Reviewerly/exHarmony huggingface dataset:
Reservoir Computing Universality With Stochastic Inputs

The universal approximation properties with respect to L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> -type criteria of three important families of reservoir computers with stochastic discrete-time semi-infinite inputs are shown. First, it is proven that linear reservoir systems with either polynomial or neural network readout maps are universal. More importantly, it is proven that the same property holds for two families with linear readouts, namely, trigonometric state-affine systems and echo state networks, which are the most widely used reservoir systems in applications. The linearity in the readouts is a key feature in supervised machine learning applications. It guarantees that these systems can be used in high-dimensional situations and in the presence of large data sets. The L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> criteria used in this paper allow the formulation of universality results that do not necessarily impose almost sure uniform boundedness in the inputs or the fading memory property in the filter that needs to be approximated.
Compositional generalization through meta sequence-to-sequence learning

People can learn a new concept and use it compositionally, understanding how to blicket twice after learning how to blicket. In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts. In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning. In this approach, models train on a series of seq2seq problems to acquire the compositional skills needed to solve new seq2seq problems. Meta se2seq learning solves several of the SCAN tests for compositional learning and can learn to apply implicit rules to variables.
Chemometric analysis for pollution source identification and human health risk assessment of water resources in Ojoto Province, southeast Nigeria

Abstract This paper quantified the level of heavy metals contamination, identified possible sources of pollution, and assessed the human health risks associated with drinking water resources in Ojoto Province, Nigeria. The study’s gross findings revealed that the suitability of some water sources for consumption purposes in this province is questionable. Based on water quality index, 57.14% of the total samples are within acceptable limits, while 42.86% are unsuitable for drinking. It was observed that the northwestern and southern parts of the study area have more of deteriorated water quality. The health hazard index revealed that 25% of the samples predispose their consumers (both adult and children populations) to high chronic health risks. Moreover, heavy metal pollution index, contamination index, and probability of cancer risk (CR) revealed that about 25% of the total samples were unsuitable and off the CR standard acceptable range of ≤ 1 × 10 −6 –1 × 10 −4 . Correlation and component factor analyses linked the origin of major ions to geogenic processes and that of the heavy metals to both natural and anthropogenic processes. Cluster analysis divided the samples into two equal classes (50% each): poor and excellent quality waters. This study indicated that Pb is the priority pollutant impacting the water quality. The various assessments revealed that waters from hand-dug wells and deeper boreholes are the least contaminated and hence best suited for drinking than waters from springs, streams, and shallow wells.
Contrastive Multi-View Representation Learning on Graphs

We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting multi-scale encodings do not improve performance, and the best performance is achieved by contrasting encodings from first-order neighbors and a graph diffusion. We achieve new state-of-the-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol. For example, on Cora (node) and Reddit-Binary (graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which are 5.5% and 2.4% relative improvements over previous state-of-the-art. When compared to supervised baselines, our approach outperforms them in 4 out of 8 benchmarks. Source code is released at: this https URL
Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.
Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors

Due to a growing number of people who carry out various adrenaline activities or adventure tourism and stay in the mountains and other inaccessible places, there is an increasing need to organize a search and rescue operation (SAR) to provide assistance and health care to the injured. The goal of SAR operation is to search the largest area of the territory in the shortest time possible and find a lost or injured person. Today, drones (UAVs or drones) are increasingly involved in search operations, as they can capture a large, controlled area in a short amount of time. However, a detailed examination of a large amount of recorded material remains a problem. Even for an expert, it is not easy to find searched people who are relatively small considering the area where they are, often sheltered by vegetation or merged with the ground and in unusual positions due to falls, injuries, or exhaustion. Therefore, the automatic detection of persons and objects in images/videos taken by drones in these operations is very significant. In this paper, the reliability of existing state-of-the-art detectors such as Faster R-CNN, YOLOv4, RetinaNet, and Cascade R-CNN on a VisDrone benchmark and custom-made dataset SARD build to simulate rescue scenes was investigated. After training the models on selected datasets, detection results were compared. Because of the high speed and accuracy and the small number of false detections, the YOLOv4 detector was chosen for further examination. YOLOv4 model results related to different network sizes, different detection accuracies, and transfer learning settings were analyzed. The model robustness to weather conditions and motion blur were also investigated. The paper proposes a model that can be used in SAR operations because of the excellent results in detecting people in search and rescue scenarios.
Image encryption algorithm based on a 2D-CLSS hyperchaotic map using simultaneous permutation and diffusion

A two-dimensional cross-mode hyperchaotic map based on logistic and sine maps (2D-CLSS) is presented. The hyperchaotic map consists of a logistic map and two sine maps with cross structure. The chaotic behavior of the system is analyzed using bifurcation diagrams, Lyapunov exponential spectra, phase diagrams, etc. The outcomes demonstrate that the system has promising ergodicity and a wide range of hyperchaotic phenomena. Using the proposed 2D-CLSS, an image cryptography algorithm is developed. This image encryption system employs a strategy that simultaneously combines permutation and diffusion to alter the location and the value of the pixels. Experiments and security simulations indicate that the scheme is effective in encrypting images with excellent security against various attacks (differential, noise and data loss, etc.).
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference; 2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification; 3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions.
SCTSC: A Semicentralized Traffic Signal Control Mode With Attribute-Based Blockchain in IoVs

Assisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.
Development of an Instructional Design Model for Mobile Blended Learning in Higher Education

The development of information and communication technology has brought a surprise and revolutionary challenge to the idea and practice of traditional education. Mobile internet technology has become the main promoter and accelerator to apply the concept of mobile learning. Mobile technology offers new opportunities to integrate face-to-face learning and online learning methods. We see a tendency to use blended learning scenarios by combining various forms of learning and integrating a variety of ways to access content using mobile technology. The objective of this study is to develop a mobile blended learning design that can systematically guide the instructor or lecturer in the lecturing processes. By combining the ideas of mobile learning and blended learning, mobile blended learning design has been developed. Implementing research and development (R&amp;D) method, the mobile blended learning design has been constructed through designing process and validation by experts. This instructional design has feasibility to use in learning; therefore, it effectively increases the popularity of mobile blended learning
Let's Encrypt

Let's Encrypt is a free, open, and automated HTTPS certificate authority (CA) created to advance HTTPS adoption to the entire Web. Since its launch in late 2015, Let's Encrypt has grown to become the world's largest HTTPS CA, accounting for more currently valid certificates than all other browser-trusted CAs combined. By January 2019, it had issued over 538~million certificates for 223~million domain names. We describe how we built Let's Encrypt, including the architecture of the CA software system (Boulder) and the structure of the organization that operates it (ISRG), and we discuss lessons learned from the experience. We also describe the design of ACME, the IETF-standard protocol we created to automate CA--server interactions and certificate issuance, and survey the diverse ecosystem of ACME clients, including Certbot, a software agent we created to automate HTTPS deployment. Finally, we measure Let's Encrypt's impact on the Web and the CA ecosystem. We hope that the success of Let's Encrypt can provide a model for further enhancements to the Web PKI and for future Internet security infrastructure.
A new global best guided artificial bee colony algorithm with application in robot path planning

Artificial bee colony has received much attention in recent years as a competitive population-based optimization algorithm. However, its slow convergence speed and one-dimensional search strategy limit it from demonstrating advantage in separable functions. To address these concerning issues, this paper introduces a coevolution framework into ABC and designs a global best leading artificial bee colony algorithm with an improved strategy to accelerate its convergence and conquer the dependency of dimension separately. A set of classical and Congress on Evolutionary Computation 2015 benchmark functions are adopted for validating the efficiency of our algorithm. In addition, in order to show the practicality of our algorithm, a robot path-planning problem is tested, and our algorithm still achieves superior results.
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization.

Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-oriented challenges which include (i) communication bottleneck since a large number of devices upload their local updates to a parameter server, and (ii) scalability as the federated network consists of millions of devices. Due to these systems challenges as well as issues related to statistical heterogeneity of data and privacy concerns, designing a provably efficient federated learning method is of significant importance yet it remains challenging. In this paper, we present FedPAQ, a communication-efficient Federated Learning method with Periodic Averaging and Quantization. FedPAQ relies on three key features: (1) periodic averaging where models are updated locally at devices and only periodically averaged at the server; (2) partial device participation where only a fraction of devices participate in each round of the training; and (3) quantized message-passing where the edge nodes quantize their updates before uploading to the parameter server. These features address the communications and scalability challenges in federated learning. We also show that FedPAQ achieves near-optimal theoretical guarantees for strongly convex and non-convex loss functions and empirically demonstrate the communication-computation tradeoff provided by our method.
Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems

Energy-efficient communication has become the center of attention from various interdisciplinary fields, such as industrial automation, healthcare, and transportation, among others. Besides, proliferation in the artificial intelligence (AI)-based sixth-generation (6G) technology for achieving the smart automation system has caught the attention of both academia and industry. Most of the intelligent automation systems are formed by IoT-based user terminal (UT) devices for multimedia (i.e., video, audio, image, and text) content delivery with high clarity and efficiency. Customer satisfaction/perception, i.e., Quality of Experience (QoE), is an essential factor to be analyzed because the Quality of Service (QoS) is not a suitable candidate to portray the feelings and expectations of users during multimedia transmission. Therefore, the energy-efficient, entropy-aware communication, and QoE analysis through IoT devices are the dire need. This article focuses on how the energy-efficient communication and user's QoE level can be captured through the UT device during multimedia transmission. Thus first, QoS-based joint energy and entropy optimization (QJEEO) algorithm is proposed. Second, the 6G-driven multimedia data structure model and framework are developed for modeling and evaluation of QoE with acquisition time. Third, the relationship between subjective test score (i.e., surveyed data) and objective performance metrics with mobility/speed of IoT-based devices for multimedia service is established. Fourth, the correlation model is proposed for integrating QoS parameters with estimated QoE perceptions. The experimental results indicate that QoE is modeled and evaluated with acquisition time and correlated with QoS parameter, i.e., packet loss ratio (PLR), and average transfer delay during energy-efficient multimedia transmission in 6G-based networks to improve the satisfaction level of customers.
Three types of incremental learning

Incrementally learning new information from a non-stationary stream of data, referred to as 'continual learning', is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed, but comparing their performances is difficult due to the lack of a common framework. To help address this, we describe three fundamental types, or 'scenarios', of continual learning: task-incremental, domain-incremental and class-incremental learning. Each of these scenarios has its own set of challenges. To illustrate this, we provide a comprehensive empirical comparison of currently used continual learning strategies, by performing the Split MNIST and Split CIFAR-100 protocols according to each scenario. We demonstrate substantial differences between the three scenarios in terms of difficulty and in terms of the effectiveness of different strategies. The proposed categorization aims to structure the continual learning field, by forming a key foundation for clearly defining benchmark problems.
Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction

Rapidly and accurately predicting on-site peak ground velocity (PGV) is important for earthquake hazard mitigation. Traditional methods used to predict PGV involve a single physics-based parameter, like the peak displacement (Pd) or squared velocity integral (IV2) techniques; deep-learning methods involve a single neural network model, like the convolutional neural network (CNN) or recurrent neural network (RNN) models, to extract feature for estimating the PGV. Here, based on the training dataset from earthquake events occurred in Japan, we construct hybrid deep-learning network (HybridNet) for predicting on-site PGV, which is consist of CNN and RNN feature extraction blocks. We use physics-based feature time series, waveforms and a site feature from a single station as the input of HybridNet model; additionally, we concatenate the features from the CNN block, RNN block and site feature to predict the on-site PGV. We show that concerning the standard deviation of error, the mean absolute error and the coefficient of determination for PGV prediction, HybridNet model exhibits better performance on the test dataset than the baseline models. Additionally, potential damage zone (PDZ) can be predicted by interpolating the predicted PGVs at the stations. Based on the predicted PGV of the HybridNet model, we investigate the feasibility of PDZ estimation on five earthquakes (M≥6.5). And we find that within a few seconds after the arrival of P-wave, the predicted PDZ is consistent well with the PGV ShakeMap obtained from the U.S. Geological Survey.
ChatGpt: Open Possibilities

ChatGPT-3 is a powerful language model developed by OpenAI that has the potential to revolutionize the way weinteract with technology. This model has been trained on a massive amount of data, allowing it to understand andgenerate human-like text with remarkable accuracy. One of the most exciting possibilities of ChatGPT-3 is its potentialto improve natural language processing (NLP) and natural language understanding (NLU) in a wide range ofapplications. In particular, ChatGPT-3 can be used to power chatbots, virtual assistants, and other conversationalinterfaces. These types of systems are becoming increasingly important as more and more people use voice and text tointeract with technology, we list ChatGpt role in each of the follwoing sections
FFTI: Image inpainting algorithm via features fusion and two-steps inpainting

In view of the faultiness that the existing image inpainting methods fail to make full use of the complete region to predict the missing region features when the object features are seriously missing, resulting in discontinuous features and fuzzy detail texture of the inpainting results, a fine inpainting method of incomplete image based on features fusion and two-steps inpainting (FFTI) is proposed in this paper. Firstly, the dynamic memory networks (DMN+) are used to fuse the external features and internal features of the incomplete image to generate the incomplete image optimization map. Secondly, a generation countermeasure generative network with gradient penalty constraints is constructed to guide the generator to rough repair the optimized incomplete image and obtain the rough repair map of the target to be repaired. Finally, the coarse repair graph is further optimized by the idea of coherence of relevant features to obtain the final fine repair graph. It is verified by simulation on three image data sets with different complexity, and compared with the existing dominant repair model for visual effect and objective data. The experimental results show that the results of the model repair in this paper are more reasonable in texture structure, better than other models in visual effect and objective data, and the Peak Signal-to-Noise Ratio of the proposed algorithm in the most challenging underwater targe dataset is 27.01, the highest Structural Similarity Index is 0.949.