Irrelevant Sources Research Articles

Towards more accurate microbial source tracking via non-negative matrix factorization (NMF).

The microbiome of a sampled habitat often consists of microbial communities from various sources, including potential contaminants. Microbial source tracking (MST) can be used to discern the contribution of each source to the observed microbiome data, thus enabling the identification and tracking of microbial communities within a sample. Therefore, MST has various applications, from monitoring microbial contamination in clinical labs to tracing the source of pollution in environmental samples. Despite promising results in MST development, there is still room for improvement, particularly for applications where precise quantification of each source's contribution is critical. In this study, we introduce a novel tool called SourceID-NMF towards more precise microbial source tracking. SourceID-NMF utilizes a non-negative matrix factorization (NMF) algorithm to trace the microbial sources contributing to a target sample. By leveraging the taxa abundance in both available sources and the target sample, SourceID-NMF estimates the proportion of available sources present in the target sample. To evaluate the performance of SourceID-NMF, we conducted a series of benchmarking experiments using simulated and real data. The simulated experiments mimic realistic yet challenging scenarios for identifying highly similar sources, irrelevant sources, unknown sources, low abundance sources, and noise sources. The results demonstrate the superior accuracy of SourceID-NMF over existing methods. Particularly, SourceID-NMF accurately estimated the proportion of irrelevant and unknown sources while other tools either over- or under-estimated them. In addition, the noise sources experiment also demonstrated the robustness of SourceID-NMF for MST. SourceID-NMF is available online at https://github.com/ZiyiHuang0708/SourceID-NMF.

Novel adaptive loss weighted transfer network for partial domain fault diagnosis

Mechanical fault transfer diagnosis has been confirmed as a feasible approach for tackling intelligent diagnosis with incomplete fault information and scarce labeled data on the basis of big data through the transfer of diagnostic knowledge from one or more conditions to any other condition. However, existing research has developed a hypothesis, i.e., the target domain shares an identical label space with the source domain, making it unfeasible to address the practical issue that the target domain label space is a subset of the source domain label space, resulting in low transfer diagnosis accuracy. To address this issue, a novel unsupervised intelligent diagnosis approach named double classifiers-dependent transfer diagnosis network is developed. In this approach, the label distribution weights are generated through the probability output of the classifier of source domain label space to target domain samples, by which small weights are assigned to irrelevant source samples to avoid negative transfer in the global-local maximum mean discrepancies (GL-MMD). In addition, classifiers of the source domain label space and the shared label space are built separately to improve the reliability of label distribution weights and GL-MMD. By training the network in the shared label space, diagnostic knowledge in partial domain issues is effectively transferred. Two cases are implemented to verify the effectiveness of the developed approach. Compared with other transfer diagnosis approaches, the developed approach achieved better diagnostic performance.

Contrastive Learning Assisted-Alignment for Partial Domain Adaptation.

This work addresses unsupervised partial domain adaptation (PDA), in which classes in the target domain are a subset of the source domain. The key challenges of PDA are how to leverage source samples in the shared classes to promote positive transfer and filter out the irrelevant source samples to mitigate negative transfer. Existing PDA methods based on adversarial DA do not consider the loss of class discriminative representation. To this end, this article proposes a contrastive learning-assisted alignment (CLA) approach for PDA to jointly align distributions across domains for better adaptation and to reweight source instances to reduce the contribution of outlier instances. A contrastive learning-assisted conditional alignment (CLCA) strategy is presented for distribution alignment. CLCA first exploits contrastive losses to discover the class discriminative information in both domains. It then employs a contrastive loss to match the clusters across the two domains based on adversarial domain learning. In this respect, CLCA attempts to reduce the domain discrepancy by matching the class-conditional and marginal distributions. Moreover, a new reweighting scheme is developed to improve the quality of weights estimation, which explores information from both the source and the target domains. Empirical results on several benchmark datasets demonstrate that the proposed CLA outperforms the existing state-of-the-art PDA methods.

Partial Domain Adaptation Without Domain Alignment.

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a related and unlabeled target domain with identical label space. The main workhorse in UDA is domain alignment and has proven successful. However, it is practically difficult to find an appropriate source domain with identical label space. A more practical scenario is partial domain adaptation (PDA) where the source label space subsumes the target one. Unfortunately, due to the non-identity between label spaces, it is extremely hard to obtain an ideal alignment, conversely, easier resulting in mode collapse and negative transfer. These motivate us to find a relatively simpler alternative to solve PDA. To achieve this, we first explore a theoretical analysis, which says that the target risk is bounded by both model smoothness and between-domain discrepancy. Then, we instantiate the model smoothness as an intra-domain structure preserving (IDSP) while giving up possibly riskier domain alignment. To our best knowledge, this is the first naive attempt for PDA without alignment. Finally, our empirical results on benchmarks demonstrate that IDSP is not only superior to the PDA SOTAs (e.g., ∼+10% on Cl → Rw and ∼+8% on Ar → Rw), but also complementary to domain alignment in the standard UDA.

Fault Diagnosis in Wind Turbines Based on Weighted Joint Domain Adversarial Network Under Various Working Conditions

In recent years, the practical cross-domain wind turbine fault diagnosis is constrained by label space and distribution differences of fault data under different working conditions. Due to the difference in feature distribution, the generalizability of traditional transfer learning models can be affected. Moreover, the difference in label space, which refers to the target domain (SCADA data of another wind turbine), covers only a subset of the source domain (SCADA data of one wind turbine) fault categories, it reduces the accuracy of recognition by the diagnostic model for the target domain failure. Therefore, a weighted joint domain adversarial network (WJDAN) is proposed in this paper to overcome the above problems. In this method, a weighting function is introduced to identify and remove irrelevant source domain fault categories, with the contribution of source domain data to the domain discriminator, maximum mean discrepancy (MMD) and classifier measured. Besides, the domain adversarial network with the intraclass MMD loss is introduced to minimize the marginal and conditional distributional discrepancies between the source domain and target domain simultaneously. As revealed by the experiments on SCADA data of different wind turbines, the proposed WJDAN outperforms other traditional transfer learning methods on wind turbine fault diagnosis.

A two-stage federated optimization algorithm for privacy computing in Internet of Things

With the advent of the Internet of things (IoT) era, federated learning plays an important role in breaking through traditional data barriers and effectively realizing data privacy and security in the process of sharing. However, the demand of the practical problems makes the algorithm still have great challenges in effectively balancing various factors, such as privacy security, accuracy, computing efficiency and so on. To challenge this problem, a two-stage federated optimization algorithm based on robust and multitasking learning is designed. In optimization client local model stage, an adaptive weight assignment mechanism is adopted to guide robust learning based on multiple untrusted sources data, which aims to ensure the credibility and robustness of the client model and obtain a reliable client model. To address the information leakage problem during the server–client global model optimization stage, a privacy patch layer is added to the client local model in multitask learning and maintain its privacy parameters stored on the client model during the global model parameter aggregation process, which aims to meet the personalized requirements and performance requirements of protecting the model privacy. To effectively measure the performance of our algorithm, two extensive experiments are carried out to verify the robustness and accuracy of model under different datasets, respectively. In addition, simulation results show that our algorithm successfully suppresses the impact of corrupted or irrelevant sources on performance, and its performance is better than the other two robust distributed learning baseline methods in the client local model optimization stage. At the same time, our algorithm achieves better accuracy performance than other advanced personalized optimization algorithms in the server–client global model optimization stage. Finally, it achieves a good balance between robustness, computational efficiency and model privacy protection.

A Study on Information Literacy among the Postgraduate Students and Research Scholars of Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

Information literacy encompasses a range of abilities that enable individuals to recognize the necessity for information, effectively locate and evaluate it, and utilize it efficiently. It is competency that equips individuals with the necessary understanding of information, its characteristics, and the various formats in which it is available. This includes the skills to discern relevant information from irrelevant sources and an ethical approach to consuming and disseminating information. In this study, the information literacy skills of postgraduate students and research scholars at Nagarjuna University were evaluated using a standardized questionnaire. The findings revealed that these groups encounter challenges when attempting to access required information. Additionally, it highlighted the need for them to enhance their proficiency in utilizing electronic resources effectively.

The role of temporal order of relevant and irrelevant dimensions within conflict tasks.

Conflict tasks are commonly used to investigate control processes under situations of relevant and irrelevant sources of information. In addition to compatibility effects at a mean behavioral level, delta plot analyses of reaction time distributions reveal that the compatibility effect generally increases with time (i.e., positive delta plot slopes) across most conflict-like tasks. Critically, the underlying causes of the increasing delta plot slopes with different types of distractors are still poorly understood. The present study tested whether the relative onset of target-to-distractor processing affects the delta plot slope. Specifically, we manipulated the temporal order of relevant and irrelevant dimensions within an Eriksen flanker task (Experiment [Exp.] 1), an Arrow-Simon task (Exp. 2), and a manual Stroop task (Exp. 3a/3b). The results of the Eriksen flanker task and Arrow-Simon task revealed that the delta plots slopes were less increasing (and instead rather decreasing) when the irrelevant dimension appears first (IR condition) compared to the reversed order (RI condition)-consistent with the idea that the underlying mechanism driving the slope of the delta plot is the temporal overlap of activation between the relevant and irrelevant dimensions. In contrast, for the Stroop task, the delta plots in the RI condition were not more increasing than the ones for the IR condition. Overall, these results suggest that the temporal properties strongly influence delta plot shape, but that the temporal dynamics operating in the flanker task and the Arrow-Simon task differs from the Stroop task, at least under conditions where relevant and irrelevant information is presented sequentially. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A game theory enhanced domain adaptation network for mechanical fault diagnosis

Transfer learning technology has been successfully applied to address the domain adaptation (DA) problem in machinery fault diagnosis. However, the partial DA problem is more suitable for industrial applications, where the target data only covers a subset of the source classes, which makes it difficult to know where to transfer the target data. To overcome this problem, a novel game theory enhanced DA network (GT-DAN) is proposed in this paper. Based on different metrics, including the maximum mean discrepancy, Jensen–Shannon divergence and Wasserstein distance, three attention matrices are constructed to describe the distribution discrepancies between the source domain and the target domain. The optimal coordination between these attention matrices is achieved by a combined weighting based on game theory to generate the optimal probability weights, which can act as a guide to filter out the irrelevant source examples in DA. Two experiments show that the proposed GT-DAN is superior to existing methods in partial DA diagnosis performance.

Reacting to Failure and Orchestrating Success: How Organizational Performance Shocks Move Attention

Organizational performance shocks – i.e., unexpected performance variations – can present challenges for organizations. Paying attention to performance shocks – before and after they happen – is important for organizational adaptation and responsiveness. Organizations should allocate attention to information sources which can signal incipient shocks and help to shape or avoid them, and to sources which can help understanding and managing occurred shocks. But do organizations reallocate their attention before and after performance shocks? We study the relationship between performance shocks and organizational attention allocation empirically with longitudinal data about information source use of knowledge workers of a large corporation. Our results indicate that performance shocks affect attention shifts of the knowledge workers, but positive and negative shocks have different effects. Positive shocks induce attention switches before they happen, but not afterwards. Negative shocks induce attention switches after they happen but not before. The results support our interpretation that allocating attention to relevant information sources and misallocating attention to irrelevant sources can shape performance shocks and attention shifts.

An Enhanced Information Retrieval-Based Bug Localization System with Code Coverage, Stack Traces, and Spectrum Information

Several strategies such as Vector Space Model (VSM), revised Vector Space Model (rVSM), and integration of additional elements such as stack trace and previously corrected bug report have been utilized to improve the Information Retrieval (IR) based bug localization process. Most of the existing IR-based approaches make use of source code files without filtering, which eventually increases the search space of the technique, thereby slowing down the bug localization process. This study developed an enhanced IR-based bug localization model as a viable solution. Specifically, an enhanced rVSM (e-rVSM) is developed based on the hybridization of code coverage, stack traces, and spectrum information. Combining the stack trace and spectrum information as additional features can enhance the accuracy of the IR-based technique by boosting the bug localization process. Code coverage analysis was conducted to remove irrelevant source files and reduce the search space of the IR technique. Then the filtered source files are preprocessed via tokenization and stemming from selecting relevant features and removing unwanted words. The preprocessed data is further analyzed by finding similarities between the preprocessed bug reports and source code files using the e-rVSM. Finally, scores for each source code and suspected buggy files are ranked in descending order. The performance of the proposed e-rVSM is tested on two open-source projects (Zxing and SWT), and its effectiveness is assessed using TopN rank (where N = 5, 10), Mean Reciprocal Rank (MRR), and Mean Average Precision (MAP). Findings from the experimental results revealed the effectiveness of e-rVSM in bug localization. In particular, e-rVSM recorded a significant Top 5 (80.2%; 65%) and Top 10 (89.1%; 75%) rank values on SWT and Zxing dataset respectively. Also, the proposed e-rVSM had MRR values of 80% and 54% on the SWT dataset and MAP values of 61.22% and 47.23% on the Zxing dataset.

Increase in body dysmorphia and eating disorders among adolescents due to social media

In this review, analysis of the studies pertaining to the role of social media in body dissatisfaction eventually leading to body dysmorphia and eating disorders was conducted.Objective:The aim was to review how social media creates the urge of attaining unrealistic body images which is impossible in real life but possible in real-life.Methods:Different search mediums including PubMed, Sciencedirect, and Google Scholar were used in order to find out the effects of social media in influencing body dissatisfaction. The inclusion criteria were based on the selection of research articles from 2014-2021 which correlated social media with eating disorder & body dysmorphia. Additionally, the articles that were not original researches, abstract only and not in English were excluded. After exclusion of irrelevant sources, duplicated article, abstract only& articles published before 2014, Total 25 articles were selected for the purpose of systematic review. Result:As per this systematic review, body dysmorphia & eating disorders were related to high levels of social media addiction. Moreover, how much social media has negatively impacted both the physical health of young adults especially in regards to disordered eating patterns as well as mental health. Social comparison in order to attain unrealistic images on social media resulting in body dysmorphia and body dissatisfaction.Conclusion:By reviewing different studies and finding out the effects of social media in body dysmorphia it is revealed that social media can highly influence various types of eating disorders like anorexia nervosa, bulimia nervosa and binge eating disorders.

A Study of Subject Selection of Commerce Students Towards Statistics

The purpose of this study is to measure the students’ attitude towards statistics subject and effect of discovery of knowledge after the completion. It is necessary to define a certain type of research parameters, so we have chosen students from higher education who take statistics as a subject in post graduate students of South Gujarat region. We randomly sampled 100 students from the management college. Factor analysis has been used here to extract the factor that affects the decision of students for directly or indirectly. We referred such parameters such as Likeness, anxiety, interest, confidence, understanding, motivation, usefulness into real world, statistical jargon, worried about test result, not to interpret test result, peer group thinking, irrelevant, computer literacy and online open sources softwares like SPSS.

Increase in body dysmorphia and eating disorders among adolescents due to social media

In this review, analysis of the studies pertaining to the role of social media in body dissatisfaction eventually leading to body dysmorphia and eating disorders was conducted.Objective:The aim was to review how social media creates the urge of attaining unrealistic body images which is impossible in real life but possible in real-life.Methods:Different search mediums including PubMed, Sciencedirect, and Google Scholar were used in order to find out the effects of social media in influencing body dissatisfaction. The inclusion criteria were based on the selection of research articles from 2014-2021 which correlated social media with eating disorder & body dysmorphia. Additionally, the articles that were not original researches, abstract only and not in English were excluded. After exclusion of irrelevant sources, duplicated article, abstract only& articles published before 2014, Total 25 articles were selected for the purpose of systematic review. Result:As per this systematic review, body dysmorphia & eating disorders were related to high levels of social media addiction. Moreover, how much social media has negatively impacted both the physical health of young adults especially in regards to disordered eating patterns as well as mental health. Social comparison in order to attain unrealistic images on social media resulting in body dysmorphia and body dissatisfaction.Conclusion:By reviewing different studies and finding out the effects of social media in body dysmorphia it is revealed that social media can highly influence various types of eating disorders like anorexia nervosa, bulimia nervosa and binge eating disorders.

DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data

Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff et al., 2016). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model can explain data behavioural patterns within common conflict-like tasks, where stimulus features include both a relevant and irrelevant source of information (e.g., Simon task). This paper introduces the R-package DMCfun, which implements the DMC model and provides functionality to fit the model to observed data. Furthermore, the package can be used to perform and illustrate distributional analyses on data from any conflict-like CRT task.

Source separation on single channel EEG: A pilot study on effect of transcranial alternating current stimulation on scalp meridian.

Brain electrical stimulation has shown the capability to modulate neural activities in a variety of ways. Compared with transcranial direct current stimulation(tDCS), transcranial alternating current stimulation (tACS) may affect brain activities differently through a frequency-based mechanism. This pilot study applied tACS to the scalp following the meridian (Jingluo) of traditional Chinese medicine to explore its potential neural modulation effect. A wearable electroencephalogram (EEG) device was used to measure the frontal activity in a female participant before and after tACS longitudinally. A combined method of singular spectrum analysis (SSA)-independent components analysis (ICA) was applied to separate potential artifacts from ocular and other irrelevant sources. The results demonstrated that SSA-ICA could effectively separate signals from different sources especially the ocular artifact. EEG spectrum analysis showed that short-term tACS could increase the power of delta waves. This study has good implications for the use of tACS and SSA-ICA method for the study of brain activities. Future research is needed to refine more optimum parameters of tACS and SSA-ICA to make the evidence more solid.Clinical Relevance- tACS may influence the brain wave oscillations through the frequency-based mechanism. SSA-ICA method helps to broaden the use of wearable EEG devices for various clinical applications.

Test fairness and assessment of differential item functioning of mathematics achievement test for senior secondary students in Cross River state, Nigeria using item response theory

This study used Item Response Theory approach to assess Differential Item Functioning (DIF) and detect item bias in Mathematics Achievement Test (MAT). The MAT was administered to 1,751 SS2 students in public secondary schools in Cross River State. Instrumentation research design was used to develop and validate a 50-item instrument. Data were analysed using the maximum likelihood estimation technique of BILOG-MG V3 software. The result of the study revealed that 6% of the total items exhibited differential item functioning between the male and female students. Based on the analysis, the study observed that there was sex bias on some of the test items in the MAT. DIF analysis attempt at eliminating irrelevant factors and sources of bias from any kind for a test to yield valid results is among the best methods of recent. As such, test developers and policymakers are recommended to take into serious consideration and exercise care in fair test practice by dedicating effort to more unbiased test development and decision making. Examination bodies should adopt the Item Response Theory in educational testing and test developers should therefore be mindful of the test items that can cause bias in response pattern between male and female students or any sub-group of consideration.
 Keywords: Assessment, Differential Item Functioning, Validity, Reliability, Test Fairness, Item Bias, Item Response Theory.

ConvTasNet-based anomalous noise separation for intelligent noise monitoring

Noise pollution has become a growing concern in public health. The availability of low-cost wireless acoustic sensor networks permits continuous monitoring of noise. However, real acoustic scenes are composed of irrelevant sources (anomalous noise) that overlap with monitored noise, causing biased evaluation and controversy. One classical scene is selected in our study. For road traffic noise assessment, other possible non-traffic noise (e.g., speech, thunder) should be excluded to obtain a reliable evaluation. Because anomalous noise is diverse, occasional, and unpredictable in real-life scenes, removing it from the mixture is a challenge. We explore a fully convolutional time-domain audio separation network (ConvTasNet) for arbitrary sound separation. ConvTasNet is trained by a large dataset, including environmental sounds, speech, and music over 150 hours. After training, the scale-invariant signal-to-distortion ratio (SI-SDR) is improved by 11.40 dB on average for an independent test dataset. ConvTasNet is next applied to anomalous noise separation of traffic noise scenes. We mix traffic noise and anomalous noise at random SNR between -10 dB to 0 dB. Separation is especially effective for salient and long-term anomalous noise, which smooth the overall sound pressure level curve over time. Results emphasize the importance of anomalous noise separation for reliable evaluation.

Optimizing Multi-Query Evaluation in Federated RDF Systems

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.

Does high state anxiety exacerbate distractor interference?

Attentional Control Theory states that anxiety can cause attention to be allocated to irrelevant sources of information by hindering the ability to control attention and focus on the information that matters. In a separate line of inquiry, action-centred views of attention state that non-target distractors involuntarily activate response codes that may cause interference with target-directed movements (distractor interference effect). Due to the proposed negative effects of anxiety on attentional control, we examined whether anxiety could also modulate distractor interference. Participants executed target-directed aiming movements to one of three targets with the potential of a distractor being presented at near or far locations. Distractors were presented at different times with respect to the target presentation in order to explore the excitatory (0, -100ms) and inhibitory (-850ms) processing of the distractor. As a broad indication of the effect of anxiety, the analysis of no distractor trials indicated a lower proportion of time and displacement to reach peak velocity under high compared to low anxiety conditions. Meanwhile, the typical excitatory influence of the distractors located near, compared to far, at a short distractor-onset asynchrony was found in movement time and overall response time. However, this distractor excitation was even greater under high compared to low anxiety in the reaction time component of the response. These findings broadly implicate the attentional control perspective, but they further indicate an influence of anxiety on the excitation rather than inhibition of responses.