Reviewerly/exHarmony huggingface dataset:
Multi-Class Fuzzy-LORE: A Method for Extracting Local and Counterfactual Explanations Using Fuzzy Decision Trees

Multi-class classification is a fundamental task in Machine Learning. However, complex models can be viewed as black boxes, making it difficult to gain insight into how the model makes its predictions and build trust in its decision-making process. This paper presents a novel method called Multi-Class Fuzzy-LORE (mcFuzzy-LORE) for explaining the decisions made by multi-class fuzzy-based classifiers such as Fuzzy Random Forests (FRF). mcFuzzy-LORE is an adaptation of the Fuzzy-LORE method that uses fuzzy decision trees as an alternative to classical decision trees, providing interpretable, human-readable rules that describe the reasoning behind the model’s decision for a specific input. The proposed method was evaluated on a private dataset that was used to train an FRF-based multi-class classifier that assesses the risk of developing diabetic retinopathy in diabetic patients. The results show that mcFuzzy-LORE outperforms prior classical LORE-based methods in the generation of counterfactual instances.
Enhanced Example Diffusion Model via Style Perturbation

With the extensive applications of neural networks in several fields, research on their security has become a hot topic. The digitization of paintings attracts our interest in the security of artistic style classification tasks. The concept of symmetry is commonly adopted in the construction of deep learning models. However, we find that low-quality artistic examples can fool high-performance deep neural networks. Therefore, we propose the enhanced example diffusion model (EDM) for low-quality paintings to symmetrically generate high-quality enhanced examples with positive style perturbations, which improves the performance of the deep learning-based style classification model. Our proposed framework consists of two parts: a style perturbation network that transforms the inputs into the latent space and extracts style features to form a positive style perturbation, and a conditional latent diffusion model that generates high-quality artistic features. High-quality artistic images are combined with positive style perturbations to generate artistic style-enhanced examples. We conduct extensive experiments on synthetic and real datasets, and find the effectiveness of our approach in improving the performance of deep learning models.
Multi-views contrastive learning for dense text retrieval

Dense text retrieval has become a widely used paradigm for recalling existing language models, and efficient dense text retrieval is essential for obtaining sufficiently accurate candidate samples. However, the existing methods for dense text retrieval, which typically use dual-encoder architectures to match similar queries and documents, suffer from a lack of information interaction at low data volumes, resulting in suboptimal performance. Additionally, existing research relies on negative sampling techniques that do not take into account the negative effects of single negative sampling bias on the robustness of the model. These limitations hinder the development of more robust dense text retrieval models. In this paper, we propose a multi-view contrast learning architecture, named MvCR, to address these issues. MvCR improves the performance of dense text retrieval by performing contrast learning with multiple views while significantly increasing the model’s ability to discriminate between positive and negative samples. Additionally, we propose a data augmentation method that focuses on increasing the number of hard negative samples with accurate and semantic matching features. The experimental results have shown that MvCR can perform as well as strong baseline models even when the data volume is small. Furthermore, MvCR achieved better results on two popular retrieval benchmarks with comparable amounts of data. Specifically, MRR@10 was 39.1(+0.9%) and Recall@50 was 87.8(+1.4%) on the MS-MARCO dataset. And Recall@5 increased to 77.2(+1.8%) and Recall@50 increased to 85.3(+1.0%) on the Natural Questions dataset.
Intent Classification by the Use of Automatically Generated Knowledge Graphs

Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision.
Improving the lightweight implementation of SNOW-V

Abstract This paper presents a proposal to improve the software implementation of SNOW-V, a candidate for encrypting 5G communications. In particular, we focus on the proposal for 32-bit architectures with limited resources, such as wireless sensor networks. SNOW-V is a pseudorandom number generator, this type of structures are traditionally implemented in hardware. The advent of 5G means that this implementation must be purely software, losing the advantages and speed of a purely hardware or even hybrid implementation. Due to the wide variety of interconnected devices in the IoT, we cannot assume or associate security with a given architecture or unlimited resource devices. For this reason, we have focused our efforts on analyzing and improving the speed of the proposed 5G encryption on lightweight architectures. Specifically, we analyze its software implementation and propose improvements to speed up the encryption process while protecting confidentiality and integrity for 32-bit devices.
ParallelTracker: A Transformer Based Object Tracker for UAV Videos

Efficient object detection and tracking from remote sensing video data acquired by unmanned aerial vehicles (UAVs) has significant implications in various domains, such as scene understanding, traffic surveillance, and military operations. Although the modern transformer-based trackers have demonstrated superior tracking accuracy, they often require extensive training time to achieve convergence, and the information from templates is not fully utilized in and integrated into tracking. To accelerate convergence and further improve tracking accuracy, we propose an end-to-end tracker named ParallelTracker that extracts prior knowledge from templates for better convergence and enriches template features in a parallel manner. Our core design incorporates spatial prior knowledge into the tracking process through three modules: a prior knowledge extractor module (PEM), a template features parallel enhancing module (TPM), and a template prior knowledge merge module (TPKM). These modules enable rich and discriminative feature extraction as well as integration of target information. We employ multiple PEM, TPM and TPKM modules along with a localization head to enhance accuracy and convergence speed in object tracking. To enable efficient online tracking, we also design an efficient parallel scoring prediction head (PSH) for selecting high-quality online templates. Our ParallelTracker achieves state-of-the-art performance on the UAV tracking benchmark UAV123, with an AUC score of 69.29%, surpassing the latest OSTrack and STARK methods. Ablation studies further demonstrate the positive impact of our designed modules on both convergence and accuracy.
DILRS: Domain-Incremental Learning for Semantic Segmentation in Multi-Source Remote Sensing Data

With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters.
copMEM2: robust and scalable maximum exact match finding

Finding Maximum Exact Matches, i.e. matches between two strings that cannot be further extended to the left or right, is a classic string problem with applications in genome-to-genome comparisons. The existing tools rarely explicitly address the problem of MEM finding for a pair of very similar genomes, which may be computationally challenging. We present copMEM2, a multithreaded implementation of its predecessor. Together with a few optimizations, including a carefully built predecessor query data structure and sort procedure selection, and taking care for highly similar data, copMEM2 allows to compute all MEMs of minimum length 50 between the human and mouse genomes in 59 s, using 10.40 GB of RAM and 12 threads, being at least a few times faster than its main contenders. On a pair of human genomes, hg18 and hg19, the results are 324 s and 16.57 GB, respectively.copMEM2 is available at https://github.com/wbieniec/copmem2.
Comparative analysis of teaching content of high school Chinese textbooks in South Korea: Focused on phonology, morphology, syntax, Chinese characters and pragmatics

This study attempted to examine the characteristics of current high school Chinese textbooks by comparing and analyzing the educational content of 11 Chinese textbooks by linguistic element. Specifically, the 11 textbooks were compared based on their educational contents on linguistic elements such as phonetics, vocabulary, grammar, Chinese characters and culture, and organized by their unique characteristics. With respect to vocabulary education in particular, the percentage of basic vocabulary in the 11 textbooks varied from 64% to 86%, but, in general, all textbooks included more than two-thirds of basic vocabulary. Additionally, they utilized various teaching methods to teach vocabulary, and actively used real-life activities or games that piqued students’ interest. With respect to grammar education, they generally all covered the contents suggested in the 2015 revised curriculum, but there was a lack of diversity in teaching and learning methods and the need to plan new teaching methods that would keep students engaged. Lastly, some textbooks introduced the Chinese language and China in general in relation to linked subjects; these connections can help students to develop a deeper and broader understanding of China and the Chinese language and is in line with the goal of fostering creative convergence talents outlined in the 2015 revised curriculum. Moving forward, various teaching and learning methods should be designed to keep students interested and engaged in China and learning the Chinese language.
CREATION OF ELECTRONIC EDUCATIONAL AND METHODOLOGICAL COMPLEXES&#x0D; WITH THE HELP OF THE ONTOLOGICAL APPROACH

The article discusses the possibilities of ontological modeling and its application for creating knowledge&#x0D; bases of electronic educational and methodological complexes. It is shown that with the help of ontological modeling&#x0D; it is possible to define a subject area, develop a conceptual model, create and fill an ontology with data, and&#x0D; finally put the ontology into action. The toolkit of ontological modeling and its possibilities for creating knowledge&#x0D; bases of electronic teaching and learning kits are considered. It is shown that the Zettelkasten technology and the&#x0D; Obsidian application can be used to build a working model of the knowledge base. An example of the application&#x0D; of the stated methodology in the construction of the knowledge base "History of the Polotsk Cadet Corps" is given.
Intelligence Amplification-Based Smart Health Record Chain for Enterprise Management System

Medical service providers generate many healthcare records containing sensitive and private information about a patient’s health. The patient can allow healthcare service providers to generate healthcare data, which can be stored with healthcare service providers. After some time, if the patient wants to share the healthcare records of one healthcare service provider with another, we can quickly exchange the healthcare record using our approaches. The challenges faced by healthcare service providers are healthcare record sharing, tampering, and insurance fraud. We have developed Health Record Chain for Sharing Medical Data using the modified SHA-512 algorithm. We have evaluated our methods, and our method outperforms in terms of storage cost and total time consumption for health record sharing. The proposed model takes 130 ms to share 100,000 records, 32 ms faster than traditional methods. It also resists various security attacks, as verified by an automated security protocol verification tool.
Distribution of gold derived from hydrothermal fluids on the modern seafloor and its impact on the gold budget of seawater

The modern seafloor hydrothermal system plays a significant role in the cycling of energy and mass between the internal and external layers of the oceanic crust and upper mantle. It continues to supply hydrothermal fluids containing three to five orders of magnitude more gold into the ocean than the amount typically present in deep seawater. It has a considerable impact on the distribution and budget of gold with respect to the large geological inventory of the ocean along with other input sources such as river water. The large amount of various types of data compiled for this study reveals that only about 0.3% of the annual hydrothermal flux of gold (2618.3 kg/a) injected into the overlying seawater column as a dissolved phase is eventually trapped in sulfide deposits near vent sites on the seafloor, while about 0.8% is trapped in metalliferous sediments that fall out from the distal nonbuoyant plume. The remaining ~98.9% of gold is delivered into the depths of the global open ocean. The global budget of gold in seawater (about 1.4 × 10 7 kg), the annual flux of hydrothermal fluids at the seafloor (about 2.6 × 10 3 kg/a), the amount delivered by river water (about 7.2 × 10 4 kg/a), and significant estuarine removal (15%) allows us to estimate the residence time of gold in the modern ocean to be about 220 years. This value is 70% shorter than that (~1000 years) reported previously. In the future, the use of appropriate artificial means to achieve more efficient precipitation of gold from the hydrothermal system at the seafloor could increase the level of enrichment of gold to obtain gold-rich hydrothermal deposits, yielding greater economic benefits.
Automatic language identification: a case study of Pahari languages

In an attempt to expand the inclusiveness of Natural Language Processing, this paper focuses on developing resources and building machine learning models to identify four languages of the Northern Indo-Aryan family, also known as Pahari languages—Nepali, Garhwali, Kumaoni, and Dogri. This is the first attempt towards building identification models for Pahari languages and developing a plain text corpus for Garhwali and Kumaoni, both of which are lesser-known and under-resourced languages/mother tongues of India. The collected corpus, including data in Nepali and Dogri, is statistically analyzed at the word level. We also trained traditional machine learning models for Pahari language identification on this corpus and found that character n-grams based Linear Support Vector Machines performed best with 99.28% accuracy.
FEATURES OF THE SELF-CONCEPT AND COPING BEHAVIOR OF PEOPLE WITH ALCOHOL ADDICTION

Despite the fact that by now alcoholism as a negative social phenomenon has been widely studied by scientists of various fields, the problem continues to be one of the most urgent and intractable. This negative social phenomenon causes enormous damage to society, adversely affects people's health, reduces life expectancy, reduces working capacity, thereby worsening the quality of human resources and limiting socio-economic development. The relevance of this study is determined by the fact that the socio-psychological analysis of the problem remains extremely insufficient at this time. Usually, the main place in its study belongs to clinical and psychological research. At the same time, the socio-psychological aspect is clearly visible in the problem under consideration. It is connected with the study of the Self-concept and interpersonal communication as significant resources for overcoming life difficulties. At the same time, coping and the mechanisms of psychological protection of a person dependent on psychoactive substances, their specifics and interrelation, as well as the phenomenon of the Self-concept itself, have also not been fully studied and require more careful consideration. In this regard, the article attempts to generalize and analyze theoretical and methodological approaches to the definition of the concept of the Self-concept of a healthy person, the features of the Self-concept and the causes of its distortion in a person with addiction, as well as to consider coping behavior models used by people with alcohol dependence, which can provide theoretical grounds for creating effective programs to prevent alcohol addiction, as well as to help form an active problem-solving behavior that allows people to cope with a pathological attraction to alcohol.
FS-GAN: Fuzzy Self-guided structure retention generative adversarial network for medical image enhancement

Improving the quality of medical images is helpful for doctors to perform clinical diagnosis and treatment. Many medical image enhancement methods can achieve good performance when training on paired data. However, acquiring paired high/low-quality medical images for training image enhancement models is challenging, and these methods are not applicable to unpaired images while also lacking structural information preservation. To address these problems, this paper proposes a fuzzy self-guided structure retention generative adversarial network (FS-GAN), which can perform unpaired learning. Specifically, we develop a fuzzy discriminator to distinguish real images from generated images in the fuzzy domain, which is able to improve the enhanced performance of the model. Moreover, we design the self-guided structure retention module (SSRM) and illumination distribution correction module (IDCM) to capture the structure information of nerve fibers in a self-guided manner and correct the illumination distribution of the image to improve the visual effect. Compared with the existing methods, the experimental results show that the enhanced images generated by the FS-GAN exhibit the most complete texture structure and uniform illumination distribution, and FS-GAN performs well in downstream application tasks.
Deep Learning-Based Denoising of Acoustic Images Generated With Point Contact Method

Abstract The versatile nature of ultrasound imaging finds applications in various fields. A point contact excitation and detection method is generally used for visualizing the acoustic waves in Lead Zirconate Titanate (PZT) ceramics. Such an excitation method with a delta pulse generates a broadband frequency spectrum and wide directional wave vector. The presence of noise in the ultrasonic signals severely degrades the resolution and image quality. Deep learning-based signal and image denoising have been demonstrated recently. This paper bench-marked and compared several state-of-the-art deep learning image denoising methods with the classical denoising methods. The best-performing deep learning models are observed to be performing at par or, in some cases, even better than the classical methods on ultrasonic images. We further demonstrate the effectiveness and versatility of the deep learning-based denoising model for the unexplored domain of ultrasound/ultrasonic data. We conclude with a discussion on selecting the best method for denoising ultrasonic images. The impact of this work may help ultrasound-based defects identification equipment manufacturers to adopt a deep learning-based denoising model for more wider and versatile use.
Learning an artificial neural network to discover bit-quad-based formulas to compute basic object properties

Shape analysis requires estimating object properties in many applications, including optical character recognition, tumor classification, skin cancer recognition, leaf and plant identification, and cell analysis. In particular, bit-quad-based linear expressions proposed by Gray and Duda for calculating the area and perimeter of binary images are widely used in the literature. Nevertheless, these formulas require computing 14 or 15 bit-quad patterns out of 16 possible, becoming critical in applications with limited computing resources. Hence, this paper introduces a method based on a single-layer artificial neural network (ANN) to discover new expressions to calculate the area and perimeter with fewer bit-quads than the original formulas without losing measuring accuracy. Besides, an iterative elimination process removes irrelevant bit-quads whose corresponding weights approach zero. After that, an inductive analysis from observing the learned weights provides interpretable formulas for the area and perimeter. Furthermore, the proposed approach is also applied to find bit-quad-based formulas for directly computing the contact perimeter property, whose original formula requires precomputing Gray's area and perimeter of the object. In addition, aiming to show our method's versatility in other applications, we address a real-world problem to discover a bit-quad-based formula to distinguish between two classes of loss of bone density caused by hyperthyroidism and aging in rats. The experimental results show that the proposed approach reduces by approximately half the bit-quads needed to calculate the area and perimeter of Gray and Duda. Likewise, the number of bit-quads to compute the contact perimeter is reduced from nine to six. Besides, the estimated value on test sets by all the found formulas is the same as their original counterparts. On the other hand, the classification of loss of bone density type using the found bit-quad-based formula reaches an accuracy of 100% in the test set. Therefore, the proposed method is an alternative to finding linear expressions with few bit-quads to measure basic object properties.