Incorrect Judgments Research Articles

Altered emotion perception in insomnia, anxiety, depression, mania, psychotic experiences and schizotypal symptoms: a dataset

This data resource provides evidence concerning the prevalence of perceptual alterations of emotional faces amongst individuals experiencing symptoms of insomnia, anxiety, depression, mania, psychotic experiences, and schizotypal tendencies. More specifically, we explored the categorisation accuracy (whether the displayed emotion was correctly identified), misperception (which emotion an incorrect judgment was perceived to be), intensity (extent of the emotion signal strength) and emotional valence (the extent and direction of perceived affect) of six facial expressions of emotion from the Karolinska Directed Emotional Faces database. Complete data from N = 572 respondents are included. The dataset is available to other researchers and is provided on Figshare. Information concerning the data records, usage notes, code availability and technical validation are presented. Finally, we present demographic and correlational data concerning psychiatric symptoms and alterations in the perception of emotional faces.

Unlocking authentic judicial reasoning: A Template-Based Legal Information Generation framework for judicial views

In the field of legal AI, the research on the task of generating judicial views is relatively weak. Judicial views typically include judgment information on the merits such as charges, law articles, and sentencing circumstances. There are several issues with using universal algorithms to generate text, such as a lack of professional expression required for legal documents, and missing or incorrect judgment information on the merits. Judicial views often involve judgment information on the merits, rather than summarizing and excerpting known information. This could be why the application of conventional text generation algorithms may not produce satisfactory results. In actual judicial scenarios, judges generate their views step by step. Firstly, identify multiple judgment information on the merits in the judicial views, and then connect them with legal templates to achieve the generation of judicial views. This article proposes a Template Based Legal Information Generation (TBLIG) framework that generates higher quality judicial views based on the authentic judicial reasoning methods used by judges. The method consists of two steps: The first step involves determining the judgment information on the merits in the judicial view, while the second step involves synchronously generating a legal template based on the case. We have carried out a large number of experiments on the dataset of indictments and non-indictments. Through comparison with other models and ablation experiments, we have proved the effectiveness of our proposed method.

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.

An efficient ensemble explainable AI (XAI) approach for morphed face detection

Numerous deep neural convolutional architectures have been proposed in literature for face Morphing Attack Detection (MADs) to prevent such attacks and lessen the risks associated with them. Although, deep learning models achieved optimal results in terms of performance, it is difficult to understand and analyze these networks since they are black box/opaque in nature. As a consequence, incorrect judgments may be made. There is, however, a dearth of literature that explains decision-making methods of black box deep learning models for biometric Presentation Attack Detection (PADs) or MADs that can aid the biometric community to have trust in deep learning-based biometric systems for identification and authentication in various security applications such as border control, criminal database establishment etc. In this work, we present a novel visual explanation approach named Ensemble XAI integrating Saliency maps, Class Activation Maps (CAM) and Gradient-CAM (Grad-CAM) to provide a more comprehensive visual explanation for a deep learning prognostic model (EfficientNet-B1) that we have employed to predict whether the input presented to a biometric authentication system is morphed or genuine. The experimentations have been performed on three publicly available datasets namely Face Research Lab London (FRLL) dataset, Wide Multi-Channel Presentation Attack (WMCA) dataset, and Makeup Induced Face Spoofing (MIFS) dataset. The experimental evaluations affirms that the resultant visual explanations highlight more fine-grained details of image features/areas focused by EfficientNet-B1 to reach decisions along with appropriate reasoning.

The effect of expertise in music reading: cross-modal competence

We hypothesize that the fundamental difference between expert and learner musicians isthe capacity to efficiently integrate cross-modal information. This capacity might be anindex of an expert memory using both auditory and visual cues built during many years oflearning and extensive practice. Investigating this issue through an eye-tracking experiment, two groups of musicians, experts and non-experts, were required to report whether afragment of classical music, successively displayed both auditorily and visually on a computer screen (cross-modal presentation) was same or different. An accent mark, associated on a particular note, was located in a congruent or incongruent way according to musical harmony rules, during the auditory and reading phases. The cross-modal competence of experts was demonstrated by shorter fixation durations and less errors. Accent mark appeared for non-experts as interferences and lead to incorrect judgments. Results are discussed in terms of amodal memory for expert musicians that can be supported within the theoretical framework of Long-Term Working Memory (Ericsson and Kintsch, 1995).

Brain Network Interconnectivity Dynamics Explain Metacognitive Differences in Listening Behavior.

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N = 40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable interindividual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional cross talk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.

Applicability of Transfer Function to Disturbance Growth Estimation and Traffic Stability

In linear stability analysis, some unrealistic assumptions (e.g., small and long wavelength disturbance) are made for the derivation of the transfer function, which may result in an inaccurate estimation of disturbance growth rate and incorrect judgment of traffic stability. To gain more insights into the relaxation of assumptions on disturbance, this paper studies the effect of disturbance magnitude and frequency on the accuracy of disturbance growth rate estimation and the applicability of transfer function to traffic stability. The disturbance growth rates of several car-following (CF) models, including both linear and nonlinear CF models, are examined through two-vehicle simulations. The calibrated growth rate is then verified with a simulation involving a long vehicle platoon. The simulation results demonstrate that the calibrated growth rate from a two-vehicle CF pair is applicable to predict the disturbance magnitude of a long platoon. In addition, a concave growth pattern is observed, for which a novel explanation is provided based on the traditional deterministic CF model for the first time.

A Transmission Rate Control Method for Active Congestion Reduction Based on Network Node Bandwidth Allocation

The control of transmission rates is currently a major topic in network research, as it plays a significant role in determining network performance. Traditional network design principles suggest that network nodes should only be responsible for forwarding data, while the sending node should manage control. However, sending nodes often lack information about network resources and must use slow-start algorithms to increase the transmission rate, potentially leading to wasted bandwidth and network congestion. Furthermore, incorrect judgments about network congestion by sending nodes may further reduce network throughput. The emergence of new Internet architectures, such as information-centric networks (ICNn), has empowered network nodes with more capabilities, including computation and caching. This paper proposes a method for transmission rate control that actively avoids congestion through network node bandwidth allocation. The sending, network, and receiving nodes each calculate the available transmission rate, and the sending node negotiates with the other nodes through a rate negotiation message to obtain the maximum transmission rate possible given the current state of the network. The network nodes notify the sending node to adjust the transmission rate to adapt to changes in the network through a rate adjustment message. Simulation experiments show that the proposed method is better than traditional methods in reducing network congestion, providing a stable transmission rate, increasing the network throughput capacity, and improving performance in high-latency and high-bandwidth networks. Additionally, the proposed transmission rate control method is fairer than traditional methods.

Camouflage Backdoor Attack against Pedestrian Detection

Pedestrian detection models in autonomous driving systems heavily rely on deep neural networks (DNNs) to perceive their surroundings. Recent research has unveiled the vulnerability of DNNs to backdoor attacks, in which malicious actors manipulate the system by embedding specific triggers within the training data. In this paper, we propose a tailored camouflaged backdoor attack method designed for pedestrian detection in autonomous driving systems. Our approach begins with the construction of a set of trigger-embedded images. Subsequently, we employ an image scaling function to seamlessly integrate these trigger-embedded images into the original benign images, thereby creating potentially poisoned training images. Importantly, these potentially poisoned images exhibit minimal discernible differences from the original benign images and are virtually imperceptible to the human eye. We then strategically activate these concealed backdoors in specific scenarios, causing the pedestrian detection models to make incorrect judgments. Our study demonstrates that once our attack successfully embeds the backdoor into the target model, it can deceive the model into failing to detect any pedestrians marked with our trigger patterns. Extensive evaluations conducted on a publicly available pedestrian detection dataset confirm the effectiveness and stealthiness of our camouflaged backdoor attacks.

Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique.

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.

Multi-transfer learning techniques for detecting auditory brainstem response

The assessment of the well-being of the peripheral auditory nerve system in individuals experiencing hearing impairment is conducted through auditory brainstem response (ABR) testing. Audiologists assess and document the results of the ABR test. They interpret the findings and assign labels to them using reference-based markers like peak latency, waveform morphology, amplitude, and other relevant factors. Inaccurate assessment of ABR tests may lead to incorrect judgments regarding the integrity of the auditory nerve system; therefore, proper Hearing Loss (HL) diagnosis and analysis are essential. In order to identify and assess ABR automation while decreasing the possibility of human error, machine learning methods, notably deep learning, may be an appropriate option. To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose HL using support vector machines (SVM). Pre-trained convolutional neural network (CNN) architectures like AlexNet, DenseNet, GoogleNet, InceptionResNetV2, InceptionV3, MobileNetV2, NASNetMobile, ResNet18, ResNet50, ResNet101, ShuffleNet, and SqueezeNet are used to extract features from the collected ABR reported images dataset in the proposed model. It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model. According to experimental findings, the ShuffleNet and ResNet50 models' TL is effective for ABR to diagnosis HL using an SVM classifier, with a high accuracy rate of 95% when using the 5-fold cross-validation method.

설명가능한 인공지능(XAI) 기술도입이 금융서비스의 혁신성과를 향상시키는가?

XAI (eXplainable Artificial Intelligence) is an artificial intelligence technology that analyzes the causal relationship of artificial intelligence decision-making, finds appropriate evidence, and explains decision-making results at the user level. Customers of the XAI model are practitioners of financial institutions that operate the model and provide results to customers, and financial consumers who use the results of the AI algorithm model. The financial industry generates profits by managing individual and institutional funds, but also has responsibility for its management. Although artificial intelligence algorithms are being applied to tasks such as the discovery of financial crimes and cyber-crime that are becoming more intelligent and repetitive credit evaluations, unexpected algorithm vulnerabilities can cause losses due to incorrect judgments. XAI presents the basis for the results of artificial intelligence algorithms in the financial sector, and financial companies to which XAI is applied can reduce the possibility of wrong decision-making by artificial intelligence. This study aims to explore the XAI characteristics and influencing factors in the financial sector that affect the reliability and satisfaction of domestic financial consumers, and examine how these factors affect innovation performance in the financial sector. DARPA mentioned user satisfaction, explanation model level, task performance improvement, reliability evaluation, and error correction level as XAI's competency evaluation factors (competency evaluation index). This study refers to DARPA's XAI competency evaluation index, G20's human-centered artificial intelligence society principles, and the Financial Services Commission's financial sector artificial intelligence guidelines, etc. In addition, the innovation performance of financial institutions according to the introduction of XAI technology and service supply was set as a dependent variable. In order to verify the research model, an online survey was conducted targeting workers in companies introducing XAI in the financial sector. As a result of the study, it was found that the XAI characteristics of the financial sector (transparency, explainability, reliability, and result demonstrability) had a significant effect on innovation performance in the financial sector.

A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.

Think More Ambiguity Less: A Novel Dual Interactive Model with Local and Global Semantics for Chinese Named Entity Recognition

Chinese is a representative East Asian language. Chinese Named Entity Recognition (CNER) aims to recognize various entities. It is significant for other NLP tasks to utilize CNER. Recent research to develop CNER systems has been dedicated to either considering word enhancement or capturing global information to strengthen local composition and alleviate word ambiguity in the meanings of words. However, information on words acquired from external lexicons is often confused, and this has led to incorrect judgments regarding the boundaries of words. Moreover, relevant studies typically use excessively complex models to capture the global semantics of sentences. To solve these two problems, we incorporate a global representation into the procedure of local word enhancement. We propose an intuitive and effective dual-module interactive network that can enhance the boundaries of words and extract the global semantics by using a rethinking mechanism to refine the importance of local composition and global information. The results of experiments on four CNER datasets showed that the proposed model can outperform other baselines in terms of the F1 score.

Inmates' Wives' Dedication to Their Partners at Nigeria's Agodi-Ibadan Medium Correctional Facility

This research set out to explain how inmates’ wives perceived the phenomenon of their incarcerated partners. The research used the personal iceberg metaphor model of Satir and interpretative phenomenology analysis to understand the lived experiences of ten non-incarcerated partners at Nigeria's Agodi-Ibadan medium correctional facility. According to the findings, the non-incarcerated partners believed their partners were not guilty of the offense that was brought against them. They also thought that society made incorrect judgments about their partners. Additionally, they believed that God had approved of their spouses' incarceration. Their co-parenting, interpersonal relationships, physical and emotional health, and socioeconomic situation were all impacted by these opinions. The research offered counselling professionals a few recommendations.

Clinical Investigation of a Rapid Non-invasive Multispectral Imaging Device Utilizing an Artificial Intelligence Algorithm for Improved Burn Assessment.

Currently, the incorrect judgment of burn depth remains common even among experienced surgeons. Contributing to this problem are change in burn appearance throughout the first week requiring periodic evaluation until a confident diagnosis can be made. To overcome these issues, we investigated the feasibility of an artificial intelligence algorithm trained with multispectral images of burn injuries to predict burn depth rapidly and accurately, including burns of indeterminate depth. In a feasibility study, 406 multispectral images of burns were collected within 72 hours of injury and then serially for up to 7 days. Simultaneously, the subject's clinician indicated whether the burn was of indeterminate depth. The final depth of burned regions within images were agreed upon by a panel of burn practitioners using biopsies and 21-day healing assessments as reference standards. We compared three convolutional neural network architectures and an ensemble in their capability to automatically highlight areas of nonhealing burn regions within images. The top algorithm was the ensemble with 81% sensitivity, 100% specificity, and 97% positive predictive value (PPV). Its sensitivity and PPV were found to increase in a sigmoid shape during the first week postburn, with the inflection point at day 2.5. Additionally, when burns were labeled as indeterminate, the algorithm's sensitivity, specificity, PPV, and negative predictive value were: 70%, 100%, 97%, and 100%. These results suggest multispectral imaging combined with artificial intelligence is feasible for detecting nonhealing burn tissue and could play an important role in aiding the earlier diagnosis of indeterminate burns.

Liquid pipeline network modeling with performance parameters sensitivity analysis due to its techno-commercial variables

After analyzing existing pipeline-network design solutions in literature, the authors have developed a novel framework model that considers important technical, financial, and environmental factors such as terrain profile, thermal impact, dynamic pump efficiency, cost escalation, tariffs, project phasing, depreciation, and carbon emissions. This comprehensive model not only prioritizes cost optimization for performance measurement but also takes into account tariffs and carbon emissions. To validate the model, the authors conduct a case study on a China multiproduct pipeline and perform sensitivity analysis. The technical model is accurate to 1.1%, except for upstream pressure at the end receiving station. Sensitivity analysis reveals that incorrect judgment of elevation profile, volume escalation, and product temperature variables can render the outcome of pipeline design infeasible. Moreover, neglecting financial components, such as lines fill cost and tankage cost, can cause a variation of 23% in the performance parameters, leading to erroneous decision-making in Pipeline Network Configuration (PNC) design and operations. Lastly, the authors discuss the significance of considering tariff and carbon-emissions performance parameters during design optimization.

NODSTAC: Novel Outlier Detection Technique Based on Spatial, Temporal and Attribute Correlations on IoT Bigdata

AbstractAn outlier in the Internet of Things is an immediate change in data induced by a significant difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause the decision-maker to make incorrect judgments about data analysis. Hence it is essential to detect outliers in any discipline. The detection of outliers becomes the most crucial task to improve sensor data quality and ensure accuracy, reliability and robustness. In this research, a novel outlier detection technique based on spatial, temporal correlations and attribute correlations is proposed to detect outliers (both Errors and Events). This research uses a correlation measure in the temporal correlation algorithm to determine outliers and the spatial correlation algorithm to classify the outliers, whether the outliers are events or errors. This research uses optimal clusters to improve network lifetime, and malicious nodes were also detected based on spatial–temporal correlations and attribute correlations in these clusters. The experimental results proved that the proposed method in this research outperforms some other models in terms of accuracy against the percentage of outliers infused and detection rate against the false alarm rate.

Improving the Accuracy of Single-Nucleotide Variant Diagnosis Using On–Off Discriminating Primers

Early detection of rare mutations through liquid biopsy can provide real-time information related to cancer diagnosis, prognosis, and treatment outcomes. Cell-free DNA samples used in liquid biopsies contain single-nucleotide variants (SNVs) with a variant allele frequency (VAF) of approximately ≤1%. Droplet digital polymerase chain reaction (ddPCR) is considered the gold standard of sequencing using liquid samples, generating amplicons from samples containing mutations with 0.001-0.005% VAF; however, it requires expensive equipment and time-consuming protocols. Therefore, various PCR methods for discriminating SNVs have been developed; nonetheless, non-specific amplification cannot be avoided even in the absence of mutations, which hampers the accurate diagnosis of SNVs. In this study, we introduce single-nucleotide variant on-off discrimination-PCR (Soo-PCR), a highly accurate and practical method that uses a 3'-end tailing primer for the on-off discrimination of low-abundance mutant-type targets, including SNVs. Soo-PCR minimizes the chance of incorrect judgments owing to its high discriminating power. Cancer markers, such as KRAS G12D, EGFR L858R, and EGFR T790M mutations, containing 0.1% VAF, were clearly detected in under 2 h with a high reliability comparable with that of ddPCR. This new method serves as a practical approach to accurately detect and evaluate low-abundance mutations in a user-friendly manner.

The Effect of Pre-segmented Instruction on Reading Fluency: A Study on Chinese-English Intermediate Learners

Intra–sentence segmentation has been proven a useful way for first language (L1) learners to increase reading fluency, including comprehension and reading rate. However, although it is widely used in China to deal with long sentences, it is still worth investigating whether it can be an effective strategy for Chinese-English intermediate learners. In addition, different methods used for segmenting also deserve to be studied. In this study, two English-speaking graduates and four Chinese- English learners are presented with nine long sentences within three different conditions (non-adjustable, segmenting different phrases with a backslash, and putting different phrases into different lines) and under each sentence there is a statement of correct or incorrect judgment. According to the results, the presegmented strategy increases the comprehension for Chinese- English intermediate learners and putting different phrases into different lines is most useful.