A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT

Nowadays, in the industrial Internet of things, address resolution protocol attacks are still rampant. Recently, the idea of applying the software-defined networking paradigm to industrial Internet of things is proposed by many scholars since this paradigm has the advantages of flexible deployment of intelligent algorithms and global coordination capabilities. These advantages prompt us to propose a multi-factor integration-based semi-supervised learning address resolution protocol detection method deployed in software-defined networking, called MIS, to specially solve the problems of limited labeled training data and incomplete features extraction in the traditional address resolution protocol detection methods. In MIS method, we design a multi-factor integration-based feature extraction method and propose a semi-supervised learning framework with differential priority sampling. MIS considers the address resolution protocol attack features from different aspects to help the model make correct judgment. Meanwhile, the differential priority sampling enables the base learner in self-training to learn efficiently from the unlabeled samples with differences. We conduct experiments based on a real data set collected from a deepwater port and a simulated data set. The experiments show that MIS can achieve good performance in detecting address resolution protocol attacks with F1-measure, accuracy, and area under the curve of 97.28%, 99.41%, and 98.36% on average. Meanwhile, compared with fully supervised learning and other popular address resolution protocol detection methods, MIS also shows the best performance.

EGAT: Extended Graph Attention Network for Pedestrian Trajectory Prediction

To improve foresight and make correct judgment in advance, pedestrian trajectory prediction has a wide range of application values in autonomous driving, robot interaction, and safety monitoring. However, most of the existing methods only focus on the interaction of local pedestrians according to distance, ignoring the influence of far pedestrians; the range of network input (receptive field) is small. In this paper, an extended graph attention network (EGAT) is proposed to increase receptive field, which focuses not only on local pedestrians, but also on those who are far away, to further strengthen pedestrian interaction. In the temporal domain, TSG-LSTM (TS-LSTM and TG-LSTM) and P-LSTM are proposed based on LSTM to enhance information transmission by residual connection. Compared with state-of-the-art methods, the model EGAT achieves excellent performance on both ETH and UCY public datasets and generates more reliable trajectories.

The seven-layer stratification theory of gastric wall under ultrasonography and its clinical application

ObjectiveHere, we develop a seven-layer gastric wall stratification theory based on the physical basis of ultrasound and histology, and further discuss its potential clinical application. Methods1. Experimental methods: Ex vivo human gastric specimens were immersed in normal saline and examined with a high-frequency probe to study the relationship between the sonograms and the corresponding anatomy of the gastric wall. 2. The study enrolled 136 patients admitted to our hospital with gastric diseases who underwent gastric contrast ultrasonography supplemented with the pathological examination. The seven-layer stratification theory was adopted during the analysis to profile sonogram characteristics with lesions originating from various layers. ResultsAll the sonograms of the in vitro human gastric specimens could be divided into seven intervals of strong and weak echoes. The pathological examinations were performed on 136 patient-derived samples as the golden criteria of diagnosis: 29 cases of gastric polyps, 10 cases of lymphomas, 5 cases of neuroendocrine tumors, 11 cases of ectopic pancreas, 22 cases of gastric stromal tumor, 19 cases of leiomyomas, 29 cases of chronic inflammation, 9 cases of diffuse invasive cancer, and 2 cases of neurilemmoma. The ultrasound and pathological examination results were consistent in 110 cases, showing a coincidence rate of 80.9%. ConclusionBy adopting the seven-layer stratification theory of the gastric wall, the ultrasound can accurately locate the position of mucosal muscularis, which is of great significance for accurate measurement of the thickness of each anatomical layer and the correct judgment of the origin and the classification of the space-occupying lesions. Keywords Gastric wall; ultrasound; seven-layer stratification; clinical application

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

Standards of Classical Architecture Criticism: Between Mathematics and Philosophy

Criticism is an intellectual process that primarily searches for beauty aspects in the works of art, including architecture. This article explores the mathematical and philosophical principles of classical architectural criticism. It is hypothesized that design criteria during the Classic period were clear and specific. The research presents theories of classical art that focus on the process of beauty interpretation. It also assesses the mathematical evaluation of architectural art and beauty through “The Golden Ratio” and “The “Fibonacci Sequence.” Classical philosophy, and its perception of beauty, is discussed as an essential basis in any artistic critical activity. The research asserts that the science of aesthetics is both objective and subjective, which explains the difference in aesthetic evaluation across eras. Objectivity stems from conditions of proportionality that must be met for an architectural art to be aesthetically judged as beautiful. Subjectivity lies in the time and place of the architectural work, whereby tendencies, tastes, and needs related to the human and geographical environment can affect the standards of beauty. This makes the evaluation of beauty in classical architecture a delicate and complex process in which many aspects must be considered to have an objective, fair, and correct judgment.

Multiple dimensions of e-healthcare ethics and its relationship to the ethical concerns of the consumer

Purpose This study aims to explore the different dimensions of e-healthcare ethics and their relationships, influencing the ethical concerns of the consumer in making ethical e-healthcare choices. Design/methodology/approach A study was conducted at two identified major hospitals of Punjab (a private hospital) and Chandigarh (a public hospital), India providing e-healthcare services with the help of a self-administered questionnaire. The respondents were identified from the waiting areas of the selected hospitals, and only those respondents were selected for the study, who agreed to be aware of e-health services and were using them for some time. The statistical analysis was done using the structural equation modeling technique and included both exploratory and confirmatory factor analysis using SPSS 20 and AMOS 21. Findings Exploratory factor analysis extracted five dimensions of ethical concerns of the consumer, which include service promotion, content quality, candor, professionalism and confidentiality. The results signify that content quality plays a significant role in ethics, followed by candor, service promotion and confidentiality. However, the relationship with professionalism did not prove to be significant for the ethical concerns of the e-health consumer. Practical implications This research delivers a practical significance in identifying the critical dimensions of the ethical concerns of the consumer while selecting e-health services. It gives an insight into the various dimensions, which should be considered by the e-health providers while crafting e-health services to make it more ethically acceptable by the consumers. Originality/value By using e-health services, consumers play an active role in their health-care decisions. The consumers need to consider ethics while choosing health-care services as an ethical judgment will also be the correct judgment. This study helps in the identification of the significant dimensions for the ethical concerns of the consumers.

The Effects of Dysphonic Voice on Speech Intelligibility in Cantonese-Speaking Adults

Purpose This study aims to investigate the effects of dysphonic voice on speech intelligibility in Cantonese-speaking adults. Method Speech recordings from three speakers with dysphonia secondary to phonotrauma and three speakers with healthy voices were presented to 30 healthy listeners (15 men and 15 women; M age = 22.7 years) under six noise conditions (signal-to-noise ratio [SNR] −10, SNR −5, SNR 0, SNR +5, SNR +10) and quiet conditions. The speech recordings were composed of sentences with five different lengths: five syllables, eight syllables, 10 syllables, 12 syllables, and 15 syllables. The effects of speaker's voice quality, background noise condition, and sentence length on speech intelligibility were examined. Speech intelligibility scores were calculated based on the listener's correct judgment of the number of syllables heard as a percentage of the total syllables in each stimulus. Results Dysphonic voices, as compared to healthy voices, were significantly more affected by background noise. Speech presented with dysphonic voices was significantly less intelligible than speech presented with healthy voices under unfavorable SNR conditions (SNR −10, SNR −5, and SNR 0 conditions). However, there was no sufficient evidence to suggest effects of sentence length on intelligibility, regardless of the speaker's voice quality or the level of background noise. Conclusions This study provides empirical data on the impacts of dysphonic voice on speech intelligibility in Cantonese speakers. The findings highlight the importance of educating the public about the impacts of voice quality and background noise on speech intelligibility and the potential of compensatory strategies that specifically address these barriers. Supplemental Material https://doi.org/10.23641/asha.13335926

A multi-fault diagnosis method of gear-box running on edge equipment

Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the edge computing device plays an especially significant role in fault diagnosis. The application of deep learning method in mechanical fault diagnosis has been gradually popularized, because it has many advantages, such as strong classification ability and accurate feature extraction ability. However, many of the completed papers and models are based on single label system and are used to diagnose single target fault. The validation set is not rigorous enough, and it is difficult to accurately simulate the faults that may occur in the actual production process. Nowadays, in the era of big data, the single label system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Hence, in the process of experiment, we used the bearing data of Case Western Reserve University(CWRU) to ensure the wide range and large quantity of data sets. A fault diagnosis method of gear and bearing in the gear-box based on multi-task deep learning model is put forward. In this method, gear and bearing faults can be diagnosed simultaneously. Through a separate task layer, this method can adaptively extract the characteristics of distinct targets from the same signal, and add a Batch Normalization layer(BN) to accelerate the convergence speed of the network. Through experiments, we conclude that it is an effective method which can judge the fault situation of gear and bearing accurately in a variety of working conditions.

NECESSARY AND SUFFICIENT LEGAL KNOWLEDGE OF A FUTURE HISTORY AND LEGAL TEACHER

The article defines the category of “knowledge of the law”, reveals their components ‒ the level, scope and content of legal information. The relation between the concepts of “information” and “knowledge” has been established. Necessary and sufficient legal knowledge for the future teacher of history and jurisprudence have been identified, which includes: a system of historical, legal and psychological-pedagogical knowledge necessary and sufficient for professional activity, their breadth, volume, depth; mastering the process of acquiring this knowledge with subsequent use of it in educational and legal activities; focus on studying the law, legal literature, solving legal situations and considering social and legal problems, seeking information on changes in the social and legal life of society and the state, studying historical and legal disciplines, the legal status of a person, free operation of elementary legal concepts, awareness of the need for legal knowledge, knowledge of human and child rights in future professional activity and their proper application in defending their views, positions. The levels of legal knowledge sufficiency are set: high, medium, low, according to the characteristics: breadth of legal knowledge, their volume, depth. The high level includes students whose breadth, volume, depth of legal knowledge make it possible to always find the right legal criterion for personal action, on the one hand, and on the other, require legitimate behaviour and correct judgment from others. For intermediate-level students, interest in legal knowledge is limited to the “required” curriculum. Students with low levels of interest in law are unstable, with many gaps in legal knowledge. In general, the level of knowledge is insufficient to understand legal relations. Students do not have the necessary skills and abilities to conduct law enforcement work with students.

The Association Between the Level of General Trust and the Judgment Accuracy of Group Members’ Cooperation in a Social Dilemma

The current study sought to examine the association between the level of general trust and the judgment accuracy of others’ cooperativeness. Based on data collected from 107 female first-year undergraduate students, we demonstrated that a high level of general trust was associated with a high level of judgment accuracy of group members’ cooperation in a social dilemma game. Additional analysis suggested that the association was present even when the judgment accuracy was divided into hit rate (i.e., the rate of correct judgment on the cooperator as a cooperative) and correct rejection rate (i.e., the rate of correct judgment on the non-cooperator as a non-cooperative) by controlling the participants’ judgment bias, Big Five personality traits, and the proportion of cooperators in the group. These results are in accordance with previous studies insofar as they suggest that high trusters are more skilled at discerning others’ trustworthiness. The current study adds to the evidence that high trusters have increased cognitive skills and supports Yamagishi’s emancipation theory of trust.