Reviewerly/exHarmony huggingface dataset:
The construction of a mathematical model for college English interactive distance teaching

Abstract To make up for the shortcomings of the traditional teaching mode, such as teacher-centered, less interaction, limited output, and inability to meet students’ personalized learning needs, the author proposes a mathematical model for college English interactive distance teaching. Based on the social culture theory and the interactive theory of distance teaching, and based on analyzing the education and teaching functions of WeChat, this paper constructs an interactive college English mixed teaching model based on WeChat and conducts teaching practice research on this model. The model emphasizes learner-centered, creates an interactive learning environment for language acquisition, and reasonably allocates learning activities and learning processes. The experimental research shows that 95.79% of the students believe that the mixed teaching mode of college English based on the WeChat platform is conducive to their English learning and that this mixed teaching mode can improve their English teaching motivation, and 94.94% of the students say that the mixed English mode is satisfactory. Conclusion: The practice has proved that the mathematical model for college English interactive distance teaching can make up for the shortcomings of the traditional teaching model, such as teacher-centered, less interaction, limited output, and unable to meet students’ personalized learning needs.
Selective interactive networks with knowledge graphs for image classification

The combination of networks and knowledge graphs (KGs) improves the accuracy and interpretability of networks in image classification. Previous methods fuse the entire KG with each sample. However, there is limited knowledge related to specific samples for the entire KG. The fusion process of the previous method includes some useless redundant knowledge, resulting in suboptimal fusion performance. In this paper, we propose a selective interactive network based on a knowledge graph, which builds a bidirectional communication bridge for knowledge and data. Specifically, we design an attention-based knowledge selection module that utilizes data to select effective knowledge from the KG, filtering out some useless knowledge involved in the subsequent fusion process. Meanwhile, we introduce knowledge selection loss that measures the consistency between knowledge and image features to achieve the effective selection of knowledge through data. Then, we employ a bilinear fusion method to combine the selected knowledge and image features to improve the recognition performance of the network. Extensive experiments conducted on the IDRiD and CUB-200-2011 datasets demonstrate the state-of-the-art performance of the proposed method, especially on the small dataset IDRiD, where the proposed method outperforms the baseline by 6.79% in accuracy. The knowledge selection mechanism establishes the association between predicted classes and the KG, and thus provides attribute descriptions for predicted classes, which has stronger interpretability. The visualization results demonstrate that our model captures more stable image features.
Apache Flink and clustering-based framework for fast anonymization of IoT stream data

In this paper, we present a novel framework that considers the expiration period time of the Internet of Things (IoT) data stream to anonymize it. IoT stands among one of most fast-growing technology in the world. Also, anonymity is one of the safeguards in place to protect data privacy. Because of the dynamic nature, vastness, and rapid changes in data streams, traditional approaches cannot be used to anonymize IoT data. The anonymization framework proposed in this paper performs its operation using a new clustering method and Apache Flink flow data processing engine. In this framework, firstly, we cluster received data. Then, if the size of the clusters doesn't meet the K-anonymity threshold, our review will continue to suppress and delete them; otherwise, the data would be anonymized and published. In this way, the framework handles both numerical and categorical data. At the end of the stream, the final remaining data will be merged and anonymized. Implementing and evaluating the framework using Scala and Apache Flink shows that the proposed approach reduces data delay by 12.33-66.62% compared with the other methods. Furthermore, in the end, combining the leftover clusters avoids information loss. In comparison with similar methods, information loss is reduced by 5.68-18.26%. The evaluation results show better performance in terms of data delay and information loss.
A novel and failsafe blockchain framework for secure OTA updates in connected autonomous vehicles

Connected Autonomous Vehicles (CAVs) are becoming data centers on wheels, amassing petabytes of data. They require a combination of software and hardware systems to operate reliably and simply in real-time. However, due to the transient nature of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) networks and CAVs' dependency on a connected software system, we need a new paradigm for secure and immutable data exchange, which emulates smart contracts. For this reason, CAV manufacturers need to maintain the latest version of software to protect CAVs from system failure and cyber threats in real-time, which is difficult with traditional centralized management. The key challenges that we must overcome are the authentication and secure sharing of the Over-The-Air (OTA) software updates and the scalability of distribution at the level of CAVs. This paper proposes a secure and scalable software updates framework in a distributed manner for the CAVs, leveraging Blockchain (BC) with smart contract technology. The framework is able to overcome slow processing speed, which is one of the significant limitations of BC in terms of lower latency and less packet overhead with a high level of security against possible cyber-attacks. We use a salting-based hashing scheme over the traditional Elliptic Curve Cryptography (ECC) Key. This process ensures multi-factor authenticated protection from malicious transactions while downloading and installing any new feature update in CAVs. Moreover, by using Hyperledger BC, our framework provides cost-free transactions while successfully upgrading and deploying OTA software patches in any system of CAVs. This paper extends our previous work [51] by analysing our proposed BC framework's immutability and load management capability. Our simulation results show that our proposed framework is robust, secure, and failsafe for CAV manufacturers and end-users.
FRRW: A feature extraction-based robust and reversible watermarking scheme utilizing zernike moments and histogram shifting

This paper introduces a feature extraction-based approach to ensure both robustness and reversibility of image. Low-order Zernike moments are utilized to embed a robust binary image as a watermark, which is used for information authentication. A reversible watermark is embedded outside the robust watermark regions and is employed for the purpose of restoring the cover image. It uses the combination of histogram shifting and prediction error, which can improve image restoration quality. Steady feature points are extracted in two ways, the speed-up robust features (SURF) algorithm and the oriented fast and rotated brief (ORB) algorithm. After extracting the feature points, the regions are obtained by extending the final selected feature points to embed the watermark. Consequently, the presented watermarking technique combines robust and reversible watermarking which has the ability to enhance the invisibility of the watermark and the clarity of image restoration. It is possible to extract the watermark even after an attack has been made on the watermarked image. Or we can recover the original image with no attacks. The results from the experiments indicate that the suggested method is resilient to geometric deformations, involving scaling and rotation, along with typical signal manipulation attacks, including noise-based attacks.
Idempotents in simple skew group rings and applications to skew cyclic codes

AbstractWe give the form of complete sets of pairwise orthogonal primitive idempotents in a simple skew group ring. Using these idempotents, we construct some classes of skew cyclic codes. We give also some examples of MDS codes and rows orthogonal and MDS matrices.Communicated by Eric JespersKEYWORDS: MDS codesMDS matricesskew cyclic codesskew group ring2020 MATHEMATICS SUBJECT CLASSIFICATION: 16S3511T71
Usability Comparison between 2D and 3D Control Methods for the Operation of Hovering Objects

This paper experimentally analyzed the cognitive load of users based on different methods of operating hovering objects, such as drones. The traditional gamepad-type control method (2D) was compared with a control method that mapped the movement directions of the drone to the natural manipulation gestures of the user using a Leap Motion device (3D). Twenty participants operated the drone on an obstacle course using the two control methods. The drone’s trajectory was measured using motion-capture equipment with a reflective marker. The distance traveled by the drone, operation time, and trajectory smoothness were calculated and compared between the two control methods. The results showed that when the drone’s movements were mapped to the user’s natural directional gestures, the drone’s 3D movements were perceived as more natural and smoother. A more intuitive drone control method can reduce cognitive load and minimize operational errors, making it more user friendly and efficient. However, due to the users’ lack of familiarity with Leap Motion, it resulted in longer distance and time and lower subjective satisfaction; therefore, a more improved 3D control method over Leap Motion is needed to address the limitations.
A Deep Reinforcement Learning Scheme for Spectrum Sensing and Resource Allocation in ITS

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity.
Reflection sensitivity of dual-state quantum dot lasers

This work experimentally and theoretically demonstrates the effect of excited state lasing on the reflection sensitivity of dual-state quantum dot lasers, showing that the laser exhibits higher sensitivity to external optical feedback when reaching the excited state lasing threshold. This sensitivity can be degraded by increasing the excited-to-ground-state energy separation, which results in a high excited-to-ground-state threshold ratio. In addition, the occurrence of excited state lasing decreases the damping factor and increases the linewidth enhancement factor, which leads to a low critical feedback level. These findings illuminate a path to fabricate reflection-insensitive quantum dot lasers for isolator-free photonic integrated circuits.
Adaptive Discretization using Voronoi Trees for Continuous POMDPs

Solving continuous Partially Observable Markov Decision Processes (POMDPs) is challenging, particularly for high-dimensional continuous action spaces. To alleviate this difficulty, we propose a new sampling-based online POMDP solver, called A daptive D iscretization using V oronoi T rees (ADVT). It uses Monte Carlo Tree Search in combination with an adaptive discretization of the action space as well as optimistic optimization to efficiently sample high-dimensional continuous action spaces and compute the best action to perform. Specifically, we adaptively discretize the action space for each sampled belief using a hierarchical partition called Voronoi tree, which is a Binary Space Partitioning that implicitly maintains the partition of a cell as the Voronoi diagram of two points sampled from the cell. ADVT uses the estimated diameters of the cells to form an upper-confidence bound on the action value function within the cell, guiding the Monte Carlo Tree Search expansion and further discretization of the action space. This enables ADVT to better exploit local information with respect to the action value function, allowing faster identification of the most promising regions in the action space, compared to existing solvers. Voronoi trees keep the cost of partitioning and estimating the diameter of each cell low, even in high-dimensional spaces where many sampled points are required to cover the space well. ADVT additionally handles continuous observation spaces, by adopting an observation progressive widening strategy, along with a weighted particle representation of beliefs. Experimental results indicate that ADVT scales substantially better to high-dimensional continuous action spaces, compared to state-of-the-art methods.
Similarity learning of product descriptions and images using multimodal neural networks

Multimodal deep learning is an emerging research topic in machine learning and involves the parallel processing of different modalities of data such as texts, images and audiovisual data. Well-known application areas are multimodal image and video processing as well as speech recognition. In this paper, we propose a multimodal neural network that measures the similarity of text-written product descriptions and images and has applications in inventory reconciliation and search engine optimization. We develop two models. The first takes image and text data, each processed by convolutional neural networks, and combines the two modalities. The second is based on a bidirectional triplet loss function. We conduct experiments using Amazon Berkeley Objects (ABO) dataset and an industry-related dataset used for the inventory reconciliation of a mechanical engineering company. Our first model achieves an accuracy of 92.37 % with ResNet152 on the ABO dataset and 99.11 % with MobileNetV3_Large on our industry-related dataset. By extending this model to a model with three inputs, two text inputs and one image input, we greatly improve the performance and achieve an accuracy of 97.57 % on the ABO dataset and 99.83 % with our industry related inventory dataset. Our second model based on the triplet loss achieves only an accuracy of 73.85 % on the ABO dataset. However, our experiments demonstrate that multimodal networks consistently perform better when measuring the similarity of products, even in situations where one modality lacks sufficient data, because it is complemented with the other modality. Our proposed approaches open up several possibilities for further optimization of search engines.
An efficient medical image encryption algorithm for telemedicine applications

Applications for healthcare execute computations that support various patient diagnostics and save private user data on cloud servers. User data is being held at ransom as a result of increasing cyberattacks on medical systems. Additionally, unreliable external parties who sell private data for profit can access mathematical calculations on data stored in the cloud. In this research, we suggest an efficient Homomorphic medical image encryption algorithm protecting medical plaintext data against unauthorised access via homomorphic encryption. To increase the security level Paillier's encryption scheme (PES) is utilized to select the optimal key. Homomorphic encryption is a method of authentication which helps one to perform observes of the encrypted information through decrypting it. Secret sharing splits up computations among a number of edge virtual nodes and hides all arithmetic operations, preventing unreliable cloud servers from figuring out what was done with the encrypted patient data. Cloud computing resources help virtual edge nodes perform computationally difficult mathematical operations and lower latency in device-edge node data transmission. During the experimental study, different factors including execution time, encryption time, and decryption time are used to evaluate the performance of the suggested approach. The proposed technique's execution time is 2% faster than the currently used HE method and 4% faster than the ECC algorithm.
ISL-GKFDA: An incomplete supervision manifold learning framework based on propagation graph of data

To improve manifold classification results using unlabeled data with high dimensional redundancy, we propose an incomplete supervision manifold learning framework. In this framework, Euclidean distance and Euclidean neighborhood are replaced by manifold distance and manifold neighborhood respectively. The pseudo label of unlabeled data is generated by constructing label propagation network in manifold neighborhood. By adding the local manifold neighborhood structure into KFDA (Kernel Fisherś Discriminant Analysis) as regularization term, the local manifold structure is stressed. Subsequently, we propose the ISL-GKFDA (Incomplete Supervision Learning (ISL) with propagation Graph of data in Kernel Fisher Discriminant Analysis) algorithm. We validate ISL-GKFDA by comparing it with FDA (Fisher's Discrimination Analysis), KFDA (Kernel FDA), ISL-FDA (ISL version FDA), ISL-KFDA (ISL version KFDA), SSDA (semi-supervised discriminant analysis based on manifold distance), and KSSDA (Kernel SSDA). In typical manifold datasets, ISL-GKFDA can more accurately capture the boundaries of binary classification; and visually in handwritten digital dataset, ISL-GKFDA can separate three data types more clearly. Finally, we use the UCI database and compare multiple algorithms. When measured by accuracy, the proposed ISL-GKFDA reaches a maximum in at least three datasets of five when labeling ratio is 5%, 15% and 30%, respectively. When measured by F1, ISL-GKFDA reaches the maximum in all datasets.
The Description Of Knowledge And Attitude Of Flood Disaster Preparedness Of Residents In Kwarasan Sub-District

Flood is a natural phenomenon triggered by natural processes and uncontrolled human activity. In early 2023, flooding in Kwarasan Sub-District occurred twice in January and February which were the worst floods after 2007. Knowledge and attitude of preparedness are important things to have because knowledge of disaster preparedness will affect a person's attitude when a disaster occurs, which be used in the long term to minimize the impact of the disaster. This research aims to know the description of the knowledge and attitude of flood disaster preparedness of residents in Kwarasan, Grogol, Sukoharjo. The type of research used is a quantitative description with survey methods. The sample in this research was 78 heads of families in the Kwarasan sub-district using purposive sampling and stratified random sampling techniques. The results of the research on preparedness knowledge were in the good category with 76 (97.4%) respondents and 2 (2.6%) respondents in the sufficient category. Then the results of the research on preparedness attitudes were in the good category with 61 (78.2%) respondents and 17 (21.8%) respondents in the sufficient category. The Conclusion is the description of the knowledge and attitude of flood disaster preparedness is mostly in the good category.
Heterogeneous network representation learning based on role feature extraction

Since most of the real-world networks are heterogeneous, existing methods cannot characterize the roles of nodes in heterogeneous networks. The neighborhood structure of nodes in heterogeneous networks largely determines the node roles, and the basic statistical features of nodes describe the topology of nodes to some extent, so extracting structural features from the adjacency matrix of networks is crucial for role-oriented network representation learning(structural equivalence). Therefore, in this paper, we propose a heterogeneous network representation learning model based on role feature extraction, called HRFE(Heterogeneous Network Representation Learning for Role Feature Extraction). Firstly, we perform feature extraction for each node in the heterogeneous network to obtain a high-dimensional feature matrix, then perform role discovery using non-negative matrix decomposition techniques to obtain a role-based node representation, and finally verify the effectiveness of the model HRFE through experiments on a large number of real datasets.
Regional scale surface of the top of the Variscan basement in the central sector of Italy

In this study, we combined regional geology and geostatistical analytical processing to model the top surface of the Variscan basement in Italy. This surface, which is overlain by the pre-Alpine sedimentary cycle (Upper Carboniferous – Permian) followed by the Alpine Mesozoic and Tertiary cycles, covers a significant portion of the Italian peninsula and the Adriatic Sea.We integrated crustal seismic profiles, deep well litho-stratigraphic information and small-scale geological maps of Italy. A regional scale map of the depth of the investigated surface was thus produced using geostatistical processing.The level of reliability of our map varies depending on the distribution of subsurface data from wells and seismic profiles. However, this method is easily replicable, providing quick updates when new data become available. The map can help to identify areas with high potential for natural deep georesources and guide the exploration.