Reviewerly/exHarmony huggingface dataset:
Importance-Performance Analysis on Perception of “Good Instruction” Recognized by Graduate Students Studying Educational Methods in Graduate School of Education

Objectives This study analyzed the importance and performance of good instruction recognized by students studying Educational Methods in graduate school of Education. Methods We conducted an online survey of 62 graduate students majoring in Educational methods from nine universities. We asked the respondents to rate the importance of the factors of five major categories determining good instruction: philosophy, direction, method, course management and the instructor’s attitude and style. They were also asked to rate the performance of the factors based on their perception of how well those factors are actually manifested in courses. Results First, the respondents recognized the philosophy and method as the most important factor, while course management was recognized as the least important. The performance level was perceived as high in instructor’s attitude and style and philosophy, while low in method. Second, the difference between the average of perceived importance and their performance level was statistically significant in all areas. Specifically, method showed the greatest difference. Third, philosophy showed a high level of perceived importance and performance. The importance of method and direction was perceived to be low, while the performance of those factors was perceived to be high. The levels of importance and performance were both low in course management, and instructor’s attitude and style show a low importance level but was demonstrated to have high performance level. Conclusions The graduate schools of Education should offer courses based on a clear understanding of course objectives. In addition, the philosophy of the courses should be maintained to pique students’ intellectual interests, inspiration and satisfaction. Moreover, graduate schools should encourage the students to discover the importance of course management. Finally, we need a more diverse array of courses using practical and effective educational methods.
Privacy protection oriented on data stream computing

Data security has always been one of the public concerns, and homomorphic encryption technology, as one of the effective means to ensure data security, has always been a hot topic of academic research. Traditional machine learning is trained in a batch environment. In practice, data owners tend to outsource data and model training tasks to third-party clouds with huge computing resources, but data outsourcing carries the risk of privacy leakage. Therefore, in order to prevent this situation, an effective method is to encrypt the data before the outsourcing, and the third-party cloud trains the machine model of the ciphertext data. This method has special requirements for encryption algorithms which should support computation directly on the ciphertext data. This paper presents a novel data flow computing privacy protection framework based on the homomorphic encryption algorithm CKKS and the flow data processing engine Flink, called SDPPF (Streaming Data Privacy Protection on Flink). The proposed framework supports the functions of data stream encryption, decryption and ciphertext computing, and expands the functions of vector point multiplication and array sum operation on the basic simultaneous operation supported by the original CKKS algorithm. This paper also selects a classical machine learning algorithm: KNN algorithm, combined with the homomorphic encryption algorithm CKKS to realize the privacy protection machine learning algorithm.
Confidence-based fuzzy assessment model of LINK16 situational quality

The development of data link technology brings new challenges to the perception and formation of battlefield situation. In view of the situation formation characteristics of LINK16, this paper analyzes the situation formation process of LINK16, proposes a new indicator system, and establishes the LINK16 based on fuzzy analytic hierarchy process. The situational quality assessment model is then used to verify the simplicity and effectiveness of the method through the simulation of typical combat scenarios of close air support. Finally, it is concluded that the confidence of the algorithm is more objective and effective than the fuzzy algorithm model and the expert experience algorithm from the aspects of the quantity, quality and integrity of the data.
Financial planning will soon incorporate virtual reality

Even before the epidemic, more and more people were making use of technology to assist with their financial planning. The financial planning business has been fast to adapt to the new standard, and as a result, many organizations now offer remote services and virtual meetings. The technology behind virtual reality (VR) is still in its infancy, with HTC, Oculus, and Sony producing the majority of the most popular VR headsets now available. Virtual reality currently has a wide range of applications, but one particularly promising one is in the field of financial planning. Virtual Reality is useful for financial planning, and it has many benefits for customers. It has been used in gaming, health-like simulations, and product demonstrations. The research will discuss, the demographics that appeal to virtual reality, the applications of virtual reality, and how financial planning can benefit from the technology.
English language training of specialist degree students of a marine university in e-learning educational environment

The article is devoted to the topical problem of educational process organization in e-learning university environment. The aim of the study is to describe the experience of teaching the English language to the Marine university students. In this paper the standard structure of the English courses has been introduced, the pedagogical experiment on revealing the efficiency of e-learning English courses focused on the increasing of motivation and formation of foreign language competence of specialist degree students at the Marine university by the example of Sevastopol State University has been represented. The scientific novelty of the research article consists in clarifying the notion “foreign language training of the Marine university students in e-learning educational environment” and identifying the criteria of assessment and levels of formation of foreign language competence of specialist degree students at the Marine university. As a result, the growth of students’ level of foreign language proficiency in the sphere of navigation has been observed owing to the use of e-learning courses. E-learning courses assist the students to increase the understanding of professionally oriented material and improve their skills of working in e-learning university environment.
Enrolling in the Metaversity: A Meta-Analysis of Virtual World University Campuses in the Metaverse

Among the innovations in higher education during the Covid-19 pandemic are university- based initiatives in the metaverse. This paper provides a meta-analysis of a series of eleven of these international university educational undertakings in the emerging networked virtual worlds, or the metaverse. Findings show that metaversity initiative are organized into at least three main areas,
Some Difficulties in Learning Speaking of English Majored Freshmen at Tay Do University, In Vietnam

“Some difficulties in learning speaking of English majored freshmen at Tay Do University” is performed to the purpose of this study help student recognize their problem in speaking English. The participants of this study are the English majored freshmen course 13 (2018-2022) from Bachelor of English class at Tay Do University. The instruments are used in this study are questionnaire and interview. The researcher will collect student’s’ information through questionnaire and paper interview question. The expected outcomes will show the difficulties in learning speaking of English-majored freshmen at Tay Do University.
Research on the Effectiveness of The System of Independent Directors of Listed Companies in China

This paper selects the financial data of Chinese A-share listed companies from 2010 to 2020 as the research sample, and empirically tests the negative impact of the number of independent directors and their ratio on financial fraud behavior from the perspective of corporate financial fraud.The research results find that the number of independent directors and the ratio of independent directors are significantly and positively correlated with corporate financial fraud behavior, which indicates that independent directors have a significant impact on The results found that the number of independent directors and the proportion of independent directors are significantly and positively correlated with the financial fraud behavior of enterprises, indicating that the independent directors did not effectively promote the current corporate management reform.
Visible imaging for high-speed moving targets via synthetic aperture

Synthetic aperture techniques and concepts are widely used in radar, far-field imaging, and other important detection areas, where the application of synthetic aperture has stringent requirements for the quality and quantity of individual apertures, so there has been a significant challenge in the application of non-interferometric laser synthetic aperture techniques - reconstructing high-resolution images. The image information needs to be acquired for a long time to constrain the phase, and the results need to be reconstructed for a long time as well. In particular, for imaging rough surfaces, the removal of scattering noise often needs to be considered. Inspired by camera array imaging and deep learning in computational imaging, we uniquely apply camera arrays to optical synthetic aperture imaging systems and introduce deep learning to achieve high-resolution, high signal-to-noise imaging results of real far-field targets in a single shot. Specifically, we design a 3 × 3 camera array to acquire nine low-resolution images in parallel, each low-resolution image corresponds to a different region on the spectrum, while using a pairwise regression network to integrate the steps of sub-map acquisition, phase retrieval, and image denoising into a unified imaging system framework, driven by the data set, the neural network can quickly find the low-resolution, low-signal-to-noise ratio input in the system framework end to the high-resolution, high-signal-to-noise output end of the system framework, rather than the usual step-by-step solution. The results show that our system is able to improve the F-number from 12 to four equivalent imaging results in 0.2s, achieving a threefold increase in resolution, which allows for continuous high-resolution imaging results compared to traditional long acquisition and reconstruction. This method has the potential to break the limitations of current optical synthetic apertures and open up new avenues for fast frame rate high resolution imaging techniques.
Research on the evolution of disruptive technological innovation cooperation network: a case study of blockchain and automatic driving technology

Overcoming technological R&D barriers and market diffusion pressures through innovative cooperation is an important channel in the evolution of disruptive technologies. Aiming at the evolution of disruptive technological innovation cooperation network, this paper combines technology life cycle theory with social network analysis method, the overall evolution and individual evolution characteristics of disruptive technological innovation cooperation network are analysed. The research results show that in the life cycle, disruptive technology has the characteristics of latent in the early stage and explosive after maturity. In terms of overall evolution, the scale of disruptive technological innovation cooperation network is expanding, but the cohesion and small-world level are decreasing, the intensity of knowledge exchange and information transmission is declining. About the core subject, there is a synergistic dynamic mechanism between the market and the government. In the evolution of cooperation model, it shows the development trend from poor cooperation model to exploring cooperation mode and cultivating cooperation mode. The research conclusion enriches and expands the theoretical research in the field of disruptive innovation, and can provide inspiration for the policy design and management optimisation of disruptive technological innovation cooperation network.
Home Occupancy Estimation Using Machine Learning

Today, Smart home technology is commonly used for remote observation and control of devices and systems, for example, heating and lighting for convenience, support, and energy saving. Smart home devices incorporate the Internet of Things (IoT) to help automate activities based on the homeowners’ preferences by working together to share the home members’ usage data. Many papers are published on home occupancy detection. Occupancy and presence can be used in the contextual smart home to accurately determine the presence of someone in buildings or houses and also able to predict various events and pre-emptive action combines inexpensive, non-intrusive sensors including CO2, temperature, sound, light, and movement with the aid of supervised learning methods like quadratic random forest and support vector machine (SVM). This method primarily focuses on reliably predicting the total occupants in an area with the help of a combination of heterogeneous sensor nodes and ML algorithms with the greatest 98.4% and the highest 0.953 F1 score. This paper primarily focuses on reliably determining the people in a room utilizing numerous sensor nodes that are heterogeneous in nature and machine learning algorithms, using various parameters such as CO2, temperature, light, sound, and motion with the help of supervised learning methods like Logistic regression, Naive Bayes, SVM Linear Kernel, KNN, Decision tree, Random Forest, SVM RBF Kernel, with the Maximum Accuracy of 99.62% and F1 score 0.996 The effectiveness of a scaled dimensional data set was further assessed using linear discriminant analysis i.e. LDA and principal component analysis i.e. PCA.
Development of Amphibian Robot for Ship Hull Cleaning

Divers and inspectors are primarily in charge of a ship’s services and maintenance, which include a wide range of difficult tasks such as cleaning ship’s hull, inspecting the ship’s surface, and so on. In general, ship surfaces are cleaned on a regular basis, whereas drydock inspections are done once a year. This practices aids in ensuring low fuel economy of a ship’s performance; nonetheless, the process to execute the work has been identified as high risk. Considering how rapidly technology has advanced, a practical solution is needed. As a result, a new and inventive strategy is required to address these difficulties. This research describes the development of a permanent magnet wall climbing robot with a detachment platform. To demonstrate the concepts, a number of simulations and tests were performed on a prototype robot. The performance of the wall-climbing robot has been investigated, and further discussions regarding the experiments have been conducted in the research.
BVFB: Training Behavior Verification Mechanism for Secure Blockchain-Based Federated Learning

There are still two problems of the existing methods of defending against poisoning attacks of the blockchain-based federated learning: 1) It is difficult to accurately identify the nodes under attack; 2) The effect of the model is greatly affected when the number of malicious nodes exceeds a half. So, an innovative secure mechanism is proposed for blockchain-based federated learning, which is called the training behavior verification mechanism. The mechanism describes the consistent training behavior rules of nodes by constructing the training behavior model, and distinguishes honest nodes from malicious nodes by comparing the differences in training behavior models on the training behavior verification algorithm. Experiments show that the new mechanism can effectively resist more than half of the label-flipping attacks and backdoor attacks, and has the advantages of higher stability and higher accuracy than methods such as Krum, Trimmed Mean, and Median.
Representation Learning Method of Graph Convolutional Network Based on Structure Enhancement

Network representation learning has attracted widespread attention as a pre-processing process for some machine learning and deep learning tasks. However, most existing methods only consider influence of nodes' low-order neighbors to represent them. Either nodes' high-order neighbor or the intrinsic characteristic attributes of nodes are ignored, leading to the effect of network representation learning that needs to be improved. This paper proposes a novel model named Structure Enhanced Graph Convolutional Network (SEGCN) to address these limitations. SEGCN consists of the following components, i.e., the network structure enhancement to transform weak relationship into strong relationship, the node feature aggregation to fuse high-order neighbor information. Hence, the SEGCN model can simultaneously integrate network structure information, attribute information, and high-order neighbor relationships together. Experimental results for node classification and node clustering on six datasets show that SEGCN achieves better effectiveness and efficiency than state-of-the-art baselines.
Simulating motion blur and exposure time and evaluating its effect on image quality

Optimizing exposure time for low light scenarios involves a trade-off between motion blur and signal to noise ratio. A method for defining the optimum exposure time for a given function has not been described in the literature. This paper presents the design of a simulation of motion blur and exposure time from the perspective of a real-world camera. The model incorporates characteristics of real-world cameras including the light level (quanta), shot noise and lens distortion. In our simulation, an image quality target chart called the Siemens Star chart will be used, and the simulation outputs a blurred image as if captured from a camera of set exposure and set movement speed. The resulting image is then processed in Imatest in which image quality readings will be extracted from the image and consequently the relationship between exposure time, motion blur and the image quality metrics can be evaluated.
The influence of image capture and processing on MTF for end of line test and validation

Slanted edge MTF measurement as per ISO12233 is the defacto standard for measuring camera sharpness at manufacturing end of line. MTF measured by slanted edge has a number of advantages for measuring sharpness, being scale invariant, and relatively robust to geometric distortion. However, slanted edge MTF measurement is known to be affected by image processing algorithms, including demosaic, edge enhancement, and denoise algorithms. To avoid these confounding factors, it is increasingly common to measure MTF directly from the raw sensor image. This approach is logical if you are assessing the optomechanical lens-imager alignment and focus. However, end-of-line production testing has specific requirements, including speed of execution, repeatability and reproducibility. These requirements are typically not considered when configuring a camera for end-of-line MTF measurement. In this study, the execution time, repeatability and reproducibility of MTF measurement for multiple image capture and image processing combinations are examined.
Design of Automatic User Identification Framework in Crowdsourcing Requirements Engineering : User Mapping and System Architecture

The requirement elicitation is the initial stage of requirement engineering where information collected from users. The process are significantly determined by the quality and quantity of information collected. The crowdsourcing is a method of information gathering from many users. The number and variety of users in the crowdsourcing are both advantages and challenges in the elicitation process. This study purposes a framework for user identification that consists of user mapping and architecture system. The identification process consists of 8 main states, start with defining context, user target and scope determination, data source determination, user data collection, data pre-processing, feature selection, data classification and user identification. The results of this study is an initial state for development of an automated tool for user identification to elicit requirement through crowdsourcing. By the framework can be generated the user classification, which can be used to apply the appropriate method for gathering information in elicitation process.
Structure of Dragonfly Optimization Algorithm

This chapter explains the structure and mathematical model of the dragonfly optimization algorithm (DFOA). The dragonfly is regarded as a small predator in nature. However, during the exploration phase, dragonflies form small groups and fly back and forth to seek food and attract prey. The DFOA can be used for solving different optimization problems. The DFOA can be easily implemented. Also, the DFOA can be coupled with different optimization algorithms. The DFOA can be used for solving multiobjective optimization problems. The DFOA is a robust optimization algorithm for training soft computing models. This chapter indicated that the DFOA was successfully used in different fields.
Rat Swarm Optimization Algorithm

This chapter reviews the application of rat swarm optimization algorithms (RSOA) for solving different optimization problems. RSOA is a robust and simple optimization algorithm. There are both male and female rats in a group of rats. A mathematical model of rats’ chasing and fighting behaviors is used to design an RSO algorithm and optimize the results. The results indicated that the RSOA was implemented for solving complex problems. The RSOA is a robust optimizer for training soft computing models. The high accuracy and fast convergence are the advantages of RSOA.
Structure of Shark Optimization Algorithm

This chapter studies the structure of the shark optimization algorithm (SSO). First, the applications of the shark algorithm are reviewed in different fields. The SSO can identify optimal solutions by balancing exploitation and exploration phases. The SSO benefits from low computation costs and fast convergence properties. The rotational movement of sharks is used to escape from the local optimums. It is suggested to explore SSO’s capability for many additional applications, such as crop planning, crop pattern optimization, irrigation water allocation, and crop yield.
An Adaptive Negotiation Dialogue Agent with Efficient Detection and Optimal Response

For negotiation dialogue tasks, instead of adopting stationary strategies, a more advanced opponent may demonstrate sophisticated behaviors by employing reasoning strategies to predict its opponent’s actions. To address this challenge, this work proposes a novel dialogue agent, which leverages the predictive power of Bayesian policy reuse and the recursive reasoning ability of theory of mind, allowing efficiently detecting the policy of opponents using either stationary or higher-level reasoning strategies and learning a best-response policy when faced with previously unseen strategies. Finally, we present the results of the proposed agent against state-of-the-art baselines on the CRAIGSLISTBARGAIN dataset and show that the agent outperforms existing agents and its efficacy of detecting new unseen strategies (This is an extended version of the paper [3] presented at the 20th IEEE International Conference on Ubiquitous Intelligence and Computing 2022.)