A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service

Experience of Online Learning from COVID-19: Preparing for the Future of Digital Transformation in Education

COVID-19 has affected traditional instructional activities. Home-based isolation and restrictive movement measures have forced most learning activities to move from an offline to an online environment. Multiple studies have also demonstrated that teaching with virtual tools during the COVID-19 pandemic is always ineffective. This study examines the different characteristics and challenges that virtual tools brought to online education in the pre-pandemic and pandemic era, with the aim of providing experience of how virtual tools supported purely online learning during a health crisis. By searching keywords in public databases and review publications, this study tries to summarize the major topics related to the research theme. These topics are the characteristics of learning supported by technologies in pre-pandemic and pandemic era, the challenges that education systems have faced during the COVID-19 pandemic. This study also compares the functions, advantages and limitations of typical virtual tools, which has rarely been done in previous studies. This study tries to present the features of virtual tools that support online learning and the challenges regarding real-life risk scenarios, and tries to provide educational institutions with a distinct perspective for efficient teaching and learning in future potential health crises

Distributed Face Recognition in Wireless Sensor Networks

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past few years. In order to construct an autonomous and robust biometric security system, this paper explores the application of face recognition technique in wireless sensor networks. Given the limited technological resources of sensor nodes, new challenges remain to be met. In this work, a facial component-based recognition mechanism is firstly applied to ensure the recognition accuracy. Secondly, in order to address the problem of resource constraints, a distributed scheme based on K-d trees is deployed for both the face image transmission and retrieval. According to the simulation results, the proposed method is capable of achieving considerable energy efficiency, while assuring the recognition accuracy

A Method for Driving Route Predictions Based on Hidden Markov Model

We present a driving route prediction method that is based on Hidden Markov Model (HMM). This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace) smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown

Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs

Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students’ characteristics or knowledge structure, while ignoring the critical roles of learning states. This study describes a dynamic personalized learning path planning algorithm to recommend appropriate knowledge points for online students based on their learning states and the difficulty of each knowledge point. The proposed method first calculates the difficulty of knowledge points automatically and constructs a knowledge difficulty model. Then, a dynamic knowledge mastery model is built based on learning behavior and normalized test scores. Finally, a path that satisfies students’ personalized changing states is generated. To achieve the aforementioned goal, a novel method that calculates the difficulty of knowledge points automatically is proposed. Moreover, the personalized learning path planning method proposed in this research is not limited to a particular course. To evaluate the method, we use a series of approaches to verify the impact of the personalized path on student learning. The experimental results demonstrate that the proposed algorithm can effectively generate personalized learning paths. Results demonstrate that the personalized path proposed by the algorithm can improve effective behavior rates, course completion rates and learning efficiency. Results also show that the personalized learning paths based on student states would help students to master knowledge

Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs

Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students’ characteristics or knowledge structure, while ignoring the critical roles of learning states. This study describes a dynamic personalized learning path planning algorithm to recommend appropriate knowledge points for online students based on their learning states and the difficulty of each knowledge point. The proposed method first calculates the difficulty of knowledge points automatically and constructs a knowledge difficulty model. Then, a dynamic knowledge mastery model is built based on learning behavior and normalized test scores. Finally, a path that satisfies students’ personalized changing states is generated. To achieve the aforementioned goal, a novel method that calculates the difficulty of knowledge points automatically is proposed. Moreover, the personalized learning path planning method proposed in this research is not limited to a particular course. To evaluate the method, we use a series of approaches to verify the impact of the personalized path on student learning. The experimental results demonstrate that the proposed algorithm can effectively generate personalized learning paths. Results demonstrate that the personalized path proposed by the algorithm can improve effective behavior rates, course completion rates and learning efficiency. Results also show that the personalized learning paths based on student states would help students to master knowledge

Comparison of coral diversity between big and small atolls: a case study of Yongle atoll and Lingyang reef, Xisha Islands, central of South China Sea

The South China Sea (SCS) includes large areas of extensive coral reef development but its reefs are still poorly known. Yongle atoll is the biggest typical atoll in the Xisha Islands, central of SCS. Lingyang Reef is an isolated small atoll within the whole big Yongle atoll. A total of 144 and 119 coral species were recorded at big Yongle atoll and small Lingyang Reef, respectively. The real coral richness might be higher because species accumulation curve did not saturate. The coral diversity pattern was similar between big Yongle atoll and small Lingyang Reef. Coral communities fell into three clusters, consistent with their habitats on reef slope, reef flat and lagoon slope. The highest coral diversity was observed on reef slopes and the lowest coral diversity was found on lagoon slope. Genera richness was a better proxy for representing coral species diversity on both the big and small atoll but percent live coral cover was not a robust proxy on the small atoll, which only explained 24% of species diversity. This study demonstrated high coral diversity with consistent pattern along habitat types, as has been shown from many other reefs. While far from exhaustive, the study allows first glimpses on how much biodiversity is contained on SCS coral reefs, and hopes to give an impetus to their conservation. The study also suggests that simplified surveys at a small scale and the use of genera richness as an effective proxy for overall diversity can indeed provide important information to rapidly monitor and evaluate the coral diversity in remote locations

The Coral Communities of Yongle Atoll: Status, Threats, and Conservation Significance for Coral Reefs in South China Sea

Xisha Islands are in the central South China Sea and form one of the four large island groups in this region. They include more than 40 islands, reefs and cays, and have considerable ecological and biodiversity value, both intrinsically and as a source of larvae for coastal ecosystems throughout the South China Sea. Yongle atoll is the biggest and one of the most important atolls in the Xisha Islands. The detailed surveys of the marine habitats in the Yongle atoll were conducted from June to July 2013. This baseline survey revealed coral communities in a relatively healthy condition. Mean coral cover of different geomorphic habitats varied from 2 to 29%. Branching corals were most important, followed by encrusting and massive growth forms (48, 29 and 17% of coral cover). Pocillopora (29% of total cover in line transects), Porites (19%), Acropora (17%) and Montipora (16%) were the four dominant genera. Communities differentiated into four clusters, namely, lower reef slope, upper reef slope, outer reef flat, and inner reef flat and lagoon slope. This baseline investigation highlighted the ecological value of these reefs. Destructive fishing and overfishing are presently the most serious threats for these coral reefs. They should receive much more scientific and conservation attention

Sulfur aerosols in the Arctic, Antarctic, and Tibetan Plateau: Current knowledge and future perspectives

Sulfur aerosols, mainly composed of sulfate and methanesulfonic acid (MSA), significantly affect the Earth’s radiation balance, biogeochemical cycles and ecosystems, especially in the polar regions with vulnerable environments. To better understand the relationship between anthropogenic activities and climate change, a comprehensive review is presented, covering sulfate and MSA concentrations and isotope composition from 18 sites in the Arctic, 22 sites in the Antarctic and 25 sites in the Tibetan Plateau. The spatio-temporal variability of sulfur aerosols and the potential factors controlling their concentrations are summarized, sulfur isotopes are used to identify the importance of anthropogenic vs. natural inputs, and ice cores are employed to reconstruct the paleo-evolution of atmospheric sulfates. Finally, this review discusses the need for future research on organosulfur aerosols, the mixing state of sulfur aerosols, their deposition fluxes and velocities, potential emissions by biomass burning, and the anticipated trends in sulfur aerosol concentrations in the Arctic, Antarctic, and Tibetan Plateau