Reviewerly/exHarmony huggingface dataset:
Jointly estimating bias field and reconstructing uniform MRI image by deep learning

Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estimate bias field and reconstruct uniform image. By modeling the quality degradation process as the product of a spatially varying field and a uniform image, the network was trained on 800 images with true bias fields from 12 healthy subjects. A network structure of bias field estimation and uniform image reconstruction was designed to compensate for the intensity loss. To further evaluate the benefit of bias field correction, a quantitative analysis was made on image segmentation. Experimental results show that the proposed BFCNet improves the image uniformity by 8.3% and 10.1%, the segmentation accuracy by 4.1% and 6.8% on white and grey matter in T2-weighted brain images. Moreover, BFCNet outperforms the state-of-the-art traditional methods and deep learning methods on estimating bias field and preserving image structure, and BFCNet is robust to different levels of bias field and noise.
A finite time discrete distributed learning algorithm using stochastic configuration network

In this paper, we propose a finite-time discrete distributed learning algorithm, denoted as the FTDDL algorithm, using the Stochastic Configuration Network (SCN) and Zero Gradient Sum (ZGS) strategy. In distributed scenarios, the communication networks that store data are modeled as algebraic graphs and the learning problems are formulated as distributed optimization problems. The FTDDL algorithm persists the universal approximation of the SCN learner. The process of the FTDDL algorithm is divided into two phases, namely “distributed hidden parameters configuration” and “distributed output weights determination”. It has been theoretically proved that the FTDDL algorithm works in distributed manner and achieves the same efficiency as the centralized SCN. According to the FTDDL algorithm, each node over the communication network transmits the updated output weights of the SCN with its direct neighbors. Thus, the FTDDL algorithm preserves data privacy and saves network bandwidth. The FTDDL algorithm is theoretically proved to converge to the global optimal output weights of the SCN within finite time. Compared with the other distributed learning algorithms using SCNs, the FTDDL algorithm can significantly reduce the iteration times. At last, some experiments are shown in Simulations part to verify the effectiveness of the FTDDL algorithm.
Obstacle avoidance using buffered voronoi cells based on local information from a laser range scanner

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FlatNet3D: intensity and absolute depth from single-shot lensless capture

Lensless cameras are ultra-thin imaging systems that replace the lens with a thin passive optical mask and computation. Passive mask-based lensless cameras encode depth information in their measurements for a certain depth range. Early works have shown that this encoded depth can be used to perform 3D reconstruction of close-range scenes. However, these approaches for 3D reconstructions are typically optimization based and require strong hand-crafted priors and hundreds of iterations to reconstruct. Moreover, the reconstructions suffer from low resolution, noise, and artifacts. In this work, we propose FlatNet3D-a feed-forward deep network that can estimate both depth and intensity from a single lensless capture. FlatNet3D is an end-to-end trainable deep network that directly reconstructs depth and intensity from a lensless measurement using an efficient physics-based 3D mapping stage and a fully convolutional network. Our algorithm is fast and produces high-quality results, which we validate using both simulated and real scenes captured using PhlatCam.
Creating Trust in Citizen Participation through Decentralized Autonomous Citizen Participation Organizations (DACPOs)

Blockchain-based Decentralized Autonomous Organizations (DAOs) are systems for transacting and storing value by automatically executing a function without the need for trusted, centralized authorities. Participative budgeting requires voting about budget allocation by communities and is often confronted with issues in trust and transparency. Yet, DAOs are hardly used for participative budgeting. In this research, we introduce Decentralized Autonomous Citizen Participation Organizations (DACPOs). In a DACPO, data and actions are recorded and autonomously executed in a decentralized way. DACPOs can be used for enabling participative budgeting and thereby provide transparency, decrease the risks of fraud and corruption, and increase citizens' trust. The viability of DACPOs depends on a number of factors, including a minimum number of citizens who participate. In further research, factors influencing the use of DACPOs for participative budgeting can be further analyzed and tested.
Ranking of Classification Algorithm in Breast Cancer Based On Estrogen Receptor Using MCDM Technique

Classification algorithm selection is an important concern for breast cancer diagnosis. The traditional routine of adopting a unique performance metric for evaluating classifiers is not adequate in the case of micro-array gene expression dataset. This paper introduces an MCDM technique to evaluate classification algorithms in breast cancer forecasting by seeing different performance measure along with feature space. An empirical study is designed to support an overall assessment of classifiers on micro-array datasets using well-known MCDM technique. TOPSIS is used to rank 11 prominent assessment criteria of different classifiers. First, the sequence order of 20 classifiers along with 11 assessment criteria is generated. Further topmost classifiers are grounded on their performances highlighting the role of feature selection in the overall process supporting the genuine assessment of classifiers over any solitary performance criteria. Result indicates that AdaBoostM1 and Iterative Classifier Optimizer are graded as topmost classifiers without and with feature selection, respectively, grounded on their performances on different measures. Furthermore, the proposed MCDM-based model can reconcile distinct or even inconsistent evaluation performance to grasp a group agreement in a complicated decision-making environment.
OVERSAMPLING TECHNIQUE-BASED DATA AUGMENTATION AND 1D-CNN AND BIDIRECTIONAL GRU ENSEMBLE MODEL FOR HUMAN ACTIVITY RECOGNITION

The success of the human activity recognition (HAR) algorithm of a wearable inertial sensor depends on the extraction of good temporal dynamics from the sequential motor movement of humans. In the past, a heuristic method was used for the feature extraction process. Recently, deep neural network (DNN)-based studies, in which this process is automatically performed in the model, have shown good results. The performance of the DNN models is good and almost all processes are automated; however, they have some limitations—data-imbalance problems in the dataset and the small data decrease the performance. In this study, the HAR public dataset was improved by introducing an oversampling technique-based data-augmentation algorithm, and a stacking ensemble model with a one-dimensional convolutional neural network and a bidirectional gated recurrent unit as the head was used for classification. The HAR public dataset used Wireless sensor data mining (WISDM) and the University of California, Irvine human activity recognition using smartphones data set (UCI-HAR). Consequently, the proposed model showed better performance than that of previous studies for both datasets. The accuracy of the proposed model was 99.49% and 99.48%; the F1-score of the model was 99.84% and 99.78%, respectively.
Research on College English Teaching Quality Assessment Method Based on K-Means Clustering Algorithm

The evaluation of college teachers’ teaching ability is very important. Currently, the indicators for evaluating the quality of college English teaching are unclear and insufficient. This paper evaluates the quality of university classroom teaching from two aspects: students’ learning effect and teachers’ teaching work. This paper employs the K-means algorithm to analyze the relationship between the indicators in the evaluation model and teachers’ teaching ability, finds out the specific factors that affect teaching activities, and guides the implementation of teachers’ teaching work. At the same time, the K-means model is used to evaluate students’ learning effect, identify the relationship between the indicators in the model and teachers’ teaching ability, and find out the specific factors that affect teachers to guide the implementation of teachers’ teaching work. Experiments show that the method proposed in this paper can solve the problem that the evaluation indicators of traditional evaluation methods are not clear and insufficient and can be better applied to teaching evaluation.
A multiview graph collaborative filtering by incorporating homogeneous and heterogeneous signals

In the industrial e-commerce recommender systems, the sparsity of user–item interaction limits the improvement of the performance of collaborative filtering recommendation. Some studies have leveraged attribute co-occurrence or similar neighbors to enhance the semantic representation quality of users and items. Previous methods consider collaborative signals of homogeneous type nodes, such as <user,user>→user and <item,item>→item. By exploiting homogeneous and heterogeneous signals of attribute and neighbor views, we design a multiview graph collaborative filtering (MVGCF) network for recommendation. The MVGCF model utilizes both co-occurrence features of various attribute values and collaborative preference of various neighbors to learn the embedding representation of nodes. Experimental results show that the MVGCF is superior to the state-of-the-art models in AUC and logloss metrics by 1.41% and 3.12% for MovieLens 1M dataset, and by 2.35% and 2.31% for BookCrossing dataset. Aiming at the sparse problem with a small amount of interaction records, our findings is that attribute co-occurrence and neighbor collaboration can improve the accuracy and provide a good explanation for e-commerce recommender systems.
On the Design and Implementation of a Blockchain-Based Data Management System for ETO Manufacturing

Engineer-to-order (ETO) is a currently popular production model that can meet customers’ individual needs, for which the orders are primarily non-standard parts or small batches. This production model has caused many management challenges, including the difficulty of tracing the production process data of products and the inability to monitor order status in real-time. In this paper, by analyzing the steps of ETO manufacturing and the business process between departments in the manufacturing industry, a blockchain-based process data management system (BPDMS) is proposed. The immutable nature of the blockchain data ensures the data’s validity and consistency in each production step. Furthermore, by embedding the sequential aggregate signature in the system, the sequence verification of discrete process steps can be completed in time. Finally, an electrical equipment assembly production platform is used to discuss the specific implementation on top of the Hyperledger Fabric, a permissioned blockchain. The experiment results show that the proposed system effectively manages the process data of ETO-type production, and the real-time querying of the production status of the orders.
The Historical Relationship between the Software Vulnerability Lifecycle and Vulnerability Markets: Security and Economic Risks

Vulnerability lifecycles and the vulnerability markets are related in a manner that can lead to serious security and economic risks, especially regarding black markets. In the current era, this is a relationship that requires careful scrutiny from society as a whole. Therefore, in this study, we analyzed the actual data relating to vulnerability-regulated markets in the case of two well-known browsers, Firefox and Chrome. Our analysis shows that financial reward is the main motivation for most discoverers, whose numbers are increasing every year. In addition, we studied the correlation between vulnerability markets and the vulnerability lifecycle from many perspectives, including theoretical concepts, and statistical approaches. Furthermore, we discussed the potential risks for people and organizations in terms of security and economics. We believe that money is the main motivation in vulnerability markets and that the latter are, in turn, the main driver of the vulnerability lifecycle, which presents several risks to the software industry and to society itself. Thus, in our opinion, if vulnerability markets can be controlled, the vulnerability lifecycle will be reduced or eliminated, along with its associated risks.
Analysis of Customer Energy Consumption Patterns using an Online Fuzzy Clustering Technique

Currently, there is a high rate of generation of new information about the Energy Consumption of customers. It is important the traceability of its consumption pattern evolution to determine in real-time the services of a smart energy management system. This paper analyses the evolution of the Energy Consumption Pattern of customers using the Learning Algorithm for Multivariable Data Analysis (LAMDA). LAMDA is a fuzzy approach for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class/cluster, through the contributions of all its descriptors. LAMDA can create new classes/clusters after the training stage (online learning). If an individual does not have enough similarity to the preexisting classes/clusters, it is evaluated with respect to a threshold called the Non-Informative Class (NIC) to define if it is a new class/cluster. Particularly, the algorithm of the LAMDA family used in this paper is LAMDA-RD (Robust Distance). In the paper is analyzed the patterns of the initial grouping of the data, as well as, the patterns through their evolution (traceability). For the analysis of the patterns different metrics are considers: Calinski- Harabasz Index and Silhouette Score.
Automatic fuzzy parameter tuning for neural network models

The area of machine learning in practical applications is constantly growing. One of the main problems with implementing such solutions is related to the research phase. This is the phase where a given machine learning architecture is modeled and tested to obtain the best model for the processed data. The main problem most often turns out to be the selection of architecture and model parameters. Hence, in this paper, we propose the use of the Takagi-Sugeno-Kang controller with simulated annealing for tuning parameters of neural networks. Based on the rules scheme, the controller will analyze various parameters provided by the network and evaluate them to create the best selector for input parameters. The proposed model is an adaptive approach that selects input parameters during analysis to achieve the best accuracy. After modeling the controller, a simulated annealing algorithm is used for finding the best values of input parameters according to the fuzzy model. The solution has been subjected to performance tests based on classic and convolutional neural networks with two databases. Based on the obtained results, the proposed method can be determined as efficient and enables quick analysis of parameters in real-time.
Comparison of aggregation classes in ensemble classifiers for high dimensional datasets

In the paper, we consider a combination of classifiers for the microarray datasets - examples of high dimensional datasets. Aggregation functions, in this case, are used to combine the output values of the constituent classifiers. Some known families of aggregation functions with several examples are studied and compared concerning their usefulness in the given classification method. Based on the proposed ranking of aggregations, the top aggregation functions considering their properties were explored to find which predispose to yield better classification results.
Aggregation of Tennis Groundstrokes on the Basis of the Choquet Integral and Its Generalizations

This paper focuses on the aggregation of two tennis groundstrokes: Forehand and backhand, which are performed in every match and training session. Recognition of tennis movements is very challenging task. Therefore it is important to correctly classify the proper patterns of various tennis players. This kind of study may be a part of training or development of a player’s skills. In this study, Support Vector Machines, Multilayer Perceptron, and Spatial-Temporal Graph Convolutional Neural Networks are applied to find a match and put the move trajectories into the proper classes. The images obtained from three dimensional data recorded using the Vicon optical motion capture system are the input data for the classifiers. The images containing forehand and backhand shots are divided into two phases: The preparation and the shot together with the racket swinging as the finishing element of the move. Due to the problems with strict classification of these data, the input is also fuzzy. In order to improve the accuracy of the forehand and backhand recognition, the non-typical Choquet-like integral aggregation functions are applied as well as traditional aggregation operators like median, or voting. In particular, the so-called pre-aggregation operators with specific t-norms, overlap functions and modification of the shape of the function under the integral sign give the best results, reaching accuracy at the level of 98.88%, which is better than the above mentioned individual classifiers.
On measuring features importance in Machine Learning models in a two-dimensional representation scenario

There is a wide range of papers in the literature about the explanation of machine learning models in which Shapley value is considered to measure the importance of the features in these models. We can distinguish between these which set their basis on the cooperative game theory principles, and these focused on fuzzy measures. It is important to mention that all of these approaches only provide a crisp value (or a fix point) to measure the importance of a feature in a specific model. The reason is that an aggregation process of the different marginal contributions produces a single output for each variable. Nevertheless, and because of the relations between features, we cannot distinguish the case in which we do not know all the features. To this aim, we propose a disaggregated model which allows the analysis of the importance of the features, regarding the available information. This new proposal can be viewed as a generalization of all previous measures found in literature providing a two dimensional graph which, in a very intuitive and visual way, provides this rich disaggregated information. This information may be aggregated with several aggregation functions with which obtain new measures to establish the importance of the features. Specifically, the aggregation by the sum results in the Shapley value. We also explain the characteristics of those graphics in different scenarios of the relations among features, to raise this useful information at a glance.
An End-to-End Trainable Deep Convolutional Neuro-Fuzzy Classifier

A key challenge in artificial intelligence is the well-known tradeoff between the interpretability of an algorithm, and its accuracy. Designing interpretable, highly accurate AI models is considered essential to broad acceptance of AI technology, and is the focus of the eXplainable Artificial Intelligence (XAI) community. We report on the design of a new deep neural network that achieves improved interpretability without sacrificing accuracy. Our design is a hybrid deep learning algorithm based in part upon fuzzy logic, which performs as accurately as existing convolutional neural networks. The network is an end-to-end trainable deep convolutional network, which replaces the final dense layers (the classifier component) with a modified ANFIS. We exploit the transparency of fuzzy logic by deriving explanations, in the form of saliency maps, based on the fuzzy rules learned in the ANFIS component.
Towards Dynamic Fuzzy Interpolation Based on Rule Assessment

Fuzzy Rule Interpolation (FRI) provides a useful mechanism to derive reasonable approximate inference outcomes when presented with a sparse rule base. However, conventional FRI techniques are exploited to generate fuzzy conclusions for observations that have no matching rules in a static rule base. Any interpolated rules created during the interpolation process are abandoned once the interpolated results are obtained, despite they may contain valuable, and general or generalisable, information about the domain problem. This paper proposes an approach that helps improve the sparse rule base dynamically, through measuring and subsequently adding certain interpolated rules that are deemed of high value into the sparse rule base. In particular, the value of an interpolated rule is assessed via its similarities with those given in the sparse rule base, in an effort to determine whether it is to be incorporated in the existing rule base. Experimental results on benchmark datasets illustrate that by the use of a dynamically enriched rule base the popular transformation-based FRI algorithm is able to produce strengthened inference outcomes over those that it generates while using just the original static rule base.
Applying d-XChoquet integrals in classification problems

Several generalizations of the Choquet integral have been applied in the Fuzzy Reasoning Method (FRM) of Fuzzy Rule-Based Classification Systems (FRBCS’s) to improve its performance. Additionally, to achieve that goal, researchers have searched for new ways to provide more flexibility to those generalizations, by restricting the requirements of the functions being used in their constructions and relaxing the monotonicity of the integral. This is the case of CT-integrals, CC-integrals, CF-integrals, CF1F2-integrals and dCF-integrals, which obtained good performance in classification algorithms, more specifically, in the fuzzy association rule-based classification method for high-dimensional problems (FARC-HD). Thereafter, with the introduction of Choquet integrals based on restricted dissimilarity functions (RDFs) in place of the standard difference, a new generalization was made possible: the d-XChoquet (d-XC) integrals, which are ordered directional increasing functions and, depending on the adopted RDF, may also be a pre-aggregation function. Those integrals were applied in multi-criteria decision making problems and also in a motor-imagery brain computer interface framework. In the present paper, we introduce a new FRM based on the d-XC integral family, analyzing its performance by applying it to 33 different datasets from the literature.