Reviewerly/exHarmony huggingface dataset:
Discussion on the Innovative Model of Remote Experimental Teaching For Engineering Education

The remote experimental teaching for engineering education is supported by the remote open experimental environment, and the experimental teaching process is implemented through remote resource sharing and remote control. The remote experiment platform of this paper is based on remote experiment as the basic carrier, connecting electronic information, artificial intelligence, structural engineering and other engineering education-related experiments, and attaching importance to technical support. Starting from the core of “importing knowledge more effectively” and the five elements of “learners, teachers, teaching tasks, learning resources and environment, and teaching practice process”, the teaching concept of remote experiments, the construction of experimental platforms and links, the content design of experimental cases, the construction and support of teaching teams, and the application of artificial intelligence have carried out model innovations. At the same time, facing the social needs and the direction of industrial development, the new technology is integrated into the experimental case teaching, and the students are guided to learn the engineering thinking method that integrates theory with practice.
Blockchain Data Sharing Query Scheme based on Threshold Secret Sharing

Blockchain is a distributed ledger that combines technologies such as timestamp, cryptography, consensus mechanism, and peer-to-peer network. In the field of data recording and management, the blockchain data query scheme based on smart contracts consumes a lot of resources, and blockchain platforms that do not support smart contracts cannot achieve convenient data query. This study proposes a blockchain data sharing query scheme based on threshold secret sharing. The secret elements used to query data are shared through the Blakley space plane equation to limit the rights of the inquirer, ensuring the security of blockchain data query. At the same time, the Blakley space plane equation coefficient matrix is used to segment the data to be uploaded to the blockchain. It solves the problem that the data cannot be directly stored in the block due to their large size. It facilitates data uploading to the blockchain. The experimental results show that the additional time consumption of the secret sharing and recovery, data segmentation, and reconstruction of this scheme is much less than the block generation time. Therefore, this solution will not affect the normal operation of blockchain applications and can improve the security and the fault tolerance rate of data query.
gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package, gscaLCA which is implemented in the R statistical computing environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis (GSCA), the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities, which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach, fuzzy clusterwise GSCA, denoted as gscaLCA, encompasses many advantages from both methods: (1) soft partitioning from FC and (2) efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function, gscaLCA, works for both binary and ordered categorical variables.In addition, gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool, gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.
Improving Discriminative Entity Retriever with Generative Tasks

Entity retrieval, which aims to retrieve a subset of candidate entities from massive KBs for each query, is essential for many entity-centric tasks, e.g., question answering and entity linking. Most of existing methods address this task using a discriminative model by formulating it as as a matching problem between the query and each candidate entity. Recent progress show that the generative model, which directly generate entity names in an autoregressive way, can obtain competitive results against discriminative models. These two types of methods model the interaction between queries and entities in different fashion, where both of them have distinct advantages. In this work, we attempt to join the discriminative and generative modeling to leverage the benefits of both. Specifically, we develop a framework to jointly train a text generative model and a discriminative retrieval model, we use two-tower structure to encode queries and entities respectively, and use Bert as our encoder. Our model achieves 0.14 improvement in NDCG@10. And we compare the results of our model with the model without generative model, and the experiment proves that the generative model is effective.
Towards Fully Synthetic Training of Indoor Object Detectors: Ablation Study

Object detection is a fundamental function of many intelligent autonomous systems such as service robots or autonomous vehicles. State-of-the-art 3D object detection methods are deep learning methods which require large amounts of annotated data for successful training. Acquiring and labelling real-world data is an expensive and time-consuming task. An alternative is to train object detectors using synthetically generated images. Since modern object detectors trained on synthetic data still do not achieve similar results to those trained on real data, generating realistic synthetic data is still an open issue. We address the problem of object detection in indoor environments based on 3D point clouds acquired by 3D cameras. We provide a detailed insight into the importance of five factors involved in generating synthetic data: camera noise, presence of background objects, positioning of objects, scene context, and object sizes. To investigate the importance of these factors, we have developed a fully modular method for generating realistic synthetic single-view point clouds for training object detectors. Our method can generate large amounts of customizable data in a short time. An interesting finding is that pre-training with our data and fine-tuning with real data improves the performance of a particular 3D object detector.
A study of bitcoin and Ethereum blockchains in the context of client types, transactions, and underlying network architecture

Blockchain technology is a growing technology having various budding platforms. Bitcoin and Ethereum are widely accepted and growing constituent of blockchain. The major difference between Bitcoin and Ethereum is that while Bitcoin is specifically is a cryptocurrency application with limited set of data types, transactions types, and limited size of data storage, Ethereum is Turing complete, general purpose blockchain, in Ethereum you do not need to concern about underlying peer-to-peer network, infrastructure details, and consensus mechanism; it abstracts all these details and provide a secure and deterministic environment to programmers for building various types of applications. This chapter constitutes a comparative study of Bitcoin and Ethereum blockchains, specifically their “Clients Types,” “Transactions,” and “Networks”. It start from explaining Bitcoin reference client, also known as satoshi client, and Bitcoin core client specifications. Information about the Bitcoin network status is obtained by Bitcoin core client for, e.g., version, protocol version, and wallet version, etc., while Ethereum clients are software which implements Ethereum specification and make communication with other peers in Ethereum network, there are two types of clients in Ethereum: full node and remote client, such as parity and geth. Explanation moves further for Bitcoin transactions, their status that is spent or unspent, and their keys used in transactions for Bitcoins. In Bitcoin it is also required to cryptographically secure random number generator for creating private keys. Ethereum transactions are also cryptographically signed messages, but in contrast with Bitcoin, transactions only can trigger a change of state of singleton blockchain or can causes a smart contract to execute. This chapter will be useful for detailed information and technical differences between Bitcoin and Ethereum platforms.
A Novel Poi Temperature Prediction Method for Heat Source System Based on Graph Convolutional Networks

The ever-increasing functional density and complexity of the heat source system, the harsh chip cooling environment, as well as the cost reduction measures that require less sensor involvement are increasingly driving the need to develop new approaches for temperature monitoring and predicting. Most current researches investigate the temperature field reconstruction of the whole system. However, the reconstruction is not necessary or practical in some cases for computing consumption and unprocurable structure. The temperature prediction of the points of interest (PoIs) like the heat-sensitive area in the electronics is essentially important for function maintenance. Thus, a solution is proposed based on the graph convolutional networks (GCN) in this paper. There are two main parts of the solution that is about modeling the PoIs and monitoring points (MoPs) into a graph, and elaborately designing a GCN adapted to this PoI temperature predictive task without losing accuracy. After model training, the real-time temperature prediction of PoIs can be realized according to the corresponding temperature of MoPs by the GCN. The results of experiments show that this method approach well prediction that the mean absolute error is less than 0.01K under the condition possessing diverse MoPs and PoIs. Moreover, the comparison experiments with the baseline methods further verify the validity of this GCN-based solution.
Software Development in Small Software Companies: Exploring the Usage of Procedures, Techniques, Methods and Models in Practice

Small software companies have a challenge with utilizing process tools, which affects practice with significant quality-related challenges. This affects the software industry significantly because SSCs dominate the industry, and most of all, over 80 percent of software products are produced by SSCs. This cross-sectional survey was conducted in 3 countries attracting 115 respondents with the primary objective of investigating the software practice concerning the utilization of process tools in SSCs. The study focused on the tools used in requirements engineering and software testing as critical process areas for quality software products. Our findings indicate that the number of personnel intertwines with the complexities arising from lengthy procedures of the tools and processes, aggregating into difficulty in tool usage. Due to the constant evolution of practices, the volatility in processes also causes slow adoption of other tools, for instance, testing that must accompany the main engineering tools during a project. These findings are significant in informing theory and communicating to the practitioners what they should do regarding process tools.
Online Feature Selection for Efficient Learning in Networked Systems

Current AI/ML methods for data-driven engineering use models that are mostly trained offline. Such models can be expensive to build in terms of communication and computing costs, and they rely on data that is collected over extended periods of time. Further, they become out-of-date when changes in the system occur. To address these challenges, we investigate online learning techniques that automatically reduce the number of available data sources for model training. We present an online algorithm called Online Stable Feature Set Algorithm (OSFS), which selects a small feature set from a large number of available data sources after receiving a small number of measurements. The algorithm is initialized with a feature ranking algorithm, a feature set stability metric, and a search policy. We perform an extensive experimental evaluation of this algorithm using traces from an in-house testbed and from two external datasets. We find that OSFS achieves a massive reduction in the size of the feature set by 1–3 orders of magnitude on all investigated datasets. Most importantly, we find that the accuracy of a predictor trained on a OSFS-produced feature set is somewhat better than when the predictor is trained on a feature set obtained through offline feature selection. OSFS is thus shown to be effective as an online feature selection algorithm and robust regarding the sample interval used for feature selection. We also find that, when concept drift in the data underlying the model occurs, its effect can be mitigated by recomputing the feature set and retraining the prediction model.
Research on Intelligent Information System Construction of Online Class Based on Computer Big Data and Virtual Reality Technology

In view of the current situation of decontextualized or weak-contextualized teaching mode in college English class, the paper proposes to employ big data technology to construct an ecological teaching context in online English class to enhance language immersion. Guided by the theory of situational cognitive learning, the situational elements of ecological teaching in college English class are analyzed and designed, and the big data virtual situation creation system is realized.
Cost-based Framework for Natural Language Argumentation Analysis

Classroom discussions are important for enriching the student's background knowledge and improving the student's logical thinking ability. Automatic evaluation of argument strengths in a discussion is helpful in such environments. In this paper, we propose a strength evaluation framework for such use. The evaluation framework is cost-based and allows attack and support relations with various semantics, recursiveness, and similarity. Its theoretical properties and application show that the proposed framework is promising.
Preoperative Path Planning Algorithm for Lung Puncture Biopsy Based on Path Constraint and Multidimensional Space Distance Optimization

Background and Objective: Lung cancer is one of the cancers with the highest mortality rate. The gold standard for lung cancer diagnosis is a pathological examination by percutaneous lung biopsy, which requires experienced clinicians to operate the puncture needle under CT guidance. The degree of injury and success rate of lung puncture surgery in patients depends significantly on the clinicians’ skill and experience.Methods: A preoperative path planning algorithm for lung puncture biopsy based on path constraints and multidimensional space distance optimization is proposed to assist clinicians in planning the needle path of lung biopsy. First, lung tissues and organs are segmented based on CT images. Then, the puncture entry positions on the skin and the target positions in the suspected tumor are screened to determine the puncture needle path. According to lung biopsy clinical requirements, three hard constraints of the puncture needle path are proposed to remove paths that do not meet clinical requirements. Six soft constraints are proposed to score the remaining paths. Finally, the optimal puncture paths are obtained by Pareto optimization based on the multidimensional space distance.Results: The experimental data were collected from 33 patients with suspected pulmonary nodules in the Zigong First People’s Hospital, of which 8 groups of CT data were scanned before and after a successful lung puncture biopsy, and 25 groups of CT data were scanned before lung biopsy. To evaluate the applicability of the proposed method in lung puncture surgery, retrospective and prospective studies were carried out on 33 groups of CT data. In the retrospective study, the average distance between the optimal algorithm path and the surgical path in 8 cases was 5.77±3.85 mm, and the paths obtained by the proposed algorithm are all clinically feasible. In the prospective study, the paths obtained by the proposed algorithm in 23 cases are all clinically feasible.Conclusions: The results demonstrated that the proposed algorithm provides an objective and effective path planning method for lung puncture biopsy and reduces the clinicians’ workload related to the planning stage.
Challenges and Risks Associated with Public Key Infrastructure

Computer networks have been sufferers of the attacker even when it was considered as an inception. First, it was firewalls, later point interruption location frameworks, after that VPNs, and now certification authorities (CAs) and Public Key Infrastructure (PKI). Public Key Cryptography (PKC) is a system that enables gatherings to convey safely using public and private key sets. PKC-based correspondences can be both credible and mystery, despite the fact that the public keys are made generally known and accessible. This paper gives a short layout of the fundamental ideas and principles associated with the activity of a PKI including issues, for example, how a PKI works, its qualities and what issues should be tended to before the utilization of PKI turns out to be increasing across the board and likewise quickly takes a glance at the diverse zones to which PKI can be connected to take care of the existing issues and inspects a scope of current reactions and difficulties. It attempts to contemplate a portion of those inquiries after examined and a present writing survey regarding the matter. We demonstrate the challenges and risks related with PKI, contrary to a basic conviction that public key authentications and accreditation administrations can be showcased freely from applications and application conditions.
Recognition and Extraction of Banking Data using OCR

The procedure of reserving data from bank records into the bank database is a long and an erroneous chore. In this module we suggest a structure that examines bank records and withdraws data from them in the form of text which can additionally be reserved in the bank dataset easily.In the system suggested, the attributes of every character written in the data inputted are drawn out and then transferred towards the neural network. Texts containing datasets that are put in writing by various persons are brought to use to teach the system. The identification system proposed provides great levels of accuracy in comparison to the already existing approaches in this field. This structure can efficiently identify texts and transform them into structural form which can be effortlessly stored and organized.
Design of Multilabel Classification for Unstructured Data Based on Multi-View Orthogonal Basis Extraction

In multi-view multi-label learning, an object can extract its features from multiple different feature extraction methods, so as to obtain multiple views that describing the same object, and transfer the unstructured data such as text data to the structured data. All multiple views from the same object correspond to the same label. Each view plays a different role in multi-label learning, and each view may contain the same and different information at the same time. In this paper, a base-coordinate decomposition method is proposed to reconstruct each view, in which each view has its corresponding base matrix, and all views of each sample share the same coordinates under their own base matrix. This coordinate is the re expression of the original object and contains the feature information of multiple views at the same time. Finally, the coordinate is used as the input of the classifier to obtain the forecast label. Furthermore, we make orthogonal constraints on the base matrix to reduce the redundant information of the base matrix of each view as much as possible and ensure the uniqueness of the internal information of the base matrix. Experiments show the excellent performance of the proposed classifier.
Deep-CNN Model for Acute Lymphocytic Leukemia (ALL) Classification Using Microscopic Blood Images

Acute lymphocytic leukemia (ALL) is a variety of malignant somatic cell cancer that influences children and teenagers. The goal of the study is to create a system that can detect cancer from blood corpuscle images mechanically. This method employs a convolutional network that takes images of blood corpuscles and determines whether or not the cell is cancer infected. The appearance of cancer in blood corpuscle images is frequently ambiguous, overlaps with other diagnosis, and can be mistaken for a variety of benign abnormalities. Machine-assisted cancer identification from blood corpuscle images at the level of skilled medical staff would be extremely beneficial in clinical settings and also in the delivery of healthcare to populations with limited access to diagnostic imaging specialists. Here, the authors proposed a convolutional neural network (CNN)-based methodology to distinguish between outdated as well as irregular somatic cell photos. With the dataset and 1188 somatic cell images, the proposed methodology achieves an accuracy of up to 96.6%.
Kinetics of the Portevin–Le Chatelier bands in polycrystalline nickel

The kinetics of localized bands of plastic deformation in the technically pure polycrystalline nickel under uniaxial tension is investigated via digital speckle correlation method. The stress-strain curves evidence the discontinuous yielding and the stress drops correspond to the nucleation of the Portevin–Le Chatelier bands, allowing one to distinguish them with respect to A, B and A+B types. The intervals of stresses and deformations are determined for various types of jumps. The dependence of the PLC band speed on the total strain is established as well.
XPS-MoSCoW

This paper presents a thorough analysis of the existing SCRUM model and the Extreme programming model for software testing and deployment and proposes a new hybrid model for software development. On reviewing the various static agile models, we concluded that the development process of the SCRUM model focused on the management aspect of software testing and deployment. In the case of Extreme programming, the engineering practices are applied to the project itself. No model effectively focuses on balancing management as well as the engineering practices of the software deployment cycle. In this paper, we have proposed an improved model called the XPS-MoSCoW Hybrid programming model that carefully integrates the features of both the SCRUM and Extreme programming models. The tasks executed using our hybrid model are prioritized using the MoSCoW prioritization rules, in which tasks with higher priorities enter first into the sprint backlog. Looking at the implementational results of this model when deployed in a startup, it is eminent how our hybrid model surpasses these static models.