False Rate Research Articles

Herschel–ATLAS Data Release III: near-infrared counterparts in the South Galactic Pole field – another 100 000 submillimetre galaxies

ABSTRACT In this paper, we present the third data release (DR3) of the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). We identify likely near-infrared counterparts to submillimetre sources in the South Galactic Pole (SGP) field using the VISTA VIKING survey. We search for the most probable counterparts within 15 arcsec of each Herschel source using a probability measure based on the ratio between the likelihood the true counterpart is found close to the submillimetre source and the likelihood that an unrelated object is found in the same location. For 110 374 (57.0 per cent) sources, we find galaxies on the near-infrared images where the probability that the galaxy is associated to the source is greater than 0.8. We estimate the false identification rate to be 4.8 per cent, with a probability that the source has an associated counterpart on the VIKING images of 0.835 ± 0.009. We investigate the effects of gravitational lensing and present 41 (0.14 deg−2) candidate lensed systems with observed flux densities >100 mJy at 500 μm. We include in the data release a probability that each source is gravitationally lensed and discover an additional 5923 sources below 100 mJy that have a probability greater than 0.94 of being gravitationally lensed. We estimate that ∼400–1 000 sources have multiple true identifications in VIKING based on the similarity of redshift estimates for multiple counterparts close to a Herschel source. The data described in this paper can be found at the H-ATLAS website.

On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion

The blind pixel suppression is the key preprocess to guarantee the real-time space-based infrared point target (IRPT) detection and tracking. Meanwhile, flickering pixels, as one of the blind pixels, is hard to suppress because of randomness. At present, common methods adopting a single feature generally need to accumulate dozens or hundreds of frames to ensure detection accuracy, which cannot update flickering pixels frequently. However, with low detection frequency, the flickering pixels are easily miss detected. In this paper, we propose an on-board flickering pixel dynamic suppression method based on multi-feature fusion. The visual and motion features of flickering pixels are extracted from the result of IRPT detection and tracking. Then, the confidence of flickering pixel evaluation strategy and selection mechanism of flickering pixel are introduced to fuse the above features, which achieves accurate flickering pixel suppression using a dozen frames. The experimental results evaluated on the real image of four scenarios show that the blind pixel false detection rate of the proposed method is no more than 1.02%. Meanwhile, evaluated on the simulated image, the flickering pixel miss suppression rate is no more than 2.38%, and the flickering pixel false suppression rate is 0. The proposed method could be an addition to most other IRPT detection methods, which guarantees the near-real-time and reliability of on-board IRPT detection applications.

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images.

Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.

A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems

Voice over Internet Protocol (VoIP) is a recent voice communication technology and due to its variety of calling capabilities, the system is expected to fuel the market value even further in the next five years. However, there are serious concerns since VoIP systems are frequently been attacked. According to recent security alliance reports, malicious activities have increased largely during the current pandemic against VoIP and other vulnerable networks. This hence implies that existing models are not sufficiently reliable since most of them do not have a hundred percent detection rate. In this paper, a review of our most recent Intrusion Detection & Prevention Systems (IDPS) developed is proposed together with a comparative analysis. The final work consisted of ten models which addressed flood intentional attacks to mitigate VoIP attacks. The methodological approaches of the studies included the quantitative and scientific paradigms, for which several instruments (comparative analysis and experiments) were used. Six prevention models were developed using three sorting methods combined with either a modified galloping algorithm or an extended quadratic algorithm. The seventh IDPS was designed by improving an existing genetic algorithm (e-GAP) and the eighth model is a novel deep learning method known as the Closest Adjacent Neighbour (CAN). Finally, for a better comparative analysis of AI-based algorithms, a Deep Analysis of the Intruder Tracing (DAIT) model using a bottom-up approach was developed to address the issues of processing time, effectiveness, and efficiency which were challenges when addressing very large datasets of incoming messages. This novel method prevented intruders to access a system without authorization and avoided any anomaly filtering at the firewall with a minimum processing time. Results revealed that the DAIT and the e-GAP models are very efficient and gave better results when benchmarking with models. These two models obtained an F-score of 98.83%, a detection rate of 100%, a false rate of 0%, an accuracy of 98.7%, and finally a processing time per message of 0.092 ms and 0.094 ms respectively. When comparing with previous models in the literature from which it is specified that detection rates obtained are 95.5% and false-positive alarm of around 1.8%, except for one recent machine learning-based model having a detection rate of 100% and a processing time of 0.53 ms, the DAIT and the e-GAP models give better results.

A Wearable ForeSeiz Headband for Forecasting Real-Time Epileptic Seizures

According to the World Health Organization (WHO), around 70 million people are affected by Epilepsy. Accurate prediction of seizures well before its commencement alarms the epileptic patients to take necessary actions to reduce the consequence of these seizures. The major objective of this work is the real-time prediction of the epileptic seizures well before its onset. A smart, bio-inspired, self-cognizant and proactive seizure predictor called ForeSeiz is designed for the purpose of forecasting of seizures. This design is suitably constructed on the basis of the Internet of Medical Things (IoMT) framework. The front-end circuitry and Seizure Predictor Tag (SPT) are designed and integrated together into a seizure predictor headband, which totally weighing of 30 grams and dissipating 52.74 mW power dissipation. The proposed ForeSeiz predictor has an Enhanced Convolutional Neural Network (ECNN) classification model, optimized using Fletcher Reeves Algorithm (FRA) along with a Phase Transition Predictor (PTP) based on the Kullback-Leibler divergence for the prediction of real-time seizures. The model is trained and tested using CHB-MIT, NINC and SRM EEG recordings via transfer learning method and yielded 97% accuracy, 0.12 FP/h false prediction rate, along with Premium Seizure Prediction Horizon (PSPH) of 66.52 minutes, prior to the onset of seizures. A compatible Seizure Prediction mobile application (SeizPred APP) is designed for the interaction to the Firebase cloud, for recording the states of the epileptic patients for further reference for the doctors and if any onset of seizure is predicted, it is immediately informed to the caretakers for further intervention actions.

Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

Microglia are tissue-resident macrophages of the central nervous system that are essential for homeostasis, immune response, neurogenesis, and neuronal plasticity. Genetic studies have strongly implicated microglial dysfunction in multiple neurodegenerative diseases. However, investigating genetically-driven changes in gene expression in microglia has been limited by lack of access to these cells from the number of subjects required to perform well-powered genomic analysis.Here we describe the transcriptome analysis of 255 primary human CD11b+ microglia samples isolated at autopsy from multiple brain regions of 100 human subjects with neurodegenerative and neuropsychiatric disorders. We performed systematic analyses to investigate various aspects of microglial heterogeneities including brain region, age, sex, and disease. By intersecting transcriptomics and genetics, we performed expression and splicing QTL analyses and by combining microglia from four different brain regions using a multivariate meta-analysis method (mashR).We observed widespread transcriptome variation associated with microglia, suggesting that these genes may play important role in diversified responses to pathological stimuli of microglia at different locations. We also observed 1,693 genes (FDR < 0.05) whose expression is associated with age including many genes in Alzheimer's (AD) (MS4A6A, FCER1G, and CR1) or Parkinson's disease (PD) (BST1 and FCGR2A) GWAS loci. We identified 3,611 eQTLs (mashR local false sign rate < 0.05), of which 50% (1,791) show region-specific effects. We identified over 300 eQTLs that colocalize with a known risk locus for a neurodegenerative or neuropsychiatric disease, nearly half of which are not found in prefrontal cortex or in peripheral monocytes. We prioritized 7 and 13 putative causal genes for AD and PD, respectively, many of which are novel genes (ITGAX, USP6NL, TSPOAP1, P2RY12, FCGR2C, and FGF20). Fine-mapping of these colocalized loci with CNS chromatin accessibility (ATAC-seq) and histone modification (H3K27ac) data nominates candidate causal variants that are within microglia-specific enhancers and are likely to modify disease susceptibility by regulating gene expression and/or splicing in microglia.In summary, we have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose molecular mechanisms of action of candidate functional variants in several neurological and neuropsychiatric diseases.

Deep face fuzzy vault: Implementation and performance

Biometric technologies, especially face recognition, have become an essential part of identity management systems worldwide. In deployments of biometrics, secure storage of biometric information is necessary in order to protect the users’ privacy. In this context, biometric cryptosystems are designed to meet key requirements of biometric information protection enabling a privacy-preserving storage and comparison of biometric data, e.g. feature vectors extracted from facial images. Until now, biometric cryptosystems have hardly been applied to state-of-the-art biometric recognition systems utilizing deep convolutional neural networks.This work investigates the application of a well-known biometric cryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors extracted through deep convolutional neural networks. To this end, a feature transformation method is introduced which maps fixed-length real-valued deep feature vectors to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted. At key binding, obtained feature sets are locked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency of different polynomial reconstruction techniques is investigated. The proposed feature transformation method and template protection scheme are agnostic of the biometric characteristic and, thus, can be applied to virtually any biometric features computed by a deep neural network. In experiments, an unlinkable improved deep face fuzzy vault-based template protection scheme is constructed employing features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). For the best configuration, a false non-match rate below 1% at a false match rate of 0.01%, is achieved in cross-database experiments on the FERET and FRGCv2 face databases. On average, a security level of up to approximately 28 bits is obtained. This work presents an effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face.

Sleep influences neural representations of true and false memories: An event-related potential study

Episodic memory is reconstructive and is thus prone to false memory formation. Although false memories are proposed to develop via associative processes, the nature of their neural representations, and the effect of sleep on false memory processing is currently unclear. The present research employed the Deese-Roediger-McDermott (DRM) paradigm and a daytime nap to determine whether semantic false memories and true memories could be differentiated using event-related potentials (ERPs). We also sought to illuminate the role of sleep in memory formation and learning. Healthy participants (N = 34, 28F, mean age = 23.23, range = 18–33) completed the learning phase of the DRM task followed by an immediate and a delayed recognition phase. The two recognition phases were separated by either a 2hr daytime nap or an equivalent wake period. Linear mixed modelling of effects at delayed recognition revealed larger LPC amplitudes for true memories in contrast to false memories for those in the wake group, and larger P300 amplitudes for false compared to true memories across sleep and wake groups. Larger LPC amplitudes for true memories were associated with enhanced true memory recognition following sleep, whilst larger P300 amplitudes were associated with similar true and false memory recognition rates. These findings are argued to reflect sleep’s ability to promote memory generalisation associated with pattern completion, whilst also enhancing true memory recognition when memory traces have a strong episodic basis (linked to pattern separation). The present research suggests that true and false memories have differing neural profiles and are reflective of adaptive memory processes.

Germline Testing Data Validate Inferences of Mutational Status for Variants Detected From Tumor-Only Sequencing.

PURPOSEPathogenic germline variants (PGVs) in cancer susceptibility genes are usually identified through germline testing of DNA from blood or saliva: their detection can affect treatment options and potential risk-reduction strategies for patient relatives. PGV can also be identified in tumor sequencing assays, which, when performed without patient-matched normal specimens, render determination of variants' germline or somatic origin critical.METHODSTumor-only sequencing data from 1,608 patients were retrospectively analyzed to infer germline versus somatic status of variants using an information-theoretic, gene-independent approach. Loss of heterozygosity was also determined. Predicted mutational models were compared with clinical germline testing results. Statistical measures were computed to evaluate performance.RESULTSTumor-only sequencing detected 3,988 variants across 70 cancer susceptibility genes for which germline testing data were available. We imputed germline versus somatic status for > 75% of all detected variants, with a sensitivity of 65%, specificity of 88%, and overall accuracy of 86% for pathogenic variants. False omission rate was 3%, signifying minimal error in misclassifying true PGV. A higher portion of PGV in known hereditary tumor suppressors were found to be retained with loss of heterozygosity in the tumor specimens (72%) compared with variants of uncertain significance (58%).CONCLUSIONAnalyzing tumor-only data in the context of specimens' tumor cell content allows precise, systematic exclusion of somatic variants and suggests a balance between type 1 and 2 errors for identification of patients with candidate PGV for standard germline testing. Although technical or systematic errors in measuring variant allele frequency could result in incorrect inference, misestimation of specimen purity could result in inferring somatic variants as germline in somatically mutated tumor suppressor genes. A user-friendly bioinformatics application facilitates objective analysis of tumor-only data in clinical settings.

False lumen thrombosis after frozen elephant trunk procedure in acute and chronic aortic dissection.

The clinical significance of the false lumen (FL) thrombosis after the thoracic aortic surgery consists in the correlation with aortic remodeling. The aim of the study was to analyze the incidence of the FL thrombosis of the thoracoabdominal aorta after the frozen elephant trunk (FET) procedure in acute and chronic aortic dissection. A total of 57 consecutive patients with type A and B aortic dissection (AD) underwent the FET procedure between March 2012 and December 2020 were included in a retrospective study. All of the patients were divided in two groups: acute (N.=18) and chronic (N.=39) AD. Early and 2-year outcomes were evaluated in both groups. Computed tomography scans were obtained along the entire aorta for the aortic status assessment. There were no statistically significant differences in early mortality and postoperative outcomes in both groups. The 2-year survival rate in acute and chronic AD was 74.7±10% vs. 71.4±7% (P=0.573), respectively. In the follow-up, the FL thrombosis rate, changes of the true lumen and total aortic diameters did not differ significantly between the analyzed groups of patients. The freedom from negative aortic remodeling was 62.2±26% in acute AD versus 76.2±11% in chronic AD (P=0.853). One (5.6%) and 4 (10.3%) distal aortic reinterventions were performed in acute and chronic AD, respectively. False lumen thrombosis rate after the FET procedure did not differ significantly in acute and chronic AD.

Seizure Prediction using Convolutional Neural Networks and Sequence Transformer Networks.

Accurate seizure prediction is important for design of wearable and implantable devices that can improve the lives of subjects with epilepsy. Such implantable devices can be used for closed-loop neuromodulation. However, there are many challenges that inhibit the performance of prediction models. One challenge in accurately predicting seizures is the nonstationarity of the EEG signals. This paper presents a patient-specific deep learning approach to improve predictive performance by transforming EEG data before extracting features for seizure prediction. In the proposed approach, a Sequence Transformer Network (STN) is first used to learn temporal and magnitude invariances in EEG data. The proposed method further computes the short-time Fourier transform (STFT) of the transformed EEG signals as input features to a convolutional neural network (CNN). A k-out-of-n post-processing method is used to reduce the significance of isolated false positives. The approach is tested using intracranial EEG from the American Epilepsy Society Seizure Prediction Challenge dataset. Leave-one-out cross validation is used to evaluate the model. The proposed model achieves an overall sensitivity of 82%, false prediction rate of 0.38/hour, and average AUC of 0.746.

Sharpness and Brightness Quality Assessment of Face Images for Recognition

Face image quality has an important effect on recognition performance. Recognition-oriented face image quality assessment is particularly necessary for the screening or application of face images with various qualities. In this work, sharpness and brightness were mainly assessed by a classification model. We selected very high-quality images of each subject and established nine kinds of quality labels that are related to recognition performance by utilizing a combination of face recognition algorithms, the human vision system, and a traditional brightness calculation method. Experiments were conducted on a custom dataset and the CMU multi-PIE face database for training and testing and on Labeled Faces in the Wild for cross-validation. The experimental results show that the proposed method can effectively reduce the false nonmatch rate by removing the low-quality face images identified by the classification model and vice versa. This method is even effective for face recognition algorithms that are not involved in label creation and whose training data are nonhomologous to the training set of our quality assessment model. The results show that the proposed method can distinguish images of different qualities with reasonable accuracy and is consistent with subjective human evaluation. The quality labels established in this paper are closely related to the recognition performance and exhibit good generalization to other recognition algorithms. Our method can be used to reject low-quality images to improve the recognition rate and screen high-quality images for subsequent processing.

HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning

Due to the large quantity of noise and complex spatial background of the remote sensing images, how to improve the accuracy of semantic segmentation has become a hot topic. Lake water body extraction is crucial for disaster detection, resource utilization, and carbon cycle, etc. The the area of lakes on the Tibetan Plateau has been constantly changing due to the movement of the Earth’s crust. Most of the convolutional neural networks used for remote sensing images are based on single-layer features for pixel classification while ignoring the correlation of such features in different layers. In this paper, the two-branch encoder is presented, which is a multiscale structure that combines the features of ResNet-34 with a feature pyramid network. Secondly, adaptive weights are distributed to global information using the hybrid-scale attention block. Finally, PixelShuffle is used to recover the feature maps’ resolution, and the densely connected block is used to refine the boundary of the lake water body. Likewise, we transfer the best weights which are saved on the Google dataset to the Landsat-8 dataset to ensure that our proposed method is robust. We validate the superiority of Hybrid-scale Attention Network (HA-Net) on two given datasets, which were created by us using Google and Landsat-8 remote sensing images. (1) On the Google dataset, HA-Net achieves the best performance of all five evaluation metrics with a Mean Intersection over Union (MIoU) of 97.38%, which improves by 1.04% compared with DeepLab V3+, and reduces the training time by about 100 s per epoch. Moreover, the overall accuracy (OA), Recall, True Water Rate (TWR), and False Water Rate (FWR) of HA-Net are 98.88%, 98.03%, 98.24%, and 1.76% respectively. (2) On the Landsat-8 dataset, HA-Net achieves the best overall accuracy and the True Water Rate (TWR) improvement of 2.93% compared to Pre_PSPNet, which proves to be more robust than other advanced models.

One-Dimensional Convolutional Neural Networks Combined with Channel Selection Strategy for Seizure Prediction Using Long-Term Intracranial EEG.

Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

False recognitions in the DRM paradigm: the role of stress and warning.

For the last decades, the factors increasing or decreasing the frequency of false memories have been of great interest. Some research also examined the effect of stress and warning on the true and false recognitions; however, so far most of the studies have yielded contradictory results or seems inadequate to understand the effect of these factors on false memory phenomenon. The purpose of this study is to examine the joint effects of stress and warning on the frequency of false and true memories elicited by the list-learning paradigm. The word lists derived from the Deese–Roediger–McDermott (DRM) paradigm were used in order to measure false and true recognition rates. Participants (N = 126) were exposed to either the Trier Social Stress Test (a stress condition) or a filler task at the beginning of the experiment (no-stress condition). Then, they were either subjected to a warning about false memories before DRM (pre-warning condition), subjected to a warning about false memories after DRM (post-warning condition), or given no warning at all (no-warning condition). Results showed that stress had a statistically significant effect on true recognition but not on false recognition. Furthermore, warning given after the DRM lists had a decreasing effect on the frequency of false memories. No significant interaction effect between stress and warning was found. Although our hypotheses were not confirmed, this study can contribute to the existing body of research by providing evidence that stress and warning have differential effects on both true and false memories derived from the DRM paradigm.

SMARTPHONE APPLICATION FOR STEMI ACTIVATION: A PILOT STUDY

BACKGROUND ST-segment Elevation Myocardial Infarction (STEMI) requires efficient communication and collaboration to ensure timely care. The current model for STEMI activation often relies on telephone and fax. While secure, fax is often slow, inefficient, and inaccessible. To speed up the process, clinicians often use text messaging, which is not secure, or activation without ECG review, which increases the risk of false activation. This study describes the feasibility of using a secure smartphone application for STEMI activation that uses a novel and secured mobile phone application. METHODS AND RESULTS A 6-month pilot study was performed using a smartphone application to facilitate communication between emergency medicine physicians in three community hospitals with interventional cardiologists at a tertiary care cardiac centre. A voluntary, anonymous post-intervention survey was conducted upon completion of the study. Analytics on use of the application were collected in an anonymous, de-identified manner using Google Analytics for Firebase. Internal and external privacy testing confirmed that the application was privacy compliant. Research Ethics Board approval was obtained. A total of 52 emergency medicine physicians, including all full-time physicians, and 10 interventional cardiologists participated in this 6-month pilot study between May 1, 2020 and October 31, 2020. Physicians were onboarded between April 24, 2020 and May 1, 2020. Data from a total of 130 patients were reviewed via the smartphone application and 69 of these resulted in STEMI activation. There were no application failures; the application had a 100% success rate in transmitting 244 ECGs during this study. During the pilot study, 68% of all STEMIs were activated via the smartphone application. The proportion of cases activated via the application over the course of the study increased from 69% to 77%. At the end of the 6-month pilot study, 17 emergency medicine physicians completed the post-intervention survey. There was high physician satisfaction in the application: 88% liked the application (0% disliked) and 82% would recommend the application to a colleague (0% would not recommend). CONCLUSION This study demonstrates real-world feasibility and usability of a novel, fast, secure, easy-to-use, point-of-care smartphone application for STEMI activation. The application did not have any technical failures, had sustained use and growth with a high degree of physician satisfaction. A larger study is underway to assess the impact of this intervention on patient outcomes, including timing of care and false activation rates. ST-segment Elevation Myocardial Infarction (STEMI) requires efficient communication and collaboration to ensure timely care. The current model for STEMI activation often relies on telephone and fax. While secure, fax is often slow, inefficient, and inaccessible. To speed up the process, clinicians often use text messaging, which is not secure, or activation without ECG review, which increases the risk of false activation. This study describes the feasibility of using a secure smartphone application for STEMI activation that uses a novel and secured mobile phone application. A 6-month pilot study was performed using a smartphone application to facilitate communication between emergency medicine physicians in three community hospitals with interventional cardiologists at a tertiary care cardiac centre. A voluntary, anonymous post-intervention survey was conducted upon completion of the study. Analytics on use of the application were collected in an anonymous, de-identified manner using Google Analytics for Firebase. Internal and external privacy testing confirmed that the application was privacy compliant. Research Ethics Board approval was obtained. A total of 52 emergency medicine physicians, including all full-time physicians, and 10 interventional cardiologists participated in this 6-month pilot study between May 1, 2020 and October 31, 2020. Physicians were onboarded between April 24, 2020 and May 1, 2020. Data from a total of 130 patients were reviewed via the smartphone application and 69 of these resulted in STEMI activation. There were no application failures; the application had a 100% success rate in transmitting 244 ECGs during this study. During the pilot study, 68% of all STEMIs were activated via the smartphone application. The proportion of cases activated via the application over the course of the study increased from 69% to 77%. At the end of the 6-month pilot study, 17 emergency medicine physicians completed the post-intervention survey. There was high physician satisfaction in the application: 88% liked the application (0% disliked) and 82% would recommend the application to a colleague (0% would not recommend). This study demonstrates real-world feasibility and usability of a novel, fast, secure, easy-to-use, point-of-care smartphone application for STEMI activation. The application did not have any technical failures, had sustained use and growth with a high degree of physician satisfaction. A larger study is underway to assess the impact of this intervention on patient outcomes, including timing of care and false activation rates.

Implanting false autobiographical memories for repeated events

ABSTRACT Research to date has exclusively focused on the implantation of false memories for single events. The current experiment is the first proof of concept that false memories can be implanted for repeated autobiographical experiences using an adapted false memory implantation paradigm. We predicted that false memory implantation approaches for repeated events would generate fewer false memories compared to the classic implantation method for single events. We assigned students to one of three implantation conditions in our study: Standard, Repeated, and Gradual. Participants underwent three interview sessions with a 1-week interval between sessions. In the Standard condition, we exposed participants to a single-event implantation method in all three interviews. In the Repeated condition, participants underwent a repeated-event implantation method in the three interviews. The Gradual condition also consisted of a repeated-event implantation method, however, in the first interview alone, we suggested to participants that they had experienced the false narrative once. Surprisingly, within our sample, false memories rates in the Standard condition were not higher compared to the Repeated and Gradual conditions. Although sometimes debated, our results imply that false memories for repeated events can be implanted in lab conditions, likely with the same ease as false memories for single events.

Lie for Me: Developmental Trends in Acquiescing to a Blatantly False Statement

A pair of studies demonstrates that simply asking children to make a blatantly false accusation in the guise of helping others can result in both immediate and long-term false claims. In the pilot study, the initial willingness to make a blatantly false statement was associated with some children making false statements a week later despite being told that the first interviewer had made mistakes during the initial interview. On a positive note, the majority of participants accurately stated that they did not have first-hand knowledge of their accusation's accuracy. Across both studies, the rate of false accusation rates was high. The main experiment demonstrated that children who were young, possessed the lowest verbal intelligence or who were from the lowest SES homes made the most accusations. These findings illustrate not only the dangers of encouraging children to make false statements, but the ease and durability of making such false statements.

Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals.

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.

A Hybrid Association Rule-Based Method to Detect and Classify Botnets

Nowadays, botnet has become a threat in the area of cybersecurity, and, worse still, it is difficult to be detected in complex network environments. Thus, traffic analysis is adopted to detect the botnet since this kind of method is practical and effective; however, the false rate is very high. The reason is that normal traffic and botnet traffic are quite close to the border, making it so difficult to be recognized. In this paper, we propose an algorithm based on a hybrid association rule to detect and classify the botnets, which can calculate botnets’ boundary traffic features and receive effects in the identification between normal and botnet traffic ideally. First, after collecting the data of different botnets in a laboratory, we analyze botnets traffic features by processing a data mining on it. The suspicious botnet traffic is filtered through DNS protocol, black and white list, and real-time feature filtering methods. Second, we analyze the correlation between domain names and IP addresses. Combining with the advantages of the existing time-based detection methods, we do a global correlation analysis on the characteristics of botnets, to judge whether the detection objects can be botnets according to these indicators. Then, we calculate these parameters, including the support, trust, and membership functions for association rules, to determine which type of botnet it belongs to. Finally, we process the test by using the public dataset and it turns out that the accuracy of our algorithm is higher.