Reviewerly/exHarmony huggingface dataset:
Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus

In this work, hourly measurements of global solar irradiances obtained from pyranometers and sunshine duration data using either Kipp &amp; Zonen CSD3 automatic sensors or Campbell–Stokes sunshine recorders were assessed through an extensive quality control procedure and statistical analysis on the measured and derived solar parameters for all the actinometric stations installed in various locations over the island of Cyprus, covering mainly the period 2019–2021. This information is useful for engineers concerning the solar energy capture systems and energy efficiency who can therefore take knowledge of the local radiation levels. Monthly mean hourly values of global radiation and sunshine duration are calculated and shown through isoline diagrams. During June or July, daily global irradiations ranged between 25 MJ/m2 and 30 MJ/m2, with the lowest occurring in the mountainous locations. On the other hand, in January or December, they ranged between 6.5 MJ/m2 and 10.5 MJ/m2. The total annual number of hours of sunshine duration ranged between 2500 and 3500, with the lowest values recorded at the mountainous sites. The clearness index and relative sunshine duration were used for the classification of the weather conditions over the island. Furthermore, the interrelationships between the said indices were used for the estimation of global radiation. This work has specifically contributed to the characterization and analysis of hourly and daily solar global radiation. Furthermore, the measurements on the ground level could be compared with satellite observations in order to improve the geographical distribution of global radiation, especially in areas where no measurements exist. The analysis could be also extended for the other shortwave radiation components (Direct, Diffuse and Photosynthetic Active Radiation (PAR)) in order to assess the solar radiation regime over the island.
TCC-net: A two-stage training method with contradictory loss and co-teaching based on meta-learning for learning with noisy labels

With the rapid development of deep neural networks, the demand for large-scale accurately labeled datasets is growing rapidly. However, human-labeled datasets often produce misleading information due to mistakes in manual labeling. Most previous works fail to control well the overfitting of the trained model to noisy labels. In this paper, we propose a novel two-stage learning framework for learning with noisy labels, called the Two-stage training method with Contradictory loss and Co-teaching based on meta-learning (TCC-net). First, a novel robust loss function called contradictory loss is designed for pre-training, which is proved to be sufficiently robust both in the experimental results and theoretical foundation. During the training stage, we co-teach two pretrained networks based on meta-learning without any auxiliary clean subset as meta-data. Unlike other co-teaching methods, we introduce two multilayer perceptron to assist in weighting the selected samples, meaning each network updates itself with weighted-selected samples by its peer network and self-perceptron. Experimental results on corrupted datasets, such as Cifar10, Cifar100, Animal10N, and Clothing1M, demonstrate that TCC-net is superior to other state-of-the-art methods on shallower layers. Specifically, we achieve 12.59% improvement on synthetic Cifar10 with 80% symmetric noise and 0.27% on the real-world Animal10N dataset. Code is released at https://github.com/QiangqiangXia/TCC-net.
From detection to understanding: A survey on representation learning for human-object interaction

Human-Object Interaction (HOI) detection is a critical topic in the visual understanding field. With the development of deep learning models, the research of HOI detection has been profoundly reshaped. Deep convolutional neural networks increased the object recognition accuracy of static images and induced a detection-based HOI detection stream. The detection-based models resolve the HOI detection problem from a classification perspective. Another stream of HOI detection methods seeks a deeper understanding of the information shown in images, and they are named HOI understanding methods in this survey paper. HOI understanding methods usually acquire external linguistic data to enable the deep models to learn more about the images. Additionally, some of the HOI understanding methods exploit graph neural networks (GNN) to increase the inference accuracy of the model.
Tin transport and cassiterite precipitation from hydrothermal fluids

Cassiterite (SnO2) is the main ore mineral of tin in magmatic–hydrothermal tin deposits, but tin transport and precipitation mechanisms from hydrothermal fluids remain poorly understood. We critically evaluated aqueous tin speciation in hydrothermal fluids from extensive experimental data and thermodynamic modeling. Sn(II) chloride complexes in hydrothermal fluids exist mainly as SnCl+, SnCl2(aq), and SnCl3−. The revised Helgeson–Kirkham–Flowers model parameters of these three tin species and two tin ions (Sn4+ and Sn2+) were derived from the correlation algorithms among these parameters, and the standard molar properties of cassiterite were optimized to be internally consistent with the available thermodynamic dataset. These thermodynamic parameters, together with the available equilibrium constant equation of Sn(IV) chloride complexes, could reproduce the available solubility data of cassiterite in acidic solutions at 400–700 °C under oxygen fugacity (fO2) levels buffered by hematite–magnetite (HM) or nickel–nickel oxide (NNO). These comparisons allow modeling chemical systems of SnO2–NaCl–HCl–H2O (liquid phase) to examine tin transport and cassiterite precipitation mechanisms under tin-mineralizing conditions: 300–500 °C, 50–150 MPa, 2 molal NaCl, and fO2 levels from QFM (quartz–fayalite–magnetite) to HM. Sn(II) chloride complexes are commonly interpreted to dominate in aqueous tin speciation under fO2 = NNO, but our modeling results indicate that considerable contents of Sn(IV) chloride complexes also exist in those reduced fluids with high HCl contents, consistent with recent in situ high-temperature experiments and molecular dynamic simulations. The Sn(II)/Sn(IV) ratios in fluids depends on fO2, temperature, and HCl contents. A considerable amount of Sn(IV) possibly exist in an early mineralization stage even under fO2 = NNO; if so, redox reactions are unnecessary to precipitate cassiterite from these mineralizing fluids. We find that even if the fO2 levels are constant, simple cooling can alter mineralizing fluids to be more oxidized (e.g., from QFM to HM) and cause cassiterite precipitation, indicating that oxidizing agents are not necessary as previously thought. This explains why cassiterite can precipitate in host rocks (e.g., sandstone or quartzite) that do not provide oxidizing agents. A simple rise in fO2 levels and pH neutralization (e.g., greisenization) also cause cassiterite precipitation. Cassiterite solubility in oxidized acidic hydrothermal fluids (NNO < fO2≤HM) is high enough to account for the tin contents of fluid inclusions from typical tin deposits, but the mineralization potential of oxdized fluids is inferior to reduced fluids (fO2≤ NNO) under the same conditions.
An Adaptive Masked Attention Mechanism to Act on the Local Text in a Global Context for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is an important research area in natural language processing, which aims to analyze the sentiment polarity of the aspect terms present in the input sentences. In recent years, many models have focused on local text or local text-aspect relations by designing models that act directly on the local text and then fusing features of the global text. In fact, this ignores the role of the global text. This paper first proposes a masked attention mechanism that acts on the local embedding part of the global embedding, based on the global attention mechanism. Previous models use two methods, called Context-features Dynamic Mask (CDM) and Context-features Dynamic Weighted (CDW), to assign weights to text vectors based on the distance to the aspect term, these methods avoid information redundancy. In this paper, the proposed method uses this masked attention mechanism to intercept the local embedding in the global embedding and then calculate the position in the dimension of the aspect term, reorder the weights corresponding to the position, and assign them to the global embedding according to the corresponding subscripts, in this way, the proposed model not only takes into account noise reduction but can also pay more attention to the feature information of the global text. Compared with the previous embedding using two pre-training models for local and global text, the model proposed in this paper can learn features of both global and local text with only one pre-training model, so it can also improve the training efficiency of the model. The proposed model achieves good results on a total of eight datasets, including the triple-classified and quadruple-classified datasets of laptops and restaurants in SemEval2014, the restaurant dataset in SemEval2016, and the Multi-Aspect Multi-Sentiment (MAMS) dataset.
A Multi-Level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference

Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">premise</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hypothesis</b> ). Recently, low-resource natural language inference has gained increasing attention, due to significant savings in manual annotation costs and a better fit with real-world scenarios. Existing works fail to characterize discriminative representations between different classes with limited training data, which may cause faults in label prediction. Here we propose a multi-level supervised contrastive learning framework named MultiSCL for low-resource natural language inference. MultiSCL leverages a sentence-level and pair-level contrastive learning objective to discriminate between different classes of sentence pairs by bringing those in one class together and pushing away those in different classes. MultiSCL adopts a data augmentation module that generates different views for input samples to better learn the latent representation. The pair-level representation is obtained from a cross attention module. We conduct extensive experiments on two public NLI datasets in low-resource settings, and the accuracy of MultiSCL exceeds other models by 1.8%, 3.1% and 4.1% on SNLI, MNLI and Sick with 5 instances per label respectively. Moreover, our method outperforms the previous state-of-the-art method on cross-domain tasks of text classification.
iSOMA swarm intelligence algorithm in synthesis of quantum computing circuits

In the present paper, we demonstrate the possibilities of designing quantum computing circuits using a specific swarm intelligence algorithm — iSOMA in the form of three experiments. All simulations are based on a simple sample of a quantum computing circuit from the Qiskit environment, which was used as a comparison circuit with the results of the three experiments already mentioned. In the first experiment, we try to find an arbitrary functional solution using iSOMA with minimal constraints on this circuit’s design. It can be said that in this experiment, iSOMA showed the highest degree of “creativity”. In the second experiment, we focused on whether iSOMA can be used to find a circuit identical to the one designed by a human or equivalent with the positions of the measurement gates fixed. In the last experiment, we highlight iSOMA’s ability to avoid unnecessary qubit usage by adding redundant qubits to a possible circuit and fixing the measurement gates to the last two qubits in the scheme. In all three experiments, we see that iSOMA can find efficient functional and often astonishing solutions — the proposed method applied to a classical circuit founded a new one preserving required properties while saving one ancilla (redundant, useless, non-used)1 qubit. All computations are implemented in the IBM Qiskit2 environment. Although these are relatively simple experiments, the results show that evolutionary algorithms can successfully design more complex quantum circuits.
Generation of Nonlinear Substitutions by Simulated Annealing Algorithm

The problem of nonlinear substitution generation (S-boxes) is investigated in many related works in symmetric key cryptography. In particular, the strength of symmetric ciphers to linear cryptanalysis is directly related to the nonlinearity of substitution. In addition to being highly nonlinear, S-boxes must be random, i.e., must not contain hidden mathematical constructs that facilitate algebraic cryptanalysis. The generation of such substitutions is a complex combinatorial optimization problem. Probabilistic algorithms are used to solve it, for instance the simulated annealing algorithm, which is well-fitted to a discrete search space. We propose a new cost function based on Walsh–Hadamard spectrum computation, and investigate the search efficiency of S-boxes using a simulated annealing algorithm. For this purpose, we conduct numerous experiments with different input parameters: initial temperature, cooling coefficient, number of internal and external loops. As the results of the research show, applying the new cost function allows for the rapid generation of nonlinear substitutions. To find 8-bit bijective S-boxes with nonlinearity 104, we need about 83,000 iterations. At the same time, the probability of finding the target result is 100%.
Dual teacher–student based separation mechanism for open set domain adaptation

Open set domain adaptation (OSDA) is a more realistic and challenging domain adaptation protocol that assumes that the label spaces of source and target domains are different. Existing OSDA methods lack the ability to identify unknown samples and suffer more from negative transfer. To mitigate the negative transfer, we propose a dual teacher–student based sample separation mechanism to accurately identify and separate known and unknown samples in the target domain. Specifically, in the teacher–student structure (TS), students are insensitive to target unknown samples because they learn only source domain knowledge. Therefore, we exploit this property of students to construct a dual teacher–student structure (DTS), which quantifies the similarity between the target samples and the source domain and improves the accuracy and robustness of the similarity assessment. In addition, we further build a selective adversarial learning model by weighting the target samples with the weights derived from the similarity assessment. This promotes cross-domain alignment of the distribution of target samples with higher weights and the distribution of source samples. Extensive experiments conducted on the benchmark dataset demonstrate the significant advantages of the proposed approach over the extant methods.
3D-Net: Monocular 3D object recognition for traffic monitoring

Machine Learning has played a major role in various applications including Autonomous Vehicles and Intelligent Transportation Systems. Utilizing a deep convolutional neural network, the article introduces a zero-calibration 3D Object recognition and tracking system for traffic monitoring. The model can accurately work on urban traffic cameras, regardless of their technical specification (i.e. resolution, lens, the field of view) and positioning (location, height, angle). For the first time, we introduce a novel satellite-ground inverse perspective mapping technique, which requires no camera calibrations and only needs the GPS position of the camera. This leads to an accurate environmental modeling solution that is capable of estimating road users’ 3D bonding boxes, speed, and trajectory using a monocular camera. We have also contributed to a hierarchical activity/traffic modeling solution using short- and long-term Spatio-temporal video analysis to understand the heatmap of the traffic flow, bottlenecks, and high-risk zones. The experiments are conducted on four datasets: MIO-TCD, UA-DETRAC, GRAM-RTM, and Leeds-Dataset including various use cases and traffic scenarios.
A novel path planning approach for AUV based on improved whale optimization algorithm using segment learning and adaptive operator selection

A novel path planning approach for autonomous underwater vehicles is proposed in this paper. It seeks a better navigation path under complex terrain and uncertain ocean currents. On the one hand, this problem is converted into a deterministic optimization problem using the order relation of interval number and vector analysis method. On the other hand, the whale optimization algorithm using segment learning and adaptive operator selection is proposed to solve this problem. Firstly, the elite and edge sets are judged by fitness and Euclidean distance, respectively. Based on them, a dynamic partitioning strategy and a weighted mean strategy are used to construct virtual individuals. The virtual individuals are incorporated into the whale optimization algorithm to construct an evolutionary pool, which can realize the balance between exploration to exploitation to improve the search ability of the algorithm. Second, an adaptive operator selection mechanism considering individual preferences is added to the algorithm. This mechanism uses the operators' historical information and future projections to guide the individual in choosing an appropriate evolutionary operator. The simulation results show that the robustness and search capability of the algorithm presented in this study are more potent than other comparative algorithms.
Feature selection with multi-class logistic regression

Feature selection can help to reduce data redundancy and improve algorithm performance in actual tasks. Most of the embedded feature selection models are constructed based on square loss and hinge loss. However, these models based on the square loss cannot directly evaluate the discriminability of the samples in the feature subspace, and these methods based on the hinge loss are difficult to solve due to their complex objective functions. To deal with these problems, a Feature Selection method with Multi-class Logistic Regression (FSMLR) is proposed in this paper. Firstly, we construct a linear function to measure the difference between the distance from samples to their regression hyperplane and the distance from these samples to regression hyperplanes of other classes, which could be used to strengthen the discriminant property of the embedded model. Then, we design a re-weighting matrix with a ℓ2,0-norm sparse condition as well as a discrete condition, which is used to select features in the subspace. Considering that it is difficult to solve the re-weighting matrix with the discrete and sparse conditions in an optimization problem, we relax these two conditions and present a feature selection model via a re-weighted multi-class logistic regression with the two relaxed constraints. Finally, we add the F-norm regularization in our model to avoid overfitting, and its unconstrained equivalent transformation with ℓ2,p-norm regularization is derived to explore the function of the re-weighting matrix. The gradient descent algorithm could be used to solve the FSMLR. Especially, when the regularization term in the equivalence problem is set to ℓ2,1-norm, the global optimal solution can be obtained. Extensive experiments on multiple public data sets prove that FSMLR outperforms other competitors.
A perceptual hash based blind-watermarking scheme for image authentication

The rise of multimedia content has increased many folds in the last decade and is expanding exponentially. And with the growing medium of technology, transmission and communication of data have become more accessible. However, this has also led to rampant manipulation of multimedia data, and as such, integrity and authentication of data have become a serious concern. This work proposes a new technique using perceptual image hashing and digital watermarking for image authentication. Here, firstly, the digital signature image to be embedded is converted to a binary hash vector. This hash vector is generated by extracting the features of KAZE and local binary pattern-histogram Fourier (LBP-HF). Secondly, on the original cover image, we first performed lifting wavelet lifting transform (LWT), and then embedding was done on all the sub-bands of the transformed coefficients. The approximation coefficient matrix is further sub-divided into 4 × 4 blocks where embedding is done on certain blocks, and in the rest of the sub-bands, embedding is directly performed on specific locations. We illuminate different performance metrics individually for both the techniques. Experimental results show that the watermarking technique achieves an average peak signal-to-noise ratio of 50.15 dB and an average structural similarity index metric of 0.9934. Moreover, the robustness of both the techniques against various single and combined attacks facilitates dual authentication of the watermarking system and enhances its security.
Reinforcing User Retention in a Billion Scale Short Video Recommender System

Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users). Retention is a long-term feedback after multiple interactions of users and the system, and it is hard to decompose retention reward to each item or a list of items. Thus traditional point-wise and list-wise models are not able to optimize retention. In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance. We formulate the problem as an infinite-horizon request-based Markov Decision Process, and our objective is to minimize the accumulated time interval of multiple sessions, which is equal to improving the app open frequency and user retention. However, current reinforcement learning algorithms can not be directly applied in this setting due to uncertainty, bias, and long delay time incurred by the properties of user retention. We propose a novel method, dubbed RLUR, to address the aforementioned challenges. Both offline and live experiments show that RLUR can significantly improve user retention. RLUR has been fully launched in Kuaishou app for a long time, and achieves consistent performance improvement on user retention and DAU.