Reviewerly/exHarmony huggingface dataset:
Automated algorithm configuration and design

tutorial Automated algorithm configuration and design Authors: Thomas Stützle IRIDIA, CoDE, ULB, Brussels, Belgium IRIDIA, CoDE, ULB, Brussels, BelgiumView Profile , Manuel López-Ibáñez University of Málaga, Spain University of Málaga, SpainView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 959–982https://doi.org/10.1145/3449726.3461404Published:08 July 2021Publication History 0citation172DownloadsMetricsTotal Citations0Total Downloads172Last 12 Months57Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Gamma-Generalized Inverse Gaussian Class of Distributions

Gamma distribution nests exponential, chi-squared and Erlang distributions; while generalized Inverse Gaussian distribution nests quite a number of distributions.The aim of this paper is to construct a gamma mixture using Generalized inverse Gaussian mixing distribution.The rth moment of the mixture is obtained via the rth moment of the mixing distribution.Special cases and limiting cases of the mixture are deduced.
Research on Heterogeneous Data Transfer Method for Power Energy Management

At present, the amount of data collected by electricity information acquisition system increases dramatically. For current data growth and business needs, based on the research results of Sqoop and DataX, which are cross-platform heterogeneous data migration tools based on big data platform, this paper proposes to adopt Spark technology to study the cross-platform heterogeneous data migration technology of big data acquisition platform (cloud platform) and Oracle database. Two example tests were conducted to verify the support and performance of the Spark migration tool for migration between data in different formats and the ability to read, parse, and support data in particular formats.
Path Planning of underwater vehicle based on improved RRT* algorithm

RRT* (rapidly exploring random tree star) algorithm is improved to solve the problems of long search time and large random tree expansion in path planning. In order to reduce the randomness of RRT* in search, the improved algorithm first introduces the target bias strategy to the sampling points, so as to enhance the target orientation. Then, the step size expansion method is used to allocate dynamic weights to the target points and the sampling points in each new point expansion, so that the expansion method is determined by the target points and the sampling points together, so as to reduce the search time. Finally, the final path is smoothed by cubic polynomial interpolation. Matlab is used to compare the standard RRT* algorithm and the improved RRT* algorithm in two-dimensional and three-dimensional environment. The results show that the search time of the improved RRT* algorithm is significantly reduced, and the final path length is significantly reduced.
Reducing Adversarial Examples Through Boundary Methods

At present, deep neural networks are widely used on a variety of tasks in computer vision, machine translation, speech recognition, etc. Unfortunately, this inexplicable black-box structure lacks robustness. In previous work, adversarial examples are proposed to describe the phenomenon that neural networks are vulnerable to be attacked. Interestingly, in addition to the widely accepted “noise” or “bugs”, recent research has shown that the adversarial examples are “non-robust features”, because the classifier trained on adversarial examples retains the ability to generalize to the original test set. In this paper, we link the relationship between large margin methods and the capabilities to defend against adversarial attacks, and further link the relationship to non-robust features. We compare the defense capabilities of the models trained by large margin loss function and general cross-entropy loss function against Fast Gradient Sign Method (FGSM) attack and Project Gradient Descent (PGD) attack and evaluate non-robust features extracted by the trained models. It is proved that the model trained with large margin loss function is more resistant to adversarial perturbation and it gets fewer non-robust features. This further indicates a direction for training robust networks: to balance model test accuracy and defense capabilities. Based on the margin method, we combined thickness to strengthen the description of the decision boundary. Through the feature space visualization, the effect of the boundary methods on the robustness of the model is intuitively illustrated.
Deep reinforcement learning in loop fusion problem

Loops’ execution time and resource consumption are one of the interest points and vital issues in the field of appraising complex scientific or computational algorithms. This issue caused the proposal of Loop performance optimization techniques such as fusion. In the literature, loop fusion merges the loops by taking into account a set of properties associated with the loops or the system on which the resulting code will be executed. The number of these factors and their interactions on the one hand, and the high runtime of available comprehensive approaches, on the other hand, reveals the need for a new method that could be concerned for further progress in solving this NP-hard problem. For the first time, Deep Reinforcement Learning Loop Fusion (DRLLF) advanced to be an ideal solution for the challenge in this article. For the proposed framework, a particular matrix is configured as the inputs of a deep neural network based on the information of the problem, namely data dependencies, data reuse, loops’ types, and computer system’s register size. These randomly generated matrixes are used in the training phase by reinforcement learning to get the imperative experience on predicting a profitable distribution over loops’ various fusion orders. In the evaluations performed, the presented algorithm was able to achieve the same or better performance in terms of speedup rate, comparing with the methods under study, approximately averaged in 7.36 percent better results. The considerable improvement observed in the results, besides the low run time, proves the comprehensiveness and superiority of this approach.
Comparison of Ciphertext Features for Data Deduplication

With the exponential growth of data storage, more and more users pay more attention to the privacy of their data, and encryption algorithms are used to encrypt sensitive data before outsourcing data to cloud servers. This paper proposes a safe comparison scheme for data before deduplication. The user encrypts the data and uploads it to the cloud server, and writes the data ciphertext hash value into the authentication record. The cloud server contrasts the data with the same characteristics uploaded twice. The safety analysis shows that the scheme has good practicability and safety.
A Fast Multi-Object Tracking Method using DDR Descriptor

In the visual intelligence system, the two main processes for visual analysis are object detection and object tracking. At present, the first process is overcome by using the YOLO-based frameworks. It shows very good results as the object detection tool using deep learning technique. However, many researchers realize that there are lots of obstacles and challenges in the second process. Therefore, the objective of this paper is to propose the Distance, Direction, and Aspect Ratio (DDR) method that can be used to improve the effectiveness of object tracking process. The main contribution of this paper is to present the technique that achieves greater than Deep SORT algorithm in term of speed for tracking objects. The experimental results show that our proposed method can be used to track object more accurately compared to a Deep SORT algorithm but consume less processing time than a Deep SORT algorithm at a frame rate, 25 frames per second on average.
Robust path planning implementation based on a hierarchical optimization approach towards optimal solution

The classical sampling algorithm, RRT*, claims to achieve an optimal solution but with a slow convergence rate or taking an infinite time. To address this limitation, this paper presents a novel hierarchical path planning approach which mainly includes two stages: finding a high-quality initial solution and optimizing key nodes of the initial path. In the first stage, the boundaries of each obstacle are firstly expanded by an amount that is equal to the size of the robot plus a safety distance. An improved RRT* algorithm with monitoring sampling strategy is then applied to find a collision-free path as quickly as possible. In second stage, the direct reachable nodes of the initial path, namely key nodes, are extracted to describe the skeleton of the obtained path. The selected key nodes yield the biasing sampling nodes to make up a new path. When a better path is found, the biasing sampling nodes replace the corresponding old ones. This process guides the path to converge to an optimal one while maintaining its reachability. It is foreseeable that the optimized path will be improved continuously as the number of iterations increases. Simulation results in various types of environments are conducted to demonstrate the superiority of the proposed approach.
LDAnet:a discriminant subspace for metric-based few-shot learning

Deep neural networks have surpassed humans in some cases, such as image recognition and image classification, with numerous labeled training samples. However, multiple tasks cannot provide enough labeled samples, training a neural network with a limited number of labeled samples is challenging. Therefore, meta-learning emerges. It aims to summarize from few-shot tasks and can quickly adapt to new categories. However, it is challenging to ensure that the trained feature extractor in the meta-train can adapt to the novel class in the meta-test. In this paper, we propose a subspace learning module to deal with the feature-mismatch problem. Specifically, we embed the linear discriminant analysis (LDA) module into the few-shot learning framework. It guarantees the feature embeddings in each few-shot task are more discriminative via increasing the inter-class distance and reducing the intra-class variances. We conduct experiments on four benchmark few-shot learning datasets, namely mini-Imagenet, CIFAR-FS, tiered-ImageNet, and CUB, to demonstrate the effectiveness of the proposed module. Experimental results show that this method has better performance than the state-of-the-art approaches.
Neural Machine Translation Based on Gumbel Tree Model optimization

The neural machine translation model has been greatly improved compared with the traditional machine translation model, and has been highly valued in the field of natural language processing. Research shows that the addition of tree model is helpful to improve the accuracy of machine translation, and the tree model can enhance the relationship between long distance words in the sentence. This paper mainly studies the NMT method combined with the Gumbel-Tree-LSTM. Experiments show that the proposed joint model can improve the translation performance of multilingual datasets.
Camouflaged Object Segmentation with Transformer

The Vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. This paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. The high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of $$E_\phi $$ [8] on the COD10K contrasted to SINet.
A Data Driven Approach for Detection and Correction of Spelling Errors in Sinhala Essays

This paper proposes novel approaches for checking and correcting spelling errors in Sinhala essays written by candidates of grade five scholarship examination. They don't have a proper mechanism to identify their spelling mistakes in essays by themselves. Spelling errors by such students may occur due to the violation of spelling rules, missing or adding of letters, missing modifiers, inaccurate spelling in a similar structure, and similar sound letters]. To mitigate such challenges, the Sinhala corpus file has been developed to identify the accurate and inaccurate spellings of the written words. The role of this application is to identify the correct and incorrect words which are entered by the user and generate the most correct words as suggestions for the incorrect words. This paper introduces three new novel approaches to detect the correctly spelled words in Sinhala essays namely object word checker method, suffixes checker method and similar word checker method. With addition to that this paper discusses three approaches to generate accurate suggestions including one novel approach. When evaluating the accuracy of the spelling error detection and correction module the overall results for precision, recall, and the f -measure were recorded as 83.05%, 85.57%, and 86.62% respectively.
MixNN: Combating Noisy Labels in Deep Learning by Mixing with Nearest Neighbors

Noisy labels are ubiquitous in real-world datasets, especially in the ones from web sources. Training deep neural networks on noisy datasets is a challenging task, as the networks have been shown to overfit the noisy labels in training, resulting in performance degradation. When trained on noisy datasets, deep neural networks have been observed to fit t he clean samples during an "early learning" phase, before eventually memorizing the mislabeled samples. We further explore the representation distributions in the early learning stage and find that the representations of similar samples from the same classes congregate regardless of their noisy labels. Inspired by these findings, we propose MixNN, a novel framework to mitigate the influence of noisy labels. In contrast with existing methods, which identify and eliminate the mislabeled samples, we modify the mislabeled samples by mixing them with their nearest neighbors through a weighted sum approach. The weights are calculated with a mixture model learning from the sample loss distribution. To enhance the performance in the presence of extreme label noise, we propose a strategy to estimate the soft targets by gradually correcting the noisy labels. We demonstrate that the estimated targets yield a more accurate approximation to ground truth labels and a better quality of the learned representations with more separated and clearly bounded clusters. Extensive experiments in two benchmarks and two challenging real-world datasets demonstrate that our approach outperforms the existing state-of-the-art methods.
Physical Examination Package Intelligent Recommendation System Based on Deep Learning

In recent years, with the rapid development of big data and artificial intelligence, deep learning, as an important tool of feature extraction, has been applied in all aspects of people’s life. Recommender system has become the main module in various mainstream applications because of its powerful ability of data processing and information retrieval. This paper focuses on the application of convolutional neural network on physical examination package recommendation system. Taking the routine physical examination package recommendation system as an example, BP neural network algorithm and convolutional neural network algorithm are used to realize package recommendation. We compare BP neural network algorithm and convolutional neural network algorithm with other machine algorithms. The experimental results show that our methods are effective.
Company Name Recognition Based on Hybrid Method

With the development of Internet, there is more and more information which needs to be progress based on language. Named Entity Recognition is a good method to promote models of machine learning to obtain the structure features and contextual constraint, thus understanding the whole sentence, while company name recognition is an important part among Named Entity Recognition. In this paper, a model based on BERT-BiLSTM-CRF and text match is put forward to realize company name recognition. The text match based on company name feature thesaurus can recognize abbreviations more efficiently, thus improving the accuracy and recall of company name recognition.
Performance Benchmarking Frameworks for Distributed Ledger Technologies

Performance, security, and privacy are important factors to consider when implementing Blockchain in real-world industrial and governance applications. Blockchain is a Distributed Ledger Technology (DLT) that ensures integrity, immutability, transparency and decentralisation. Various Distributed Ledger Technology platforms have emerged over time. These platforms differ in terms of architecture, access permissions, mathematical models, and consensus algorithms. A performance benchmark for DLTs will aid in analysing them based on performance metrics. This paper presents a comparative analysis of existing benchmarking frameworks for DLT performance evaluation. The paper concludes with future research challenges.
The Improvement of Computing for 3d Information Reproduction of a Human Body Scanner with Various Sensors: Human Body 3d Scanner

Collector Operating Characteristic (ROC) bends are plots of genuine awesome expense versus bogus eminent charge which are helpful for assessing twofold order models, but intense to use for becoming more acquainted because the way that the Area Under the Curve (AUC) is non-arched. ROC bends can moreover be utilized in various issues that have bogus awesome and real fine charges, for example, changepoint identification. In this more noteworthy normal setting, the ROC bend is currently not typically monotonic, thus in case circles existing the Area Under the Curve (AUC) can be higher than one. Another differentiable substitute misfortune including alluded is suggested as the Area Under Min (AUM) (FP, FN). The AUM slope can be successfully figured and utilized in another dominating calculation. In this experimental, twofold arrangement and changepoint recognition issues are investigated. It is found that, this new AUM minimization calculation results in expanded AUC and comparable speed comparative with going before baselines.
Fuzzy Cognitive Mapping Analysis Metrics for Team Communication

As fuzzy cognitive mapping becomes more ubiquitous and transferable between fields, there is a growing necessity to be able to compare its results. However, the comparison of these maps is not widespread and often seems to be circumvented. This paper gathers graph analysis metrics and demonstrates how they can be used for fuzzy cognitive map comparison. For this, various metrics are described and applied to these fuzzy cognitive maps, specifically when differentiating team performance levels. This paper shows how existing metrics can be applied and what their results imply, and current gaps in the domain of fuzzy cognitive map comparison.
Local search algorithms for the composite retrieval problem

Abstract In this paper, we address a problem arising in information retrieval (IR) called composite retrieval problem (CRP) of diverse and complementary bundles. The CRP aims to group items into bundles and then select a subset of such bundles, so that we can maximise the similarity of the items within a bundle and, simultaneously, can maximise the complementarity of the selected bundles. To this end, the CRP approach considers the existing relations among items' attributes, leading to the selection of bundles that satisfy users' expectations without the needing for any refining query and, thus, improving the searching experience, with respect to traditional IR approaches. In this study, we propose three efficient yet straightforward algorithms, namely Local Search, Iterative Local Search and Variable Neighbourhood Search. Further, two different neighbourhood moves are evaluated at each algorithm. Although the first neighbourhood move is focused on the exploitation of the nearby search space, the second one is focused on the exploration of larger portions of the search space. All these algorithms are applied to two real‐world publicly available instances and compared to the state‐of‐the‐art algorithms in CRP. Obtained results suggest that combining both neighbourhood moves leads to better results in terms of both, the complementarity of the bundles and similarity of the items. Further, results show that, after statistical analysis, the proposed algorithms are significantly better, for the vast majority of the experiments performed in this study, when compared to the state‐of‐the‐art algorithms in CRP.
Missing data filling method based on Aitchison simplex space

Because the Aitchison simplex spatial data is limited by bounded and definite sum constraints, the data generally does not meet the multivariate normal distribution, and there is a strict or approximate linear relationship between some variables, it is very difficult to establish its data model. In multiple regression analysis, a small change in the sample attribute value will greatly disturb the estimated value of the regression coefficient, resulting in the extremely unstable regression coefficient, and the existing general statistical analysis methods cannot be used to properly interpret and process the data. To solve this problem, based on the relevant definitions of complete algebraic operations in Aitchison simplex space, this paper proposes a filling method based on missing data in simplex space: firstly, the k-means method is used for initial filling in simplex space, then the equidistant logarithm ratio transformation is carried out, and finally the principal component method is used to correct the initial filling value. The example results show that the effect of using the principal component correction filling method based on the proposed complete algebraic operation system of simplex space is better than that of other filling methods.