Reviewerly/exHarmony huggingface dataset:
Shallow decision trees for explainable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si14.svg"><mml:mi>k</mml:mi></mml:math>-means clustering

A number of recent works have employed decision trees for the construction of explainable partitions that aim to minimize the k-means cost function. These works, however, largely ignore metrics related to the depths of the leaves in the resulting tree, which is perhaps surprising considering how the explainability of a decision tree depends on these depths. To fill this gap in the literature, we propose an efficient algorithm with a penalty term in its loss function to favor the construction of shallow decision trees – i.e., trees whose leaves are not very deep, which translate to clusters that are defined by a small number of attributes and are therefore easier to explain. In experiments on 16 datasets, our algorithm yields better results than decision-tree clustering algorithms recently presented in the literature, typically achieving lower or equivalent costs with considerably shallower trees.
BFMNet: Bilateral feature fusion network with multi-scale context aggregation for real-time semantic segmentation

As a key technology for scene understanding, real-time semantic segmentation is an important topic in the field of computer vision in recent years. However, some lightweight networks designed for real-time semantic segmentation only have limited receptive field, so they cannot effectively perceive multi-scale objects in images. In addition, although the fast downsampling feature extraction network reduces the amount of computation, it has the problem of loss of detailed information, resulting in poor prediction accuracy. In this paper, we propose an efficient lightweight semantic segmentation network called BFMNet to address these issues. First, we use a lightweight bilateral structure to encode the semantic and detailed information from images respectively and introduce feature interactions during the encoding process. Furthermore, we design a novel Multi-Scale Context Aggregation Module (MSCAM) to help the network perceive the information of multi-scale objects, which is crucial for semantic segmentation. Finally, we introduce a new fusion module (AEFM) that uses attention perception to facilitate bilateral feature fusion. Our network achieves competitive results on three popular semantic segmentation benchmarks: Cityscapes, CamVid and COCO-Stuff. Specifically, on a single 2080Ti GPU, our network yields 77.7% mIoU at 63.7 FPS on Cityscapes test set with the input resolution of 768×1536. Considering the speed-accuracy trade-off, we also report the results with 1024×2048 input resolution: 78.9% mIoU at 31.4 FPS. On the Camvid test set, our network achieves 75.6% mIoU at 95.8 FPS, while on the COCO-Stuff validation set, our network achieves 31.2% mIoU.
Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran

For the purpose of multi-element geochemical modeling of stream sediment data, we investigate the use of unsupervised clustering analysis (CA). For the various reasons including straightforward implementation, fast computation, and scaling to big dataset, the K-means (KM) algorithm was implemented to cluster the geochemical data to reveal anomalous populations linked to porphyry and skarn Cu deposits in Baft district, NE Iran. However, one of the biggest disadvantages of KM method is the randomly selection of cluster centroids which may increase the systemic uncertainty in unsupervised geochemical modeling and also run time. To address this, genetic (GA) and firefly (FA) metaheuristic optimization algorithms were incorporated into the KM method (namely GKM and FKM models) for determination of cluster centroids, and then, optimal definition of geospatial patterns of anomaly-background classes using clr-transformed values of stream sediment geochemical data. To do so, sample catchment basins (SCBs) of anomalous geochemical dataset were used to spatially map the anomaly populations derived from KM, GKM and FKM methods. We have used the advantages of quantitative evaluation measures namely normalized density index (NDI) and success-rate curves to assess the effectiveness of multi-element geochemical anomaly in determining exploratory targets. The results not only affirms that CA is a useful method to decompose the geochemical anomaly-background populations, but also hybridization of clustering methods via optimization algorithms can efficiently increase the certainty of mineralized zones to be used in further exploration stages.
The universal approximation theorem for complex-valued neural networks

We generalize the classical universal approximation theorem for neural networks to the case of complex-valued neural networks. Precisely, we consider feedforward networks with a complex activation function σ:C→C in which each neuron performs the operation CN→C,z↦σ(b+wTz) with weights w∈CN and a bias b∈C. We completely characterize those activation functions σ for which the associated complex networks have the universal approximation property, meaning that they can uniformly approximate any continuous function on any compact subset of Cd arbitrarily well. Unlike the classical case of real networks, the set of "good activation functions"—which give rise to networks with the universal approximation property—differs significantly depending on whether one considers deep networks or shallow networks: For deep networks with at least two hidden layers, the universal approximation property holds as long as σ is neither a polynomial, a holomorphic function, nor an antiholomorphic function. Shallow networks, on the other hand, are universal if and only if the real part or the imaginary part of σ is not a polyharmonic function.
From target tracking to targeting track: A data-driven yet analytical approach to joint target detection and tracking

This paper addresses the problem of real-time detection and tracking of a non-cooperative target in the challenging scenario with almost no a-priori information about target birth, death, dynamics and detection probability. Furthermore, there are false and missing data at an unknown yet low rate in the measurements. The only information given in advance is about the target-measurement model and the constraint that there is no more than one target in the scenario. To solve these challenges, we model the movement of the target by using a polynomial trajectory function of time (T-FoT), which aims to estimate the continuous-time trajectory of the target rather than a series of discrete-time point estimates as is done in most existing filters/trackers. Data-driven T-FoT initiation and termination strategies are proposed for identifying the (re-)appearance and disappearance of the target. During the existence of the target, real target measurements are distinguished from clutter if the target indeed exists and is detected, in order to update the T-FoT at each scan for which we design a least-squares estimator. Overall, our approach is Markov-free, data-driven yet analytical. Simulations using either linear or nonlinear systems are conducted to demonstrate the effectiveness of our approach in comparison with the Bayes optimal Bernoulli filters. The results show that our approach is comparable to the perfectly-modeled filters, even outperforms them in some cases while requiring much less a-priori information and computing much faster.
Joint multimodal sentiment analysis based on information relevance

Social media users are increasingly turning to express opinions with both images and text, while the visual content and text description may cover some conflicting information diverse from each other. Information relevance refers to the matching degree between cross-modal features at the emotional semantic level, which is not systematically studied. In order to exploit the discriminative features and the internal correlation among different modalities, the mid-level representation extracted by a visual sentiment concept classifier is used to determine information relevance, with the integration of other features, including attended textual and visual features. Then grid search is applied to tune weighting coefficients of the decision fusion scheme, followed by a multimodal adaptive method for joint sentiment analysis based on image-text relevance. The superiority of our architecture approach is demonstrated experimentally by comparing it with several state-of-the-art baselines, such as the vision-aware language modeling approach and contrastive learning-based model. The results indicate that fused multiple features lead to more precise classification than unimodal ones, while the contributions of each single modality differ obviously in emotional expression. Besides, the performance of every involved model varies markedly per dataset of different content correlation, which proves that it is of extensive theoretical significance and application prospect to introduce the image-text relevance classifier into a multimodal task.
A deep recursive multi-scale feature fusion network for image super-resolution

Recently, Convolutional Neural Networks (CNNs) have achieved great success in Single Image Super-Resolution (SISR). In particular, the recursive networks are now widely used. However, existing recursion-based SISR networks can only make use of multi-scale features in a layer-wise manner. In this paper, a Deep Recursive Multi-Scale Feature Fusion Network (DRMSFFN) is proposed to address this issue. Specifically, we propose a Recursive Multi-Scale Feature Fusion Block (RMSFFB) to make full use of multi-scale features. Besides, a Progressive Feature Fusion (PFF) technique is proposed to take advantage of the hierarchical features from the RMSFFB in a global manner. At the reconstruction stage, we use a deconvolutional layer to upscale the feature maps to the desired size. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed DRMSFFN in comparison with the state-of-the-art methods in both quantitative and qualitative evaluations.
Artificial intelligence in breast cancer histopathology

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.
Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology

Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The objective of this study was to demonstrate the accuracy of AI-based image analysis for thyroid fine-needle aspiration cytology (FNAC) and to propose its application in clinical practice.In total, 148,395 microscopic images of FNAC were obtained from 393 thyroid nodules to train and validate the data, and EfficientNetV2-L was used as the image-classification model. The 35 nodules that were classified as atypia of undetermined significance (AUS) were predicted using AI training.The precision-recall area under the curve (PR AUC) was >0.95, except for poorly differentiated thyroid carcinoma (PR AUC = 0.49) and medullary thyroid carcinoma (PR AUC = 0.91). Poorly differentiated thyroid carcinoma had the lowest recall (35.4%) and was difficult to distinguish from papillary thyroid carcinoma, medullary thyroid carcinoma, and follicular thyroid carcinoma. Follicular adenomas and follicular thyroid carcinomas were distinguished from each other by 86.7% and 93.9% recall, respectively. For two-dimensional mapping of the data using t-distributed stochastic neighbor embedding, the lymphomas, follicular adenomas, and anaplastic thyroid carcinomas were divided into three, two, and two groups, respectively. Analysis of the AUS nodules showed 94.7% sensitivity, 14.4% specificity, 56.3% positive predictive value, and 66.7% negative predictive value.The authors developed an AI-based approach to analyze thyroid FNAC cases encountered in routine practice. This analysis could be useful for the clinical management of AUS and follicular neoplasm nodules (e.g., an online AI platform for thyroid cytology consultations).
The Potential of TikTok’s Key Features as a Pedagogical Strategy for ESL Classrooms

The predominance of the use of the English language on social media entails its potential usefulness in regard to ESL learning. Amongst all social media platforms, none can boast the ubiquity of TikTok. This application has been, and continues to be, one of the largest and most influential social media platforms, as well as a massive success across a range of fields with the most active users in the world. In the field of education, it has the potential to enhance educational pedagogies. Although TikTok has various features aligned to language learning, TikTok’s application within the education field is still largely unexplored. This scoping review aims to identify the key features of TikTok that can be used to enhance pedagogical strategies in sustaining the teaching of language skills in ESL classrooms. Although studies have been conducted on this subject, they have often lacked clarity and also lacked the possibility of synthesis in regard to the contribution of this platform to language education. Thus, this scoping review is conducted in order to identify the useful TikTok elements that strengthened pedagogical strategies in ESL classrooms. A total of 60 publications with publication dates spanning from 2018 to 2021 were chosen from searches on the EBSCOhost, Google Scholar, and ERIC databases in order to illustrate the current state. The outcome of using the identified features of TikTok and key pedagogical TikTok strategies for the purposes of ESL learning were the two themes used to synthesize the data. Findings indicated that video-related features and the duet challenge feature in TikTok have potential in promoting engaging and meaningful learning, in the context of a virtual environment. This potential has enabled the use of major pedagogical strategies, such as digital assignment; active learning; collaborative and team-based learning; experiential learning; and incidental and constructive learning. Although using TikTok is beneficial for the purposes of language learning, the findings of this study also revealed several limitations associated with the platform, such as users’ behavior, poor infrastructure, and an absence of online discussion forums. This review contains suggestions for the purposes of assisting teachers in terms of maximizing their use of TikTok, via overcoming the challenges that were highlighted in previous studies.
A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans

Liver tumours are diseases with high morbidity and high deterioration probabilities, and accurate liver area segmentation from computed tomography (CT) scans is a prerequisite for quick tumour diagnosis. While 2D network segmentation methods can perform segmentation with lower device performance requirements, they often discard the rich 3D spatial information contained in CT scans, limiting their segmentation accuracy. Hence, a deep residual attention-based U-shaped network (DRAUNet) with a biplane joint method for liver segmentation is proposed in this paper, where the biplane joint method introduces coronal CT slices to assist the transverse slices with segmentation, incorporating more 3D spatial information into the segmentation results to improve the segmentation performance of the network. Additionally, a novel deep residual block (DR block) and dual-effect attention module (DAM) are introduced in DRAUNet, where the DR block has deeper layers and two shortcut paths. The DAM efficiently combines the correlations of feature channels and the spatial locations of feature maps. The DRAUNet with the biplane joint method is tested on three datasets, Liver Tumour Segmentation (LiTS), 3D Image Reconstruction for Comparison of Algorithms Database (3DIRCADb), and Segmentation of the Liver Competition 2007 (Sliver07), and it achieves 97.3%, 97.4%, and 96.9% Dice similarity coefficients (DSCs) for liver segmentation, respectively, outperforming most state-of-the-art networks; this strongly demonstrates the segmentation performance of DRAUNet and the ability of the biplane joint method to obtain 3D spatial information from 3D images.
Text2Light

High-quality HDRIs (High Dynamic Range Images), typically HDR panoramas, are one of the most popular ways to create photorealistic lighting and 360-degree reflections of 3D scenes in graphics. Given the difficulty of capturing HDRIs, a versatile and controllable generative model is highly desired, where layman users can intuitively control the generation process. However, existing state-of-the-art methods still struggle to synthesize high-quality panoramas for complex scenes. In this work, we propose a zero-shot text-driven framework, Text2Light , to generate 4K+ resolution HDRIs without paired training data. Given a free-form text as the description of the scene, we synthesize the corresponding HDRI with two dedicated steps: 1 ) text-driven panorama generation in low dynamic range (LDR) and low resolution (LR), and 2 ) super-resolution inverse tone mapping to scale up the LDR panorama both in resolution and dynamic range. Specifically, to achieve zero-shot text-driven panorama generation, we first build dual codebooks as the discrete representation for diverse environmental textures. Then, driven by the pre-trained Contrastive Language-Image Pre-training (CLIP) model, a text-conditioned global sampler learns to sample holistic semantics from the global codebook according to the input text. Furthermore, a structure-aware local sampler learns to synthesize LDR panoramas patch-by-patch, guided by holistic semantics. To achieve super-resolution inverse tone mapping, we derive a continuous representation of 360-degree imaging from the LDR panorama as a set of structured latent codes anchored to the sphere. This continuous representation enables a versatile module to upscale the resolution and dynamic range simultaneously. Extensive experiments demonstrate the superior capability of Text2Light in generating high-quality HDR panoramas. In addition, we show the feasibility of our work in realistic rendering and immersive VR.
SocialHaterBERT: A dichotomous approach for automatically detecting hate speech on Twitter through textual analysis and user profiles

Social media platforms have evolved into an online representation of our social interactions. We may use the resources they provide to analyze phenomena that occur within them, such as the development and viralization of offensive and hostile content. In today’s polarized world, the escalating nature of this behavior is cause for concern in modern society. This research includes an in-depth examination of previous efforts and strategies for detecting and preventing hateful content on the social network Twitter, as well as a novel classification approach based on users’ profiles, related social environment and generated tweets. This paper’s contribution is threefold: (i) an improvement in the performance of the HaterNet algorithm, an expert system developed in collaboration with the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security (Ministry of the Interior) that is capable of identifying and monitoring the evolution of hate speech on Twitter using an LTSM + MLP neural network architecture. To that end, a model based on BERT, HaterBERT, has been created and tested using HaterNet’s public dataset, providing results that show a significant improvement; (ii) A methodology to create a user database in the form of a relational network to infer textual and centrality features. This contribution, SocialGraph, has been independently tested with various traditional Machine Learning and Deep Learning algorithms, demonstrating its usefulness in spotting haters; (iii) a final model, SocialHaterBERT, that integrates the previous two approaches by analyzing features other than those inherent in the text. Experiment results reveal that this last contribution greatly improves outcomes, establishing a new field of study that transcends textual boundaries, paving the way for future research in coupled models from a diachronic and dynamic perspective.
Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement

Integrating artificial intelligence (AI) systems into decision-making tasks attempts to assist people by augmenting or complementing their abilities and ultimately improve task performance. However, when considering recommendations from modern "black box" intelligent systems, users are confronted with the decision of accepting or overriding AI's recommendations. These decisions are even more challenging to make when there exists a significant knowledge imbalance between the users and the AI system—namely, when people lack necessary task knowledge and are therefore unable to accurately complete the task on their own. In this work, we aim to understand people's behavior in AI-assisted decision-making tasks when faced with the challenge of knowledge imbalance and explore whether involving users in an AI's prediction generation process makes them more willing to follow the AI's recommendations and enhances their perception of collaboration. Our empirical study reveals that the involvement of users in generating AI recommendations during a task with notable knowledge imbalance causes them to be more willing to agree with the AI's suggestions and to perceive the AI agent and their collaboration as a team more positively.
An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness.