Reviewerly/exHarmony huggingface dataset:
Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit

Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has not been fully explored. To address this gap, we study controversy on Reddit, a popular network of online discussion forums. We collect discussions from various topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc. (Code and dataset are publicly available at https://github.com/ChenK7166/controversy-emotion ). We find controversial comments express more anger and less admiration, joy, and optimism than non-controversial comments. Moreover, controversial comments affect emotions of downstream comments, resulting in a long-term increase in anger and a decrease in positive emotions. The magnitude and direction of emotional change differ by forum. Finally, we show that emotions help better predict which comments will become controversial. Understanding the dynamics of emotions in online discussions can help communities to manage conversations better.
Towards Secure and Efficient Multi-generation Cellular Communications: Multi-mode SNOW-3G/V ASIC and FPGA Implementations

There is a definite upward trend in the number of cellular communication nodes that, together with high rate of digital content transactions, led to continuous progress on generational technologies (3G, 4G, 5G, and more recently 6G). To facilitate smooth deployments on multi-generation environments, communication systems have to be flexible, i.e., being able to support more than one security mechanisms. In this article, two multimode architectures are introduced, realizing both SNOW 3G and SNOW-V stream ciphers. The first SNOW-3G/V multimode architecture targets area efficiency. Thus, extensive and sophisticated resource sharing is performed, without degrading the overall performance. The second one, going one step further, aims at increasing the performance of underlying SNOW 3G mechanism through parallelism, without compromising the overall area efficiency. The introduced architectures are captured in SystemVerilog and implemented in FPGA (AMD Xilinx Virtex 7 - xc7v585tffg1761–3) and ASIC (22nm FD-SOI) technologies. Frequency, Area, and Throughput metrics are concerned and comparisons with existing implementations of individual SNOW 3G and SNOW-V mechanisms are made. Regarding the area, comparisons with the sum of the area of individual designs are also made. Experimental results showed that: (a) there is limited degradation of achieved performance of multimode architectures, (b) remarkable resource sharing is achieved in both multimode architectures, and (c) the overall performance of the proposed multimode architectures is great, comparable with almost all existing individual cipher designs. Finally, it has to be stressed that it is the first time that multimode designs are proposed that include SNOW 3G and SNOW-V mechanisms.
Survey: Unconventional Categories of Chatbots that make use of Machine Learning Techniques

A growing trend in computer interaction approaches is human-computer interaction, which describes communication between a person and a computer and is known as a chatbot. A conversational agent, often known as a chatbot, is a computer program that attempts to provide responses that appear human during a conversation. Studies on several chatbot applications, are been carried out in this research on the areas of Natural Language Processing (NLP), the Natural Language understanding (NLU), and Intent recognition in recent years. There are several NLU applications in various fields, including Chatbots, search in natural language, voice-driven assistants, web- scale information extraction, legal discovery, and content summarization. Intent classification is one of the biggest challenges in NLP. Some of the chatbots classifies students requests to make them simpler to comprehend. A chatbot is an appliance of software that replicates and processes human communication to offer real-time digital assistance. Various tasks that were previously handled by human agents, like assistance to customers, medical counselling, digital commerce and issues addressing, are now being assigned to chatbots. Due to the appearance of machine learning techniques, the main step in chatbot development was defining the rules that would be used to create responses. Here are some strategies for chatbot applications and the machine-learning techniques that underpin them.
Solving linear optimization problems subject to bipolar fuzzy relational equalities defined with max-strict compositions

There have been several studies in the literature that looked at bipolar fuzzy relation equations using three compositions, max-min, max-product, and max-Lukasiewicz. This paper addresses an optimization problem involving a general class of bipolar fuzzy relation equations. A point to be noted is that the constraints are formed as a system of bipolar fuzzy equations, in which the fuzzy compositions are defined by an arbitrary continuous t-norm. The paper shows that a feasible solution set consists of a finite number of closed convex cells whose lower and upper bounds are determined by corner (extreme) points. In addition, it will be illustrated that, in contrast to the (ordinary) fuzzy relational equations, corner points may neither be the minimum (maximum) nor minimal (maximal) solutions to the feasible region. The generality of the problem leads us to divide the constraints into four classes (each with a different structure) and then investigate the specific characteristics of the equations in each class. Additionally, to evaluate the feasibility of the problem in general, some necessary and sufficient conditions are presented. Due to the high computational complexity of the complete solution of FREs, six rules are introduced to reduce the size of the original problem in such a way that the effort to solve the problem is significantly decreased, followed by an algorithm for solving the current optimization problems. Accordingly, it is shown that the problem has a finite number of local optimal solutions and a global optimal solution can always be obtained by choosing a point having the minimum objective value compared to all the local optimal solutions. In the final section, we will examine an example to demonstrate how the algorithm works.
A novel predefined-time noise-tolerant zeroing neural network for solving time-varying generalized linear matrix equations

Due to the time-varying nature of time-varying linear systems, many traditional time-invariant methods cannot solve time-varying linear systems. The emergence of zeroing neural networks (ZNN) has led to a long and intensive development in this field. Researchers have mainly studied or improved the ZNN model for time-varying linear systems in the direction of activation functions and design parameters. This paper proposes a novel modified versatile activation function (MVAF) from the perspective of activation function optimization, and a modified versatile activation function zeroing neural network (MVAFZNN) model for solving time-varying generalized linear matrix equations is constructed. The proposed model converges faster than the versatile activation function zeroing neural network (VAFZNN) model and other ZNN models. Theoretical analysis shows that the MVAFZNN model can solve time-varying systems in a predetermined time. Two numerical experiments verify the validity of the proposed model, and two circuit experiments show that the proposed model can be used to calculate circuit parameters.
RCFT: re-parameterization convolution and feature filter for object tracking

Abstract Siamese-based trackers have been widely studied for their high accuracy and speed. Both the feature extraction and feature fusion are two important components in Siamese-based trackers. Siamese-based trackers obtain fine local features by traditional convolution. However, some important channel information and global information are lost when enhancing local features. In the feature fusion process, cross-correlation-based feature fusion between the template and search region feature ignores the global spatial context information and does not make the best of the spatial information. In this paper, to solve the above problem, we design a novel feature extraction sub-network based on batch-free normalization re-parameterization convolution, which scales the features in the channel dimension and increases the receptive field. Richer channel information is obtained and powerful target features are extracted for the feature fusion. Furthermore, we learn a feature fusion network (FFN) based on feature filter. The FFN fuses the template and search region features in a global spatial context to obtain high-quality fused features by enhancing important features and filtering redundant features. By jointly learning the proposed feature extraction sub-network and FFN, the local and global information are fully exploited. Then, we propose a novel tracking algorithm based on the designed feature extraction sub-network and FFN with re-parameterization convolution and feature filter, referred to as RCFT. We evaluate the proposed RCFT tracker and some recent state-of-the-art (SOTA) trackers on OTB100, VOT2018, LaSOT, GOT-10k, UAV123 and the visual-thermal dataset VOT-RGBT2019 datasets, which achieves superior tracking performance with 45 FPS tracking speed.
MFFN: Multi‐path feedback fusion network for lightweight image super resolution

Abstract Recently, convolutional neural network (CNN) has shown great power in single image super resolution (SISR) reconstruction, and achieving significant improvements over traditional methods. Despite the great success of these CNN‐based methods, direct application of these methods to some edge devices is impractical due to the large computational overhead required. To address this problem, a novel, lightweight SISR network focusing on speed and accuracy, called the multi‐path feedback fusion network (MFFN), has been designed in this paper. Specifically, in order to extract features more effectively, the authors propose a novel fusion attention feedback block (FAFB) as the main building block of MFFN. The FAFB consists of a backbone branch and several hierarchical branches. The backbone branch is composed of stacked enhanced pixel attentional blocks (EPAB), which are responsible for incremental deep feature learning on the feature map. And the hierarchical branches are responsible for extracting feature maps with different sizes of receptive fields and fusing these feature maps with the features extracted from the trunk branches to achieve multi‐scale feature learning, which the authors refer to this design as the multi‐scale fusion block (MSFB). Extra enhancement information (EIE) is added to each EPAB input, which enables the backbone branch to learn more effectively. On the other hand, the outputs of the cascade branches are further complemented by an additional feedback fusion enhancement block (FFEB) before being fused with the output of the trunk branches to achieve more comprehensive and accurate feature learning. Numerous experiments have shown that MFFN achieves higher accuracy than other state‐of‐the‐art methods on benchmark test sets.
Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China.
Correlation between the surface morphology of the intrusions and the formation of mineralization within skarn deposits: A numerical simulation study of the Qiaomaishan skarn Cu deposit, Middle and Lower Yangtze River Metallogenic Belt, China

Skarn deposits are high grade hydrothermal deposits distributed throughout the world, containing multiple mineral resources. However, the formation of skarn deposits is extremely complex, hindering our in-depth understanding of these deposits with high economic value. The Qiaomaishan Cu deposit is a typical skarn deposit located in the Liqiao-Tongshan orefield, Middle-Lower Yangtze River Metallogenic Belt (MLYRMB). This study applied numerical simulation approach to quantitatively describe the formation of copper mineralization within the Qiaomaishan skarn deposit, and explored the correlation between the surface morphology of intrusion and the formation of mineralization. This numerical model links five main processes related to ore formation within hydrothermal ore-forming system and their complex coupled relationship, namely chemical reactions, heat conduction, rock deformation, fluid migration, and materials diffusion. Results show that rough surface morphology of the intrusions is an ore controlling factor within the Qiaomaishan skarn deposit. The uneven surface of the intrusions greatly affects the time spent in cooling, thereby affecting the generation and spatial distribution of chalcopyrite. The concave part of the surface of the intrusions lives a longer cooling period (1000–2500 years) than the convex part (110–160 years) from 310 to 260 °C, which directly causes the difference in the temporal-spatial distribution of chalcopyrite. Results also show that the factors involved in prospectivity modeling must undergo strict conditional independence analysis, and numerical simulation method can be used to effectively achieve this goal. However, there are also some problems unsolved, such as difficulties caused by lack of data and oversimplified ore-forming chemical reactions. Future development of both geological research and modification of the law of mass action will undoubtedly improve the accuracy and practicability of numerical simulation and our understanding of the Qiaomaishan as well as other skarn deposit, providing information for mineral deposit research from the perspective of computational geoscience.
A mobile-based system for translating sign language and facilitating communication between deaf and non-deaf people

With increasing number of deaf individuals, we need a clear communication between deaf and non-deaf for both social and economical development.
Frequency and content dual stream network for image dehazing

Image dehazing can improve image clarity and visual effect, which plays a pivotal role in many computer vision tasks. Existing dehazing methods are mostly based on a single feature stream and tend to ignore the low-frequency characteristics of haze. In this paper, we propose a dual stream network for image dehazing. To enhance the edge information and texture detail of the image, we construct a frequency stream based on attention octave convolution. We decompose the features into high and low-frequency branches in the frequency stream to obtain different structural information. By adding a residual channel attention block, the attention octave convolution can extract frequency features more efficiently and effectively. Due to the lower resolution of low-frequency features in the frequency stream, the frequency stream features alone are insufficient for recovering the overall content of the image. Therefore, a content stream was added to compensate for the information lost in the frequency stream. By fusing the outputs of two feature streams, the network achieves an enhanced dehazing performance. The results show that our method is superior to other state-of-the-art algorithms in quantitative evaluation and visual impact.
Curvelet coefficient prediction-based image super-resolution method for precision measurement

The precision of image-based measurements is mainly limited by the resolution of the camera hardware. Currently, increasing the resolution of measurement images by super-resolution method is an effective way to improve the precision of vision measurement. However, most image super-resolution methods are only based on the prediction of spatial information, therefore the reconstruction quality and localization accuracy of the edge regions are often insufficient to meet the requirements of precision measurements. For this reason, a Curvelet coefficient prediction method for image super-resolution (CPSR) is proposed to achieve accurate super-resolution results of edge regions in this paper. Firstly, the image is decomposed into sub-bands of Curvelet coefficients at different scale to extract frequency features. Then, deep residual networks are built and trained to fit the mapping function between low- and high-resolution coefficient sub-bands. In addition, Curvelet loss and edge localization loss functions are designed to obtain Curvelet coefficient errors of each scale and edge localization errors of sub-pixel level. The proposed method is evaluated using public super-resolution datasets and actual precision measurement images. Experimental results show that CPSR generates images with better visual effects, and has strong generalization ability for the reconstruction of different edge patterns. Furthermore, compared with the common super-resolution method based on deep learning, vision measurements on the reconstructed image of CPSR achieve smaller measurement errors, indicating that CPSR achieves more accurate edge localization. Experimental results verified the effectiveness of the proposed method.
mldr.resampling: Efficient reference implementations of multilabel resampling algorithms

Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes limited to the pseudocode provided by their authors in a paper. This Original Software Publication presents mldr.resampling, a software package that provides reference implementations for eleven multilabel resampling methods, with an emphasis on efficiency since these algorithms are usually time-consuming.
Adversarial Imitation Learning with Controllable Rewards for Text Generation

Supervised fine-tuning of large language models (LMs) does not always provide good text-generation performance in terms of quality and diversity. This is because such models maximize the likelihood of correct subsequent words based on previous contexts encountered in the training phase, instead of evaluating the entire structure of the generated texts. In this context, fine-tuning methods for LMs using adversarial imitation learning (AIL) have been proposed to improve the trade-off relationship between quality and diversity. This method leverages the evaluations of the discriminators without requiring manually designed metrics. Previously proposed AIL methods cannot control the shapes of the reward functions and constrain updates of LMs using fixed ranges, independent of the quality, e.g., proximal policy optimization. This study proposes a combination of an AIL method and an approximation of mixture distributions (AMDAIL), synergizing with LMs for text generation. AMDAIL exhibits two features: (1) controlling the distribution of the bounded reward values by varying the shape of the bounded reward function, and (2) a variable constraint to promote updates using the confidence of the discriminator as the quality of the texts. The proposed method exhibits stable behavior in the training phases and improves the trade-off relationship between the quality and diversity in the inference phases.
A KNN-Based Non-Parametric Conditional Independence Test for Mixed Data and Application in Causal Discovery

Testing for Conditional Independence (CI) is a fundamental task for causal discovery but is particularly challenging in mixed discrete-continuous data. In this context, inadequate assumptions or discretization of continuous variables reduce the CI test’s statistical power, which yields incorrect learned causal structures. In this work, we present a non-parametric CI test leveraging k-nearest neighbor (kNN) methods that are adaptive to mixed discrete-continuous data. In particular, a kNN-based conditional mutual information estimator serves as the test statistic, and the p-value is calculated using a kNN-based local permutation scheme. We prove the CI test’s statistical validity and power in mixed discrete-continuous data, which yields consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes.
Adversarial Sample Detection Through Neural Network Transport Dynamics

We propose a detector of adversarial samples that is based on the view of neural networks as discrete dynamic systems. The detector tells clean inputs from abnormal ones by comparing the discrete vector fields they follow through the layers. We also show that regularizing this vector field during training makes the network more regular on the data distribution’s support, thus making the activations of clean inputs more distinguishable from those of abnormal ones. Experimentally, we compare our detector favorably to other detectors on seen and unseen attacks, and show that the regularization of the network’s dynamics improves the performance of adversarial detectors that use the internal embeddings as inputs, while also improving test accuracy.
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal

An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention automatically labeled ones. Previous methods for label denoising have primarily focused on detecting outliers and their permanent removal – a process that is likely to over- or underfilter the dataset. In this work, we propose AGRA: a new method for learning with noisy labels by using Adaptive GRAdient-based outlier removal (We share our code at: https://github.com/anasedova/AGRA .) Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process. By comparing the aggregated gradient of a batch of samples and an individual example gradient, our method dynamically decides whether a corresponding example is helpful for the model at this point or is counter-productive and should be left out for the current update. Extensive evaluation on several datasets demonstrates AGRA’s effectiveness, while a comprehensive results analysis supports our initial hypothesis: permanent hard outlier removal is not always what model benefits the most from.