Reviewerly/exHarmony huggingface dataset:
An Enhanced Social Spider Colony Optimization for Global Optimization

An improved variant of the social spider optimization algorithm is introduced. It is inspired by the hunting and mating behaviors of spiders in nature. We call it enhanced social spider colony optimization (ESSCO). The performance of ESSCO is evaluated using the benchmark CEC 2020. To validate the proposed algorithm, the obtained statistical results are compared to eleven recent state-of-the-art metaheuristic algorithms. The comparative study shows the competitiveness of ESSCO in finding efficient solutions to the considered test functions.Zitouni, Farouq Harous, Saad Maamri, Ramdane
Dynamic Ensemble Selection for Imbalanced Data Stream Classification with Limited Label Access

Real data streams often, in addition to the possibility of concept drift occurrence, can display a high imbalance ratio. Another important problem with real classification tasks, often overlooked in the literature, is the cost of obtaining labels. This work aims to connect three rarely combined research directions i.e., data stream classification, imbalanced data classification, and limited access to labels. For this purpose, the behavior of the desisc-sb framework proposed by the authors in earlier works for the classification of highly imbalanced data stream was examined under the scenario of limited label access. Experiments conducted on synthetic and real streams confirmed the potential of using desisc-sb to classify highly imbalanced data streams even in the case of low label availability.
Design and Implementation of Web front End of Computer Game Platform Based on Electron

This paper studies the current situation of computer game platforms, summarizes the advantages and disadvantages of existing platforms, and proposes to build an interactive computer game platform with a personalized interface based on the Electron front-end framework, using JavaScript, HTML and CSS technology stacks. The system integrates the characteristics of different chess games, designs a reasonable data structure representing the chessboard and chess pieces, and establishes a game model for specific chess types. The interface design of the battle platform follows certain specifications. In addition to the regular menu interface and game battle interface, the corresponding functions are also designed and realized, such as timer, replaying disk, and penitence chess. The chess motion animation display is realized and the chess game is controlled according to the chess rules. By using the computer game platform, the game operation and rule judgment can be realized automatically. Experiments show that the system is feasible and effective, and it has practical significance for the future research of computer games.
Position Heaps for Cartesian-Tree Matching on Strings and Tries

The Cartesian-tree pattern matching is a recently introduced scheme of pattern matching that detects fragments in a sequential data stream which have a similar structure as a query pattern. Formally, Cartesian-tree pattern matching seeks all substrings \(S'\) of the text string S such that the Cartesian tree of \(S'\) and that of a query pattern P coincide. In this paper, we present a new indexing structure for this problem, called the Cartesian-tree Position Heap (CPH). Let n be the length of the input text string S, m the length of a query pattern P, and \(\sigma \) the alphabet size. We show that the CPH of S, denoted \(\mathsf {CPH}(S)\), supports pattern matching queries in \(O(m (\sigma + \log (\min \{h, m\})) + occ )\) time with O(n) space, where h is the height of the CPH and \( occ \) is the number of pattern occurrences. We show how to build \(\mathsf {CPH}(S)\) in \(O(n \log \sigma )\) time with O(n) working space. Further, we extend the problem to the case where the text is a labeled tree (i.e. a trie). Given a trie \(\boldsymbol{T}\) with N nodes, we show that the CPH of \(\boldsymbol{T}\), denoted \(\mathsf {CPH}(\boldsymbol{T})\), supports pattern matching queries on the trie in \(O(m (\sigma ^2 + \log (\min \{h, m\})) + occ )\) time with \(O(N \sigma )\) space. We also show a construction algorithm for \(\mathsf {CPH}(\boldsymbol{T})\) running in \(O(N \sigma )\) time and \(O(N \sigma )\) working space.
Algebras of Sets and Coherent Sets of Gambles

In a recent work we have shown how to construct an information algebra of coherent sets of gambles defined on general possibility spaces. Here we analyze the connection of such an algebra with the set algebra of sets of its atoms and the set algebra of subsets of the possibility space on which gambles are defined. Set algebras are particularly important information algebras since they are their prototypical structures. Furthermore, they are the algebraic counterparts of classical propositional logic. As a consequence, this paper also details how propositional logic is naturally embedded into the theory of imprecise probabilities.
Hierarchical complementary learning for weakly supervised object localization

Weakly supervised object localization (WSOL) is a challenging problem that aims to localize objects without ground-truth bounding boxes. A common approach is to train the model that generates a class activation map (CAM) to localize the discriminative features of the object. Unfortunately, the limitation of this method is that they detect just a part of the object and not the whole object. To solve this problem, previous works have removed some parts of the image (Zhang et al., 2018; Zhang et al., 2018; Singh and Lee, 2017; Choe and Shim, 2019) to force the model to detect the full object extent. However, these methods require one or many hyper-parameters to erase the appropriate pixels on the image, which could involve a loss of information. In this paper, we propose a Hierarchical Complementary Learning Network method (HCLNet) that helps the CNN to perform better on classification and localization. HCLNet uses a complementary CAM to generate multiple maps that detect different parts of the object. Unlike previous works, this method does not need any extra hyper-parameters, as well as does not introduce a big loss of information. In order to fuse these different maps, two different fusion strategies known as the addition strategy and the l1-norm strategy have been used. These strategies allow to detect the whole object while excluding the background. Extensive experiments show that HCLNet obtains better performance than state-of-the-art methods.
A QoS-driven Customizable Forecasting Framework for Blockchain Transaction Fee Recommendation

In this paper, we propose the QoS-driven Customizable Forecasting Framework (QCFF) to recommend Decentralized Applications (DApps) a transaction fee (TF) to reduce cost of confirming a transaction in blockchain (BC). QCFF provides a uniform API for DApps to access various TF prediction models according to QoS requirements. In addition, a customized TF prediction model can be easily plugged into QCFF. The design and implementation show that QCFF is feasible and applicable for DApps to acquire a recommended TF from various TF prediction models according to their QoS requirements. According to the preliminary analysis, QCFF can help DApps saving near 3.5 USD per transaction according to the ETH price as 2,800 USD in 5/20/2021.
Study on Blended Smart Teaching Deeply Integrating Information Technology

The development of blended smart teaching deeply integrating information technology requires teachers to be competent in smart teaching at first. In this paper, based on teaching objectives, C language programming course is taken as an example to study the design concept of blended smart teaching, the design of blended teaching model based on Rain Classroom, a smart teaching tool, and effect evaluation method for blended learning. In addition, essential elements for the development of blended smart teaching deeply integrating information technology are summarized in combination with the practical teaching. It has been found that the blended smart teaching deeply integrating information technology is the inevitable outcome of the development of the times and also the reform trend of higher education, which is of extremely important practical significance and provides a new teaching design for course teaching in the post-epidemic era.
Digital Speech Makeup: Voice Conversion Based Altered Auditory Feedback for Transforming Self-Representation

Makeup (i.e., cosmetics) has long been used to transform not only one’s appearance but also their self-representation. Previous studies have demonstrated that visual transformations can induce a variety of effects on self-representation. Herein, we introduce Digital Speech Makeup (DSM), the novel concept of using voice conversion (VC) based auditory feedback to transform human self-representation. We implemented a proof-of-concept system that leverages a state-of-the-art algorithm for near real-time VC and bone-conduction headphones for resolving speech disruptions caused by delayed auditory feedback. Our user study confirmed that conversing for a few dozen minutes using the system influenced participants’ speech ownership and implicit bias. Furthermore, we reviewed the participants’ comments about the experience of DSM and gained additional qualitative insight into possible future directions for the concept. Our work represents the first step towards utilizing VC to design various interpersonal interactions, centered on influencing the users’ psychological state.
A Study of The Robustness of Raw Waveform Based Speaker Embeddings Under Mismatched Conditions

In this paper, we conduct a cross-dataset study on parametric and non-parametric raw-waveform based speaker embeddings through speaker verification experiments. In general, we observe a more significant performance degradation of these raw-waveform systems compared to spectral based systems. We then propose two strategies to improve the performance of raw-waveform based systems on cross-dataset tests. The first strategy is to change the real-valued filters into analytic filters to ensure shift-invariance. The second strategy is to apply variational dropout to non-parametric filters to prevent them from overfitting irrelevant nuance features. By combining these strategies, we achieve results comparable to spectral based systems on both the VoxCeleb and VOiCEs datasets. Futhermore, we demonstrate that the learned filters carry little noise compared to existing non-parametric learnable front-ends.
Building a Reusable Defect Resolution Time Prediction Model Based on a Massive Open-Source Dataset: An Industrial Report

This research describes an attempt to build a model aimed to reproduce predictive ability of an experienced human tester and provide analytical insights beyond statistical dependencies. An important step in this direction is working out an approach to create a dataset that has a sufficient size and is organized as a set of features allowing to train a model yielding reliable results without being dependent on data from specific projects. In this paper, we outline our experience of designing a general approach for time-to-resolve prediction for defect reports to assist in defect management tasks. We describe the process of dataset preparation based on the massive open-source data and the experiments using bi- and multi-labeling classification. The article covers the evolved understanding of the task, approaches to classification, and comparison of the results.
Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.
Influence of surface roughness on the spectroscopic characterization of jadeite and greenstones archaeological artifacts: The axe-god pendants case study

Spectroscopic techniques are commonly used for the non-invasive characterization of the molecular and elemental composition of greenstone archaeological artifacts. The surface topography of these artifacts is greatly influenced by the crafting and polishing techniques employed in their making. However, no study of the effect of roughness on spectra has ever been reported for greenstones. Here we show that infrared, Raman and X-ray fluorescence spectra are strongly influenced by the sample's surface roughness. Spectral changes were seen in both geological (45 jadeite and green stone samples) and archaeological artifacts (12 axe-God pendants); in every case, the variations were more prominent in samples with higher arithmetic average height values. The results show that these changes can affect the interpretation of the spectroscopic data and limit the efficacy of statistical analysis. Consequently, any spectroscopic characterization of this type of samples should be performed preferably in areas with lower values of roughness parameters. Overall, FT-IR appears to be the most advantageous technique to distinguish the differences in mineral composition of this type of samples during in situ studies; its performance was evaluated with an innovative statistical analysis that treats the spectra as functional data. Additionally, the results suggest that confocal Raman spectroscopy is an ideal complementary technique that enhances mineralogical characterization, nevertheless its applicability is limited to laboratory settings.
Milliseconds Color Stippling

Stippling is a popular and fascinating sketching art in stylized illustrations. Various digital stippling techniques have been proposed to reduce tedious manual work. In this paper, we present a novel method to create high-quality color stippling from an input image in milliseconds. The key idea is to obtain stipples with predetermined incremental 2D sample sequences, which algorithms generate with sequential incrementality and distributional uniformity features. Two typical sequences are employed in our work: one is constructed from incremental Voronoi sets, and the other is from Poisson disk distributions. A threshold-based algorithm is then applied to determine stipple appearance and guarantee result quality. We extend color stippling with multitone level and radius adjustment to achieve improved visual quality. Detailed comparisons of the two sequences are conducted to explore further the strengths and weaknesses of the proposed method. For more information, please visit https://gitlab.com/maleiwhat/milliseconds-color-stippling.
A Mixed Reality Approach to Visualizing Cultural Heritage Artefacts

This paper reviews some existing technologies for mixed reality visualization of cultural and historical heritage artefacts. It describes a project, whose main aim is to virtually restore partly destroyed archaeological object by first building a 3D model of the original artefact and then rendering it on top of the real object in real world. It also proposes that when the user viewpoint does not change much in space, then cheaper hologram projecting technologies, like 3Dholonet or musion3D, originally designed for public performances, can be used for mixed reality visualization. A pilot project is being implemented, whose main idea is to build a virtual 3D model and project it as a hologram on top of the real object using two different technologies. The two resulting scenes will be evaluated by a group of pilot users, who will be asked to share their virtual experience and compare and rank the two approaches. The paper draws some conclusions and addresses future work
Graph Neural Networks in Natural Language Processing

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Ego-Deliver: A Large-Scale Dataset For Egocentric Video Analysis

The egocentric video provides a unique view of event participants to show their attention, vision, and interaction with objects. In this paper, we introduce Ego-Deliver, a new large-scale egocentric video benchmark recorded by takeaway riders about their daily work. To the best of our knowledge, Ego-Deliver presents the first attempt in understanding activities from the takeaway delivery process while being one of the largest egocentric video action datasets to date. Our dataset provides a total of 5,360 videos with more than 139,000 multi-track annotations and 45 different attributes, which we believe is pivotal to future research in this area. We introduce the FS-Net architecture, a new anchor-free action detection approach handling extreme variations of action durations. We partition videos into fragments and build dynamic graphs over fragments, where multi-fragment context information is aggregated to boost fragment classification. A splicing and scoring module is applied to obtain final action proposals. Our experimental evaluation confirms that the proposed framework outperforms existing approaches on the proposed Ego-Deliver benchmark and is competitive on other popular benchmarks. In our current version, Ego-Deliver is used to make a comprehensive comparison between algorithms for activity detection. We also show its application to action recognition with promising results. The dataset, toolkits and baseline results will be made available at: https://egodeliver.github.io/EgoDeliver_Dataset/
Modeling and Optimization of NO&lt;i&gt;&lt;sub&gt;x&lt;/sub&gt;&lt;/i&gt; Emission from a 660 MW Coal-Fired Boiler Based on the Deep Learning Algorithm

With the increasingly strict environmental protection policies, restrictions on NOx emissions are becoming increasingly stringent. This paper focuses on modeling and optimizing NOx emission for a coal-fired boiler with advanced deep learning approaches. Three types of deep recurrent neural network models, including recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU), are developed to model the relationship between operational parameters and NOx emission of a 660 MW boiler. The hyperparameters of the models are selected by grid search and the effects of the hyperparameters on the prediction results are analyzed. Compared with the traditional back propagation neural network (BP), support vector machine (SVM) models and deep belief network (DBN), the deep recurrent neural network models have higher prediction accuracy. The experimental results show that the GRU-based NOx prediction model has the best prediction performance among the proposed models. Then, the predicted NOx emission is used as the objective of searching the optimal parameters for the boiler combustion through the grey wolf optimization (GWO) algorithm. The searching process of GWO is convergent. According to the simulation results, the declines in the NOx emissions in the two selected cases were 19.49% and 17.96%, which are reasonable achievements for the boiler combustion process.
HDA-Net: Horizontal Deformable Attention Network for Stereo Matching

Stereo matching is a fundamental and challenging task which has various applications in autonomous driving, dense reconstruction and other depth related tasks. Contextual information with discriminative features is crucial for accurate stereo matching in the ill-posed regions (textureless, occlusion, etc.). In this paper, we propose an efficient horizontal attention module to adaptively capture the global correspondence clues. Compared with the popular non-local attention, our horizontal attention is more effective for stereo matching with better performance and lower consumption of computation and memory. We further introduce a deformable module to refine the contextual information in the disparity discontinuous areas such as the boundary of objects. Learning-based method is adopted to construct the cost volume by concatenating the features of two branches. In order to offer explicit similarity measure to guide learning-based volume for obtaining more reasonable unimodal matching cost distribution we additionally combine the learning-based volume with the improved zero-centered group-wise correlation volume. Finally, we regularize the 4D joint cost volume by a 3D CNN module and generate the final output by disparity regression. The experimental results show that our proposed HDA-Net achieves the state-of-the-art performance on the Scene Flow dataset and obtains competitive performance on the KITTI datasets compared with the relevant networks.
Hand Gesture Alphabet Recognition for American Sign Language using Deep Learning

Speech impairment limits a person's capacity to speak and communicate with others, forcing them to adopt other communication methods such as sign language. Sign language is not that widely used technique by the deaf. To solve this problem, we developed a powerful hand gesture detection tool that can easily monitor both dynamic and static hand motions with ease. Gesture recognition aims to translate sign language into voice or text for individuals who have a rudimentary comprehension of that, which will be a tremendous help in communication between deaf-mute and hearing people. We describe the design and implementation of an American Sign Language (ASL) fingerspelling translator based on spatial feature identification using a convolutional neural network.
SEA: Graph Shell Attention in Graph Neural Networks

A common problem in Graph Neural Networks (GNNs) is known as over-smoothing. By increasing the number of iterations within the message-passing of GNNs, the nodes’ representations of the input graph align and become indiscernible. The latest models employing attention mechanisms with Graph Transformer Layers (GTLs) are still restricted to the layer-wise computational workflow of a GNN that are not beyond preventing such effects. In our work, we relax the GNN architecture by means of implementing a routing heuristic. Specifically, the nodes’ representations are routed to dedicated experts. Each expert calculates the representations according to their respective GNN workflow. The definitions of distinguishable GNNs result from k-localized views starting from the central node. We call this procedure Graph Sh ell Attention (SEA), where experts process different subgraphs in a transformer-motivated fashion. Intuitively, by increasing the number of experts, the models gain in expressiveness such that a node’s representation is solely based on nodes that are located within the receptive field of an expert. We evaluate our architecture on various benchmark datasets showing competitive results while drastically reducing the number of parameters compared to state-of-the-art models.