Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Blockchain Technology for Government Information燬haring

Government information sharing (GIS) refers to that act of required or provided for duty government information, commercial information and public welfare information, and it is a basic issue of government services.However, the existing GIS has low transparency and is lack of flexibility between different departments.Aiming at such problems, this paper takes blockchain as a solusion, and systematically summarizes the development of digital GIS, the advantages and challenges of blockchain and its theoretical research and practical applications.Specifically, it reviews e-government interactive structure, big data and other solutions, analyses their imperfections, and puts forward blockchain-based solutions.The blockchain improves government service efficiency and data security, meanwhile it faces challenges in throughput and supervision.In order to meet these challenges, researchers propose solutions based on three data exchange scenarios: government-to-government, government-to-enterprise and government-toindividual.In addition to researches, blockchain-based GIS is put into use.The electronic licensing project of Nanjing (China) government is taken as an example to illustrate that blockchain has the ability to openly and securely verify, track, and exchange data, so as to improve management ability and public service level of government agencies.
Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value.One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients' projects.The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients.The projects belong to OSMO vendors, having offices in developing countries while providing services to developed countries.In the current study, Extreme Learning Machine's (ELM's) variant called Deep Extreme Learning Machines (DELMs) is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model.The proposed DELM's based model evaluations achieved 90.017% training accuracy having a value with 1.412 × 10 -3 Root Mean Square Error (RMSE) and 85.772% testing accuracy with 1.569 × 10 -3 RMSE with five DELMs hidden layers.The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies.The current study also concludes DELMs as the most applicable and useful technique for OSMO client's project assessment.
Deep Learning-Based Program-Wide Binary Code Similarity for Smart Contracts

Recently, security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks.There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts.Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis.However, due to the difference between common programs and smart contract, such as diversity of bytecode generation and highly code homogeneity, directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy, poor scalability and the limitation of binary similarity on function level.Therefore, this paper investigates graph neural network to detect smart contract binary code similarity at the program level, where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts.In particular, two improved Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN) models are explored to encode the contract graphs into quantitatively vectors, which can capture the semantic information and the program-wide control flow information with temporal orders.Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings.To evaluate the effectiveness and efficient of our proposed method, extensive experiments are performed on two real-world datasets, i.e., smart contracts from Ethereum and Enterprise Operation System (EOS) blockchain-based platforms.The results show that our proposed approach outperforms three state-of-the-art methods by a large margin, achieving a great improvement up to 6.1% and 17.06% in accuracy.
Constructing Representative Collective Signature Protocols Using The GOST R34.10-1994 Standard

The representative collective digital signature, which was suggested by us, is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis, the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper, we use the discrete logarithmic problem on prime finite fields, which has been implemented in the GOST R34.10-1994 digital signature standard, to build the proposed collective signature protocols.These protocols help to create collective signatures: Guaranteed internal integrity and fixed size, independent of the number of members involved in forming the signature.The signature built in this study, consisting of 3 components (U, R, S), stores the information of all relevant signers in the U components, thus tracking the signer and against the "disclaim of liability" of the signer later is possible.The idea of hiding the signer's public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.
Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

A self-adaptive resource provisioning on demand is a critical factor in cloud computing.The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests.Therefore, a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload.In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy (CARSS) Framework that formally addresses these issues and is more expressive than traditional approaches.The decision making in CARSS is based on more than one factors.The MAPE-K based framework determines the state of the resources based on their current utilization.Timed-Arc Petri Net (TAPN) is used to model system formally and behaviour is expressed in TCTL, while TAPAAL model checker verifies the underline properties of the system.
Combing Type-Aware Attention and Graph Convolutional Networks for燛vent燚etection

Event detection (ED) is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers (ETs), which are defined as the phrase or word most clearly expressing event occurrence.Thus, current approaches require both annotated triggers as well as event types in training data.Nevertheless, triggers are non-essential in ED, and it is time-wasting for annotators to identify the "most clearly" word from a sentence, particularly in longer sentences.To decrease manual effort, we evaluate event detection without triggers.We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks (TA-GCN) for event detection.Specifically, the task is identified as a multi-label classification problem.We first encode the input sentence using a novel type-aware neural network with attention mechanisms.Then, a Graph Convolutional Networks (GCN)-based multilabel classification model is exploited for event detection.Experimental results demonstrate the effectiveness.
GrCol-PPFL: User-Based Group Collaborative Federated Learning Privacy Protection Framework

With the increasing number of smart devices and the development of machine learning technology, the value of users' personal data is becoming more and more important.Based on the premise of protecting users' personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data.However, since FL can still leak the user's original data by exchanging gradient information.The existing privacy protection strategy will increase the uplink time due to encryption measures.It is a huge challenge in terms of communication.When there are a large number of devices, the privacy protection cost of the system is higher.Based on these issues, we propose a privacy-preserving scheme of user-based group collaborative federated learning (GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission mechanism.All groups work in parallel at the same time.The server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model parameter.We use the public datasets of modified national institute of standards and technology database (MNIST) to test the model accuracy.The experimental results show that GrCol-PPFL not only ensures the accuracy of the model, but also ensures the security of the user's original data when users collude with each other.Finally, through numerical experiments, we show that by changing the number of groups, we can find the optimal number of groups that reduces the uplink consumption time.
Top 10 Differences between Machine Learning Engineers and Data Scientists

Data science and machine learning are subjects largely debated in practice and in mainstream research. Very often, they are overlapping due to their common purpose: prediction. Therefore, data science techniques mix with machine earning techniques in their mutual attempt to gain insights from data. Data contains multiple possible predictors, not necessarily structured, and it becomes difficult to extract insights. Identifying important or relevant features that can help improve the prediction power or to better characterize clusters of data is still debated in the scientific literature. This article uses diverse data science and machine learning techniques to identify the most relevant aspects which differentiate data science and machine learning. We used a publicly available dataset that describes multiple users who work in the field of data engineering. Among them, we selected data scientists and machine learning engineers and analyzed the resulting dataset. We designed the feature engineering process and identified the specific differences in terms of features that best describe data scientists and machine learning engineers by using the SelectKBest algorithm, neural networks, random forest classifier, support vector classifier, cluster analysis, and self-organizing maps. We validated our model through different statistics. Better insights lead to better classification. Classifying between data scientists and machine learning engineers proved to be more accurate after features engineering.
Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-based MRC models (for example, restrictions on word count), such models cannot effectively integrate significant features, such as syntax relations, semantic connections, and long-distance semantics between sentences, leading to the inability of the available models to better understand the intrinsic connections between text and questions to be answered based on it. In this paper, a multi-granularity syntax guidance (MgSG) module that consists of a “graph with dependence” module and a “graph with entity” module is proposed. MgSG selects both sentence and word granularities to guide the text model to decipher the text. In particular, syntactic constraints are used to guide the text model while exploiting the global nature of graph neural networks to enhance the model’s ability to construct long-range semantics. Simultaneously, named entities play an important role in text and answers and focusing on entities can improve the model’s understanding of the text’s major idea. Ultimately, fusing multiple embedding representations to form a representation yields the semantics of the context and the questions. Experiments demonstrate that the performance of the proposed method on the Stanford Question Answering Dataset is better when compared with the traditional BERT baseline model. The experimental results illustrate that our proposed “MgSG” module effectively utilizes the graph structure to learn the internal features of sentences, solve the problem of long-distance semantics, while effectively improving the performance of PrLM in machine reading comprehension.
Music for Nine Post Cards jako przykład kankyo ongaku, czyli „muzyki unoszącej się w powietrzu jak dym”

Music for Nine Post Cards as an Example of Kankyo Ongaku i.e ‘Music that Floats in the Air Like a Smoke’ The aim of the article is an attempt to present the genesis and describe the first compositions that formulated the Japanese ambient trend, i.e. kankyo ongaku. The subject of the research is both the history of the creation of the series by Hiroshi Yoshimura, Music for Nine Post Cards, which became the cornerstone of later albums from the ambient and minimal music trend in Japan, as well as individual works included in the cycle under study. Based on the results of the automated and auditory analysis of Music for Nine Post Cards, the structure of musical works and their stylistic features are determined. For automated analysis, Sonic Visualiser was used—an application used to create a graphical visualisation of the music content of audio files. As a result of the research, the answer was obtained as to how Hiroshi Yoshimura and Satoshi Ashikawa realised the original assumptions of kankyo ongaku.
Curriculum meets platform: A reconceptualisation of flexible pathways in open and higher education

In open and higher education, digital technologies are increasingly used to enable flexible learning pathways and unbundle programs into separate courses. Whereas technologies have been praised for enhancing the flexibility of curricula, the implications of going digital have yet to be fully explored in curriculum studies. This article aims to critically investigate how an open education platform, OpenLearn, describes, prescribes, and enacts a particular form of curriculum. Rather than understanding platforms as passive tools for facilitating education, the article draws on theoretical and methodological ideas from science and technology studies (STS) to approach “curriculum” as a collection of socio-technical practices in which platforms play an active role. The findings of our analysis detail how networks of human and other-than-human actors are situated in a wider ecology and enact five curricular practices: prescribing, mobilising, enrolling, evaluating, and rebundling. We propose “platform curriculum” as a sensitising concept to investigate how technologies enable and constrain these practices instead of simply flexibilising them. With this article, we argue for the further adoption of STS in curriculum studies to disentangle the specific ways in which technologies, too, shape education.
Comparison of full-field solution between virtual and experimental digital image correlation for model verification

Traditionally, an iterative procedure is used for the calibration of material model parameters using the global response of experiments that develop homogeneous strain/stress fields. The calibrated model is then used in those applications where the response is heterogeneous in nature depending on the nature of loading and/or structural geometry. Generally, experimental data is associated with its inherent noise, whereas simulation data is much cleaner. Hence, some discrepancies occur while comparing the simulation data with experimental data locally. In the present work, a novel procedure of virtual speckling is developed and verified for the von Mises elastoplastic model on aluminum alloy under uniaxial tension test. First, digital image correlation (DIC) is performed on the deforming samples to obtain the full-field strain history. Subsequently, virtual speckles are generated on the simulated domain, followed by Finite Element Method (FEM) simulations. Using the virtual speckles and deformation from FEM, the full-field strain responses are evaluated using DIC. Finally, the DIC responses from the experiments and simulations under uniaxial tensile tests are compared to identify the effectiveness of the DIC technique and accuracy of the calibrated parameters.
Quasi-cyclic and generalized quasi-cyclic codes and uniqueness of their generators

In this paper, we use a novel approach to describe generator polynomials of quasi-cyclic (QC) and generalized QC (GQC) codes over finite fields. Our study of QC- and GQC-codes will be general and not only restricted to one-generator codes. We prove that generator polynomials of QC-codes and GQC-codes are unique. Further, we use our results to obtain an expression for the dimensions of QC-codes and GQC-codes. As an application of our construction of these codes, we obtain many optimal linear codes over finite fields [Formula: see text] and [Formula: see text].
Automatic Modulation Classification with Neural Networks via Knowledge Distillation

Deep learning is used for automatic modulation recognition in neural networks, and because of the need for high classification accuracy, deeper and deeper networks are used. However, these are computationally very expensive for neural network training and inference, so its utility in the case of a mobile with memory limitations or weak computational power is questionable. As a result, a trade-off between network depth and network classification accuracy must be considered. To address this issue, we used a knowledge distillation method in this study to improve the classification accuracy of a small network model. First, we trained Inception–Resnet as a teacher network, which has a size of 311.77 MB and a final peak classification accuracy of 93.09%. We used the method to train convolutional neural network 3 (CNN3) and increase its peak classification accuracy from 79.81 to 89.36%, with a network size of 0.37 MB. It was also used similarly to train mini Inception–Resnet and increase its peak accuracy from 84.18 to 93.59%, with a network size of 39.69 MB. When we compared all classification accuracy peaks, we discover that knowledge distillation improved small networks and that the student network had the potential to outperform the teacher network. Using knowledge distillation, a small network model can achieve the classification accuracy of a large network model. In practice, choosing the appropriate student network based on the constraints of the usage conditions while using knowledge distillation (KD) would be a way to meet practical needs.
Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis

In recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis.Therefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work.The present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified.The present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.
Super-Resolution Virtual Scene of Flight Simulation Based on Convolutional Neural Networks

We propose a method to establish super-resolution (SR) virtual scenes based on multi-spectral remote sensing images. Multi-spectral remote sensing images are processed, then the SR scenes are realized based on the convolutional neural network (CNN) with a special training set. The results show that the training set proposed in this paper can improve the generalization ability of CNN in processing remote sensing images, and different operation sequences can significantly affect the restoration quality of images. The terrain model is established in the physics engine based on the elevation data. Moreover, the flight simulation software with real-time SR virtual scenes is designed and implemented.
An Adaptive Self-detection and Self-classification Approach Using Matrix Eigenvector Trajectory

Motivated by the rapid development of the next generation artificial intelligence, we propose a novel adaptive self-detection and self-classification algorithm using matrix eigenvector trajectory in the paper. This algorithm’s mathematical inferences are also described and proved theoretically. The proposed algorithm is used in a multi-class bearing faults classification problem to validate its effectiveness. Results show that in an online data processing scenario, it can automatically adapt to new data patterns so that self-detection and self-classification can be realized by monitoring the eigenvector evolution trajectory. By comparing with other machine learning algorithms, we have validated that the proposed algorithm does not require explicit training, its required data processing time dropped more than 78% and achieved the same classification accuracy on new testing data.