Due to the COVID-19 pandemic, traditional teaching has been migrated online. Different from traditional face-to-face teaching, when students learn online, the online learning platform will generate various data. And t...
Due to the COVID-19 pandemic, traditional teaching has been migrated online. Different from traditional face-to-face teaching, when students learn online, the online learning platform will generate various data. And these data make it possible for us to analyze students’ final academic performance. In this paper, we use structural equation modeling (SEM) to analyze the relationship between students’ learning factors. It is found that students’ lab scores (LS), exercise scores (LS) and participation in the discussion (PID) have a direct impact on their final programming scores (FPS). This paper also finds that students’ assignment submissions have an indirect influence on their final programming scores (FPS) but have a direct effect on lab scores (LS). In addition, students’ participation in the discussion (PID) has an indirect influence on assignment submissions (AS) and lab scores (LS). The research in this paper can provide instructional designers with references for instructional design.
An approach to smoothing the fluctuations of largescale wind power is investigated using vehicle-to-grid(V2G)***,an energy management and optimization system is designed and *** using the wavelet packet decomposition ...
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An approach to smoothing the fluctuations of largescale wind power is investigated using vehicle-to-grid(V2G)***,an energy management and optimization system is designed and *** using the wavelet packet decomposition method,the target grid-connected wind power,the required electric vehicle(EV)power,and supercapacitor power are *** energy management model for EVs is then developed by introducing a knapsack problem that can evaluate the needs of an EV ***,an optimized dispatch strategy for EVs and wind power is developed by using a dynamic programming method.A case study demonstrates that the energy management and optimization method for V2G systems achieves noticeable performance improvements over benchmark techniques.
Many applications have an inherent tolerance for insignificant inaccuracies. Full adders are key arithmetic functions for many error-tolerant applications. Approximate full adders are considered an efficient technique...
Many applications have an inherent tolerance for insignificant inaccuracies. Full adders are key arithmetic functions for many error-tolerant applications. Approximate full adders are considered an efficient technique to trade off energy relative to performance and accuracy. In this paper, we propose four approximate full adders with low overhead. The proposed and the existing approximate full adders are classified into two groups according to their error distances. Simulation results show that, compared with the existing approximate full adders, in the first group, the proposed ones can reduce power-area-delay product (PADP) by 61.83%, power by 54.15%, area by 44.67%, and delay by 22.78 % on average; in the second group, the proposed ones can reduce PADP by 97.01%, power by 93.43%, area by 24.98%, and delay by 36.14% on average.
A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via n...
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Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not a priori be evident which sensors provide useful information about the tar...
This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna sel...
This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.
Dense video captioning, with the objective of describing a sequence of events in a video, has received much attention recently. As events in a video are highly correlated, leveraging relationships among events helps g...
Dense video captioning, with the objective of describing a sequence of events in a video, has received much attention recently. As events in a video are highly correlated, leveraging relationships among events helps generate coherent captions. To utilize relationships among events, existing methods mainly enrich event representations with their context, either in the form of vision (i.e., video segments) or combining vision and language (i.e., captions). However, these methods do not explicitly exploit the correspondence between these two modalities. Moreover, the video-level context spanning multiple events is not fully exploited. In this paper, we propose MRCap, a novel relationship-based model for dense video captioning. The key of MRCap is a multi-modal and multi-level event relationship module (MMERM). MMERM exploits the correspondence between vision and language at both the event level and the video level via contrastive learning. Experiments on ActivityNet Captions and YouCook2 datasets demonstrate that MRCap achieves state-of-the-art performance.
Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low ...
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The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking,...
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Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such...
Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to in-ferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and pre-vents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed cur-vature space, we propose an incremental search scheme, through which the growing geometric structures are en-coded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capa-ble of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.
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