member of a group of participants and provides an upper bound on one of the parameters of the algorithm. The algorithm terminates in one round.The proposed algorithm improves an earlier algorithm (AIDA) and mirrors its working. Simulation is used to show that AIDA may not terminate when the number of participants is large. We then discuss the software architecture and performance improvements offered by the proposed algorithm.
Misalignment in Semantic User Model Elicitation via Conversational Agents: A Case Study in Navigation Support for Visually Impaired People

Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.
Study of security problem in cloud computing with access control based on the blockchain

Classic cloud data sharing systems are based on a network architecture and large storage providers. However, these providers are responsible for transferring and storing the data. This type of cloud storage model has some weaknesses, such as data availability, centralized data storage, high operational cost and security issues. In this paper, we study the security problem related to the data stored in the cloud by focusing on the use of blockchain combined with the attribute-based encryption (ABE) algorithm. The studied system meets the security requirements of the cloud, such as confidentiality and non-forgery. In addition, the smart contract solves the problem of cloud storage, such as the return of erroneous results as in the traditional cloud server.
Chinese Word Segmentation with Many Rare Terms in Low-Resource Scenarios

In constructing the domain-specific knowledge graphs, we can use the texts accumulated in the domain as data sources for analysis. However, in many domains, there are many rare terms in the text that make the generic corpus inapplicable, but no domain-specific corpus is available. Using the existing Chinese word segmentation (CWS) corpus and methods, this type of texts cannot be effectively segmented. For such special texts without applicable corpus, this paper proposes a domain dictionary-based Chinese word segmentation method based on the BiLSTM-CNN-CRF method. We firstly manually label a part of the samples, then combine randomly selected words from the dictionary into the manually labeled sentences to generate pseudo-labeled data, and merge the two to get a composite training set. Then we preprocess the texts, replace the rare terms with non-segmentable strings to further improve the accuracy of word segmentation. The experimental results show that our approach has higher accuracy, recall and F1 score in the task of segmenting texts with many rare terms in low-resource scenarios. Our approach can be applied to the task of Chinese word segmentation in specific domains containing rare terms.
Criteria for Assessing Media Literacy and Media Competence

The concepts of “media literacy” and “media competence” are determined, scientifically substantiated and criteria and indicators for their evaluation are developed. Due to the fact that the media and communications are in constant transformation, the directions and models for the development of media education are identified.
The Filters for Websites by Organizing Distance Learning Processes

The paper provides a systematic analysis of the task to minimize the risks of aggressive influences in social networks and educational structures. The article analyz-es the goals and objectives of countering sites with un-desirable (low-quality) content on students or school-children when organizing independent work. The classes of aggressive sites and their impact are analyzed. Recog-nition of aggressive and “Trojan” content or resource for learning purposes is a multifaceted problem, often with uncertainties and “white” noise. This work proposes ap-proaches to counteract this noise. The analysis is valua-ble to develop measures and approaches to filtering ag-gressive sites. A model for evaluating the aggressive ca-pabilities of the site is also considered.
Ab Initio Gene Identification Using Apache Spark

Computer-assisted gene identification in deoxyribonucleic acid (DNA) sequences is a challenging task, which requires intelligent pattern recognition algorithms as well as significant computing power. Gene recognition has increased importance today, when medical treatment of a patient should be adapted to the inherited features of each individual; these features may be identified through the patients’ DNA. Gene recognition is also important for identify pathological organisms and their resistance to different treatments (e.g. antibiotics). In order to allow adaptive treatment of patients, gene identification must be fast, accessible and precise. In this paper we concentrate on formulating the problem of gene identification, on determining the most popular approaches and also on finding a parallelization procedure that can reduce the execution time of an identification algorithm.
An experimental study of group-by and aggregation on CPU-GPU processors

Abstract Hash-based group-by and aggregation is a fundamental operator in database systems. Modern discrete GPUs (graphics processing units) have been considered to accelerate the performance. However, the data transfer through the PCIe (peripheral component interconnect express) bus would reduce gains. On recent architectures, the GPU and the CPU (central processing unit) are built into the same chip which removes the data transmission and offers new performance opportunities. Yet there has been no systematic analysis of grouping and aggregation algorithms on such architectures. In this paper, we study the behaviors of various hash-based grouping and aggregation methods on coupled architectures to provide meaningful guidelines. We conduct an extensive experimental study and analysis on the single CPU, the coupled GPU, and both processors. Six dimensions are considered in analyzing the hashing methods carefully: (1) hashing scheme, (2) hash function, (3) data size, (4) group cardinality, (5) load factor, and (6) data distribution. Two additional dimensions are also explored: (7) shared and independent hash tables and (8) running on single processors and co-processing. We hope the results in our study could help database researchers to choose the right direction in terms of algorithm design and system optimization.
LambdaObjects

Existing cloud computing (or serverless) architectures provide convenient abstractions for application developers, but exhibit high latencies and do not support strong consistency guarantees. These limitations stem not only from the overheads due to virtualization, but also because storage and compute layers are disaggregated.
Development Countermeasures of College English Education Based on Deep Learning and Artificial Intelligence

College English education aims at cultivating students’ English application ability and promoting the development of students’ English communication level. However, at present, many college students still stay in English learning to cope with the examination. The cultivation of oral English and other comprehensive ability is little. Therefore, the development direction of college English education should be based on the actual needs of students in social work and life, and formulate a plan more suitable for contemporary development needs. In this context, to better provide countermeasures for the development of college English education, this paper provides a new English education model based on artificial intelligence (AI), and evaluates students’ comprehensive English ability through deep learning (DL). This paper not only constructs a college English teaching model and evaluation system through artificial intelligence methods but also evaluates the quality of English pronunciation through DBN. It also designs comparative experiments, and the consistency between manual evaluation and DBN-based evaluation is obtained. The results show that the English pronunciation evaluation system based on DBN has a high consistency with manual evaluation. Among them, in the speech rate evaluation, the adjacent consistency rate reaches 99.58%, showing that the evaluation model constructed in this paper is effective and verifying the feasibility of applying the artificial intelligence method to college English education.
Application analysis of digital fund prediction model based on neural network

Summary With the development of the economy, digital funds have developed rapidly, and traditional big data processing technologies have been unable to effectively solve the update of digital funds information. This research uses neural network to study the digital fund forecasting model based on big data technology and deep learning algorithm. First, on the basis of the feedforward neural network model, the network structure and network parameters are optimized to effectively improve the algorithm's ability to learn the characteristics of digital capital information. Through the first‐order approximation, the number of iterations in the model parameter update is effectively reduced, and the computational efficiency of the algorithm is improved. By improving the model network structure, increasing the number of neurons in the hidden layer, and improving the dynamic change learning ability of the algorithm, the dynamic characteristics of the incremental file information can be effectively obtained. Through the experimental simulation calculation, it can be seen that compared with the traditional algorithm, the algorithm proposed in this study has certain advantages in terms of convergence speed, convergence ability and solution error, and the algorithm has good stability and strong inheritance, and is suitable for digital capital forecasting aspects of research.
Blockchain-enabled decentralized privacy-preserving group purchasing for retail energy plans

Retail energy markets are increasingly consumer-oriented, thanks to a growing number of energy plans offered by a plethora of energy suppliers, retailers and intermediaries. To maximize the benefits of competitive retail energy markets, group purchasing is an emerging paradigm that aggregates consumers' purchasing power by coordinating switch decisions to specific energy providers for discounted energy plans. Traditionally, group purchasing is mediated by a trusted third-party, which suffers from the lack of privacy and transparency. In this paper, we introduce a novel paradigm of decentralized privacy-preserving group purchasing, empowered by privacy-preserving blockchain and secure multi-party computation, to enable users to form a coalition for coordinated switch decisions in a decentralized manner, without a trusted third-party. The coordinated switch decisions are determined by a competitive online algorithm, based on users' private consumption data and current energy plan tariffs. Remarkably, no private user consumption data will be revealed to others in the online decision-making process, which is carried out in a transparently verifiable manner to eliminate frauds from dishonest users and supports fair mutual compensations by sharing the switching costs to incentivize group purchasing. We implemented our decentralized group purchasing solution as a smart contract on Solidity-supported blockchain platform (e.g., Ethereum), and provide extensive empirical evaluation.
Geochemical Analogy Viscosity of Mid-Ocean Ridge Basalt as an Indicator for Determining the Location of Seafloor Hydrothermal Fields?

Many achievements have been made in the exploration of seafloor polymetallic hydrothermal vents, however, it needs to further study how to discovery more potential new hydrothermal or massive sulfide fields. In this study, we collected 9818 major-element geochemical data sets of basalt samples from the Mid-Atlantic Ridge (MAR, 78°N–53°S) and the seafloor topographic data for eleven known hydrothermal vents (e.g., TAG, Snake Pit, MAR 22°30′N, Lilliput). Based on the different spatial distances between basalts and hydrothermal vents, we classified all the basalts into three categories: 1) basalt near known vent (focal objects of the research, 29 samples), 2) basalt around known vent (observed objects, 155 samples), and 3) other basalt of the MAR (as background basalt). Meanwhile, we used major element components of basalts to calculate the analogy viscosity and analyzed the possible correlation between analogy viscosity and the spatial distribution of seafloor hydrothermal vents. We found that the analogy viscosity of basalt near known vent fall into a narrow medium value range (∼46.8–68.5 Pa s) contrasted with the range of background basalt values range (∼4.0–356.0 Pa·s). It infers that these basalts on the Mid-Atlantic Ridge with the medium values of analogy viscosity favor the formation of hydrothermal vents. However, due to the complexities of magmatic activity and fault structure, as well as the multi-stage water-rock reaction, whether the analogy viscosity of basalts can be used as an indicator for determining the location of seafloor hydrothermal field or not needs further verification. Hence, this paper provides a possible rule (the variation of analogy viscosity estimated by major element components in the basalts) for exploration the location of hydrothermal vents rather than a fully developed method.