Reviewerly/exHarmony huggingface dataset:
CREATING A CLUSTER IN A GRAPH DATABASE NEO4J

The digital transformation for all sectors of the national economy of Russia requires from designers of information systems a high speed of processing a huge number of requests for the provision and processing of various types of information, “the ability to quickly adapt to environmental changes and optimize their work “fleetingly”, adjusting to the client’s expectations” [Mamayeva 2018]. To meet that requirement, database clusters are widely used. Each cluster is a system of one master server and several slave servers, between which data replication is configured. The article proposes a technique for deploying and configuring a cluster of the Neo4j graph database, an example of correct data replication between server instances and replicas, and describes the principle of developing a client application for working with a cluster. The material of the work can be used in information systems built on the basis of the cloud storage technologies, which will ensure high performance, availability, elastic scalability and rapid deployment [Anisiforov, Il’in 2012]. Deployment of a Neo4j cluster is possible on modern platforms: AuraDB, Google Cloud Platform, Amazon EC2, Microsoft Azure and others.
English Online Learning System Based on Web

With the renewal and development of the Internet, online English learning based on the Internet has gradually become the mainstream teaching method. At present, the traditional form of teaching has been difficult to meet the needs of modern education, so more and more students begin to use the network environment platform, which is an advanced and universal learning method, to meet the needs of the development of modern education. In recent decades, countries all over the world have shown amazing strength in the tide of scientific and technological development. China’s relevant departments have also increased investment in education year by year, gradually narrowing the gap with developed countries. The reason why English online teaching is deeply loved by the public is that it overcomes multiple obstacles such as distance, time, capital and efficiency, and realizes a qualitative leap across platforms, fields and disciplines. English online network teaching subverts the rigid education model, helps students with lack of teaching resources in remote mountainous areas, and enables teachers and students to communicate in English online anytime and anywhere for common progress.
DLC: An Optimization Framework for Full-State Quantum Simulation

Quantum simulation on classical computers is one of the main approaches to evaluate quantum computation devices and develop quantum algorithms. Some quantum simulators have been proposed, mainly divided into two categories: full-state simulators and tensor network simulators. The former consumes a lot of memory to hold the quantum state vectors. Therefore, the time overheads cost by calculation are much lower than that cost by memory-accesses and communications. Traditional optimization techniques such as latency hiding are not suitable for quantum simulation, and high-performance devices like GPGPUs cannot be fully utilized. This paper proposes DLC (Data Locality and Communication) optimizer to perform data locality and data layout optimizations. Both optimizations are based on the identification of amplitudes that can be processed by a sequence of quantum gates. They not only increase the data locality on the GPU side, but also reduces the date communication overheads and the times of data exchanges. In addition, layout data dynamically can significantly reduce the memory space on the GPGPU side for data communication. We evaluate our scheme on a small-scale CPU + GPU cluster. Experimental results show that for quantum circuits having 30–34 qubits, the ratio of communication to calculation increases from 12 to 79%, and a performance improvement 1.25–7× is achieved. Theoretically, our optimizations will be more effective as the number of qubits increases.
Zebra Crossing Detection and Recognition Using Automatic Grab Cut Algorithm, Radon Transform, and Improved Mask R-Cnn

Zebra crossing is a vital traffic sign in our today’s traffic system to ensure pedestrian security and safety. This paper proposes a zebra-crossing detection and recognition algorithm by using automatic grab cut algorithm, radon transform, and improved mask region based convolutional neural networks (R-CNN). The contrast and sharpness of the zebra-crossing image is enhanced by using the contrast limited adaptive histogram equalization (CLAHE) method. Then region of interest (ROI) of zebra-crossing is extracted by applying the automatic grab cut algorithm. Uniform local binary pattern is utilized for extracting the features of this ROI. Finally, the extracted handcrafted features are concatenated with the deep features produced by improved Mask R-CNN for classification. Experimental results indicate that the presented method detects and recognizes the zebra crossing region effectively from different images with an accuracy of 99.33%. In addition, it shows better results than the other state-of-the-art methods in terms of accuracy.
A trial of cooperative aerial manipulation by caged drone formation

In this paper, we propose a method for transporting an object in a non-fixed state between the object and the aircraft or between the aircraft and each other using multiple caged unmanned aerial vehicles without a grasping mechanism. We also give an overview of the experiments using this method and their results.
Rebound Attacks on $$\textsf{SKINNY}$$ Hashing with Automatic Tools

In ToSC’20, a new approach combining Mix-Integer Linear Programming (MILP) tool and Constraint Programming (CP) tool to search for boomerang distinguishers is proposed and later used for rebound attack in ASIACRYPT’21 and CRYPTO’22. In this work, we extend these techniques to mount collision attacks on $$\textsf{SKINNY}$$ -128-256 MMO hashing mode in classical and quantum settings. The first results of 17-round (and 15-round) free-start collision attack on this variant of $$\textsf{SKINNY}$$ hashing mode are presented. Moreover, one more round of the inbound phase is covered leading to the best existing classical free-start collision attack of 19-round on the $$\textsf{SKINNY}$$ -128-384 MMO hashing.
Research on the Coupling Coordination Development of Urban Agglomeration and Industrial Cluster——Based on Wuhan Metropolitan Area Empirical Analysis

The coupled development of urban agglomerations and industrial clusters can promote the formation of an interactive network system between the industrial chain and the urban chain, and jointly promote the optimal allocation of resources and the optimization and upgrading of the industrial structure. In this paper, by constructing an evaluation index system for coupling coordination between urban agglomerations and industrial clusters, using the TOPSIS method and coupling coordination degree model, the comprehensive development level and coupling coordination degree of Wuhan metropolitan area and industrial clusters from 2016 to 2020 are measured. The research shows: During 2016-2020, the coupling degree of Wuhan metropolitan area has been significantly improved, reaching the coupling maturity stage; at the same time, the coupling coordination degree has also shown a significant upward trend, realizing the transformation from a disordered urban agglomeration to a coupled urban agglomeration , Which shows that the coupling degree between metropolitan area and industrial clusters has been significantly improved in the past five years.
A Study on Good Laboratory Practice in Hospitals with Reference to Specimen Collection and Handling for Microbiological Studies

Background Currently the non- clinical laboratory study of Good Laboratory Practice GLP is playing an important role in the laboratory studies of microbiological lab in Quality Control of Culture Media which must be reliability reproducibility and quality laboratory test. It provides the frame work of laboratories to Plan Perform Monitor Record amp Report their activities. Aim of the study To assess the existing Good Laboratory Practice which reflects the study of microbial studies about specimen collection and lab reported test in process. Methodology GLP is based on survey and cross - checking of some procedures through laboratory testing which has to be dealt. GLP was initially adopted by US-FDA-GLP and was accepted by OECD and without modifying the essence of US-FDA GLP was practiced globally. Results In this review we discuss the objective and need for study of microbial laboratory implementation study of GLP questionnaire survey by the different GLP guidelines amp this in the field of clinical research and academic research. Conclusion We strongly encourage that GLP is a vital criteria in educational and medical research according to guidelines study of microbiological lab diagnostic investigation.
A Comparison of Information Retrieval Pre-processing Algorithms Applied to African Historical Data

African historical data presents unique challenges to search algorithms because much of the data was produced by colonial authorities or archivists far from the source of the data. Contemporary datasets include descriptions of museum artefacts in European museums and books written by colonial administrators, both of which encode African history. These are both arguably biased collections and the information retrieval algorithms used to search through such data collections may not provide modern researchers with relevant results. The goal of this study was therefore to investigate the degree to which common text and image pre-processing algorithms affect the quality of search results when users search through a current African historical data collection. Nine common algorithms were compared in terms of recall, precision and NDCG. The results indicate that text pre-processing performs better when stemming and stopping are used but thesaurus use may depend on the thesaurus chosen. Results from the image pre-processing experiment indicate that shape detectors generally work better than colour detectors.
Penyelenggaraan Teras Baca Sebagai Solusi Guna Meingkatkan Miat Baca Anak Di Masa Pandemi Covid-19 Di Dusun Jatisari, Desa Krenceng, Kecamatan Kepung, Kabupaten Kediri

Books are one of the sources of knowledge that add insight. By reading one can find out what is in the corner of the world. This makes books often referred to as a window to the world. Book reading is also an activity that has many benefits and is well applied at the earliest possible age or the age of children. But in fact, not many children have a high interest in reading due to several factors. Especially like today where the development of technology is very fast and rapid causing all information to be easily obtained through gadgets. So that the activity of reading books began to be abandoned. The existence of the Teras Baca program aims to restore interest in reading in children, provide baha reading for them and as a place where they learn. In this study, researchers used the PAR (Participacory Action Research) method. The PAR method is one of the research models that look for something to connect the research process into the process of social change. Namely by directly holding a Teras Baca program for children. With this program, it is expected to foster interest and instill reading habits for children.
A Mismatch Elimination Method Based on Reverse Nearest Neighborhood and Influence Space

To tackle the problem of usually high feature mismatching rate for the scene containing similar structures and repeated textures, a mismatch elimination method based on the reverse nearest neighborhood and influence space is proposed under the framework of clustering-based analysis. More specifically, after obtaining a tentative noisy set of matching candidates, a new feature matching dataset is built by making use of the global information, which could better represent the local similarity, the geometric and the motion consistencies between matching pairs. Then a new salable radius clustering method is designed to determine the clusters by combining the reverse nearest neighbors and influence space, by which outliers are deleted from the clusters and mismatches are effectively discarded. With the standard dataset DTU and ancient Chinese architectural dataset, the experimental results show that, the average precision, recall rate and <italic>F</italic> score of our method can over 90% compared with other feature matching method.
Preface

This book intends to provide a continuous assessment of the crisis in governance in Africa. As it is, there are huge deficits in the capacity of African states to harness vast human and material resources to promote good governance. This manifests in pervasive corruption, collapsed service delivery, collapsed state-owned enterprises, eroded social trust, capital flight, escalating levels of poverty and wars, human insecurity, and stunted growth. The public sector is the pulse of service delivery because the entire governance system revolves around the sourcing of materials and services, mostly from the private sector, in order to achieve its public policy intents. The procurement process, therefore, ordinarily ought to yield positive economic outcomes and an efficiency-driven system in favour of the government itself and its service recipients. However, this more often than not is not the case. Despite its enormous wealth, the African continent is in an economic quagmire, a dilemma that requires multi-facet research activities. This is the motivation for this book.
A Vision Transformer-based handwritten alphanumeric characters recognizing intelligent pen

Digitizing handwriting is mostly performed either by using offline methods such as Optical character recognition or using the combination of a special stylus and pad. This approach is costlier. Therefore, in this study, a deep-learning-based English alphabet and Arabic numeral character recognition method is proposed. A particular digital pen, equipped with an inertial sensor (three-axis accelerometer and three-axis gyroscope) and three force sensors, developed in our previous paper was utilized for this study. Vision transformer (ViT) which is drawing huge attention in the field of image classification and sequential tasks was adopted as a neural network model. The model achieved an excellent F1-score result of 0.993 during testing. This confirmed a promising capability of the developed digital pen for character recognition using neural network approaches.
Leave Catalytic Traces: Land-based infrastructures for environmental mitigation at Fly Ranch, Nevada, USA

This article focuses on Leave Catalytic Traces, a design entry for the Land Art Generator Initiative (LAGI) Fly Ranch 2020 Design Competition, located in Fly Ranch, Nevada, USA. Co-hosted by LAGI, an organization that develops artistic renewable energy infrastructure, and the Burning Man Project, which organizes the annual nine-day art festival Burning Man nearby, the programmatic requirements of the competition included the mitigation of the festival’s environmental impact, ecological restoration and a demonstration of renewable energy generation through infrastructural sculpture at Fly Ranch. In response, this research through design case study investigates: (1) Land-based Infrastructures (LBI) as resilient infrastructure and a flexible process-driven framework for site design; (2) a temporary event harnessing participatory processes as a generative strategy; and (3) acupunctural land-based interventions as dynamic ‘sculptures’. The article argues that the work proposes a new design and research process that combines speculative futures and projective imaginaries, which are tested through the development of a process-driven design approach by deploying sitespecific land-based interventions.
Origin and Evolution

The origin of blockchains can be traced back to Bitcoin [220]. We start this chapter with a review of Bitcoin. We first explain its core data structure which is a ledger comprising of multiple blocks. We then expand on its core innovation, namely the Proof-of-Work (PoW) consensus protocol, which allows mutually distrusting peers to securely agree on a common history.
Peningkatan Publikasi Ilmiah Melalui Pelatihan Penulisan Artikel Ilmiah Bagi Mahasiswa

This service activity aims to provide understanding and knowledge to students about writing scientific articles so that they can improve scientific publications. This activity is not only carried out by providing theories about writing scientific articles, but also coaching clinic activities as a form of training for students. The implementation method is carried out online or online through zoom media. The presentation of the material was given directly by the resource person to partner students, then students were given one week to apply the material obtained through the coaching clinic. The coaching clinic is carried out with the aim of maximizing monitoring of students during the practice of writing articles.
A Study of the Effectiveness of Correction Factors for Log Transforms in Ensemble Models

We consider the problem of making predictions about long-tailed interval variables. Such variables are commonplace in revenue prediction, where a small part of the population has very large positive or negative values. Log transforms are often used in such problems, and when modeled in a log space, a correction factor is required when converting back to the original space. In this work, we study the effectiveness of two different approaches of applying correction factors at the individual model level and the whole model level. Particularly, we consider ensembles of simpler models (decision trees) with individual correction factors, compared with XGBoost, an averaging model using an overall correction factor. We show that the ensembles of simple models outperform XGBoost, when the correction factors are applied separately to each component model.
Author Matching Classification with Anomaly Detection Approach for Bibliomethric Repository Data

Authors name disambiguation (AND) is a complex problem in the process of identifying an author in a digital library (DL). The AND data classification process is very much determined by the grouping process and data processing techniques before entering the classifier algorithm. In general, the data pre-processing technique used is pairwise and similarity to do author matching. In a large enough data set scale, the pairwise technique used in this study is to do a combination of each attribute in the AND dataset and by defining a binary class for each author matching combination, where the unequal author is given a value of 0 and the same author is given a value of 1. The technique produces very high imbalance data where class 0 becomes 98.9% of the amount of data compared to 1.1% of class 1. The results bring up an analysis in which class 1 can be considered and processed as data anomaly of the whole data. Therefore, anomaly detection is the method chosen in this study using the Isolation Forest algorithm as its classifier. The results obtained are very satisfying in terms of accuracy which can reach 99.5%.
GRUZ: Practical Resource Fair Exchange Without Blockchain

A fair exchange protocol allows two parties to exchange their secret messages fairly. The protocol is said to be fair if either both parties receive secrets from each other or neither of them does. However, complete fairness was proven not always possible by Cleve (STOC 1986) as a corrupted party can always abort early to obtain more advantages than the other party. Thus, we should consider partial fairness. In this paper, we specifically discuss resource fairness (one of partial fairness), which means parties require similar computation resources to recover secrets even if the protocol is aborted at an arbitrary round. One of the methods used to achieve resource fairness is gradual release, where two parties gradually release their private information. We put forward a protocol named GRUZ (Gradual Release Using Zero-knowledge) to realize gradual release in the two-party exchange problem using zero-knowledge from garbled circuits (ZKGC) without blockchain that many past works rely on. Two parties first encrypt messages with their secret keys. Then they gradually release their secret keys one bit at a time alternately so that either party gains an advantage of at most one bit if he/she aborts. The authenticity of the exchanged keys is guaranteed by zero-knowledge proof. We implement this protocol with standard primitives AES, SHA256, and ECDSA, which are compatible with real-world applications such as digital currency exchange. We show that our protocol is practical by analyzing its running time and communication costs.
Non-Parametric Stochastic Policy Gradient with Strategic Retreat for Non-Stationary Environment

In modern robotics, effectively computing optimal control policies under dynamically varying environments poses substantial challenges to the off-the-shelf parametric policy gradient methods, such as the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic policy gradient (TD3). In this paper, we propose a systematic methodology to dynamically learn a sequence of optimal control policies non-parametrically, while autonomously adapting with the constantly changing environment dynamics. Specifically, our non-parametric kernel-based methodology embeds a policy distribution as the features in a non-decreasing Euclidean space, therefore allowing its search space to be defined as a very high (possible infinite) dimensional RKHS (Reproducing Kernel Hilbert Space). Moreover, by leveraging the similarity metric computed in RKHS, we augmented our non-parametric learning with the technique of AdaptiveH— adaptively selecting a time-frame window of finishing the optimal part of whole action-sequence sampled on some preceding observed state. To validate our proposed approach, we conducted extensive experiments with multiple classic benchmarks and one robotics benchmark equipped with dynamically changing environments. Overall, our methodology has outperformed the well-established DDPG and TD3 methodology by a sizeable margin in terms of learning performance.
A Bearing Fault Diagnosis Method based on Improved LSTM-cascade CatBoost

Bearing faults are widely concerned in the field of fault diagnosis, it has numerous excellent detection algorithms currently. In this paper, a new model called LSTM-Cascade CatBoost is applied, which can directly classify bearing vibration signals in the case of multiple granularities and high dimensions without signal processing. The model is based on gcForest, which can automatically adjust its complexity to the size of the dataset and it uses LSTM to improve its feature extraction ability. CatBoost is used as the base classifier of cascade forest to improve classification accuracy. Experimental results show that this model has high accuracy in CWRU and XJTU-SY datasets. Besides, it not only demonstrates that the feature extraction ability of LSTM is significantly better than that of multi-grained scanning, but CatBoost as a base classifier can further improve the accuracy of a cascade forest.