The widespread adoption of technology-enhanced learning in various knowledge disciplines has pushed forward the development of information technology-assisted media for language learning and ...teaching. However, most of the existing electronic-learning (e-learning) solutions have underexplored and under-addressed given specific characteristics of grammar learning, which is one of the most demanding areas of language education. The lack of pedagogically informed instructional design to enhance learning performance on the current system can result in low motivation and engagement due to an imbalance and excessive increase of the cognitive load. This paper attempts to address these deficiencies posed by the existing systems by proposing smart communication networks that are driven by the student learning experience to manage cognitive load in the context of grammar learning. The e-grammar learning networks serve as a collaborative learning platform that combines a pedagogically informed instructional model named attention, relevance, confidence, and satisfaction (ARCS) and cyber interaction among teaching/learning agents. From the technological perspective, our numerical simulations demonstrate the desirable performance indicators of the proposed networks to facilitate information exchange and learning. From the education perspective, our empirical studies show that the overall smart network-enabled e-grammar learning system has desirable characteristics to motivate learners (<inline-formula> <tex-math notation="LaTeX">m = 3.78 </tex-math></inline-formula>) and manage their overall cognitive load ( m = 1.73), which suggest the promising capability of the proposed system.
Summary
Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such ...signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.
Summary
In recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for ...points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.
Structural collapses are widespread, owing to a surge in climatic changes, earthquakes, and terrorism. Therefore, there are some technological rescue methods in practice that involve sensors, radars, ...cameras, microphones, and robots. However, deployment of these techniques faces at least one issue amongst cost, availability, and technical expertise, which limits their application in developing countries. So, there is a dire need for a low-cost and easy deployable rescue method. Recently, we witnessed an increasing trend of using Wi-Fi radios as sensing modality for various applications, including breathing detection and localization, thus leading to device-free communication. Based on this, we may envisage having a Wi-Fi rescue solution. However, Wi-Fi signals cannot easily penetrate through collapsed structures due to the multilayered obstacle scenario. So, in this study, we focus our research on the proper information analysis and abstraction of debris and also present the possible methodology to have better coverage for Wi-Fi signals using Wi-Fi radar edge. We define two objectives of this work; 1) debris information analysis and 2) the Wi-Fi signal propagation mechanism, respectively. We achieve our first goal by conducting site surveys of earthquake-hit areas that enable us to analyze the causes and types of structural collapses followed by debris concept selection model. We employ a bijective soft set approach to accurately select the debris based on the complexity and nature of structural engineering followed. Moreover, we use the Wi-Fi Halow radar edge for wireless signal propagation and perform extensive simulations at low power. Finally, we compare both methods and discuss prospects.
The development of mobile cloud computing technology has made location-based service (LBS) increasingly more popular. Given the continuous requests to cloud LBS servers, the amounts of location and ...trajectory information collected by LBS servers are continuously increasing. Privacy awareness for LBS has been extensively studied in recent years. Among the privacy concerns about LBS, trajectory privacy preservation is particularly important. Based on privacy preservation models, previous work have mainly focused on peer-to-peer and centralized architectures. However, the burden on users is heavy in peer-to-peer architectures, because user devices need to communicate with LBS servers directly. In centralized architectures, a trusted third party (TTP) is introduced, and acts as a bridge between users and the LBS server. Anonymity technologies, such as k-anonymity, mix-zone, and dummy technologies, are usually implemented by the TTP to ensure safety. There are certain drawbacks in TTP architectures: Users have no physical control of the TTP. Moreover, the TTP is more attractive to adversaries, because substantially more sensitive information is stored by the TTP. To solve the above-mentioned problems, in this paper, we propose a fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust. Compared with cloud computing, fog computing is a promising technique that extends the cloud computing to the edge of a network. Moreover, fog computing provides local computation and storage abilities, wide geo-distribution, and support for mobility. Therefore, mobile users' partial important information can be stored on a fog server to ensure better management. We take the principles of similarity, intersection, practicability, and correlation into consideration and design a dummy rotation algorithm with several properties. The effectiveness of the proposed method is validated through extensive simulations, which show that the proposed method can provide enhanced privacy preservation.
Due to the severe resource constraints in wireless sensor networks (WSNs), designing an efficient target tracking algorithm for WSNs in terms of energy efficiency and high tracking quality becomes a ...challenging issue. WSNs usually provide centralized information, e.g., the locations and directions of a target, choosing sensors around the target, etc. However, some ready strategies may not be used directly because of high communication costs to get the responses for tracking tasks from a central server and low quality of tracking. In this paper, we propose a fully distributed algorithm, an auction-based adaptive sensor activation algorithm (AASA), for target tracking in WSNs. Clusters are formed ahead of the target movements in an interesting way where the process of cluster formation is due to a predicted region (PR) and cluster members are chosen from the PR via an auction mechanism. On the basis of PR calculation, only the nodes in the PR are activated and the rest of the nodes remain in the sleeping state. To make a trade-off between energy efficiency and tracking quality, the radius of PR and the number of nodes are adaptively adjusted according to current tracking quality. Instead of fixed interval (usually used in existing work), tracking interval is also dynamically adapted. Extensive simulation results, compared to existing work, show that AASA achieves high performance in terms of quality of tracking, energy efficiency, and network lifetime.
•We present an auction mechanism for the problem of target tracking in WSNs.•We propose an adaptive sensor activation algorithm for the target tracking.•We improve prior trilateration algorithm for low target localization errors.•The proposed algorithms provide high energy-efficiency and tracking quality.
The ranking algorithm in the recommender system aims at optimizing accuracy during training so that it pays too much attention to the relevance of the individual and ignores the mutual influence ...between the items in the list. In response to this problem, we propose dither, a re‐ranking model for the recommender system. We deploy the re‐ranking algorithm as an independent module after the ranking algorithm to achieve the function of decoupling from it. Our model formalizes the re‐ranking problem as a multi‐objective optimization problem. It re‐ranks the initial ranking list by balancing multiple indicators to generate an improved list and updates the list during frequent user interactions with the system. Through a case study on the MovieLens 100 K data set, the workflow, and effects of the dither model are demonstrated. In addition, the re‐ranking algorithm shows the performance advantages of our model over existing methods.
Our model deploys the re‐ranking algorithm as an independent module after the ranking algorithm to achieve decoupling from the ranking algorithm. We formalize the re‐ranking problem as a multi‐objective optimization problem.
Storage is one of the main services that the cloud provides users. Utilizing the cloud to store large amounts of data can alleviate the cost of storage and the hardware investment on the local side, ...which is very important for resource-restricted clients. An emerging issue for clients is how to verify the database in the could after outsourcing their data to the cloud. Many verifiable database (VDB) schemes have been proposed by researchers that can solve this issue. However, some of the existing schemes cannot satisfy the practical requirements of database verifiability. In this paper, we propose a secure verifiable database scheme that is based on the polynomial commitment for cloud computing, which can realize the verifiability of database records in the cloud. Moreover, the proposed scheme can support public verifiability in that all clients in the system can verify the database. In addition, we use the BLS signature and the index-hash table to construct dynamic operations for the database. Security analysis shows that our scheme can achieve real-world security requirements. The simulation results show that our scheme is more efficient than similar schemes.
Data privacy is one of the intriguing problems in e-commerce site. For personal or business purposes, users have to disclose their private data to these e-commerce sites. Often such businesses use ...these highly sensitive data for computing artificial intelligence-driven analyses like recommendation generation without user consent. In the case of recommendation generation, data need to be analyzed at the business platforms. An automated personalization, based on artificial intelligence, on a list of products with respect to user interest is generated by a recommender system. However, the secure utilization of user data is absent in such systems. This paper proposes Private-Rec , a privacy-preserving platform for a recommendation system through the integration of artificial intelligence and blockchain. In Private-Rec , blockchain gives the user a secure environment through the distributed attribute in which data can be used with the required permission. Under this platform, users receive incentives (i.e., point, discount) from the recommended company for sharing their data to be used for computing recommendations. The Private-Rec platform has been studied empirically.
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