COVID-19, Spatial Social Distance and Relative Poverty Governance

The sudden global outbreak of the new crown pneumonia epidemic at the end of 2019 has caused a huge impact on the world’s economic and social development. It has also brought new challenges to the governance of relative poverty in China. This paper compares the connotation and characteristics of relative poverty, and argues that relative poverty mainly encompasses three levels: income, viability and rights. Starting from the perspective of maintaining spatial social distance, the main strategy to prevent and control the new crown epidemic, the paper explores the inner influence mechanism of relative poverty governance in the prevention and control of the epidemic, and concludes that maintaining spatial social distance has a series of impacts from poverty alleviation, including poor agricultural production and marketing, relatively poor educational facilities, difficult operation of rural scenic spots, hindered non-agricultural employment and work, relocation facing unemployment, and obvious social psychological anxiety. Accordingly, the following measures are proposed to address relative poverty management with digital economy tools as the main orientation: innovate digital production and marketing matching mechanisms, improve online education infrastructure, launch digital cloud tourism dual-line experience, strengthen digital economy employment traction, and cultivate digital social information literacy, etc

Measuring mass transfer of AM CVn binaries with a space-based gravitational wave detector

The formation mechanism of AM CVn binary has not been well understood yet. Accurate measurements of the mass transfer rate can help to determine the formation mechanism. But unfortunately such observation by electromagnetic means is quite challenging. One possible formation channel of AM CVn binary is a semi-detached white dwarf binary. Such system emits strong gravitational wave radiation which could be measured by the future space-based detectors. We can simultaneously extract the mass transfer rate and the orbital period from the gravitational wave signal. We employ a post-Keperian waveform model of gravitational wave and carry out a Fisher analysis to estimate the measurement accuracy of mass transfer rate through gravitational wave detection. Special attention is paid to the observed sources in Gaia Data Release 2. We found that we can accurately measure the mass transfer rate for those systems. Comparing to electromagnetic observations, gravitational wave detection improves the accuracy more than one order. Our results imply that the gravitational wave detection will help much in understanding the formation mechanism of AM CVn binaries

Seismic Fragility Analysis of Precast RC Shear Wall-Frame Structures with Connection Defects

As observed in many evidences during past earthquakes, the quality of precast concrete (PC) connections is one of the main factors that affect the seismic reliability of PC structures. In the context of Chinas rapid development of PC structures in high seismic regions, it is important to assess the effect of connection deficiency on their seismic performance. This paper proposes a framework for seismic fragility analysis of PC shear wall-frame structures whose wall panels are assembled through grout sleeve connections that are susceptible to insufficient grouting. The uncertainties associated with the defected sleeve connections are taken into account, and then the probabilistic response of shear wall model is estimated through Point Estimate (PE) method. Then, a generic shear wall-frame building is modeled on platform of OpenSees. Seismic fragility analysis is performed to the structures with different degrees of connection deficiency, showing that the seismic performance is significantly affected by connection deficiencies, and great effort should be taken for the quality control of grout sleeve connections in construction site

AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education

This study investigates the use of Artificial Intelligence (AI)-powered, multi-role chatbots as a means to enhance learning experiences and foster engagement in computer science education. Leveraging a design-based research approach, we develop, implement, and evaluate a novel learning environment enriched with four distinct chatbot roles: Instructor Bot, Peer Bot, Career Advising Bot, and Emotional Supporter Bot. These roles, designed around the tenets of Self-Determination Theory, cater to the three innate psychological needs of learners - competence, autonomy, and relatedness. Additionally, the system embraces an inquiry-based learning paradigm, encouraging students to ask questions, seek solutions, and explore their curiosities. We test this system in a higher education context over a period of one month with 200 participating students, comparing outcomes with conditions involving a human tutor and a single chatbot. Our research utilizes a mixed-methods approach, encompassing quantitative measures such as chat log sequence analysis, and qualitative methods including surveys and focus group interviews. By integrating cutting-edge Natural Language Processing techniques such as topic modelling and sentiment analysis, we offer an in-depth understanding of the system's impact on learner engagement, motivation, and inquiry-based learning. This study, through its rigorous design and innovative approach, provides significant insights into the potential of AI-empowered, multi-role chatbots in reshaping the landscape of computer science education and fostering an engaging, supportive, and motivating learning environment

A deep learning model for network intrusion detection with imbalanced data

With an increase in the number and types of network attacks, traditional firewalls and data encryption methods can no longer meet the needs of current network security. As a result, intrusion detection systems have been proposed to deal with network threats. The current mainstream intrusion detection algorithms are aided with machine learning but have problems of low detection rates and the need for extensive feature engineering. To address the issue of low detection accuracy, this paper proposes a model for traffic anomaly detection named a deep learning model for network intrusion detection (DLNID), which combines an attention mechanism and the bidirectional long short-term memory (Bi-LSTM) network, first extracting sequence features of data traffic through a convolutional neural network (CNN) network, then reassigning the weights of each channel through the attention mechanism, and finally using Bi-LSTM to learn the network of sequence features. In intrusion detection public data sets, there are serious imbalance data generally. To address data imbalance issues, this paper employs the method of adaptive synthetic sampling (ADASYN) for sample expansion of minority class samples, to eventually form a relatively symmetric dataset, and uses a modified stacked autoencoder for data dimensionality reduction with the objective of enhancing information fusion. DLNID is an end-to-end model, so it does not need to undergo the process of manual feature extraction. After being tested on the public benchmark dataset on network intrusion detection NSL-KDD, experimental results show that the accuracy and F1 score of this model are better than those of other comparison methods, reaching 90.73% and 89.65%, respectively

Study on Resource Configuration on Cloud Manufacturing

The purpose of manufacturing is to realize the requirement of customer. In manufacturing process of cloud system, there exist a lot of resource services which have similar functional characteristics to realize the requirement. It makes the manufacturing process more diverse. To develop the quality and reduce cost, a resource configuration model on cloud-manufacturing platform is put forward in this paper. According to the generalized six-point location principle, a growth design from the requirement of customers to entities with geometric constraints is proposed. By the requirement growing up to product, a configuration process is used to match the entities with the instances which the resources in the database could supply. Different from most existing studies, this paper studies the tolerance design with multiple candidate resource suppliers on cloud manufacturing to make the market play a two-level game considering the benefit of customers and the profit of resources to give an optimal result. A numerical case study is used to illustrate the proposed model and configuration process. The performance and advantage of the proposed method are discussed at the end

AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning

In-band Network Telemetry (INT) has emerged as a promising network measurement technology. However, existing network telemetry systems lack the flexibility to meet diverse telemetry requirements and are also difficult to adapt to dynamic network environments. In this paper, we propose AdapINT, a versatile and adaptive in-band network telemetry framework assisted by dual-timescale probes, including long-period auxiliary probes (APs) and short-period dynamic probes (DPs). Technically, the APs collect basic network status information, which is used for the path planning of DPs. To achieve full network coverage, we propose an auxiliary probes path deployment (APPD) algorithm based on the Depth-First-Search (DFS). The DPs collect specific network information for telemetry tasks. To ensure that the DPs can meet diverse telemetry requirements and adapt to dynamic network environments, we apply the deep reinforcement learning (DRL) technique and transfer learning method to design the dynamic probes path deployment (DPPD) algorithm. The evaluation results show that AdapINT can redesign the telemetry system according to telemetry requirements and network environments. AdapINT can reduce telemetry latency by 75\% in online games and video conferencing scenarios. For overhead-aware networks, AdapINT can reduce control overheads by 34\% in cloud computing services.Comment: 14 pages, 19 figure

Context-aware Event Forecasting via Graph Disentanglement

Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspects of the whole society. The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future. Most existing studies on event forecasting formulate it as a problem of link prediction on temporal event graphs. However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information. To address these limitations, we propose a novel task of context-aware event forecasting which incorporates auxiliary contextual information. First, the categorical context provides supplementary fine-grained information to the coarse-grained events. Second and more importantly, the context provides additional information towards specific situation and condition, which is crucial or even determinant to what will happen next. However, it is challenging to properly integrate context into the event forecasting framework, considering the complex patterns in the multi-context scenario. Towards this end, we design a novel framework named Separation and Collaboration Graph Disentanglement (short as SeCoGD) for context-aware event forecasting. Since there is no available dataset for this novel task, we construct three large-scale datasets based on GDELT. Experimental results demonstrate that our model outperforms a list of SOTA methods.Comment: KDD 2023, 9 pages, 7 figures, 4 table

Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning

This study explores the integration of generative artificial intelligence (AI), specifically large language models, with multi-modal analogical reasoning as an innovative approach to enhance science, technology, engineering, and mathematics (STEM) education. We have developed a novel system that utilizes the capacities of generative AI to transform intricate principles in mathematics, physics, and programming into comprehensible metaphors. To further augment the educational experience, these metaphors are subsequently converted into visual form. Our study aims to enhance the learners' understanding of STEM concepts and their learning engagement by using the visual metaphors. We examine the efficacy of our system via a randomized A/B/C test, assessing learning gains and motivation shifts among the learners. Our study demonstrates the potential of applying large language models to educational practice on STEM subjects. The results will shed light on the design of educational system in terms of harnessing AI's potential to empower educational stakeholders