Research on the development of carrier intelligent cloud network under the background of IPv6+

With the increasingly mature 5G technology in our country, the government has comprehensively promoted IPv6 scale deployment, the rapid improvement of network quality of the three operators, and gradually transformed to IPv6+, the carrying network is more fl exible, and the user opening service is more convenient, which has promoted the development of intelligent cloud network of China’s carriers. Operators should actively respond to the challenges of IPv6+ era, based on their own intelligent cloud network development needs, the use of SRv6 technology, promote cloud network integration, carrying a variety of online services; Provide integrated cloud network products and services, build an intelligent operation and maintenance system, and improve user satisfaction; To build IPv6 networking capability of the whole network and build intelligent cloud network; Do a good job in the construction of IPv6 network information security, improve the security defense capability of intelligent cloud network, ensure the smooth operation of network, and inject new vitality into the 2B industry market for operators

Validation of the Oregon Scientific BPU 330 for self-monitoring of blood pressure according to the International Protocol

Li Li1, XinYu Zhang1, ChunHong Yan1, QingXiang Liang21Biomedical Engineering Lab, Faculty of Information Engineering, ShenZhen University, ShenZhen, China; 2Bao An People’s Hospital, ShenZhen, ChinaObjective: Extensive marketing of devices for self-measurement of blood pressure has created a need for purchasers to be able to satisfy themselves that such devices have been evaluated according to agreed criteria. The Oregon Scientific BPU 330 blood pressure monitor is an electronic device for upper arm measurement. This study assessed the accuracy of the Oregon Scientific BPU 330 blood pressure monitor according to the International Protocol by the Working Group on Blood Pressure Monitoring of the European Society of Hypertension for validation of blood pressure measuring devices.Method: 52 participants over 30 years of age were studied in the validation. Nine blood pressure measurements were taken alternately with a mercury sphygmomanometer by two observers, and by the supervisor, using the BPU 330 device. A total of 33 participants were selected for the analysis. The validation was divided into two phases. Phase 1 included 15 participants. If the device passed phase 1, 18 more participants were included. The 99 pairs of measurements were compared according to the International Protocol. The device was given a pass/fail recommendation based on its accuracy compared with the mercury standard (within 5, 10, and 15 mmHg), as well as the number met in the ranges specified by the International Protocol.Results: The mean and standard deviation of the difference between the mean of the observers and the BPU 330 device were 1.7 ± 4.7 mmHg and 2.8 ± 3.9 mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. In phase 1, the device passed with a total of 33, 43, and 44 SBP readings; 38, 44, and 45 DBP readings were within 5, 10, and 15 mmHg, respectively. In phase 2.1, 81, 95, and 96 for SBP, and 83, 95, and 98 for DBP readings fell within the zones of 5, 10, and 15 mmHg, respectively. In phase 2.2, the last phase, 28 participants fell within the zone of two of the three comparisons, lying within 5 mmHg for SBP and 29 participants for DBP. No participants fell within the zone of all three of their comparisons over 5 mmHg apart for both SBP and DBP.Conclusion: The BPU 330 can be recommended for self-monitoring of blood pressure in the adult population, according to the International Protocol.Keywords: blood pressure, self-monitoring, hypertension, International Protoco

Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.Comment: Accepted by CIKM 2023, 10 pages, 5 figures, 4 table

Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student's distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.Comment: Accepted by ICDM 2023, 10 pages, 5 figures, 4 table

Detection of Nutrient-Related SNP to Reveal Individual Malnutrition Risk

Malnutrition is a result of complicated reasons from diet and food behavior and also related to genetic background which has been revealed by studies in recent decades. Traditionally, nutrition status are measured and expressed with indexes of anthropometric, diet survey, clinical symptom, biochemistry, behavior, etc. These measurement has been used in national nutrition monitoring, clinic nutrition therapy, mother and children nutrition care, nutrition intervention projects, and scientific studies. However, genetic and epigenetic information on nutrition explain malnutrition in a genetic view that would supply additional new theory and methodology for the growing requirement in terms of personalized and precise nutrition. In this chapter, an introduction on the detection of nutrient-related SNP to reveal individual malnutrition risk is discussed

Multi-Factors Aware Dual-Attentional Knowledge Tracing

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.Comment: Accepted by CIKM 2021, 10 pages, 10 figures, 6 table

No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths

Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).Comment: Accepted by CIKM 2023, 10 pages, 8 figures, 5 table