Reviewerly/exHarmony huggingface dataset:
Building a Mineral Resources Data Platform for Critical Mineral Exploration

The increasing importance of addressing the climate crises through carbon neutrality is accelerating the transition to cleaner energy, such as in the case of electricity. This has led to a surge in demand for critical minerals, such as battery materials, that are essential for electrification, and rare earth elements (REE) that are essential for high-tech industries. Therefore, securing a stable supply chain of critical minerals is essential for a country's sustainable development. To build a foundation and database for exploration of critical minerals that do not have established ore genesis models and specialized exploration technologies yet, KIGAM has developed a mineral resources data platform that can increase exploration efficiency and share exploration data anywhere in the world, and its features are introduced here.
Signals, Systems, and Filters

In this chapter, analog signals and systems are treated. Analog circuits, namely, voltage and current ones, must be causal, i.e., physically realizable. The concepts involving linearity, nonlinearity, time-invariant, time-variant, and linear time-invariant are briefly explained in this chapter. In addition, it is dealt with linear time-invariant circuits. Total harmonic distortion is defined, and an NMOS transistor-based simple amplifier is exhibited as an example. Definitions of Laplace and Fourier transforms are given. Ideal transfer functions (TFs) of the low-pass filter (LPF), band-pass filter (BPF), notch filter, high-pass filter (HPF), and all-pass filter are presented. Ideal TFs of the second-order and first-order filters are discussed in which all the non-inverting and inverting phase and gain responses with respect to frequency are demonstrated. Several examples for the second-order ideal filters are given. Gains of the second-order LPF, BPF, and HPF are evaluated. A video filter as a high-order filter is given as an example.
The Contribution of Artificial Intelligence to Stimulating the Innovation of Educational Services and University Programs in Public Administration

In the new contemporary pandemic paradigm, higher education in public administration and beyond knows challenges, barriers, and opportunities for the academic process. Shocks, environmental changes and physical distancing in recent years have led to the implementation of efficient solutions in educational services, based on artificial intelligence (AI). Thus, in the global academic landscape, many international universities have adopted a ‘chatbot’, a digital interface designed to stimulate conversation with people. Modern algorithmic communications have effects, such as: increasing the number of enrollments in public administration programs, improving academic experience and active student participation, while reducing administrative tasks on university staff. This paper aims to provide a glimpse on the perceptions of the potential impact of conversational artificial intelligence on public administration (PA) programs from universities and to reflect on its implications for university leaders and staff work and also on students’ engagement. The paper will focus on how AI tools will help university programs to be more effective, and how technology will support the limited human resources in this sector. In order to gather the data for this research, the interview was used as well as case studies from international universities that have implemented a chatbot. The research findings capture technological and digital advances that will continue to shape the higher education landscape and its curricula, especially in PA programs. As a result, leaders will need to monitor how artificial intelligence, in particular conversational agents, influences universities’ images; attraction and retention for students in PA programs and other important partners; staff productivity, and how to be more proactive in initiating pilot projects. This is one of the few publications that looks at the expectations for conversational AI in higher education today. In this sense, implementing a chatbot can be a competitive advantage in a market where modern technologies weigh heavily and make a difference.
Speaker-Aware Interactive Graph Attention Network for Emotion Recognition in Conversation

Recently, E motion R ecognition in C onversation (ERC) has attracted much attention and has become a hot topic in the field of natural language processing. Conversation is conducted in chronological order; current utterance is more likely influenced by nearby utterances. At the same time, speaker dependency also plays a core role in the conversation dynamic. The combined effect of the sequence-aware information and the speaker-aware information makes the emotion’s dynamic change. However, past works used simple information fusion methods to model the two kinds of information but ignored their interactive influence. Thus, we propose a novel method entitled SIGAT ( S peaker-aware I nteractive G raph A ttention Ne t work) to solve the problem. The core module is a mutual interactive module in which a dual-connection (self-connection and interact-connection) graph attention network is constructed. The advantage of SIGAT is modeling the speaker-aware and sequence-aware information in a unified graph and updating them simultaneously. In this way, we model the interactive influence of them and obtain the final representations, which have richer contextual clues. Experimental results on the four public datasets demonstrate that SIGAT outperforms the state-of-the-art models.
Triumvirate: A Python/C++ package for three-point clustering measurements

Triumvirate is a Python/C++ package for measuring the three-point clustering statistics in large-scale structure (LSS) cosmological analyses.Given a catalogue of discrete particles (such as galaxies) with their spatial coordinates, it computes estimators of the multipoles of the three-point correlation function, also known as the bispectrum in Fourier space, in the tri-polar spherical harmonic (TripoSH) decomposition proposed by Sugiyama et al. (2019).The objective of Triumvirate is to provide efficient end-to-end measurement of clustering statistics which can be fed into downstream galaxy survey analyses to constrain and test cosmological models.To this end, it builds upon the original algorithms in the hitomi code 1 developed by Sugiyama et al. (2018Sugiyama et al. ( , 2019)), and supplies a user-friendly interface with flexible input/output (I/O) of catalogue data and measurement results, with the built program configurable through external parameter files and tracked through enhanced logging and warning/exception handling.For completeness and complementarity, methods for measuring two-point clustering statistics are also included in the package.
Quantum hypothesis testing via robust quantum control

Abstract Quantum hypothesis testing plays a pivotal role in quantum technologies, making decisions or drawing conclusions about quantum systems based on observed data. Recently, quantum control techniques have been successfully applied to quantum hypothesis testing, enabling the reduction of error probabilities in the task of distinguishing magnetic fields in presence of environmental noise. In real-world physical systems, such control is prone to various channels of inaccuracies. Therefore improving the robustness of quantum control in the context of quantum hypothesis testing is crucial. In this work, we utilize optimal control methods to compare scenarios with and without accounting for the effects of signal frequency inaccuracies. For parallel dephasing and spontaneous emission, the optimal control inherently demonstrates a certain level of robustness, while in the case of transverse dephasing with an imperfect signal, it may result in a higher error probability compared to the uncontrolled scheme. To overcome these limitations, we introduce a robust control approach optimized for a range of signal noise, demonstrating superior robustness beyond the predefined tolerance window. On average, both the optimal control and robust control show improvements over the uncontrolled schemes for various dephasing or decay rates, with the robust control yielding the lowest error probability.
Hydis: A Hybrid Consistent KVS with Effective Sync Among Replicas

Distributed storage systems distribute user loads across regions. User requests from different geographical locations are directed to the nearest data center, benefiting reduced service latency and improved service quality. However, the consistency among regions holds against availability and richness of the underlying data services. To address these constraints, our study proposes Hydis, a hybrid consistency distributed key-value storage system based on optimized replica synchronization. Hydis guarantees high availability and scalability for geographically distributed systems and uses Conflict-free Replicated Data Types to construct HybridLattice that supports various consistency models. A novel Writeless-Consistency strategy is proposed to improve the synchronization efficiency between replicas, and a dynamic synchronization optimization based on this strategy is implemented for consistency algorithm to effectively reduce the synchronization overhead of distributed storage systems. A performance evaluation of the Hydis cluster deployed on Alibaba Cloud showed that the strong consistency algorithm in Hydis outperformed the Raft algorithm by 1.8X. Additionally, the causal consistency algorithm in Hydis outperformed the traditional Vector Clock algorithm by 2.5X.
Spear-Phishing Detection Method Based on Few-Shot Learning

With the further development of Internet technology, various online activities are becoming more frequent, especially online office and online transactions. This trend leads that the network security issues are increasingly prominent, the network security situation is more complex, and the methods and means of attacks are emerging in endlessly. Due to the characteristics of spear-phishing such as target accuracy, attack durability, camouflage concealment and damage severity, it has become the most commonly used initial means for attackers and APT organizations to invade targets. Thus, automated spear-phishing detection based machine learning and deep learning have become the focus of researchers in recent years. However, because of a smaller range and less attack frequency, the number of spear-phishing emails is very limited. How to detect spear-phishing based on machine learning and deep learning with small samples has become a key issue. Meanwhile, in machine learning and deep learning, few-shot learning aims to study a better classification model trained with only a few samples. Therefore, we propose a spear-phishing detection method based on few-shot learning that combines the basic features and the message body of emails. We propose a simple word-embedding model to analyzes the message body, which can process the message body of different lengths into text feature vectors with the same dimension, thus retaining the semantic information to the greatest extent. Then the text feature vectors are combined with the basic features of emails and input into commonly used machine learning classifiers for detection. Our proposed simple word-embedding method does not require the complex training of the model to learn a large number of parameters, thereby reducing the dependence of the model on a large number of training data. The experimental results show that the method proposed in this paper achieves better performance than the existing spear-phishing detection method. Especially, Especially, the advantages of our detection method are more obvious with small samples.
A parallel strategy to accelerate neighborhood operation for raster data coordinating CPU and GPU

This study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy.
Coherent states of photonic dimers

The concept of coherent states of single photons is generalized to the scenario wherein the fundamental building blocks are entangled two-photon states rather than single-photon states. To provide a concrete discussion, the case of photonic dimers (two-photon bound states) is explored. The coherent states of photonic dimers are defined. The correlation functions are calculated and expressed in terms of series expansion which is suitable for computational investigations. The quadrature properties and the squeezing effects are computed and compared with the experimental results at suitable regimes. Finally, the statistical properties of the coherent states of photonic dimers in the BEC and BCS limits are investigated. The correlation functions are shown to exhibit a BEC-BCS crossover behavior when the two-photon pairing correlations (i.e., the size of photonic dimers) are comparable to the average spacing between the dimer pairs.
Enhanced Checkerboard Detection Using Gaussian Processes

Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades. While some detectors are able to handle partially occluded checkerboards, they fail when a large occlusion completely divides the checkerboard. We propose a new checkerboard detection pipeline for occluded checkerboards that has a robust performance under varying levels of noise, blurring, and distortion, and for a variety of imaging modalities. This pipeline consists of a checkerboard detector and checkerboard enhancement with Gaussian processes (GP). By learning a mapping from local board coordinates to image pixel coordinates via a Gaussian process, we can fill in occluded corners, expand the board beyond the image borders, allocate detected corners that do not fit an initial grid, and remove noise on the detected corner locations. We show that our method can improve the performance of other publicly available state-of-the-art checkerboard detectors, both in terms of accuracy and the number of corners detected. Our code and datasets are made publicly available. The checkerboard detector pipeline is contained within our Python checkerboard detection library, called PyCBD. The pipeline itself is modular and easy to adapt to different use cases.
POSS-CNN: An Automatically Generated Convolutional Neural Network with Precision and Operation Separable Structure Aiming at Target Recognition and Detection

Artificial intelligence is changing and influencing our world. As one of the main algorithms in the field of artificial intelligence, convolutional neural networks (CNNs) have developed rapidly in recent years. Especially after the emergence of NASNet, CNNs have gradually pushed the idea of AutoML to the public’s attention, and large numbers of new structures designed by automatic searches are appearing. These networks are usually based on reinforcement learning and evolutionary learning algorithms. However, sometimes, the blocks of these networks are complex, and there is no small model for simpler tasks. Therefore, this paper proposes POSS-CNN aiming at target recognition and detection, which employs a multi-branch CNN structure with PSNC and a method of automatic parallel selection for super parameters based on a multi-branch CNN structure. Moreover, POSS-CNN can be broken up. By choosing a single branch or the combination of two branches as the “benchmark”, as well as the overall POSS-CNN, we can achieve seven models with different precision and operations. The test accuracy of POSS-CNN for a recognition task tested on a CIFAR10 dataset can reach 86.4%, which is equivalent to AlexNet and VggNet, but the operation and parameters of the whole model in this paper are 45.9% and 45.8% of AlexNet, and 29.5% and 29.4% of VggNet. The mAP of POSS-CNN for a detection task tested on the LSVH dataset is 45.8, inferior to the 62.3 of YOLOv3. However, compared with YOLOv3, the operation and parameters of the model in this paper are reduced by 57.4% and 15.6%, respectively. After being accelerated by WRA, POSS-CNN for a detection task tested on an LSVH dataset can achieve 27 fps, and the energy efficiency is 0.42 J/f, which is 5 times and 96.6 times better than GPU 2080Ti in performance and energy efficiency, respectively.
Model-based clustering of multiple networks with a hierarchical algorithm

The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the collection of networks. Using a Bayesian framework, model selection is performed in an automated way since the algorithm stops when the best number of clusters is attained. The algorithm is computationally efficient, when carefully implemented. The aggregation of clusters requires a means to overcome the label-switching problem of the stochastic block model and to match the block labels of the networks. To address this problem, a new tool is proposed based on a comparison of the graphons of the associated stochastic block models. The clustering approach is assessed on synthetic data. An application to a set of ecological networks illustrates the interpretability of the obtained results.
Sig‐Wasserstein GANs for conditional time series generation

Abstract Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high‐dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time‐series data. Furthermore, long time‐series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig‐WGAN framework by integrating Wasserstein‐GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time‐series model. In particular, we develop the conditional Sig‐ metric that captures the conditional joint law of time series models and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators, which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state‐of‐the‐art benchmarks with respect to measures of similarity and predictive ability.
ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection

Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).
Analyzing Data Reference Characteristics of Deep Learning Workloads for Improving Buffer Cache Performance

Due to the recent growing data size of deep learning workloads, loading data from storage is increasingly becoming a performance bottleneck for neural network systems. In this article, we analyze the data reference characteristics of neural network workloads and observe that they are significantly different from conventional desktop workloads. In particular, during the training phase of deep learning, data blocks are referenced in a fully random manner, which significantly degrades the performance of a buffer cache. To handle this situation, this article suggests a new data shuffling scheme that aims to accelerate data loading in deep neural networks. Unlike the default shuffling method used in PyTorch that randomly shuffles full dataset in every epoch, the proposed scheme defines a shuffling unit called bundle, and enhances the locality of data references to improve buffer cache performances. Specifically, the proposed scheme performs data shuffling by the unit of a bundle, and the bundles used in each epoch are arranged alternately, thereby improving the locality of references at the viewpoint of the buffer cache. Based on simulation and measurement studies, we show that the hit rate of the buffer cache is improved by 37.2%, and the data loading time is also shortened by 11.4% without degrading the model’s training efficiency.