Reviewerly/exHarmony huggingface dataset:
Parameter Identification of Nonlinear Systems Model Based on Improved Differential Evolution Algorithm

Aiming at the difficulty of optimizing the parameter estimation of nonlinear models, a new method for parameter identification of nonlinear system models based on improved differential evolution algorithm based on diversity evaluation index is proposed. By establishing a population reconstruction mechanism, based on the population diversity index, the concept of population similarity is proposed to guide the selection of evolutionary strategies and the adaptive adjustment of process parameters, thereby balancing the global and local search functions at different stages. The population size decreasing strategy effectively reduces the amount of computation and improves the convergence speed and algorithm efficiency. In order to verify the performance of the algorithm, several types of standard functions with typical complex mathematical characteristics are simulated and applied to the identification of a type of thermal system model parameters. The results show that the improved algorithm has high model parameter identification accuracy and faster convergence speed, which effectively improves the accuracy and efficiency of model establishment, and provides a feasible way to solve the model parameter identification problem in practical systems.
Revisiting Embedding Based Graph Analyses: Hyperparameters Matter!

Graph embeddings have been widely used for many graph analysis tasks. Mainstream factorization-based and graph-sampling-based embedding learning schemes both involve many hyperparameters and design choices. However, existing techniques often adopt some heuristics for these hyperparameters and design choices with little investigation into their impact, making it unclear what is the exact performance gains of these techniques on graph analysis tasks. Against this background, this paper presents a systematic study on the impact of an extensive list of hyperparameters for both factorization-based and graph-sampling-based graph embedding techniques for homogeneous graphs. We design generalized factorization-based and graph-sampling-based techniques involving these hyperparameters, and conduct a comprehensive set of experiments with over 3,000 embedding models trained and evaluated per dataset. We reveal that much of the performance gains are indeed due to optimal hyperparameter settings/design choices rather than the sophistication of embedding models; appropriate hyperparameter settings for typical embedding techniques can outperform a sizeable collection of 18 state-of-the-art graph embedding techniques by 0.30-35.41% across different tasks. Moreover, we find that there is no one-size-fits-all hyperparameter setting across tasks, but we can indeed provide a list of task-specific practical recommendations for these hyperparameter settings/design choices, which we believe can serve as important guidelines for future research on embedding based graph analyses.
Self-supervised Adaptive Kernel Nonnegative Matrix Factorization

The kernel function to imply nonlinear mapping is crucial for kernel nonnegative matrix factorizations (KNMF), which determines whether the model can properly represent the data. However, the analysis and adaptive learning of parameters in the kernel function are lacking in existing methods. The kernel parameters of most methods are randomly selected, and the purpose and motivation of kernel function construction is weak. To address this problem, a self-supervised adaptive kernel nonnegative matrix factorization (SAKNMF) is proposed to handle image recognition tasks. Kernel methods are used to achieve non-linearization. Firstly, the data is devided into multiple clusters by fuzzy clustering as self-supervised information. Then, according to the self-supervision information, the discriminative constraints that can adaptively learn the kernel parameters are constructed in the objective function. Based on the new objective function, the Gaussian kernel function parameters, basis matrix and coefficient matrix are jointly optimized. The kernel function constructed by the learned parameters enables the nonlinear mapped basis images to cover the mapped samples in the same cluster and distinguish other different clusters. So that the model can finally learn features that reflect the sample difference and have good generalization. Experiments show that our method can learn appropriate kernel width in different datasets, and the recognition accuracy is better than the state-of-the-art methods.
GAE-Based Document Embedding Method for Clustering

Document embedding methods for clustering using deep neural networks (DNNs) have been proposed recently. However, the existing DNN-based document embedding methods for clustering have a problem of either generating document embeddings dependent on a given number of document clusters or generating document embeddings that do not take into account the characteristic of high similarity between documents belonging to the same document cluster. In this paper, we propose a new document embedding method for clustering by using a graph autoencoder (GAE). To this end, we construct an undirected and weighted sparse graph from a set of documents wherein each document is represented by a node, and all the weighted edges created in the graph have high cosine similarities between the two end nodes. We then apply the proposed GAE to the graph to compute node embedding vectors. Each node embedding vector in the graph is used as a document embedding vector. This paper presents in-depth experimental analyses of the proposed method. Experimental results on various real document data sets demonstrate that the proposed approach affords the significant performance improvement over the existing document embedding methods.
Criticism of the Risk Management Process in Scrum Methodology

Risk management in software engineering projects describes an integrated design to prevent project failure with methods, processes, and artifacts that continually identify, analyze, control, and monitor risks. For example, changes in people's lifestyles during the COVID-19 pandemic pose unexpected risks to the information technology industry. Agile is known as a methodology that is responsive and adapts quickly to change. Scrum is the most frequently used method based on the 2016 Agile development survey results. Many studies have produced a risk management framework for Scrum in recent years. However, repeating the risk analysis process and selecting a response to risk becomes a burden for stakeholders, so a framework is needed that can become a support system to help make decisions. This paper used a comparative study of risk management framework literature and literature that utilizes risk management tools and a case study of risk classification using 34k GitHub Issues for data mining. This study proposed a new framework that integrates datasets and machine learning into a risk management framework. The novelty in this paper is that the risk priority scheme is carried out using Long Short-Term Memory (LSTM) and Multinomial Naive Bayes (MNB). Further analysis can be carried out to test the overall effectiveness of the framework.
The Influence of AR Scene Complexity on Map Reading Tasks: A User Study using Eye-Tracking Approach

It is generally agreed that augmented reality(AR) maps can effectively integrated various virtual location based information and the real geographical environment. Unlike the conventional printed and digital maps, AR maps use real scene directly as base map, which provides a stronger sense of immersion. However, it is unknown whether and how the introduction of real world may affect the cognition of AR maps. In this study, we conducted a controlled experiment using eye- tracking approach to explore the influence of AR scene complexity on map reading tasks. Both indoor and outdoor scenes in varied complexity levels were considered. Then a total of 34 participants were recruited to perform several POI search tasks with the AR Maps, and their response time, accuracy rate and eye movement data were recorded. We analyzed the effect of AR scene complexity on map reading from three aspects, i.e. perceptual efficiency, cognitive load and cognitive performance. The results show that when the amount of present POI is reasonable, AR scene complexity has little effect on POI searching. This finding could help us to set visual strategy for displaying virtual map symbols with complex real scene and support AR map design.
AHP Learning Website Development with The RAD Method for Deaf Student

The learning target is to implement character education that is integrated with technology so that it can improve the quality of human resources. Learning using the website can be used for students, especially deaf students because learning is fun and there is a high element of creativity that makes it easier to remember learning material. This shows that education is starting to realize the need for a website to increase effectiveness and efficiency, especially for deaf students. This study develops a website-based learning application for deaf students. The material raised is AHP (Analytical Hierarchy Process) in the course of forecasting techniques. The material was chosen because based on a previous survey if the course is one of the subjects that is quite difficult. The application development method uses Rapid Application Development. The trial was conducted on media and material experts as well as 6 deaf students. Both trials produced good test results and were included in the valid and very good categories. Website-based applications do not get major revisions. Thus, this application is feasible to use.
Data Bricks Space Mission: Teaching Kids about Data with Physicalization

The Data Bricks Space Mission is a prototype activity based on data physicalization for teaching kids about data. The design of the activity is based on a literature review and interviews with elementary school teachers, and targets kids aged 10-12. Using Lego bricks and a fictional space adventure story, teachers can use the Data Bricks Space Mission activity to empower kids to produce data, communicate their findings, and gain a better understanding of the relationship between data and the world around them.
Balancing the Stability-Plasticity Dilemma with Online Stability Tuning for Continual Learning

Balancing the stability-plasticity dilemma is an omnipresent challenge in continual learning. The dilemma is that the ability of a model to learn new knowledge (plasticity) comes at the expense of the ability to remember past knowledge (stability) and vice versa. Some continual learning algorithms incorporate a constant hyper-parameter to control this trade-off. We argue that the trade-off should be dynamically tuned rather than kept constant. We propose a method to dynamically balance stability and plasticity in a semi-online and fully online manner. Our method is applicable to a range of underlying regularisation based strategies and class incremental problems. Our experiments in three continual learning benchmarks suggest that dynamic stability tuning methods can improve accuracy and decrease forgetting of continual learning strategies throughout their lifetime.
Improved Human-Object Interaction Based Lightweight Openpose

In order to improve the performance as well as the requirement of lightweight, the human-object interaction detection network of GGNet including the improved Lightweight Openpose is proposed. The Lightweight Openpose algorithm is introduced to substitute for the feature extraction module in the GGNet network as the feature extraction network. The improved GGNet network has an accuracy of 96.6% and a recall of 82.6%, which is a significant improvement in speed and accuracy compared to using the original GGNet feature extraction network.
Averaging Solution of Differential Equations as Value for Argumentation Graph with Similarity

In this paper, we present a new method to get the value of claims and relations of an argumentation graph. By the method, the values of claims and relations are determined by the average of the solutions of the differential equation derived from the argumentation graph. We show that the overhead of obtaining multiple solutions can be reduced by parallel execution and the values obtained by the method agree with our intuition. We can get a confidence interval of each value and a rough grouping of claims and relations by the method. We show the results for toy graphs, large artificial graphs, and a natural language argumentation graph.
Research on Computer Intelligent Test Paper Generation for English Test Based on Genetic Algorithm

Intelligent test paper generation is the core of online examinations, based on a test bank, which generates test papers efficiently and quickly through simple operations. Commonly used algorithms for intelligent test paper generation include randomised algorithms, back-testing methods and genetic algorithms. Simple randomized test paper generation method and backtracking algorithm have the disadvantage of too strong randomness, and it is difficult to ensure that the generated test papers meet the requirements. Using computer simulation and the problem solving process, with the characteristics of strong global optimization, overcomes the limitations of the traditional randomized test paper generation method and solves the problem of intelligent test paper generation in a scientific and reasonable way. The university English web-based examination system must continue to advance and needs to focus on solving the problem of intelligent test paper generation. The findings of this paper, which shorten the time used to extract test questions, are important in promoting English teaching reform.
Cross Elitist Learning Multifactorial Evolutionary Algorithm

Multifactorial Evolutionary Algorithm (MFEA) is a popular optimization algorithm in recent years. It has implicit parallelism and can solve different problems at the same time in the same search space. However, premature convergence is a significant shortcoming of MFEA. The main reason for this phenomenon is the lack of population diversity and the inability to migrate effectively between tasks. To address this shortcoming, this paper proposes a cross elitist learning multifactorial evolutionary algorithm (CEL-MFEA). Firstly, the cross elitist strategy guides the individuals to learn from the elite individual, which can improve the convergence of the proposed CEL-MFEA. Secondly, the Nelder-Mead algorithm is used to provide an effective knowledge transfer between different tasks. Finally, the opposition learning mechanism is employed to ensure population diversity. The comprehensive ability of the proposed CEL-MFEA is evaluated by 9 classical multifactorial optimization problem test sets. Experimental results show that the CEL-MFEA is more competitive than MFEA algorithm and several popular multifactorial optimization algorithms.
Towards real-world navigation with deep differentiable planners

We train embodied neural networks to plan and navigate unseen complex 3D environments, emphasising real-world deployment. Rather than requiring prior knowledge of the agent or environment, the planner learns to model the state transitions and rewards. To avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations. Although they work well in small simulations, we address two major limitations that hinder their deployment. First, we observed that current differentiable planners struggle to plan long-term in environments with a high branching complexity. While they should ideally learn to assign low rewards to obstacles to avoid collisions, these penalties are not strong enough to guarantee collision-free operation. We thus impose a structural constraint on the value iteration, which explicitly learns to model impossible actions and noisy motion. Secondly, we extend the model to plan exploration with a limited perspective camera under translation and fine rotations, which is crucial for real robot deployment. Our proposals significantly improve semantic navigation and exploration on several 2D and 3D environments, succeeding in settings that are otherwise challenging for differentiable planners. As far as we know, we are the first to successfully apply them to the difficult Active Vision Dataset, consisting of real images captured from a robot. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available: https://github.com/shuishida/calvin
Portable Technology of Low-Cost Image Digitizing for the Screening of Uterine Cancer in Latin America

Cervical cancer death rates have been increasing during the last five years in the world, especially in Brazil and Latin America. One of the reasons for this phenomenon is the stalling of preventive cervical cancer screening tests, due to the lack of specialists with proper technologies for massive screening. The COVID-19 pandemic has worsened the situation as several Pap smearing tests have been postponed. The goal of this work is to present the design of a portable, low-cost device for digital screen test analysis to support cervical cancer detection in low-income and remote areas. We were able to design and test a low-cost, easy to implement device for conventional Pap smearing with high accuracy to detect anomalies in cervical cells. The total cost of the device is lower than 400 dollars.
Observer Attack on Stream Ciphers

This paper proposes an application of a new observer theory for non-linear systems developed previously to solve the Cryptanalysis problem of a special class of pseudorandom generators which are commonly used in Cryptography. The Cryptanalysis problem addressed here is that of the recovery of internal state of the non-linear dynamic stream generator from the output stream. The proposed methodology is termed as observability attack. It is also shown that for a special class of generators, the computations are of complexity O(D4) in pre-computation and of O(D) for online computation, where D = σdi=0 (n i) for this class of stream generators with n states and d the degree of the output function. The attack is technically applicable over general finite fields as well as most dynamic systems arising from models of stream ciphers and appropriate bounds on computation are estimated. From these complexity bounds, it follows that this attack is feasible in realistic cases and gives important estimates of time and memory resources required for Cryptanalysis of a class of stream ciphers.
Automating staged rollout with reinforcement learning

Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultaneously considering multiple product or process metrics explicitly. This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects.
Blockchain Based Regulatory Technology Deployment for Real Estate Transaction

This research investigates opportunities for deploying blockchain-based regulatory technology (RegTech) in the real estate industry, particularly the mortgage application process in real estate transactions. The real estate industry entails a broad range of regulatory requirements. Processes to fulfil these requirements are primarily inefficient and costly. Blockchain features many characteristics that can effectively deploy RegTech to make real estate transactions more efficient, accurate, faster and at a lower cost. This paper conducts a literature review and semi-structured interviews with a range of industry stakeholders to reveal four main categories of problems in the sector, examine five use cases of blockchain deployments, study in-depth the mortgage process as the primary target area for blockchain-based RegTech deployment, propose six desired attributes of an ideal mortgage process and offer a solution framework to encompass these attributes. The study also considers the limitations of blockchain-based RegTech solutions and suggests future considerations for such development.
SSL-DC: Improving Transductive Few-Shot Learning via Self-Supervised Learning and Distribution Calibration

Few-shot learning, aiming to distinguish unseen classes by training with few labeled samples, is still challenged by the overfitting problem. The transductive few-shot learning paradigm enables us to reduce overfitting by training a highly discriminative feature representation via self-supervised learning since the entire unlabeled samples are allowed to be accessed. In this paper, we propose a simple but efficient approach based on self-supervised pre-training and nearest class prototype search, which can obtain a significant improvement in the performance of transductive few-shot learning tasks without external samples. However, since the class prototype is obtained through limited support samples, it is easily affected by biased samples. Therefore, we propose to train a conditional generative adversarial network to estimate the distribution of features instead of assuming it follows Gaussian distribution as previous arts. Thus, we can generate features that are closed to real features from the estimated distribution to calibrate the distribution of the class prototype. Finally, more detailed experiments show that our method can exceed plenty of recent transductive few-shot learning methods significantly and achieve 9.83% and 4.38% improvements over the existing best method under the transductive 5-way 1-shot and 5-shot settings with ResNet-12 on the miniImageNet.
Information Technologies as a Tool for Bachelor’s Engineering Teamwork Skills Development

The digitalization of higher education and current realities of society actualize the search for modern information technologies to develop the teamwork skills of future engineers, contributing to the synergy effect in the academic sphere. The aim of the study is to identify the possibilities of integrating information technologies in the educational process to develop teamwork skills of undergraduate engineering students in the course of Technical English. The article applied theoretical analysis of pedagogical literature, used a survey-diagnostic method. Engineering Team Performance Scale was chosen as a diagnostic tool to analyze the effectiveness of team interactions and Fey's Acceptance of Others Scale was used. The study involved 107 undergraduate engineering bachelors. The presented results confirm that the use of information technologies (online service of interactive visual communication Genially, online platform PowToon and web service Trello) significantly influences the efficiency of team interaction process and the result of team activities.
An Effective Multi-objective Evolutionary Algorithm Based on Decomposition for A Robot Rescue Path Planning Problem

The paper addresses a post-disaster robot rescue path planning problem where a number of people are trapped and a robot is used to rescue them. The objective functions are to minimize the number of deaths and the number of serious injuries. At first, a path matrix between two trapped people is obtained by using an improved A* algorithm. And then, an improved multi-objective evolutionary algorithm is proposed to find rescue paths with Pareto optimal or near-optimal solutions. At last, the proposed algorithm is compared with other multi-objective algorithms. The experimental results demonstrate the proposed algorithm can effectively plan a shorter path to reduce the number of deaths and serious injuries after disasters.