False Recognition Research Articles

Towards Nonintrusive and Secure Mobile Two-Factor Authentication on Wearables

Mobile devices are promising to apply two-factor authentication to improve system security. Existing solutions have certain limits of requiring extra user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors available in most wrist-worn wearables. PPGPass simultaneously performs a password/pattern/signature authentication and a physiological-based authentication. To realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts so that users do not have to deliberately keep their bodies still. In addition, to deal with noncancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials. We leverage the great power of Random Forest and Support Vector Data Description to detect adversaries and verify a user's identity. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors. Extensive experiments demonstrate that PPGPass can achieve the false acceptance rate of 3.11% and the false recognition rate of 3.71%, which confirms its high effectiveness, security, and usability.

Neurocognitive bases of self-monitoring of inner speech in hallucination prone individuals

Verbal hallucinations in schizophrenia patients might be seen as internal verbal productions mistaken for perceptions as a result of over-salient inner speech and/or defective self-monitoring processes. Similar cognitive mechanisms might underpin verbal hallucination proneness in the general population. We investigated, in a non-clinical sample, the cerebral activity associated with verbal hallucinatory predisposition during false recognition of familiar words —assumed to stem from poor monitoring of inner speech—vs. uncommon words. Thirty-seven healthy participants underwent a verbal recognition task. High- and low-frequency words were presented outside the scanner. In the scanner, the participants were then required to recognize the target words among equivalent distractors. Results showed that verbal hallucination proneness was associated with higher rates of false recognition of high-frequency words. It was further associated with activation of language and decisional brain areas during false recognitions of low-, but not high-, frequency words, and with activation of a recollective brain area during correct recognitions of low-, but not high-, frequency words. The increased tendency to report familiar words as targets, along with a lack of activation of the language, recollective, and decisional brain areas necessary for their judgement, suggests failure in the self-monitoring of inner speech in verbal hallucination-prone individuals.

Accuracy of pilot balloon palpation for cuff pressure assessment in small versus large sized tubes: a prospective non-randomized observational study

Pilot balloon palpation is still a commonly used method to evaluate cuff pressure of the endotracheal tube after intubation. This study determined whether the size of the tracheal tube influenced the accuracy of pilot balloon palpation. A prospective observational analysis of 208 patients intubated with an endotracheal tube of internal diameter (ID) 6.0 or 8.0 was conducted. An anesthesiologist judged the cuff pressure by manual pilot balloon palpation, and then measured the cuff pressure with a pressure gauge. Cuff pressure exceeding 20–30 cmH2O was defined as false recognition. The intracuff pressure was significantly higher in ID 6.0 tube than in the ID 8.0 tube (41.9 ± 18.8 cmH2O vs. 30.3 ± 11.9 cmH2O, p < 0.001). The number of patients that were mistakenly perceived to have appropriate cuff pressure by pilot balloon palpation was significantly higher in the ID 6.0 group compared to the ID 8.0 group (85 (81.7%) vs. 64 (61.5%), p = 0.001). Therefore, a smaller tube size may further increase risk of inaccurate measurement by pilot balloon palpation and although pressure gauge is recommended for all sizes to maximize accuracy, groups with increased risk factors should be targeted for standardized use of the pressure gauge.

An Offline Handwriting Age Range Prediction System Using an Optimized Deep Learning Technique

One of the distinguishing features of an individual is handwriting and it has been established that everyone has unique handwriting differing from one another. This unique feature evolves with time and is influenced by a variety of factors such as gender, physical and mental health, and age among others. Also, the recent development in using individual peculiar features for forensic investigations in banks and other allied institutions either for signature verification or identification spelled the need to develop a smart system that can predict the age range with offline handwriting recognition. It is on this background that this research employed an optimized deep learning technique comprising of Gravitational Search Algorithm and Convolutional Neural Network (CNN-GSA) for offline handwriting age range prediction. A local database was populated with samples of the signature captured with a digital camera (5 megapixels), the CNN was employed for feature extraction, GSA was utilized to select optimal CNN parameters used for classification while the combined CNNGSA was utilized for an offline handwritten-based age prediction system. The performance evaluation of the approach proposed was done using sensitivity, specificity, precision, false positive rate, recognition accuracy, and processing time for all the variants, while the superiority of the system developed was ascertained by comparing it with the original CNN.

A Novel Deep Multifeature Extraction Framework Based on Attention Mechanism Using Wearable Sensor Data for Human Activity Recognition

Human activity recognition (HAR) utilizing wearable sensors is a prominent topic in academia and has been widely used in health monitoring, medical treatment, motion analysis, and other fields. Although HAR technology based on deep learning has made some progress, there are still some problems, such as incomplete feature extraction and low utilization rate of features, which may result in false recognition. To settle the problems, we propose a novel deep multifeature extraction framework based on attention mechanism (DMEFAM), which includes the temporal attention feature extraction layer (TAFEL), the channel and spatial attention feature extraction layer (CSAFEL), and the output layer. The TAFEL is comprised of bidirectional gated recurrent unit (Bi-GRU) and self-attention (SA) mechanism, and the CSAFEL is comprised of the convolutional block attention module (CBAM) and the residual network-18 (ResNet-18). In this framework, the combination of deep learning neural networks, SA mechanism, and CBAM assigns different important degrees to features, increases the diversities of feature extraction, and improves the accuracy of HAR. To improve the practicability of the proposed framework, a daily and aggressive activity dataset (DAAD) is collected by our laboratory for performance evaluation. The experiments are conducted on the wireless sensor data mining laboratory (WISDM) dataset, the University of California Irvine-HAR (UCI-HAR) dataset, and the collected DAAD dataset. The results show that the proposed DMEFAM can upgrade the recognition performance effectually compared to other advanced HAR frameworks, with the recognition accuracies of 97.9%, 96.0%, and 99.2% on the WISDM dataset, the UCI-HAR dataset, and the DAAD dataset, respectively.

The Influence of Focused Attention and Open Monitoring Mindfulness Meditation States on True and False Memory

Previous investigations into the effect of mindfulness meditation on false memory have reported mixed findings. One potential issue is that mindfulness meditation involves different styles that establish distinct cognitive control states. The present work aimed to address this issue by comparing the effects of single-session focused attention (FAM) and open monitoring (OMM) mindfulness meditation styles on true and false memory recall. Strengthened cognitive control states associated with FAM were predicted to increase true memory recall and decrease false memory recall. Conversely, weakened cognitive control established by OMM was predicted to increase false memory recall. Thirty-four meditation-naïve participants (23 females, mean age = 23.4 years, range = 18–33) first completed pre-meditation learning and recall phases of the Deese-Roediger-McDermott (DRM) task. Participants then completed a single session of FAM or OMM prior to a second, post-meditation, round of DRM task learning and recall phases with a novel word list. Finally, participants completed a recognition test with true and false memory, and distractor words. Both FAM and OMM groups demonstrated significant increase in false memory recall between pre- and post-meditation recall tests but these groups did not differ with respect to true and false memory recall and recognition. The present findings are consistent with previous reports of increased false memory arising from mindfulness meditation. Distinct cognitive control states associated with FAM and OMM states do not result in distinct true and false memory formation, at least in meditation-naïve adults.

Short-Term Phantom Recollection in 8-10-Year-Olds and Young Adults.

Illusory conscious experience of the "presentation" of unstudied material, called phantom recollection, occurs at high levels in long-term episodic memory tests and underlies some forms of false memory. We report an experiment examining, for the first time, the presence of phantom recollection in a short-term working memory (WM) task in 8- to 10-year-old children and young adults. Participants studied lists of eight semantically related words and had to recognize them among unpresented distractors semantically related and unrelated to the studied words after a retention interval of a few seconds. Regardless of whether the retention interval was filled with a concurrent task that interfered with WM maintenance, the false recognition rate for related distractors was very high in both age groups, although it was higher in young adults (47%) than children (42%) and rivaled the rate of target acceptance. The conjoint recognition model of fuzzy-trace theory was used to examine memory representations underlying recognition responses. In young adults, phantom recollection underpinned half of the false memories. By contrast, in children, phantom recollection accounted for only 16% of them. These findings suggest that an increase in phantom recollection use may underlie the developmental increase in short-term false memory.

Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis

We propose deep learning-based models for the risk detection of underground pipelines. To build effective diagnosis models, we construct two types of deep neural network frameworks. First, we propose an image segmentation model with two parallel encoder structures for analyzing RGB and thermal images to detect risk-suspected regions due to pipeline rupture. In addition, for pipeline risk-stage recognition, we compare the performance of various image classification models and select the optimal model. Then, by integrating the image segmentation and classification models, we propose a fused model that performs risk-suspected region detection and risk-level detection using a single inference. The proposed image segmentation model achieves IoU and Dice coefficient values of 0.8373 and 0.9024, respectively, indicating higher performance in suspected-region detection compared with other competing models and demonstrating robust performance against false recognition. In risk-level detection, we confirm that the DenseNet-based model has the highest performance among the classification models, scoring 94.10% and 95.65% in terms of heat and leak accuracy, respectively. Finally, we validate that the fusion model, which recorded 0.8160, 0.8711, 93.90%, and 95.05% in terms of IoU, Dice coefficient, heat accuracy, and leak accuracy, respectively, can process 30 frames of video per second in real-time at the cost of a slight drop in performance.

The effect of memory instructions on within- and between-language false memory

AbstractWe examined the effect of memory instructions on false memory using the Deese/Roediger–McDermott paradigm in second-language learners. Participants studied lists of words in L1 and L2 (e.g., note, sound, piano…) associatively related to a non-presented critical lure (e.g., MUSIC). In a later recognition test, critical lures appeared in the same or the other language of their lists (i.e., within- and between-language conditions). In Experiment 1, participants should only endorse an item when study and test languages matched (i.e., restrictive instructions); that is, they should retrieve language information. In Experiment 2, participants should endorse studied items regardless of the language (i.e., inclusive instructions). With restrictive instructions, false recognition was higher in within- than between-language conditions, whereas with inclusive instructions, this result was replicated only when words were studied in L1, but not L2. Results suggested that second-language learners show false memory in their L2 and that the effect of language shift on false recognition depended on the study language.

Electrical Appliance Recognition Algorithm Based on Neural Network and Kernel Density Estimation

With the rapid development of the social economy, the need for social intelligent transformation is increasingly urgent. In order to identify the electrical equipment quickly and accurately and reduce the false recognition rate, we propose a new identification algorithm, which uses the neural network model to pre-identify the connected electrical equipment and then conducts a secondary test through the kernel density estimation model. The experimental results show that the algorithm has a high recognition rate for the trained model and, at the same time, solves the misrecognition problem of the new electrical appliance neural network model in the production environment.

Chip detection algorithm based on lightweight E-YOLOv5 convolutional neural network

To solve the chip location recognition problem, this paper proposes a lightweight E-YOLOv5 based chip detection algorithm based on the You Only Look Once version 5 (YOLOv5s) algorithm. For the problem of the difficult distinction between chip detection points and light spots, a simulated exposure algorithm is used to process part of the training set images to enhance model robustness; the existing model network is complex, and EfficientNet, a lightweight feature extraction network, is introduced to reduce the model size; for the problem of imprecise model recognition due to small detection points, Selective Kernel Neural Network (SKNet) module is introduced into EfficientNet is introduced to enhance the feature extraction ability of the model and improve the training efficiency, and Efficient Intersection over Union Loss (EIoU_Loss) is used as the loss function to reduce the false recognition rate. Experiments show that the algorithm in this paper improves by 3.85% and 3.92% in precision, recall rate, 28.89% in loss value, nearly 20% in model size and training time, and 46.67% in image processing speed on CPU compared with YOLOv5s. The experimental results show that the proposed algorithm outperforms other algorithms and is able to distinguish and identify chip locations precisely and stably.

Spreading of phonological activation in dyslexia throughout life

We studied the developmental course of the spreading of phonological activation in individuals with and without dyslexia by means of a phonological version of the Deese/Roediger-McDermott false memory task. The false memory effect is assumed to reflect the spreading of activation from the presented stimuli to similar words in the lexicon. We assessed 35 volunteers with dyslexia and a group of matched controls with ages ranging from 6 to 58 years. They were first presented with six lists of ten two-syllable words each, all of which shared one syllable with a reference unpresented word. Then, they answered a recognition questionnaire including 24 presented words, 18 phonologically related unpresented words, and 12 unrelated unpresented words. False recognition of phonologically related words increased with age in the control group but stayed at very low levels in the dyslexia group. Our study indicates the existence of a deficit in the spreading of phonological activation in individuals with dyslexia throughout life.

OCDMA Codeword Switching Technique to Avoid Interference of Time-of-Flight LiDAR System for Autonomous Vehicles

The direct time-of-flight (d-ToF) technique is the most promising method for light detection and ranging (LiDAR) systems in long-range autonomous applications. However, the receiver cannot distinguish its own pulse signal from those of other LiDAR systems. This causes mutual interference between LiDAR systems, which degrades object recognition. The optical code division multiple access (OCDMA) modulation technique can be used to avoid mutual interference. However, it is difficult to secure sufficient orthogonal codes, because each laser channel uses a designated codeword. In addition, when interference occurs in a specific direction, it can occur continuously. Adversarial attacks on a LiDAR system can trick a vehicle to perform false object recognition without physical contact. This can endanger passengers, pedestrians, and other vehicles. Therefore, we propose an OCDMA codeword switching technique that can effectively solve these problems in driving environments. First, we analyzed the characteristics of optical orthogonal codes (OOC) and defined the interference generated in LiDAR systems. The proposed technique exhibits interference robustness by using a code index buffer based on differences in the received optical signal power. In addition, verification of the hardware implementation and its complexity was analyzed using LiDAR testbench and an FPGA. The interference and continuous interference of the proposed technique in various driving environments were verified using simulation model. Compared with using only a designated codeword, the proposed technique reduces interference by 82.59% and 85.51%, and continuous interference by 92.24% and 96.94% in straight-line and curved driving, respectively.

Children's gist-based false memory in working memory tasks.

False memories are well established episodic memory phenomena. Recent research in young adults has shown that semantically related associates can be falsely remembered as studied items in working memory (WM) tasks for lists of only a few items when a short 4-second interval was given between study and test. The present study reported two experiments yielding similar effects in 4- (n = 32 and 33, 18 and 14 females, respectively) and 8-year-old children (n = 33 and 34, respectively, 19 females in both). Short lists of semantically related items specifically tailored for young children were retained over a brief interval. Whether or not the interval was filled with a concurrent task that impeded or not WM maintenance, younger children were as prone to falsely recognize related distractors as their older counterparts in an immediate recognition test, and also in a delayed test. In addition, using the conjoint recognition model of the fuzzy-trace theory, we demonstrated that the retrieval of gist traces of the list themes was responsible for the occurrence of short-term false memories in 4- and 8-year-old children. Gist memory also underpinned the occurrence of false recognition in the delayed test. These findings suggest that young children are as likely to make gist-based false memories as older children in working memory tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Preschoolers’ memory recall of new information: relation to parental reminiscing

ABSTRACT The influence of parental reminiscing style (the way parents discuss past events with their children) on the development of children’s autobiographical memory has been well documented. The specific mechanisms involved in this effect, however, remain unknown. We explored the association between specific components of parental reminiscing and preschoolers’ episodic memory. Fifty-three parent–child dyads (M Child Age = 53.13 months, 29 females) from Belgium were tested twice within a delay of about nine months. At the first time point, parental reminiscing style was assessed via a parent–child discussion of a prior standardised event (a museum visit) focusing on both the structure of parental interactions and the addressed content. At each time point, children were administered with story–recall tasks in the form of true–false recognition about previously heard stories. Generalised linear mixed-effect models were conducted on an item-by-item basis. Results indicated that parental reminiscing is associated with preschoolers’ recognition memory performance at both time points, but not all reminiscing components equally influence children’s performance. Specifically, parents’ concretisations and metamemory talk were found to impact children’s memory. The identification of these components provided insights for exploring the processes underlying the reminiscing–memory influence (in-depth encoding of information, binding processes, and metacognition).

Older adults can use memory for distinctive objects, but not distinctive scenes, to rescue associative memory deficits

ABSTRACT Associative memory deficits in aging are frequently characterized by false recognition of novel stimulus associations, particularly when stimuli are similar. Introducing distinctive stimuli, therefore, can help guide item differentiation in memory and can further our understanding of how age-related brain changes impact behavior. How older adults use different types of distinctive information to distinguish overlapping events in memory and to avoid false associative recognition is still unknown. To test this, we manipulated the distinctiveness of items from two stimulus categories, scenes and objects, across three conditions: (1) distinct scenes paired with similar objects, (2) similar scenes paired with distinct objects, and (3) similar scenes paired with similar objects. Young and older adults studied scene-object pairs and then made both remember/know judgments toward single items as well as associative memory judgments to old and novel scene-object pairs (“Were these paired together?”). Older adults showed intact single item recognition of scenes and objects, regardless of whether those objects and scenes were similar or distinct. In contrast, relative to younger adults, older adults showed elevated false recognition for scene-object pairs, even when the scenes were distinct. These age-related associative memory deficits, however, disappeared if the pair contained an object that was visually distinct. In line with neural evidence that hippocampal functioning and scene processing decline with age, these results suggest that older adults can rely on memory for distinct objects, but not for distinct scenes, to distinguish between memories with overlapping features.

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique.

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

Mobile Big Data Analytics for Human Behavior Recognition in Wireless Sensor Network Based on Transfer Learning

Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.

A fire alarm judgment method using multiple smoke alarms based on Bayesian estimation

Smoke alarms are the most widely used fire sensors; however, their false-alarm rate is high. Owing to the difficulties in replacing all installed smoke alarms in a short period, improving the fire detection accuracy of the installed alarms is a significant challenge for the fire emergency response team. Therefore, this study proposes a fire alarm judgment method with multiple smoke alarms based on Bayesian estimation. To this end, we propose a novel criterion called fire alarm authenticity, wherein the alarm time intervals of multiple smoke alarms are used to calculate the posterior probability distribution of the location and intensity of the fire source without installing any other type of sensors. Subsequently, an alarm sequence classification method based on the proposed criterion is developed to judge a real fire and is validated through simulations of both real fires and false alarms. The results show that the proposed method can identify 77.5% of false alarms based on no real fires to be misjudged. Applications in real fire alarm systems are also discussed, which indicate that the false alarm recognition rate reaches 80%. The proposed method provides a theoretical basis for reducing the existing false-alarm rate with multiple same-type fire alarms or detectors.