Multi-modal Open World User Identification

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.

Individual coactivation patterns improve the subject identification and their behavior association

Brain states can be characterized by recurring coactivation patterns (CAPs). Traditional CAP analysis is performed at the group-level, while the human brain is individualized and the functional connectome has shown the uniqueness as fingerprint. Whether stable individual CAPs could be obtained from a single fMRI scan and could individual CAPs improve the identification is unclear. An open dataset, the midnight scan club was used in this study to answer these questions. Four CAP states were identified at three distinct levels (group-, subject- and scan-level) separately, and the CAPs were then reconstructed for each scan. Identification rate and differential identifiability were used to evaluate the identification performance. Our results demonstrated that the individual CAPs were unstable when using a single scan. By maintaining high intra-subject similarity and inter-subject differences, subject-level CAPs achieved the best identification performance. Brain regions that contributed to the identifiability were mainly located in higher-order networks (e.g., frontal-parietal network). Besides, head motion reduced the intra-subject similarity, while its impact on identification rate was non-significant. Finally, a pipeline was developed to depict brain-behavior associations in dataset with few samples but dense sampling, and individualized CAP dynamics showed an above-chance level correlation with IQ.

Infant Cry Recognition System

Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.

Anomaly based Intrusion Detection using Neural Networks in 5G Network

Ensuring the organizations of tomorrow is set to be a difficult space due to expanding digital protection dangers and enlarging assault surfaces made by the Internet of Things (IoT), expanded organization heterogeneity, expanded utilization of virtualisation innovations and circulated structures. This paper proposes SDS (Software Defined Security) which is a method gives mechanized, adaptable and versatile framework. SDS will tackle momentum progresses in AI to plan a CNN (Convolutional Neural Network) utilizing NAS (Neural Architecture Search) to distinguish irregular organization traffic. SDS can be applied to an interruption location framework to make a more proactive and start to finish protection for a 5G organization. To test this presumption, ordinary and irregular organization streams from a mimicked climate have been gathered and examined with a CNN. The outcomes from this strategy are promising as the model has recognized harmless traffic with a 100% exactness rate and irregular traffic with a 96.4% identification rate. This exhibits the viability of organization stream investigation for an assortment of normal pernicious assaults and furthermore gives a suitable alternative to discovery of encoded vindictive organization traffic. Keywords: 5G Security, IoT Security, Automated Intrusion Detection Systems, Convolutional Neural Networks, Artificial Intelligence, Software Defined Security

Assessment of sentinel lymph node detection by radioisotope scan and methylene blue alone in operable oral cavity cancer to assess its accuracy for metastasis

Background: Squamous cell carcinoma (SCC) is the most common malignancy affecting the oral cavity. It typically metastasizes into the regional cervical lymph nodes before spreading to distant organs.Methods: A prospective study on sentinel lymph node biopsy (SLNB) in early oral cancers using methylene blue dye and sentinel node localisation using pre-operative lymphoscintigraphy and intraoperative gamma probe in early oral cavity cancer.Results: Present study had a male to female ratio of 1.9:1, with (65.5%) male and (34.5%) female patients. Present study had a side distribution of disease more on left side with 138 patients (62.7%) and ratio of left to right was approximately 1.7:1. In present study most predominating gross morphological pattern of growth was ulcerative (35%) followed by ulcero-infilterative (25%). Buccal mucosa was the most common sub-site of origin of carcinoma in oral cavity, followed by tongue, with 83 (37.7%) and 64 (29.1%) patients. Identification rate of methylene blue dye was 91.7% (100 out of 109 patient). Identification rate of radionuclide tracer was 94.6% (105 out of 111 patient). In methylene blue dye group out of 103 metastatic sentinel lymph nodes, 9 metastatic sentinel lymph nodes were detected on IHC.Conclusions: With the above results it can be concluded the SLNB study is liable in detection of actual positive node and can avoid unnecessary neck dissections in patients with SCC with negative sentinel lymph node, as having very low risk of occult lymphatic metastases in the remaining lymphatic drainage.

Whole-genome analysis-based phylogeographic investigation of Streptococcus pneumoniae serotype 19A sequence type 320 isolates in Japan.

After the introduction of the seven-valent pneumococcal conjugate vaccine, the global spread of multidrug resistant serotype 19A-ST320 strains became a public health concern. In Japan, the main genotype of serotype 19A was ST3111, and the identification rate of ST320 was low. Although the isolates were sporadically detected in both adults and children, their origin remains unknown. Thus, by combining pneumococcal isolates collected in three nationwide pneumococcal surveillance studies conducted in Japan between 2008 and 2020, we analyzed 56 serotype 19A-ST320 isolates along with 931 global isolates, using whole-genome sequencing to uncover the transmission route of the globally distributed clone in Japan. The clone was frequently detected in Okinawa Prefecture, where the U.S. returned to Japan in 1972. Phylogenetic analysis demonstrated that the isolates from Japan were genetically related to those from the U.S.; therefore, the common ancestor may have originated in the U.S. In addition, Bayesian analysis suggested that the time to the most recent common ancestor of the isolates form Japan and the U.S. was approximately the 1990s to 2000, suggesting the possibility that the common ancestor could have already spread in the U.S. before the Taiwan 19F-14 isolate was first identified in a Taiwanese hospital in 1997. The phylogeographical analysis supported the transmission of the clone from the U.S. to Japan, but the analysis could be influenced by sampling bias. These results suggested the possibility that the serotype 19A-ST320 clone had already spread in the U.S. before being imported into Japan.

Does failed mapping predict sentinel lymph node metastasis in cN0 breast cancer?

Aims: The clinical significance of nonvisualized sentinel lymph nodes (non-vSLNs) is unknown. The authors sought to determine the incidence of non-vSLNs on lymphoscintigraphy, the identification rate during surgery, factors associated with non-vSLNs and related axillary management. Patients & methods: A total of 30,508 consecutive SLN procedures performed at a single institution from 2000 to 2017 were retrospectively studied. Associations between clinicopathological factors and the identification of SLNs during surgery were assessed. Results: Non-vSLN occurred in 525 of the procedures (1.7%). In 73.3%, at least one SLN was identified intraoperatively. Nodal involvement was only significantly associated with SLN nonidentification (p < 0.001). Conclusion: Patients with non-vSLN had an increased risk for SLN metastasis. The detection rate during surgery was consistent, reducing the amount of unnecessary axillary dissection.

Evaluation of the Performance of Filmarray Blood Culture Identification Panel on Detecting Blood Cultures Containing Activated Carbon Powder

ObjectiveThe FilmArray Blood Culture Identification (BCID) panel is a rapid microfluidic PCR amplification microbial detection system. Several studies have evaluated its clinical performance on the basis of blood culture bottles containing resins. However, proportion of hospitals in China use bottles with carbon power, which the performance of FilmArray has not been fully investigated. Therefore, this study is conducted to explore the accuracy of the panel using blood culture bottles with carbon power.Method147 venous blood cultures containing carbon powder were used to assess the microbial and antibiotic resistance detection ability of the FilmArray panel. Outcomes were compared with results of the clinical combination method and their consistency was analyzed.ResultsFilmArray detected single microorganism in 121 samples, multiple microorganism in 9 cases and the consistency rate between the two methods was 90.6%. Among the 150 microorganisms detected, 85.1% (40/47) of staphylococcus contained the antibiotic resistant mecA gene, 15.3% (9/59) of Enterobacter detected the KPC gene, 7.7% (1/13) of Enterococcus has the vanA gene and the consistency with their clinical drug-resistant phenotypes were 93.6%, 86.4% and 100%, respectively.ConclusionThe identification rate of the FilmArray BCID panel using venous blood cultures with activated carbon powder was highly consistent with the outcomes of previous researchers using non-carbon powder blood culture bottles. It is capable of providing rapid and reliable results in the detection of pathogens present in automated blood culture systems.

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in νμCC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

Imperfect Debugging in Software Systems by SRGM with TEF

In the Proposed work we are going to assimilate two important process called TEF and imperfect debugging in software systems for analyzing FDP and FCP. Byapplying the tools called debuggers we are going to identify the failures and going to correct them in order to attain the high quality reliability. As we know, testingeffort function is predicted during this time by allocating the resources which influences considerably only for the fault identification rate and also for the correction of such faults. Additionally, new faults may be included for evaluating as the feedback. In this technique, first it is proposed to demonstrate for the inclusion of TEF and fault introduction into FDP and later develop FCP as delayedFDP with a proper effort for correction. The FCP as well FCP as paired specific models which are extracted based on the basis of types of assumptions of introducing fault introduction as well as correction effort. In addition, the optimal policy of software releasefor different criteria with examples was also presentedin this work. Keywords: FDP, FCP, TEF, Fault