Incorporating Frequency Information Research Articles

Generalized possibility computation tree logic with frequency and its model checking

In recent years, there has been significant research in the field of possibilistic temporal logic. However, existing works have not yet addressed the issue of frequency, which is a common form of uncertainty in the real world. This article aims to fill this gap by incorporating frequency information into possibilistic temporal logic and focusing on the model-checking problem of generalized possibility computation tree logic (GPoCTL) with frequency information. Specifically, we introduce generalized possibility computation tree logic with frequency (GPoCTLF). Although its syntax can be considered as an extension of frequency constraints of the always operator (□) in GPoCTL, they are fundamentally different in semantics and model-checking methods. To facilitate this extension, useful frequency words such as “always”, “usually”, “often”, “sometimes”, “occasionally”, “rarely”, “hardly ever” and “never” are defined as fuzzy frequency operators in this article. Therefore, this article focuses on investigating the model-checking problem of the frequency-constrained always operator. In addition, we analyze the relationship between some GPoCTLF path formulas and GPoCTL path formulas. Then, we provide a model-checking algorithm for GPoCTLF and analyze its time complexity. Finally, an example of a social network is used to illustrate the calculation process of the proposed method and its potential applications.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance.Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter.To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively.These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method’s ability to generalize well in SSVEP analysis.

Image-based reinforced concrete component mechanical damage recognition and structural safety rapid assessment using deep learning with frequency information

Safety assessment of post-event damaged structures is vital and significant because it directly affects life security, structural repair, and economic loss, especially in earthquakes. This study applies deep learning (DL) approaches to structural health monitoring and post-earthquake reconnaissance. An image-based procedure for reinforced concrete (RC) component damage recognition and structural safety rapid assessment is established. An RC component mechanical damage image dataset is built first, and comprehensive component damage recognition tasks (CDRTs) are proposed. EfficientNet-V2 is selected as the baseline model to perform CDRTs. The discrete wavelet transform module that integrates frequency information is then proposed to improve the performance and interpretability of the baseline model. Finally, component and structural damage are linked and structural safety rapid assessment methods based on the results of CDRTs are proposed. This study overcomes the limitations of manual inspections and advances the applications of DL techniques in civil engineering by incorporating frequency information.

Active Learning of Discriminative Subgraph Patterns for API Misuse Detection

A common cause of bugs and vulnerabilities are the violations of usage constraints associated with Application Programming Interfaces (APIs). API misuses are common in software projects, and while there have been techniques proposed to detect such misuses, studies have shown that they fail to reliably detect misuses while reporting many false positives. One limitation of prior work is the inability to reliably identify correct patterns of usage. Many approaches confuse a usage pattern's frequency for correctness. Due to the variety of alternative usage patterns that may be uncommon but correct, anomaly detection-based techniques have limited success in identifying misuses. We address these challenges and propose ALP (Actively Learned Patterns), reformulating API misuse detection as a classification problem. After representing programs as graphs, ALP mines discriminative subgraphs. While still incorporating frequency information, through limited human supervision, we reduce the reliance on the assumption relating frequency and correctness. The principles of active learning are incorporated to shift human attention away from the most frequent patterns. Instead, ALP samples informative and representative examples while minimizing labeling effort. In our empirical evaluation, ALP substantially outperforms prior approaches on both MUBench, an API Misuse benchmark, and a new dataset that we constructed from real-world software projects.

Frequency-Aware Summarization of Resting-State fMRI Data

Many brain imaging modalities reveal interpretable patterns after the data dimensionality is reduced and summarized via data-driven approaches. In functional magnetic resonance imaging (fMRI) studies, such summarization is often achieved through independent component analysis (ICA). ICA transforms the original data into a relatively small number of interpretable bases in voxel space (referred to as ICA spatial components, or spatial maps) and corresponding bases in the time domain (referred to as time-courses of corresponding spatial maps) In this work, we use the word “basis” to broadly refer to either of the two factors resulting from the transformation. A precise summarization for fMRI requires accurately detecting co-activation of voxels by measuring temporal dependence. Accurate measurement of dependence requires a proper understanding of the underlying temporal characteristics of the data. One way to understand such characteristics is to study the frequency spectrum of fMRI data. Researchers have argued that information regarding the underlying neuronal activity might be spread over a range of frequencies as a result of the heterogeneous temporal nature of the neuronal activity, which is reflected in its frequency spectrum. Many studies have accounted for heterogeneous characteristics of the frequency of the signal by either directly inspecting the contents of frequency domain-transformed data or augmenting their analyses with such information. For example, studies on fMRI data have investigated brain functional connectivity by leveraging frequency-adjusted measures of dependence (e.g., when a correlation is measured as a function of frequency, as with “coherence”). Although these studies measure dependence as a function of frequency, the formulation does not capture all characteristics of the frequency-based dependence. Incorporating frequency information into a summarization approach would enable the retention of important frequency-related information that exists in the original space but might be lost after performing a frequency-independent summarization. We propose a novel data-driven approach built upon ICA, which is based on measuring dependence as a generalized function of frequency. Applying this approach to fMRI data provides evidence of existing cross-frequency functional connectivity between different areas of the brain.

Incorporating Frequency Information in a Collocation Dictionary: Establishing a Methodology

This paper describes the methodology applied to incorporate frequency information in the Diccionario de Colocaciones del Español, a dictionary developed within the framework of Explanatory and Combinatorial Lexicology. The main reasons for including lexical frequency information in DiCE were to enrich the dictionary content with information potentially useful for language learners, and to filter currently listed collocates. Obtaining and providing frequency information on collocations is not a straightforward task. In our case, we have opted for assigning a frequency score to each individual lexical unit which constitutes the base of a collocation, while collocations are assigned a frequency score taking into account both the absolute frequency of the collocation itself and the frequency of the lexical unit constituting its base. As a result, collocates are presented in the dictionary under the lexical entry of the base organized according to their syntactic pattern and their meaning, and ordered according to their frequency. Through assigning frequency scores to collocations we also foresee obtaining a frequency list of all collocations included in DiCE, valuable for language teaching and testing purposes.

Chinese text segmentation: A hybrid approach using transductive learning and statistical association measures

Chinese text segmentation (CTS) is a fundamental step in building any Chinese or cross-language information retrieval system. This paper identifies and proposes solutions to two main challenges facing today’s CTS systems: segmenting words longer than the context window and identifying words not derived from affixation or composition. Our methods exploit unlabeled data, making them scalable at little extra cost. To tackle the first problem, we use a transductive learning approach to automatically construct a dictionary, and then refine it by improving its test set coverage while reducing its over-fitting tendency. In addition, we incorporate frequency information to discriminate overlapping matching words. For the second problem, we employ statistical association measures non-parametrically through a natural but novel feature representation scheme. To demonstrate the generality of our approach, we verify our system on the most reputable CTS evaluation standard – the SIGHAN bakeoff, which contains datasets in both traditional and simplified Chinese. These datasets are provided by representative academic or industrial research institutes. The experimental results show that with only training data and unlabeled test data and with no external dictionaries, our approach effectively overcomes the above-mentioned problems and reduces segmentation errors by an average of 27.8% compared with the traditional approach. Notably, our approach improves the recall of new words, the most informative words, by 4.7% on average. Also, our approach outperforms the best SIGHAN CTS system, which requires many external resources. Additional analysis shows that our approach has the potential to gain accuracy as the test data increases.

Data association for deghosting in Y-shaped passive linear array sonars

This paper deals with data association using three sets of passive linear array sonars (PLAS) geometrically positioned in a Y-shaped configuration, fixed in an underwater environment. The data association problem is directly transformed into a 3D assignment, which is known to be NP hard. For generic passive sensors, it can be solved using conventional algorithms, while in PLAS, it becomes a formidable task due to the presence of bearing ambiguity. Thus, the central issue of the problem in PLAS is how to eliminate the bearing ambiguity without increasing tracking error. To solve this problem, the 3D assignment algorithm used the likelihood value of only those observed bearing measurements is modified by incorporating frequency information in consecutive time-aligned scans. The region of possible ghost targets is first established by the geometrical relation of PLAS with respect to target. The ghost targets are then confirmed and eliminated by generating multiple observations in consecutive scans. Representative simulations demonstrate the effectiveness of the proposed approach.

Polymorphism in Systematics and Comparative Biology

▪ Abstract Polymorphism, or variation within species, is common in all kinds of data and is the major focus of research on microevolution. However, polymorphism is often ignored by those who study macroevolution: systematists and comparative evolutionary biologists. Polymorphism may have a profound impact on phylogeny reconstruction, species-delimitation, and studies of character evolution. A variety of methods are used to deal with polymorphism in phylogeny reconstruction, and many of these methods have been extremely controversial for more than 20 years. Recent research has attempted to address the accuracy of these methods (their ability to estimate the true phylogeny) and to resolve these issues, using computer simulation, congruence, and statistical analyses. These studies suggest three things: that (a) the exclusion of polymorphic characters (as is commonly done in morphological phylogenetics) is unjustified and may greatly decrease accuracy relative to analyses that include these characters; (b) methods that incorporate frequency information on polymorphic characters tend to perform best, and (c) distance and likelihood methods designed for polymorphic data may often outperform parsimony methods. Although rarely discussed, polymorphism may also have a major impact on comparative studies of character evolution, such as the reconstruction of ancestral character states. Finally, polymorphism is an important issue in the delimitation of species, although this area has been somewhat neglected methodologically. The integration of within-species variation and microevolutionary processes into studies of systematics and comparative evolutionary biology is another example of the benefits of exchange of ideas between the fields of population genetics and systematics.