Similar Methods Research Articles

Visible wounds of invisible repression: A perspective on the importance of investigating the biological and psychological impact of political repression.

Exposure to psychological trauma is a well-accepted risk factor for the development of mental and somatic diseases. However, chronic stressors not fulfilling the criteria of traumatic experience can have similarly adverse health consequences. While the harmful impact of chronic stressors is generally recognized among researchers, there is a lack of acknowledgment within clinical, political, and societal entities. This becomes evident in the experiences of victims of political repression in the former German Democratic Republic (GDR), an authoritarian state in East Germany. Repression in the GDR included covert measures, such as "Zersetzung" (engl: disintegration), consisting of wiretapping, spreading rumors, or provoking failure in professional and social domains. It aimed to systematically undermine the psychosocial integrity of individuals, inducing anxiety, social isolation, and confusion. This article integrates findings on repression in the GDR with existing trauma and chronic stress literature. "Zersetzung" shares key features with severe psychosocial chronic stressors. Like trauma, experiencing "Zersetzung" likely dysregulated the biological stress systems, thereby predisposing victims to the health consequences they frequently experience to the present day. Certain severe chronic stressors, such as "Zersetzung," do not appear to differ in their negative health consequences from Criterion A traumatic events. Identifying the biological and psychological impact of political repression techniques is essential, not only for public acknowledgment, and proper health care of victims of GDR repression, but also for those individuals suffering from similar repression methods today. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Investigation of protein family relationships with deep learning.

In this article, we propose a method for finding similarities between Pfam families based on the pre-trained neural network ProtENN2. We use the model ProtENN2 per-residue embeddings to produce new high-dimensional per-family embeddings and develop an approach for calculating inter-family similarity scores based on these embeddings, and evaluate its predictions using structure comparison. We apply our method toPfam annotation by refining clan membership for Pfam families, suggesting both new members of existing clans and potential new clans for future Pfam releases. We investigate some of the failure modes of our approach, which suggests directions for future improvements. Our method is relatively simple with few parameters and could be applied to other protein family classification models. Overall, our work suggests potential benefits of employing deep learning for improving our understanding of protein family relationships and functions of previously uncharacterized families. github.com/iponamareva/ProtCNNSim, 10.5281/zenodo.10091909.

Securing the Socio-Cyber World: Multiorder Attribute Node Association Classification for Manipulated Media

With the rapid development of information technology, social network has become an indispensable part of daily life. People have been able to get news from all over the world through social networks for a long time. People spend more time online than they do in real life. However, the information we get in the world of social network is not purely benign. Due to the development of artificial intelligence technology, more and more tampered media information appears in social networks, some for entertainment, while others become the dark side of social networks, of which the most harmful is to people in the media tamper. For fake news and misinformation caused by media tampering, we need to trace the source and clearly distinguish the truth from the manipulated. This article proposes an image media forgery classification method of multiorder attribute nodes. First, we use different methods to extract the edge, texture, grayscale, and color attributes of the image. Second, according to the characteristics of different attributes, we calculate the first-order entropy of edge attributes, the second-order entropy of texture attributes, local entropy of grayscale, and color properties. Finally, we represent each image with some nodes and build a graph convolutional network (GCN) to classify real and fake images. Experimental results on mainstream media manipulation datasets show that our method is the state-of-the-art compared with similar methods.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN .

Methods and Algorithms for Searching Similarities between Texts

The review of the analytical article is a comprehensive study of text analysis modern methods in order to identify and measure the degree of their similarity, which itself is a very important and relevant task, since it examines and analyzes the tools used to solve it. The introduction discusses the purpose of this work, the relevance of the problem, and the importance of developing effective methods for comparing texts. The main part of the article examines and analyzes such methods as “jaccard similarity”, “shingle algorithm”, “levenshtein distance”, “tf-idf” and “bm25”, “bert” and the use of neural networksseparately. The application of a particular method is illustrated by examples presented in tabular form and illustrations. When considering and analyzing the “jaccard similarity”, the methods of its application and limitations are considered. When analyzing the “shingles algorithm”, the advantages of the method in the context of similarity search are revealed. The publication discusses methods based on line spacingin detail, including levenshtein distance. In this case, special attention is paid to the scope of its application and its advantages over other methods. By reviewing statistical methods such as 'tf-idf' and 'bm25', theanalysis of their application and effectiveness in text similaritysearching is given. The article is not limited by analyzing only traditional methods, but it also covers modern ones, including 'bert' and the use of neural networks. These methods are compared with each other, their advantages and disadvantages of use are identified. The conclusion section provides a comparative analysis of all presented methods based on the principle of objectivity, highlighting their characteristics and areas of application. The importance of choosing the most appropriate method for text similaritysearching is noted, depending on the specific search goals, tasks and requirements, and a conclusion is given about the most used, vast and productive method i.e. The use of neural networks. The conclusions emphasize that the article, devoted to a comparative analysis of various methods for similarity searching between texts, has the main goal of developing recommendations to choose the optimal method.

Impact of bariatric surgery, lifestyle change, and pharmacotherapy on fertility in men with obesity: a systematic review protocol.

This review will determine whether various health interventions designed to reduce weight (lifestyle change, bariatric surgery, pharmacotherapy) in men with obesity are associated with improved fertility markers. The review will also establish whether the degree of weight loss achieved through these methods is associated with improvement. Current preconception guidelines provide limited information for men with obesity. Small studies implementing lifestyle changes in men are associated with improvement in sperm quality, whereas bariatric surgery has not been associated with improvements in sperm quality. Determining the benefit of different interventions and the relationship to weight lost is necessary to optimize male fertility. The population will be men younger than 50 years with overweight (BMI >25 kg/m 2 ) or obesity (BMI >30 kg/m 2 ). The exposure of interest will be an intervention undertaken to improve health or reduce weight, categorized as lifestyle change, bariatric surgery, or pharmacotherapy. Outcomes will include time to conception, fecundity rate, assisted reproduction outcomes, and semen quality measures. Secondary analysis will determine whether degree of weight loss achieved is associated with degree of improvement. This review will follow the JBI methodology for systematic reviews of etiology and risk. Databases to be searched will include PubMed, Embase (Ovid), Cochrane Central Register of Controlled Trials, Web of Science Core Collection, and Scopus. Articles not published or translated into English will be excluded. Methodological quality will be assessed using the JBI critical appraisal tools. Data will be extracted using a tool developed by the reviewers. Statistical meta-analysis will be performed where possible to synthesize outcomes of similar methods. PROSPERO CRD42022349665.

A study of concept similarity in Wikidata

Robust estimation of concept similarity is crucial for applications of AI in the commercial, biomedical, and publishing domains, among others. While the related task of word similarity has been extensively studied, resulting in a wide range of methods, estimating concept similarity between nodes in Wikidata has not been considered so far. In light of the adoption of Wikidata for increasingly complex tasks that rely on similarity, and its unique size, breadth, and crowdsourcing nature, we propose that conceptual similarity should be revisited for the case of Wikidata. In this paper, we study a wide range of representative similarity methods for Wikidata, organized into three categories, and leverage background information for knowledge injection via retrofitting. We measure the impact of retrofitting with different weighted subsets from Wikidata and ProBase. Experiments on three benchmarks show that the best performance is achieved by pairing language models with rich information, whereas the impact of injecting knowledge is most positive on methods that originally do not consider comprehensive information. The performance of retrofitting is conditioned on the selection of high-quality similarity knowledge. A key limitation of this study, similar to prior work lies in the limited size and scope of the similarity benchmarks. While Wikidata provides an unprecedented possibility for a representative evaluation of concept similarity, effectively doing so remains a key challenge.

A Study of Multi-criterion Fuzzy Decision-making Methods: Recent Trends and Directions

A plausible soft computing model for addressing ambiguity and vagueness in decision-making circumstances is the concept of intuitionistic fuzzy sets (IFSs). Utilizing similarity-distance metrics, cases like diagnostic analysis, Finance and Investment, pattern recognition, Risk Analysis and Assessment, etc. have been investigated. Numerous methods of distance and similarity have been suggested and utilized to solve deciding circumstances. Although the existing similarity measures and their distance counterparts are fairly significant, they have certain accuracy and conceptual alignment issues that need to be addressed in order to improve output reliability. As a result, a unique similarity-distance technique is introduced in this paper. To demonstrate the benefits of the innovative similarity-distance strategy over related current approaches, a comparative analysis is presented. The theoretical and philosophical aspects of the approach are set aside, and the analysis is solely focused on the algorithmic (technical) point of view. Applying the ranking algorithm yields a solution. A comparative study is presented to illustrate the advantages of the suggested measures. The outcomes of applying the suggested similarity metrics are confirmed by a technique known as Topsis. The outcomes are more logical, consistent, and productive in a skeptical setting.

Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.

LogCSS: Log anomaly detection based on BERT-CNN with context-semantics-statistics features

With the development of artificial intelligence, deep-learning-based log anomaly detection proves to be an important research topic. In this paper, we propose LogCSS, a novel log anomaly detection framework based on the Context-Semantics-Statistics Convolutional Neural Network (CSSCNN). It is the first model that uses BERT (Bidirectional Encoder Representation from Transformers) and CNN (Convolutional Neural Network) to extract the semantic, temporal, and correlational features of the logs. We combine the features with the statistic information of log templates for the classification model to improve the accuracy. We also propose a technique, DOOT (Deals with the Out-Of-Templates), for online template matching. The experimental research shows that our framework improves the average F1 score of the six best algorithms in the industry by more than 5% on the open-source dataset HDFS, and improves the average F1 score of the six best algorithms in the industry by more than 8% on the BGL dataset, LogCSS also performs better than other similar methods on our own constructed dataset.

Dynamic evaluation of road and bridge engineering construction safety risk based on Fuzzy Dynamic Bayesian Network research

To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies.

Similarity measure between generalized intuitionistic fuzzy sets with three kinds of negation

The similarity measure of intuitionistic fuzzy sets is a primary method for dealing with uncertainty and fuzzy problems and is commonly used in fuzzy decision-making and pattern recognition. The current mainstream similarity measure is based on the classical fuzzy set with only one negation, which often violates the intuitionistic problem in applications because the actual semantics of multiple negations are not considered. To solve the inconsistency and irrationality problems in the classical similarity methods, we introduce three negations (contradiction negation, opposition negation, and mediation negation) in the intuitionistic fuzzy set to obtain the generalized intuitionistic fuzzy set and prove its related property theorem. On this basis, our similarity measure adopts a mediational negation to represent non-membership, which fully utilizes the multiple negation information of non-membership and hesitancy and avoids the loss of fuzzy information. We verify the method’s rationality, validity, and originality through pattern recognition experiments and numerical examples, which improves the performance of intuitionistic fuzzy set similarity in practical applications and provides a new approach for future research on intuitionistic fuzzy inference.

Trajectory outlier detection method based on group division

Trajectory-outlier detection can be used to discover the fraudulent behaviour of taxi drivers during operations. Existing detection methods typically consider each trajectory as a whole, resulting in low accuracy and slow speed. In this study, a trajectory outlier detection method based on group division is proposed. First, the urban vector region is divided into a series of grids of fixed size, and the grid density is calculated based on the urban road network. Second, according to the grid density, the grids were divided into high- and low-density grids, and the code sequence for each trajectory was obtained using grid coding and density. Third, the trajectory dataset is divided into several groups based on the number of low-density grids through which each trajectory passes. Finally, based on the high-density grid sequences, a regular subtrajectory dataset was obtained within each trajectory group, which was used to calculate the trajectory deviation to detect outlying trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.

Scoring alignments by embedding vector similarity.

Sequence similarity is of paramount importance in biology, as similar sequences tend to have similar function and share common ancestry. Scoring matrices, such as PAM or BLOSUM, play a crucial role in all bioinformatics algorithms for identifying similarities, but have the drawback that they are fixed, independent of context. We propose a new scoring method for amino acid similarity that remedies this weakness, being contextually dependent. It relies on recent advances in deep learning architectures that employ self-supervised learning in order to leverage the power of enormous amounts of unlabelled data to generate contextual embeddings, which are vector representations for words. These ideas have been applied to protein sequences, producing embedding vectors for protein residues. We propose the E-score between two residues as the cosine similarity between their embedding vector representations. Thorough testing on a wide variety of reference multiple sequence alignments indicate that the alignments produced using the new $E$-score method, especially ProtT5-score, are significantly better than those obtained using BLOSUM matrices. The new method proposes to change the way alignments are computed, with far-reaching implications in all areas of textual data that use sequence similarity. The program to compute alignments based on various $E$-scores is available as a web server at e-score.csd.uwo.ca. The source code is freely available for download from github.com/lucian-ilie/E-score.

Phoneme Hallucinator: One-Shot Voice Conversion via Set Expansion

Voice conversion (VC) aims at altering a person's voice to make it sound similar to the voice of another person while preserving linguistic content. Existing methods suffer from a dilemma between content intelligibility and speaker similarity; i.e., methods with higher intelligibility usually have a lower speaker similarity, while methods with higher speaker similarity usually require plenty of target speaker voice data to achieve high intelligibility. In this work, we propose a novel method Phoneme Hallucinator that achieves the best of both worlds. Phoneme Hallucinator is a one-shot VC model; it adopts a novel model to hallucinate diversified and high-fidelity target speaker phonemes based just on a short target speaker voice (e.g. 3 seconds). The hallucinated phonemes are then exploited to perform neighbor-based voice conversion. Our model is a text-free, any-to-any VC model that requires no text annotations and supports conversion to any unseen speaker. Quantitative and qualitative evaluations show that Phoneme Hallucinator outperforms existing VC methods for both intelligibility and speaker similarity.

GWS: Rotation object detection in aerial remote sensing images based on Gauss–Wasserstein scattering

The majority of existing rotating target detectors inherit the horizontal detection paradigm and design the rotational regression loss based on the inductive paradigm. But the loss design limitation of the inductive paradigm makes these detectors hardly detect effectively tiny targets with high accuracy, particularly for large-aspect-ratio objects. Therefore, in view of the fact that horizontal detection is a special scenario of rotating target detection and based on the relationship between rotational and horizontal detection, we shift from an inductive to a deductive paradigm of design to develop a new regression loss function named Gauss–Wasserstein scattering (GWS). First, the rotating bounding box is transformed into a two-dimensional Gaussian distribution, and then the regression losses between Gaussian distributions are calculated as the Wasserstein scatter; By analyzing the gradient of centroid regression, centroid regression is shown to be able to adjust gradients dynamically based on object characteristics, and small targets requiring high accuracy detection rely on this mechanism, and more importantly, it is further demonstrated that GWS is scale-invariant while possessing an explicit regression logic. The method is performed on a large public remote sensing dataset DOTA and two popular detectors and achieves a large accuracy improvement in both large aspect ratio targets and small targets detection compared to similar methods.

Quantifying the spatial associations among terrain parameters from digital elevation models

AbstractTerrain parameters describe the morphological characteristics of a surface and are an important part of the application of geoscience. At present, there is still a lack of scientific and systematic research on the associations among terrain parameters. In this article, 14 representative terrain parameters are selected to explore their spatial association relationships by using the quantification methods of linear correlation degree, distribution similarity and influence degree. Then, we discuss the effects of the digital elevation model (DEM) cell size and landform region on the associations among terrain parameters. Furthermore, the mechanism of the association relationships among terrain parameters is also analyzed to achieve a better understanding of terrain analysis. This study revealed that the similarity in calculation methods, the derivation of terrain parameters from other parameters, the geographic significance of representations, and the characteristics and appearances of landforms are significant factors contributing to the associations of terrain parameters. The terrain parameters with strong associations remained stable with changes in cell size and landform region. However, terrain parameters with weak associations significantly respond to this variety of factors. We quantify the associations among terrain parameters, provide a new perspective for examining the associations among terrain parameters, and provide guidance for the selection of terrain parameters in geoscience research.

Community Preferences for the Care of Older People at the End of Life: How Important is the Disease Context?

Population preferences for care at the end of life can inform palliative care policy and direction. Research investigating preferences for care at the end of life has focused predominantly on the context of advanced cancer, with relatively little attention to other life-limiting illnesses that are common causes of death. We aimed to investigate preferences for the care of older people at the end of life in three different disease contexts. The purpose was to understand if population preferences for care in the last 3 weeks of life would differ for patients dying from cancer, heart failure or dementia. Three discrete choice experiments were conducted in Australia with a general population sample using similar methods but different end-of-life disease contexts. Some attributes were common across the three experiments and others differed to accommodate the specific disease context. Each survey was completed by a different panel sample aged ≥45years (cancer, n=1548; dementia, n=1549; heart failure, n=1003). Analysis was by separate mixed logit models. The most important attributes across all three surveys were costs to the patient and family, patient symptoms and informal carer stress. The probability of choosing an alternative was lowest (0.18-0.29) when any one of these attributes was at the least favourable level, holding other attributes constant across alternatives. The cancer survey explored symptoms more specifically and found patient anxiety with a higher relative importance score than the symptom attribute of pain. Dementia was the only context where most respondents preferred to not have a medical intervention to prolong life; the probability of choosing an alternative with a feeding tube was 0.40 (95% confidence interval 0.36-0.43). This study suggests a need for affordable services that focus on improving patient and carer well-being irrespective of the location of care, and this message is consistent across different disease contexts, including cancer, heart failure and dementia. It also suggests some different considerations in the context of people dying from dementia where medical intervention to prolong life was less desirable.

Applying a Context-based Method to Build a Knowledge Graph for the Blue Amazon

ABSTRACT Knowledge graphs are employed in several tasks, such as question answering and recommendation systems, due to their ability to represent relationships between concepts. Automatically constructing such a graphs, however, remains an unresolved challenge within knowledge representation. To tackle this challenge, we propose CtxKG, a method specifically aimed at extracting knowledge graphs in a context of limited resources in which the only input is a set of unstructured text documents. CtxKG is based on OpenIE (a relationship triple extraction method) and BERT (a language model) and contains four stages: the extraction of relationship triples directly from text; the identification of synonyms across triples; the merging of similar entities; and the building of bridges between knowledge graphs of different documents. Our method distinguishes itself from those in the current literature (i) through its use of the parse tree to avoid the overlapping entities produced by base implementations of OpenIE; and (ii) through its bridges, which create a connected network of graphs, overcoming a limitation similar methods have of one isolated graph per document. We compare our method to two others by generating graphs for movie articles from Wikipedia and contrasting them with benchmark graphs built from the OMDb movie database. Our results suggest that our method is able to improve multiple aspects of knowledge graph construction. They also highlight the critical role that triple identification and named-entity recognition have in improving the quality of automatically generated graphs, suggesting future paths for investigation. Finally, we apply CtxKG to build BlabKG, a knowledge graph for the Blue Amazon, and discuss possible improvements.

Hybrid multi-criteria decision making approach proposal for evaluating workloads of aircraft maintenance technicians.

Aircraft maintenance technicians (AMTs) have the most difficulty in terms of total workload criteria working in line maintenance. This is a very important problem for the Aircraft Maintenance Organization. A systematic and scientific approach is required for its solution. This study proposes a new Multi-Criteria Decision Making (MCDM) based approach to evaluating the total workloads of AMTs to identify the most challenging AMT tasks in the aircraft maintenance organization. A new hybrid MCDM approach is proposed by integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity (TOPSIS) methods to compare AMTs on the basis of workloads according to license categories. The hybrid method proposed in this study evaluates the total workload under three main titles: mental, physical, and environmental workload. Focusing on AMTs working in line maintenance of an aircraft maintenance organization, this study revealed that the most important workload criteria determined by the DEMATEL method are lower back strain, upper back strain, time pressure, and air temperature criteria. The results of the TOPSIS method showed that the license categories of AMTs are sorted according to the workloads as follows: A, B2, B1, and B1 + B2. The AMTs holding a "Category A" license have fewer workloads than the other categories. The findings of the study reveal some measures that might allow authorities to minimize the workload of AMTs. In addition, the study contributes to the literature because there are few studies that systematically analyze total workloads by using MCDM methods.