Reviewerly/exHarmony huggingface dataset:
A Connection Cloud Model Coupled With Improved Conflict Evidence Fusion Method for Prediction of Rockburst Intensity

As a common geological hazard in underground engineering, rockburst has many uncertain factors and interactions, and is of a complicated mechanism.Based on the information fusion theory, a methodology using a novel connection cloud model, which can overcome the defect of the traditional cloud model requiring factors to be normal distribution when determining the basic probability assignment function of the evidence is proposed for predicting the rockburst intensity.And concepts of Lance-distance and Deng-entropy are also introduced here to improve the traditional evidence fusion algorithm.The practical application shows that the result obtained from the proposed model is consistent with that in the actual site testing, so this model is feasible and effective for the prediction of the rockburst intensity.Furthermore, compared with results from other six improved evidence fusion rules, the improved evidence fusion method proposed here has higher fusion convergence precision, faster convergence rate, and more accurate and reliable results, this may provide a new reference for other similar uncertain problems.
A Complete Characterization of the APN Property of a Class of Quadrinomials

In this paper, by the Hasse-Weil bound, we determine the necessary and sufficient condition on coefficients <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a_{1},a_{2},a_{3}\in {\mathbb F} _{2^{n}}$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=2m$ </tex-math></inline-formula> such that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f(x) = {x}^{3\cdot 2^{m}} + a_{1}x^{2^{m+1}+1} + a_{2} x^{2^{m}+2} + a_{3}x^{3}$ </tex-math></inline-formula> is an APN function over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{2^{n}}$ </tex-math></inline-formula> . Our work together with the follow-up work by Chase and Lisoněk indicates that all such APN quadrinomials <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f(x)$ </tex-math></inline-formula> are affine equivalent to two instances of Gold functions, which resolves the first half of an open problem by Carlet at the International Workshop on the Arithmetic of Finite Fields, 83-107, 2014.
Evolutionary Hyperparameter Optimisation for Sentence Classification

The performance that Deep Neural Networks can achieve on a specific task is impacted significantly by the hyperparameters selected. In order to compare the performance of various Deep Neural Network architectures on sentence classification, an optimised set of hyperparameters needs to be found for each architecture. In this work we use a simple Genetic Algorithm to optimise the hyperparameters of three different architectures and we evaluate their performance on a suite of sentence classification benchmarks. We found that a single Genetic Algorithm is capable of optimising a variety of different architectures and that the evolved configurations found can compete with those chosen by experts while using fewer overall trainable parameters. The three architectures tested are a recurrent neural network and two types of convolutional networks with a large difference in complexity. Of the three architectures, optimised for sentence classification, a simple Convolutional Neural Network was the overall best performer consistently achieving good performance while using very few trainable parameters.
Intelligent Prediction Mathematical Model of Industrial Financial Fraud Based on Data Mining

The essence of enterprise financial modeling is to use mathematical models to classify and sort out all kinds of enterprise information according to the main line of value creation and on this basis to complete the analysis, prediction, and value evaluation of enterprise financial situation. A reasonable financial model is also an effective means to reduce financial fraud. In this paper, a financial fraud identification model is constructed based on empirical data. In the process of model construction, the primary feature set is selected according to the financial fraud motivation theory, and then, the original feature set is obtained by Mann–Whitney test on the primary feature set, and the final fraud identification feature set is selected from the original feature set by using Relief and Boruta algorithms. Finally, based on the final fraud identification feature set, the data algorithms such as decision tree, logistic regression, support vector machine, and random forest are used to identify financial fraud. The experimental results show that the combination of financial fraud identification features constructed by the Relief algorithm and random forest model has the best recognition effect. The evaluation indexes of the G mean value and the F value were 75.86% and 78.33%, respectively.
Designing an Augmented Reality Digital Game for Adaptive Number Knowledge Development

Although the affordances of augmented reality have been widely explored, the study of AR in education is still in its infancy. The most pressing issue is a need for research and evidence to connect the design of augmented reality programs to learning science theories. To address this gap, this paper presents the iterative design of an augmented reality mathematics board game, The Nomads. The design of the game leverages three learning science theories—cognitive load theory, multimedia theory, and collaborative learning theory. This chapter will provide a detailed description of The Nomads and an in-depth analysis of how the aforementioned theories have informed the design and the development of the game.
QMIX Algorithm for Coordinated Welding of Multiple Robots

This paper considers the coordinated welding control based on deep multi-agent reinforcement learning. The discrete-time states and actions with local observation for the welding robots and the characteristics of the weld lines (e.g., the starting points of the weld lines and the ending points of the weld lines) are defined, which is suitable to use the monotonic value function factorisation for deep multi-agent reinforcement learning (QMIX) algorithm. A novel reward composed of trajectory optimization, collision avoidance and the task done is designed, which is proved by the simulation in the grid-world environment.
DeLend: A P2P Loan Management Scheme Using Public Blockchain in 6G Network

Financial institutions have made lives easier for a lot of individuals and organizations that would earlier use to face capital shortage now and then. Therefore, it becomes necessary to make the financial systems more reliable, secure, time-conserving, and cost-effective. Although several approaches have already been proposed, all of these tend to fail on at least one of the key features, i.e., trust. Motivated by this, in this paper, we propose DeLend, an Ethereum blockchain-based peer-to-peer (P2P) lending system. In DeLend, the problems of security, trust, and reliability have been solved with the help of Ethereum-based smart contracts (SCs). To make the system middlemen-free and much more cost-effective, we use the interplanetary file system (IPFS) protocol as a data storage. Through extensive simulation, we show that DeLend requires less bandwidth, which makes it a suitable enabling technology for the next generation of cellular networks, i.e. 6G. Finally, DeLend’s performance evaluation demonstrates its efficacy compared to traditional lending schemes.
Analyzing and Evaluating Boosting-Based CNN Algorithms for Image Classification

CNN is the most widely used method for image classification. In many occasions, its accuracy is improved by combining it with several boosting techniques. Determining such an efficient boosting model can be very time conserving and benevolent for many image classification applications. This study aims at analyzing and evaluating the performance of CNN added with various boosting techniques, namely XGBoost, Gradient Boost and AdaBoost. At first, CNN mode is trained, and image features are extracted by using convolutional layers, then for training the boosting algorithms intermediate data is extracted from three different layers of the CNN model. After comparing the performance of each individual techniques, it is revealed that boosted CNNs overperforms the others. While comparing the performances of the three CNN-Boosting algorithms, using data extracted from the flatten layer and the first dense layer, CNN-Gradient Boost performs better with an average of approximately 0.1921% more test accuracy and 0.2455% more F1 score for the flatten layer and 0.0505% more test accuracy and 0.0479% more F1 score for the first dense layer. For the third dense layer, CNN-XGBoost achieves better result with 0.0505% more test accuracy and 0.0479% more F1 score than the others. This finding is beneficial to improve performance of any application requiring image classification.
Training for Implicit Norms in Deep Reinforcement Learning Agents through Adversarial Multi-Objective Reward Optimization

We propose a deep reinforcement learning algorithm that employs an adversarial training strategy for adhering to implicit human norms alongside optimizing for a narrow goal objective. Previous methods which incorporate human values into reinforcement learning algorithms either scale poorly or assume hand-crafted state features. Our algorithm drops these assumptions and is able to automatically infer norms from human demonstrations, which allows for integrating it into existing agents in the form of multi-objective optimization. We benchmark our approach in a search-and-rescue grid world and show that, conditioned on respecting human norms, our agent maintains optimal performance with respect to the predefined goal.
The Influence of Virtual Reality Technology on the Cultivation of Agricultural Students

Virtual reality learning environment is a virtual learning environment created by the use of virtual reality technology, which expands the physical learning environment and provides learners with richer learning experience and learning resources. It has the characteristics of immersion, interaction, and imagination. From these three characteristics, this paper studies the virtual reality learning environment to promote the deeper learning of agricultural students, immersion teaching provides experience support for agricultural students' deeper learning, interaction provides emotional support for agricultural students' deeper learning, and imagination provides situational support for agricultural students' deeper learning. The virtual reality technology is helpful to the cultivation of higher-order thinking of agricultural talents. It is expected to provide a reference for the future exploration of virtual reality technology to the cultivation of agricultural talents.
Digital Twin Modeling and Operating State Assessment of Substation Equipment

The comprehensive perception of substation equipment status is the precondition of ensuring safe operation and realizing intelligent patrol inspection of substation. With the development of digital power grid, higher requirements are put forward for the fine management of power equipment. Digital twin technology is one of the ideal ways to realize the real-time perception and evaluation of the running state of equipment by using digital technology such as sensor, Internet of Things, virtual reality and artificial intelligence. Aiming at the problem of precision constraint and error accumulation of physical model, a digital twin modeling method for substation equipment based on multi-parameter perceptual data and a method for equipment operation state evaluation is proposed.
Identifying and classifying goals for scientific knowledge

Science progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize statements of ignorance, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals.We present a novel ignorance taxonomy driven by the role statements of ignorance play in research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10 000 annotations and used it to train classifiers that achieved over 0.80 F1 scores.Corpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python.
A Pre-Trained Vs Fine-Tuning Methodology in Transfer Learning

Abstract Transfer learning from a pre-trained and fine-tuning methodology has been utilized for the image classification. In this paper, we classify images of cats and dogs. It is much faster and easier than training from scratch using a pre-trained network with transfer learning. This saved network was trained previously on a huge dataset and known as a pre-trained model. The pre-trained model can be used in two ways, either it is used in the same way or it utilizes transfer learning approach to adapt this pre-trained network to a specific goal. Since the idea for image classification using transfer learning is that it is trained on the general and large dataset and further this model will be efficiently worked on the visual perception task of the real world. In fine-tuning, instead of tuning with the weights of generic feature map of based model, it is tuned accordingly to the dataset. In this paper, we take benefit of these learned feature maps deprived of having to start from scratch by utilizing a deep learning technique on a huge dataset and analyze the various methodology. The experimental results show that fine-tuning methodology outperformed over the pre-trained with feature extraction on accuracy.
Relatable Clothing: Soft-Attention Mechanism for Detecting Worn/Unworn Objects

We have identified a need in visual relationship detection and biometrics related research for a dataset and model which focuses on person-clothing pairs. Previous to our Relatable Clothing dataset, there were no publicly available datasets usable for “worn” and “unworn” clothing detection. In this paper we propose a novel visual relationship model architecture for “worn” and “unworn” clothing detection that makes use of a soft attention mechanism for feature fusion between a conventional ResNet backbone and our novel person-clothing mask feature extraction architecture. The best proposed model achieves 98.62% accuracy, 99.50% precision, 98.31% recall, and 99.14% specificity on the Relatable Clothing dataset, outperforming our previous iterations. We release our models which can be found on the Relatable Clothing GitHub repository (https://github.com/th-truong/relatable_clothing) for future research and applications into detecting and analyzing person-clothing pairs.
SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink

Existing SPARQL query engines and triple stores are continuously improved to handle more massive datasets. Several approaches have been developed in this context proposing the storage and querying of RDF data in a distributed fashion, mainly using the MapReduce Programming Model and Hadoop-based ecosystems. New trends in Big Data technologies have also emerged (e.g., Apache Spark, Apache Flink); they use distributed in-memory processing and promise to deliver higher data processing performance. In this paper, we present a formal interpretation of some PACT transformations implemented in the Apache Flink DataSet API. We use this formalization to provide a mapping to translate a SPARQL query to a Flink program. The mapping was implemented in a prototype used to determine the correctness and performance of the solution. The source code of the project is available in Github under the MIT license.
Effect of Climate Changes on Renewable Production in the Mediterranean Climate: Case Study of the Energy Retrofit for a Detached House

One of the strategies of the European Green Deal is the increment of renewable integration in the civil sector and the mitigation of the impact of climate change. With a statistical and critical approach, the paper analyzes these aspects by means of a case study simulated in a cooling dominated climate. It consists of a single-family house representative of the 1980s Italian building stock. Starting from data monitored between 2015 and 2020, a weather file was built with different methodologies. The first objective was the evaluation of how the method for selecting the solar radiation influences the prevision of photovoltaic productivity. Then, a sensitivity analysis was developed, by means of modified weather files according to representative pathways defined by the Intergovernmental Panel on Climate Change Fifth Assessment Report. The results indicate that the climate changes will bring an increment of photovoltaic productivity while the heating energy need will be reduced until 45% (e.g., in March) and the cooling energy need will be more than double compared with the current conditions. The traditional efficiency measures are not resilient because the increase of the cooling demand could be not balanced. The maximization of installed photovoltaic power is a solution for increasing the resilience. Indeed, going from 3.3 kWp to 6.9 kWp for the worst emission scenario, in a typical summer month (e.g., August), the self-consumption increases until 33% meanwhile the imported electricity passes from 28% to 17%.
Differential Evolution Neural Network Optimization with Individual Dependent Mechanism

With the increase of scenes where Neural Networks are used as a classifier, the expectation of the classifying accuracy for the network has risen. To improve classifying accuracy, Differential Evolution (DE) has been applied as an optimization method for Neural Networks. Compared to other DE methods, Differential Evolution with an Individual-Dependent Mechanism (IDE) takes in account of the differences between the fitness value of individuals. As a result, IDE has better results in terms of accuracy. Therefore in this paper, a Neural Network optimizer using IDE is proposed for further Neural Network improvement. Moreover, while most optimizers would use the loss function as the fitness value in DE, a method using accuracy is proposed because DE does not require the activation function to be differentiable. Experiments using the proposed framework has been conducted on classification problems, and comparative studies with other self-adaptive mutations and traditional optimizing methods have been performed. Experimental results showed that the proposed method outperformed other conventional methods in terms of accuracy.
Supervised and Unsupervised Machine Learning for Cancer Classification: Recent Development

This is models with the ability to detect and classify cancer is important in the industrial of healthcare. The most difficult aspect for such model is the classification of cancer, which can be addressed using machine learning methods. The methods are used to improve classification accuracy between system output and test data. The classification process becomes more difficult due to vast data information. This paper presents an overview on current development of cancer classification techniques using machine learning methods, which have received increasing attention within the area of healthcare. This review will mainly focus on the development of machine learning methods for classification of cancer diseases. Recently, there are various researchers proposed different kinds of methods for cancer classification. The results show that the successful of cancer classification is dependent on the machine learning models. Besides, various types of healthcare data used in the experiments would also be discussed in this paper. The development of many optimization methods for cancer classification has brought a lot of improvement in the healthcare field. There is demand for further improvements in optimization methods to develop better machine learning models for cancer classification.
Requirement Engineering Challenges for Social Sector Software Development: Insights from Multiple Case Studies

Diversity is a great challenge for software engineers in the social sector context. The objective of this paper is to contribute to the identification of the RE processes and associated challenges in releasing the software in the social sector markets for which an exploratory case study is conducted. The outcome of the case study indicates that the diversity limits the ability to involve the representative samples of user populations using the same set of RE tools and techniques as one size fits all solution for all segments. The diverse user base must be partitioned into different segments, with each segment triggered using a suitable set of RE techniques i.e., traditional and crowd-based RE. The diverse perspectives learned as a result of the interaction with each segment, must be merged together into a single perspective about the software meant to be used in the social sector. There is a need for a new RE process specially designed for handling the complexities of the social sector, which this paper terms as Social Sector Requirement Engineering (SSRE). There is an increased need for collaboration between government social sector institutions and software engineers to get access to diverse customers to improve software quality.
Global motion planning and redundancy resolution for large objects manipulation by dual redundant robots with closed kinematics

Abstract The multi-arm robotic systems consisting of redundant robots are able to conduct more complex and coordinated tasks, such as manipulating large or heavy objects. The challenges of the motion planning and control for such systems mainly arise from the closed-chain constraint and redundancy resolution problem. The closed-chain constraint reduces the configuration space to lower-dimensional subsets, making it difficult for sampling feasible configurations and planning path connecting them. A global motion planner is proposed in this paper for the closed-chain systems, and motions in different disconnected manifolds are efficiently bridged by two type regrasping moves. The regrasping moves are automatically chosen by the planner based on cost-saving principle, which greatly improve the success rate and efficiency. Furthermore, to obtain the optional inverse kinematic solutions satisfying joint physical limits (e.g., joint position, velocity, acceleration limits) in the planning, the redundancy resolution problem for dual redundant robots is converted into a unified quadratic programming problem based on the combination of two diff erent-level optimizing criteria, i.e. the minimization velocity norm (MVN) and infinity norm torque-minimization (INTM). The Dual-MVN-INTM scheme guarantees smooth velocity, acceleration profiles, and zero final velocity at the end of motion. Finally, the planning results of three complex closed-chain manipulation task using two Franka Emika Panda robots and two Kinova Jaco2 robots in both simulation and experiment demonstrate the effectiveness and efficiency of the proposed method.