Reviewerly/exHarmony huggingface dataset:
Sensitivity matrices as keys to local structural system properties of large-scale nonlinear systems

Sensitivities are shown to play a key role in a highly efficient algorithm, presented in this paper, to establish three fundamental structural system properties: local structural identifiability, local observability, and local strong accessibility. Sensitivities have the advantageous property to be governed by linear dynamics, also if the system itself is nonlinear. By integrating their linear dynamics over a short time period, and by sampling the result, a sensitivity matrix is obtained. If this sensitivity matrix satisfies a rank condition, then the local structural system property under investigation holds. This rank condition will be referred to in this paper as the sensitivity rank condition (SERC). Applying a singular value decomposition (SVD) to the sensitivity matrix not only determines its rank but also pinpoints exactly the system components causing a possible failure to satisfy the local structural system property. The algorithm is highly efficient because integration of linear systems over short time-periods and computation of an SVD are computationally cheap. Therefore, it allows for the handling of large-scale systems in the order of seconds, as opposed to conventional algorithms that mostly rely on Lie series expansions and a corresponding Lie algebraic rank condition (LARC). The SERC and LARC algorithms are mathematically and computationally compared through a series of examples.
Control with adaptive Q-learning: A comparison for two classical control problems

This paper evaluates adaptive Q-learning (AQL) and single-partition adaptive Q-learning (SPAQL), two algorithms for efficient model-free episodic reinforcement learning (RL), in two classical control problems (Pendulum and CartPole). AQL adaptively partitions the state–action space of a Markov decision process (MDP), while learning the control policy, i.e., the mapping from states to actions. The main difference between AQL and SPAQL is that the latter learns time-invariant policies, where the mapping from states to actions does not depend explicitly on the time step. This paper also proposes the SPAQL with terminal state (SPAQL-TS), an improved version of SPAQL tailored for the design of regulators for control problems. The time-invariant policies are shown to result in a better performance than the time-variant ones in both problems studied. These algorithms are particularly fitted to RL problems where the action space is finite, as is the case with the CartPole problem. SPAQL-TS solves the OpenAI Gym CartPole problem, while also displaying a higher sample efficiency than trust region policy optimization (TRPO), a standard RL algorithm for solving control tasks. Moreover, the policies learned by SPAQL are interpretable, while TRPO policies are typically encoded as neural networks, and therefore hard to interpret. Yielding interpretable policies while being sample-efficient are the major advantages of SPAQL. The code for the experiments is available at https://github.com/jaraujo98/SinglePartitionAdaptiveQLearning.
Revisiting the building of past snapshots — a replication and reproduction study

Abstract Context Building past source code snapshots of a software product is necessary both for research (analyzing the past state of a program) and industry (increasing trustability by reproducibility of past versions, finding bugs by bisecting, backporting bug fixes, among others). A study by Tufano et al. showed in 2016 that many past snapshots cannot be built. Objective We replicate Tufano et al.’s study in 2020, to verify its results and to study what has changed during this time in terms of compilability of a project. Also, we extend it by studying a different set of projects, using additional techniques for building past snapshots, with the aim of extending the validity of its results. Method (i) Replication of the original study, obtaining past snapshots from 79 repositories (with a total of 139,389 commits); and (ii) Reproduction of the original study on a different set of 80 large Java projects, extending the heuristics for building snapshots (300,873 commits). Results We observed degradation of compilability over time, due to vanishing of dependencies and other external artifacts. We validated that the most influential error causing failures in builds are missing external artifacts, and the less influential is compiling errors. We observed some facts that could lead to the effect of the build tool on past compilability. Conclusions We provide details on what aspects have a strong and a shallow influence on past compilability, giving ideas of how to improve it. We could extend previous research on the matter, but could not validate some of the previous results. We offer recommendations on how to make this kind of studies more replicable.
Product Processing Quality Classification Model for Small-Sample and Imbalanced Data Environment

With the rapid development of machine learning technology, how to use machine learning technology to empower the manufacturing industry has become a research hotspot. In order to solve the problem of product quality classification in a small sample data and imbalanced data environment, this paper proposes a data generation model called MSMOTE-GAN, which is based on Mahalanobis Synthetic Minority Oversampling Technology (MSMOTE) and Generative Adversarial Network (GAN). Among them, MSMOTE is proposed to solve the problem of the sample biased to the majority class expanded by methods such as GAN in a sample imbalanced environment. Based on the traditional SMOTE method, the sample distance measurement method is modified from Euclidean distance to Mahalanobis distance, taking into account the correlation between attributes and the influence of dimensions on the sample distance. In the data generation model, MSMOTE is used to balance the positive and negative samples in the data. GAN generates fake data with the same distribution as the original data based on a balanced data set and expands the sample size to solve the problems of overfitting and insufficient model expression ability that occur when the sample size is too small. The quality classification framework of water heater liner based on the data generation model and Random Forest is constructed, and the process of the quality classification of water heater liner under the environment of small sample data and imbalanced data is fully described. This paper compares the MSMOTE-GAN model, Bootstrap, and tableGAN on the water heater liner production line data set and the public data set. The experimental result shows that the expanded data set of the MSMOTE-GAN model can effectively improve the performance of the classification model.
Combined satellite and portable XRF exploration mapping of the Zaaiplaats tin field, South Africa

Abstract The Zaaiplaats tin field is host to two of the historically most significant tin mines in South Africa. The geological maps of Zaaiplaats and Groenfontein have not been updated since the 1980s and 1950s respectively and warrant a renewed investigation. Cassiterite is hosted in the Bobbejaankop and Lease granites, of the Zaaiplaats and Groenfontein tin mines, which are part of the Lebowa Granite Suite of the Bushveld Complex. Tin mineralisation is primarily hosted as low-grade disseminations and within networks of high-grade hydrothermal pipes and lenticular ore-bodies. One difficulty in mapping such formations arises from the limited lithological variability between mineralised and unmineralised granitic facies. In order to map the granitic lithologies and discriminate alteration zones, an integrated approach is applied by combining remote sensing and in situ portable X-ray fluorescence (pXRF) mapping. The pXRF large ion lithophile (LIL) element distribution mapping, specifically correlating Rb, Sr and Ba with Sn, points to the concentration of late-stage magmatic-hydrothermal fluids, which are associated with endogranitic mineralisation. The use of the Rb/Ba ratio highlights regions of late-stage magmatic-hydrothermal alteration, effectively delineates granitic facies and identifies zones of potential tin mineralisation. Spectral image processing techniques were used as tools to support the mapping of these granites, their variable degrees of magmatic-hydrothermal alteration and regions of endogranitic disseminated mineralisation. We thus exemplify that an expert-based and synergic approach, combining inexpensive in situ pXRF and freely available satellite remote sensing data, supports the mapping and identification of endogranitic mineralisation in early exploration stages. Furthermore, due to its versatility, this approach can easily be applied to other styles of plutonic mineralisation.
The Two-Stage Algorithm for Extraction of the Significant Pharmaceutical Named Entities and Their Relations in the Russian-Language Reviews on Medications on Base of the XLM-RoBERTa Language Model

The Internet contains a large amount of heterogeneous information, the extraction and structuring of which is currently a relevant task. This is especially relevant for tasks of social importance, in particular the analysis of the experience of using pharmaceutical products. In this paper, we propose a two-step sequential algorithm for extracting named entities and the relationships between them. Its creation was made possible by the availability of a marked-up corpus of Internet users’ reviews of medicines (Russian Drug Review Corpus). The basis of the algorithm is the language model XLM-RoBERTa-sag, which is pre-trained on a large corpus of unlabeled texts of reviews. The developed algorithm achieves the accuracy of identifying related entities: 71.6 and relations: 80.5, which is the first estimate of the accuracy of the solution of the considered problem on the Russian-language drug review texts.
A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets

Compared to fuzzy utility itemset mining (FUIM), temporal fuzzy utility itemset mining (TFUIM) has been proposed and paid attention to in recent years. It considers the characteristics of transaction time, sold quantities of items, unit profit, and transformed semantic terms as essential factors. In the past, a tree-structure method with two phases was previously presented to solve this problem. However, it spent much time because of the number of candidates generated. This paper thus proposes a one-phase tree-structure method to find the high temporal fuzzy utility itemsets in a temporal database. The tree was designed to maintain candidate 1-itemsets with their upper bound values meeting the defined threshold constraint. Besides, each node in this tree keeps the required data of a 1-itemset for mining. We also designed an algorithm to construct the tree and gave an example to illustrate the mining process in detail. Computational experiments were conducted to demonstrate the one-phase tree-structure method is better than the previous one regarding the execution time on three real datasets.
Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval

Content-based remote sensing (RS) image retrieval (CBRSIR) is a critical way to organize high-resolution RS (HRRS) images in the current big data era. The increasing volume of HRRS images from different satellites and sensors leads to more attention to the cross-source CSRSIR (CS-CBRSIR) problem. Due to the data drift, one crucial problem in CS-CBRSIR is the modality discrepancy. Most existing methods focus on finding a common feature space for various HRRS images to address this issue. In this space, their similarity relations can be measured directly to obtain the cross-source retrieval results straight. This way is feasible and reasonable, however, the specific information corresponding to HRRS images from different sources is always ignored, limiting retrieval performance. To overcome this limitation, we develop a new model for CS-CBRSIR in this paper named dual modality collaborative learning (DMCL). To fully explore the specific information from diverse HRRS images, DMCL first introduces ResNet50 as the feature extractor. Then, a common space mutual learning module is developed to map the specific features into a common space. Here, the modality discrepancy is reduced from the aspects of features and their distributions. Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. The comprehensive experiments are conducted on a public dataset. Compared with many existing methods, the behavior of our DMCL is stronger. These encouraging results for a public dataset indicate that the proposed DMCL is useful in CS-CBRSIR tasks.
Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue

External document knowledge is helpful for dialogue systems to generate high-quality responses. Although several knowledge-grounded dialogue models have been designed, external knowledge cannot be comprehensively exploited due to the complex relationships among dialogue context, knowledge, and responses. To this end, we propose a novel transformer-based model, named TransIKG, which incorporates external document knowledge for dialogue generation. TransIKG comprises a two-step integration mechanism, including correlation integration and overall integration. Correlation integration is designed to fully exploit the pairwise mutual information among dialogue context, knowledge, and responses, while overall integration adopts an integration gate to capture global information. Furthermore, we utilize the positional information of dialogue turns to better represent the dialogue context and enhance the generalization ability of our model on out-of-domain documents. Finally, we propose a novel knowledge-aware pointer network to generate knowledge-enhanced response tokens. Experimental results on two benchmark datasets demonstrate that our model outperforms state-of-the-art models on both open-domain and domain-specific dialogues.
SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from Denmark with various resolutions

The aim of the SOLETE dataset is to support researchers in the meteorological, solar and wind power forecasting fields. Particularly, co-located wind and solar installations have gained relevance due to the rise of hybrid power plants and systems. The dataset has been recorded in SYSLAB, a laboratory for distributed energy resources located in Denmark. A meteorological station, an 11 kW wind turbine and a 10 kW PV array have been used to record measurements, transferred to a central server. The dataset includes 15 months of measurements from the 1st June 2018 to 1st September 2019 covering: Timestamp, air temperature, relative humidity, pressure, wind speed, wind direction, global horizontal irradiance, plane of array irradiance, and active power recorded from both the wind turbine and the PV inverter. The data was recorded at 1 Hz sampling rate and averaged over 5 min and hourly intervals. In addition, there are three Python source code files accompanying the data file. RunMe.py is a code example for importing the data. MLForecasting.py is a self-contained example on how to use the data to build physics-informed machine learning models for solar PV power forecasting. Functions.py contains utility functions used by the other two.
A mapping-based constraint-handling technique for evolutionary algorithms with its applications to portfolio optimization problems

A novel Constraint-Handling Technique (CHT) for Evolutionary Algorithms (EAs) applied to constrained optimization problems is proposed. It is assumed that the feasible region of the constrained optimization problem is defined by a convex-hull of multiple vertices. On the other hand, without loss of generality, the search space of EA is given by a hyper-cube. The proposed CHT called Convex-Hull Mapping (CHM) transforms the real vector in the search space of EA into the solution in the feasible region. It is also proven that CHM performs a surjective mapping from the search space of EA to the feasible region. Although the proposed CHM can be applied to any EAs, one of the latest EAs, or Adaptive Differential Evolution (ADE), is used in this paper. By using ADE, CHM is compared with conventional CHTs in a real-world optimization problem in the field of finance, namely the portfolio optimization problem. Portfolio optimization is the process of determining the best proportion of investment in different assets according to some objective. Specifically, to reveal the characteristic of CHM depending on the number of the above vertices, three different formulations of the portfolio optimization problem are employed to evaluate the performance of ADE using CHM. Numerical experiments show that CHM is better than conventional CHTs in most cases. Moreover, the hybrid method combining CHM with a conventional CHT outperforms the original CHT.
TextureMe: High-Quality Textured Scene Reconstruction in Real Time

Three-dimensional (3D) reconstruction using an RGB-D camera has been widely adopted for realistic content creation. However, high-quality texture mapping onto the reconstructed geometry is often treated as an offline step that should run after geometric reconstruction. In this article, we propose TextureMe , a novel approach that jointly recovers 3D surface geometry and high-quality texture in real time. The key idea is to create triangular texture patches that correspond to zero-crossing triangles of truncated signed distance function (TSDF) progressively in a global texture atlas. Our approach integrates color details into the texture patches in parallel with the depth map integration to a TSDF. It also actively updates a pool of texture patches to adapt TSDF changes and minimizes misalignment artifacts that occur due to camera drift and image distortion. Our global texture atlas representation is fully compatible with conventional texture mapping. As a result, our approach produces high-quality textures without utilizing additional texture map optimization, mesh parameterization, or heavy post-processing. High-quality scenes produced by our real-time approach are even comparable to the results from state-of-the-art methods that run offline.
Meta hyperbolic networks for zero-shot learning

Zero-Shot Learning (ZSL) aims at generalizing the classification experience from seen classes to unseen classes with auxiliary side information, among which word vectors of class names and class attributes are popular ones. While word vectors technique is more practical for large datasets, its performance is usually slightly worse than that of attributes since it requires no human annotations. In this paper, we focus on the utilization of word vectors in ZSL by exploiting their hierarchical knowledge for large dataset and propose Meta Hyperbolic Networks (MHN). Specifically, we present Poincaré Graph Convolutional Networks (P-GCN), which transforms the word vectors into a Poincaré ball, and then encodes them with Poincaré Graph Convolutional layers. During training, the image classifiers from Convolutional Neural Networks (CNN) and P-GCN weights are aligned to ensure an accurate mapping between them. Moreover, we further develop a short-term memory episode learning on it to relieve the model's inherent bias towards seen classes. Extensive experiments on the popular ImageNet dataset show the competitive performance for both ZSL and generalized ZSL.
Data Stream Classification by Using Stacked CARU Networks

RNN based networks have been widely used in various applications to obtain impressive performance, and CARU has more advantages in NLP tasks. However, the RNN exerts a great pressure on the CARU unit when a single layer is used. In this work, we propose to implement a multi-layer design, which can gradually extract the main features through multiple CARU units. The advantage of this is that it can consider part of speech and content. It allows each layer to perform its work clearly while alleviating long-term dependencies. Using seven popular data streams, the performance of multi-layer CARU is compared and evaluated with many state-of-the-art technologies. Experiments show that our design can improve the classification performance of various data sets. In addition, the design and implementation can be easily deployed in RNN based systems.
Multi-queues can be state-of-the-art priority schedulers

Designing and implementing efficient parallel priority schedulers is an active research area. An intriguing proposed design is the Multi-Queue: given n threads and m ≥ n distinct priority queues, task insertions are performed uniformly at random, while, to delete, a thread picks two queues uniformly at random, and removes the observed task of higher priority. This approach scales well, and has probabilistic rank guarantees: roughly, the rank of each task removed, relative to remaining tasks in all other queues, is O (m) in expectation. Yet, the performance of this pattern is below that of well-engineered schedulers, which eschew theoretical guarantees for practical efficiency.
Video Object Segmentation Using Kernelized Memory Network With Multiple Kernels

Semi-supervised video object segmentation (VOS) is to predict the segment of a target object in a video when a ground truth segmentation mask for the target is given in the first frame. Recently, space-time memory networks (STM) have received significant attention as a promising approach for semi-supervised VOS. However, an important point has been overlooked in applying STM to VOS: The solution (=STM) is non-local, but the problem (=VOS) is predominantly local. To solve this mismatch between STM and VOS, we propose new VOS networks called kernelized memory network (KMN) and KMN with multiple kernels (KMN M). Our networks conduct not only Query-to-Memory matching but also Memory-to-Query matching. In Memory-to-Query matching, a kernel is employed to reduce the degree of non-localness of the STM. In addition, we present a Hide-and-Seek strategy in pre-training to handle occlusions effectively. The proposed networks surpass the state-of-the-art results on standard benchmarks by a significant margin (+4% in JM on DAVIS 2017 test-dev set). The runtimes of our proposed KMN and KMN M on DAVIS 2016 validation set are 0.12 and 0.13 seconds per frame, respectively, and the two networks have similar computation times to STM.
Neural Network-Based Price Tag Data Analysis

This paper compares neural networks, specifically Unet, MobileNetV2, VGG16 and YOLOv4-tiny, for image segmentation as part of a study aimed at finding an optimal solution for price tag data analysis. The neural networks considered were trained on an individual dataset collected by the authors. Additionally, this paper covers the automatic image text recognition approach using EasyOCR API. Research revealed that the optimal network for segmentation is YOLOv4-tiny, featuring a cross validation accuracy of 96.92%. EasyOCR accuracy was also calculated and is 95.22%.