Reviewerly/exHarmony huggingface dataset:
The Development of Intelligent Education and Teachers’ Professional Informatization Led by an Intelligent Ubiquitous Network

This study applies intelligent ubiquitous network technology to the field of education and leads to the benign development of intelligent education and teachers' professional informatization. This study proposes the concepts of eco-classroom, smart education, and flipped classroom and conducts practical research on the teaching effectiveness of the three elements of eco-classroom: teachers, students, and environment. Because of the problems of inaccurate positioning of data fusion, single application function, and difficulty of automatic correlation of data stored in multiple networks in the intelligent ubiquitous sensing environment, the method and path of multidimensional data fusion governance are proposed and put into practice to achieve good results in practice. Based on the characteristics of ubiquitous learning in time, space, and dimensional changes between reality and time, combined with the law of knotted learning and Bloom's mastery learning theory, the five components of the teaching model and their correlations are sorted out, and the teaching model of higher education under the vision of ubiquitous learning is constructed to achieve the goal of students' mastering knowledge and skills and promote the innovation of the education and teaching model. The model interaction site is created, a ranking system is developed, and professional standards for the new era are developed to help educators reconstruct their professional competencies. The concept and characteristics of knowledge sharing in the ubiquitous network environment are analyzed, the knowledge sharing model in the ubiquitous network learning environment is constructed, the status of knowledge sharing practice in the ubiquitous network environment is studied, and suggestions for improvement are put forward to optimize knowledge sharing in the ubiquitous network learning environment.
Protection of Digital Elevation Model—One Approach

The quality of DEMs is especially being considered based on the spatial resolution of the created digital models and the type of terrain of the interest area. DEM analysis was performed specifically for LiDAR-based DEMs, as well as a comparison with results obtained using the radar recording method (Shuttle Radar Topography Mission). The application of DEM data for security-sensitive systems requires the fulfillment of security requirements, such as authenticity, integrity, confidentiality, and non-repudiation. For this purpose, the authors have developed a model for non-repudiation and protecting DEM data. The model simulation shows it is possible to detect even the smallest changes made in the transmission or the DEM location, as well as proving the data authenticity and non-repudiation of the sender. DEM data security testing has shown that DEM data is effectively protected from the source of origin to the end entity location.
Extending Partial Domain Adaptation Algorithms to the Open-Set Setting

Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA.
Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning

With the development of communication technology and the increasingly complex wireless communication channel environment, the requirements for radio modulation recognition are also increased to avoid interference and improve the efficiency of radio spectrum resources. To achieve high recognition accuracy with less computational overload, we propose a radio signal modulation recognition method based on deep learning, which uses a pruning strategy to reduce computational overload, based on the original model, CNN-LSTM-DNN (CLDNN), and the double-layer long short-term memory (LSTM). Effect factors are analyzed in terms of recognition accuracy by adjusting the parameters of each network layer. The results of the experiments show that the model not only has a greater precision improvement than some existing models, but also reduces the computational resources necessary.
A Federated Incremental Learning Algorithm Based on Dual Attention Mechanism

Federated incremental learning best suits the changing needs of common Federal Learning (FL) tasks. In this area, the large sample client dramatically influences the final model training results, and the unbalanced features of the client are challenging to capture. In this paper, a federated incremental learning framework is designed; firstly, part of the data is preprocessed to obtain the initial global model. Secondly, to help the global model to get the importance of the features of the whole sample of each client, and enhance the performance of the global model to capture the critical information of the feature, channel attention neural network model is designed on the client side, and a federated aggregation algorithm based on the feature attention mechanism is designed on the server side. Experiments on standard datasets CIFAR10 and CIFAR100 show that the proposed algorithm accuracy has good performance on the premise of realizing incremental learning.
Foreword to the Special Issue on Advances in Secure AI: Technology and Applications

I am pleased to introduce the Special Issue on “Advances in Secure AI: Technology and Applications” [...]
TensorRT acceleration based on deep learning photoelectric target detection

One of the important research directions in the field of target detection in computer vision, among which deep learning-based target detection can extract advanced features and has higher detection accuracy than traditional detection algorithms. The inference speed of convolutional neural networks in embedded platforms is low, and the practical application value is low. Therefore, for the background of optoelectronic device detection and camera detection, on the embedded platform NVIDIA Jeston Nano, the ResNet18 convolutional neural network is used to identify the photoelectric target. Use TensorRT to accelerate the process of network model simplification and engine construction, and accelerate the network inference time. Experimental results show that when the input image resolution is 640*480, the inference time of tensorRT technology after running the network on the NVIDIA Jeston Nano device is in the range of 0.04-0.06s, and the single-area photoelectric target detection inference is accelerated by 2.38 times and the multi-area photoelectric target detection inference is accelerated by 2.74 times, which provides support for practical applications.
Design and development of interactive virtual assembly system for spinning machine

The equipment structure and operation status of spinning machine were analyzed, and an interactive virtual system for spinning machines was developed. The 3D model required by the system was established by UG software, and the 3D model was optimized by 3DSMAX software. In this paper, Unity3D was chosen as the development environment of the system. According to the system functional requirements, the interactive functions of the system were developed through the C# language. The interactive function development based on gesture operation was realized by using the Kinect device. The movement of the model was controlled by recognizing the user's gesture action, the assembly process of the longitudinal drive device of the spinning machine was completed. Finally, a virtual assembly system including motion simulation function, interactive perspective function and virtual assembly function was developed. It has been verified by an example that the system has good interactivity and immersion, and effectively improved the assembly quality and design efficiency of products.
Performance analysis of 1/2 bit rate convolutional encoder

In modern times, people usually use channel coding technology to ensure the effectiveness and reliability of information transmission. As a better coding method, convolutional coding is widely used in wireless communication standards such as GSM, cdma20000 and IS-95. This paper introduces the coding principle of convolutional code, uses Viterbi decoding, and verifies the performance of convolutional code through MATLAB simulation. Simulations have found that when the bitrate is 1/2, the performance of the convolutional encoding increases with the length of the constraint. And in the decoding phase, the soft judgment Viterbi algorithm due to the hard judgment.
FTPS: Efficient fault‐tolerant dynamic phrase search over outsourced encrypted data with forward and backward privacy

Summary With the popularity of cloud computing, more and more users store sensitive information in cloud servers. In order to protect the data over the cloud server, symmetric encryption with keyword search has been developed and the phrase search has been proposed subsequently to overcome the inefficiency produced by single/multi‐keyword search. However, most existing phrase search schemes fails to support fault‐tolerant search which is essential to users. Therefore, this paper proposes a fault‐tolerant dynamic phrase search scheme with forward privacy and backward privacy (FTPS). Piecewise Linear Chaotic Map and minhash function are used to blur information, and Bloom filter based index is constructed to realize efficient search and dynamic update simultaneously. Security analysis proves that FTPS can properly preserve the privacy of search user, and experimental results show that FTPS is practical.
System Statuses in Academic Libraries: Increasing Transparency and Improving the User Experience

"is facebook down" trended on Google on October 4, 2021, when Facebook became temporarily globally unavailable. Web users proactively seek information when they encounter issues online. System status dashboards are one way libraries can leverage this behavior to share changes to resources or services and improve transparency, reference services, and the user experience. The University at Albany Libraries Discovery Services Librarian and User Experience Librarian implemented Springshare’s System Status Management tool to share system statuses with library staff and the campus community. The institutional context, implementation, and impact are described in this article. The authors reviewed Association of Research Libraries members’ library websites to investigate if and how system statuses are being used to communicate with patrons. Results indicate that the majority of libraries reviewed do not employ a dedicated system status dashboard to share information with their constituents, but may use other types of alerts instead.
Transformation of coherent state in Hadamard gate via multi-photon catalysis

Abstract Based on two-mode continuous Hadamard gate (TCHG) and multi-photon catalysis, this paper presents a scheme to generate non-classical quantum states via coherent state. When considering the m-photon input and m-photon detection in a -mode, the output state is obtained for the input coherent in b -mode. An interesting finding is that as the amplitude of the output coherent state increases, both the detection efficiency and the fidelity of the output quantum state gradually approach zero. Thus, only certain low-intensity quantum light field states can pass through Hadamard gate. In addition, the quantum statistical distribution and squeezing properties of the output quantum states are also analyzed in detail. Our results show that TCHG is a powerful tool for generating non-classical quantum states under multi-photon catalysis.
Multilevel Features-Guided Network for Few-Shot Segmentation

The purpose of few-shot semantic segmentation is to segment unseen classes with only a few labeled samples. However, most methods ignore the guidance of low-level features for segmentation, leading to unsatisfactory results. Therefore, we propose a multilevel features-guided network using convolutional neural network techniques, which fully utilizes features from each level. It includes two novel designs: (1) a similarity-guided feature reinforcement module (SRM), which uses features from different levels, it enables sufficient guidance from the support set to the query set, thus avoiding the situation that some feature information is ignored in deep network computation, (2) a method that bridges query features at each level to the decoder to guide the segmentation, making full use of local and edge information to improve model performance. We experiment on PASCAL-5i and COCO-20i datasets to demonstrate the effectiveness of the model, the results in 1-shot setting and 5-shot setting on PASCAL-5i are 64.7% and 68.0%, which are 3.9% and 6.1% higher than the baseline model, respectively, and the results on the COCO-20i are also improved.
Social partnership and entrepreneurship: trends and prospects of development in the student environment

The article discusses the features of the following concepts: social partnership, entrepreneurship, social entrepreneurship. The issues of trends in students' choice of a conflict resolution strategy within the framework of social partnership and the attitude to entrepreneurship in the student environment are raised. The answers of students of various majors on choosing their own strategy for overcoming conflict situations and identifying leading career orientations that allow determining trends and prospects for the development of social partnership and entrepreneurship in the student environment are analyzed.
Model-driven design space exploration for multi-robot systems in simulation

Abstract Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.
Possible advantages that may be enhanced with the adoption of research triangulation or mixed methodology

In research, triangulation refers to the use of various datasets, methodologies, theories, and/or investigators to answer a research question. This work highlighted some of the advantages of triangulation in qualitative and quantitative research. It shows that triangulation is helpful in the development of complementary analysis which can facilitate the completeness of a research and help develop an insight.
Perceived Usability and User Experience in Augmented Reality Tools for the Treatment of Spider Phobia

Abstract The use of augmented reality (AR) environments to treat small animal phobias is an alternative to traditional in vivo exposure treatments that allow supporting the therapy through the virtual, gradual and controlled exposure of the patient to the animal to which he/she is afraid. In this paper, we compare three different AR tools used in exposure therapy for spider phobia with thirty users; namely, a mobile haptic AR system, an immersive AR environment, and a non-immersive AR environment. An in vivo (direct) interaction with a real spider was also used as a reference during the comparison. To compare these four conditions, each subject participated in an exposure therapy session using all of them. The perception of usefulness and experience of use of each of the tools were evaluated using a Technology Acceptance Model on-exit questionnaire and the results were obtained through indirect observation analysis. The results showed that there are no significant differences regarding the perception of usefulness among the three applications and that the haptic AR system generated the least discomforting experience of use.
Design Space Exploration of Galois and Fibonacci Configuration Based on Espresso Stream Cipher

Fibonacci and Galois are two different kinds of configurations in stream ciphers. Although many transformations between two configurations have been proposed, there is no sufficient analysis of their FPGA performance. Espresso stream cipher provides an ideal sample to explore such a problem. The 128-bit secret key Espresso is designed in Galois configuration, and there is a Fibonacci-configured Espresso variant proved with the equivalent security level. To fully leverage the efficiency of two configurations, we explore the hardware optimization approaches toward area and throughput, respectively. In short, the FPGA-implemented Fibonacci cipher is more suitable for extremely resource-constrained or high-throughput applications, while the Galois cipher compromises both area and speed. To the best of our knowledge, this is the first work to systematically compare the FPGA performance of cipher configurations under relatively fair cryptographic security. We hope this work can serve as a reference for the cryptography hardware architecture research community.
A POI Recommendation Algorithm Based on the Heterogeneous Graph Convolution Network

Point-of-interest (POI) recommendation is a type of recommendation task, which generates a list of places that users may be interested in. There is a complex heterogeneous graph structure between users and points of interest. The current recommendation algorithms are generally based on Euclidean space data, and the algorithms based on graph structure also generally use homogeneous graph convolution. To solve these problems, the author proposes a heterogeneous graph convolution network algorithm based on hierarchical subgraphs (HGCNR). By constructing user-centered subgraph layers and interest point-cantered subgraph layers, respectively, the author performs heterogeneous graph convolution on different subgraphs to obtain more effective user node information and interest point node information and form recommendation result. Experiments on two public data sets show that HGCNR can effectively improve the recommendation performance of interest points and achieve better recommendation results.
Event-set differential privacy for fine-grained data privacy protection

Privacy-preserving data statistics and analysis has become an urgent problem nowadays. Differential privacy (DP), as a rigorous privacy paradigm, has been widely adopted in various fields. However, in the context of large-scale mobile applications where each user has multiple records, both user-level DP and record-level DP cannot achieve a good compromise between stringent privacy and high data utility. A more satisfying privacy paradigm with desired granularity becomes very necessary. To this end, this paper proposes a fine-grained privacy paradigm called α-event-set differential privacy, which prevents adversaries from inferring any one of α event-sets owned by the user in data statistics and analysis. We theoretically introduce the definition, properties, and baseline mechanisms of α-event-set DP. Besides, we implement and evaluate α-event-set DP on mean estimation, histogram estimation, and machine learning applications, respectively. The experimental results have shown that α-event-set DP is able to achieve a fine-grained granularity of privacy protection while allowing high data utility.
A novel edge gradient distance metric for automated evaluation of deformable image registration quality

To develop a new registration quality metric, based on the distance between image edges, for automated evaluation and comparison of DIR algorithms.Canny filter is used to create binary gradient images from input images to be compared. A small subregion of one binary image is translated relative to the other. The translational distance maximizing overlap of edges in the subregion is the local edge gradient distance to agreement (EGDTA); repeating over all subregions provides an EGDTA map. The method was tested on phantom and pelvic CT images, by applying a known deformable vector field (DVF). The method was then applied to evaluate two DIR algorithms (SICLE and Demons) for pelvic CTs from five patients. Three SICLE variants were used: Grayscale-driven (G), Contour-driven (C), and Grayscale + Contour-driven (G + C). For each patient, a planning CT was registered to three repeat CTs using the above DIR algorithms. Mean EGDTA values in concentric ring regions of interest close to and far away from the pelvic organ contours were compared.EGDTA maps and imposed DVF deformations on phantom and CT images demonstrated agreement. In comparison of the three variants of SICLE: C had lower EGDTA values close to the pelvic organs, while G showed much better performance in the regions distant from the organs compared to C; and G + C registration exhibited the lowest or comparable EGDTA value among three variants. Demons achieved the lowest EGDTA values.The EGDTA metric shows potential as an automated means of evaluating and comparing DIR algorithms.
Comparing the impact of word writing and typing via mobile devices on L2 vocabulary learning

Copying words by writing and typing are two effective methods in traditional learning, but their merits for learning L2 words have seldom been compared. Because of technological advances and the growing popularity of utilising smart mobile products to acquire L2 vocabulary, this study investigated the effectiveness of writing and typing techniques in increasing L2 word acquisition in a mobile-assisted environment and compared their impacts on learning outcomes. Both methods were found to help learners achieve better performances than the control condition, but their advantages differed. Writing improved both immediate and delayed learning, whereas typing only helped with longer-term learning. Based on these findings, people who teach or learn L2 vocabulary in either conventional fixed settings or mobile-assisted environments are encouraged to employ both writing and typing approaches. Programmers are also suggested to integrate the two approaches in their future development of vocabulary learning apps on smart portable devices.
Generalized Solution of Inverse Problem for Ising Connection Matrix on d-Dimensional Hypercubic Lattice

We analyze a connection matrix of a d-dimensional Ising system and solve the inverse problem, restoring the constants of interaction between spins, based on the known spectrum of its eigenvalues. When the boundary conditions are periodic, we can account for interactions between spins that are arbitrarily far. In the case of the free boundary conditions, we have to restrict ourselves with interactions between the given spin and the spins of the first d coordination spheres.