Reviewerly/exHarmony huggingface dataset:
Cloud platform teaching expansion and practical innovation of landscape design course driven by information thinking

The information age has come, and some colleges and universities still use the previous teaching mode in their courses. Teachers learn by playing PPT and students listen to classes and take notes. In this way, classroom teaching without passion will make students lose their enthusiasm for learning, and the learning effect is difficult to guarantee. Driven by information thinking, science and technology have developed rapidly, and new teaching methods and teaching tools continue to appear in college teaching, gradually subverting the traditional teaching concept. The construction of cloud platform can make teachers and students get what they need. The use of big data and the construction of cloud platform in many courses can bring better experience to teachers and students. This paper analyzes the important role of cloud computing in college education and teaching, and discusses the expansion and practical innovation of cloud platform teaching in landscape design course based on the role of cloud platform teaching in landscape design course.
Use of Blockchain for Securing Electronic Health Records During COVID-19 Pandemic

Electronic health records (EHRs) are utilized to protect and exchange health data in most healthcare facilities, replacing paper-based systems. EHRs are real-time, patient-centered records that enable authorized users to access information quickly and securely. Apart from the fundamental standards of information secrecy, confidentiality, and integrity, there is a requirement for interoperability and user control over entree to their information. There are still concerns about the Electronic Health Record (EHR) system, such as data security, trust, and administration. Global risk management has been affected by the SARS-CoV2 epidemic. Various apps, tools, and websites that save data and offer security were established during the COVID-19 epidemic. Simultaneously, people will be reluctant to provide personal information out of fear of privacy and social shame. Blockchain technology has become a transformative technology capable of ensuring the immutableness, secrecy, and customer access characteristics of deposited data while facilitating dispersed storage among users. The integration of blockchain technology and machine learning systems enables developing a generalizable prediction system that may aid in the containment of pandemic risk on national territory. This article shows the usage of blockchain technology to secure electronic health records during the COVID-19 pandemic and analyzes how blockchain can be used to maintain HER to handle the pandemic efficiently.
Towards Privacy in Deep Learning

Machine learning often operates on privacy-sensitive data and this data comes with numerous threats. Differentially private learning is assumed to be the state-of-the-art coun-termeasure and many machine learning tool-kits implement differentially private gradient descent. However, we increasingly see that the protection by differentially private learning may fail and many data scientists reject it due to its large impact on model accuracy. In this paper we discuss why and how differentially private learning fails against practical privacy attacks.
Regularized Sequential Latent Variable Models with Adversarial Neural Networks

The highly structured sequential data, such as from speech and handwriting, often contain complex relationships between the underlaying variational factors and the observed data. This paper will present different ways of using high level latent random variables in RNN to model the variability in the sequential data. We have developed the two-steps training algorithms of such RNN model under the VAE (Variational Autoencoder) principle. We proposed novel approach of using adversarial training to regularize the latent variable distributions in the variational RNN model. Contrary to competing approaches, our approach has theoretical optimum in the model training and provides better model training stability. Our approach also improves the posterior approximation in the variational inference network by a separated adversarial training step. Numerical results simulated from TIMIT speech data show that reconstruction loss and evidence lower bound converge to the same level and adversarial training loss converges to 0.
Chaos-Based Applications of Computing Dynamical Systems at Finite Resolution

In this paper, a novel Pseudo Random Number Generator (PRNG) is introduced as an application of computing discrete time dynamical systems at finite resolution in chaos-based cryptography and chaotic optimization. It is based on constructing a combinatorial representation of a given chaotic map, expressing it as a graph, and walking randomly on the resulting graph. The limitations of current methods that use floating-point numbers in finite time compared to the proposed method that uses rational numbers at finite resolution are demonstrated.
Text Recommendation Algorithm Fused with BERT Semantic Information

Faced with the problem of text recommendation with massive data on the Internet, the use of a recommendation method based on deep learning combined with semantic information will improve the accuracy of the recommendation results. Therefore, we propose a HyReB (Hybrid Recommendation algorithm combining BERT and CNN network). The algorithm HyReB uses the BERT word vector as the input of the CNN network and incorporates external semantic information in features extraction and topic classification. Then we combine BERT and TextRank algorithms to extract text keywords and calculate the BERT word vector similarity of topic word. Finally, we do the weighted calculation of the label proportion of the recommended text and the similarity of the topic word vector to make the text top-N recommendation. The HyReB algorithm makes user interest extraction more refined and incorporates BERT semantic information into the text recommendation. Experiments show that the feature extraction of HyReB is more accurate and has a better recommendation effect when performing small-scale accurate text recommendation.
An Adaptive Black-box Defense against Trojan Attacks on Text Data

Trojan backdoor is a poisoning attack against Neural Network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans into model parameters for backdoor breaches through a poisoned training process. Most of the proposed defenses against Trojan attacks assume a white-box setup, in which the defender either has access to the inner state of NN or can run back-propagation through it. Moreover, most of exiting works that propose white-box and black-box methods to defend Trojan backdoor focus on image data. Due to the the difference in the data structure, these defenses cannot be directly applied for textual data. We propose T-TROJDEF which is a more practical but challenging black-box defense method for text data that only needs to run forward-pass of the NN model. T-TROJDEF tries to identify and filter out Trojan inputs (i.e., inputs augmented with the Trojan trigger) by monitoring the changes in the prediction confidence when the input is repeatedly perturbed. The intuition is that Trojan inputs are more stable as the misclassification only depends on the trigger, while benign inputs will suffer when perturbed due to the perturbation of the classification features.
Link User Identities Across Social Networks Based on Contact Graph and User Social Behavior

With the rapid development of Social Networking Services (SNSs), linking online user IDs is becoming increasingly important to internet service providers.Existing methods can achieve matching adjacent IDs between different services, where adjacent IDs mean the IDs that send message loggings at the same physical location.However, nonadjacent IDs also need to be matched in reality, which is a key challenge.In this paper, a new method based on users social behaviors and contact graph is put forward to realize linking of IDs across domains.This method can be used for matching both adjacent IDs and nonadjacent IDs.Specifically, all the IDs are mapped to contact graph.And we utilize a set matching algorithm based on the contact graph to find out the set of candidate IDs and generate confidence score by means of this algorithm to select the most appropriate matching.Our experimental results show that our algorithm is capable of identifying not only the set of adjacent IDs that belong to one same user but also the set of nonadjacent IDs that belong to one same user.
K-Cosine-Medoids Clustering Algorithm

Clustering is a data mining method that aims to partition data into multiple clusters by minimizing inter-cluster similarity and maximizing intra-cluster similarity. K-medoids is one of the most simple and well-known clustering algorithms. However, due to the presence of local outliers and randomly selecting initial medoids, the performance of the algorithm sometimes deteriorates. In this paper, we propose K-cosine-medoids algorithm that extends the idea of K-medoids algorithm and works on minimizing the aforementioned shortcomings. By implementing a systematic approach of selecting initial medoids similar to K-means++ algorithm and using cosine similarity for assigning data points to different clusters and updating medoids, we have observed a significant improvement in terms of accuracy compared to the standard K-medoids algorithm and a number of its variants.
UAV Electric Patrol Path Planning Based on Improved Ant Colony Optimization-A* Algorithm

Due to the control accuracy and the complexity of three-dimensional path planning, it is difficult for multi rotor UAV to locate accurately according to point cloud data. In order to overcome these problems, according to the control accuracy of UAV and taking the minimum how much the energy is consumed as the objective function, an improved ant colony-A&#x002A; optimization algorithm based on UAV performance and wind speed constrained path planning model is established in this paper. The improved ant colony-A&#x002A; algorithm is used to find the approximate optimal trajectory on the premise of covering all shooting points. The simulation results show that compared with the air transmission line UAV patrol image shooting manual, the how much the energy is consumed of the improved algorithm is greatly reduced.
AN ANALYSIS OF FACULTY PROFILES ON IRINS OF DR. BABASAHEB AMBEDKAR OPEN UNIVERSITY, AHMEDABAD

The paper plans to study IRINS in general and analyse the faculty profiles of Dr. Babasaheb Ambedkar Open University concerning IRINS. The research tool comprises several faculty profiles, number of publications, department wise publications, resources impact and analysis of the top faculty profile, etc. IRINS is a web-based Research Information Management (RIM) service provided by the Information and Library Network Centre (INFLIBNET) in collaboration with the Central University of Punjab, Bathinda. It showcases the research contribution of the professionals working in and associated with the institution. The data is collected from the Indian Research Information Network System faculty profile of Dr. Babasaheb Ambedkar Open University (BAOU), Ahmedabad (https://baou.irins.org/) and the Indian Research Information Network System (https://irins.org/irins/).
CONVOLUTIONAL AUTOENCODER FOR ANOMALY DETECTION IN CROWDED SCENES

Monitoring abnormal events in a crowded scene is essential these days, especially with the increase in surveillance cameras in most, if not all, places. This has made the field of computer vision an active field of research in recent periods. Identifying abnormal events in a crowded scene as soon as possible is essential, especially from the security side. It is a cumbersome and challenging task for humans because there are many surveillance cameras and overcrowding. Computer vision can solve this problem and get accurate and high results. This paper proposes a convolutional neural network architecture for anomaly detection in videos. The proposed convolutional neural network model has been trained to recognize anomaly frames. The performance of the proposed network has been evaluated using Avenue and UCSD standard datasets designed to identify anomalies events in crowded scenes. Experimental results show that the proposed model outperforms state-of-the-art methods, achieving an AUC of 71.96% and 89.52% on Avenue and UCSD Pde2 datasets. A detailed analysis of the experiments used to choose the parameters is presented and compared with other methods.
Research on Mainstream DataBase Security Analysis Technology of Big Data Platform

With the rapid development of the Internet and information technology, big data, characterized by large amount, high speed and diversity, has become a basic strategic resource and an Important core productivity. The application of database based on big data platform is more and more extensive, and it has gradually become an important foundation for field support and industrial application. Database security is the basic guarantee for the operation and application of big data platform. However, traditional database security is not suitable for new databases under big data platform, and its security has become an urgent problem to be solved. Based on the research and summary of the existing achievements in the security field of mainstream databases under the big data platform, this paper takes the attack behavior as the main research object, and expounds the security analysis technology from the perspective of operating system layer, database layer and distributed file system layer, and points out the future development direction.
Great Diversity in the Utilization and Reporting of Latent Growth Modeling Approaches in Type 2 Diabetes: A Literature Review

Introduction: The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D, many questions remain regarding the utilization of these methods. Objective: To review the literature of longitudinal studies using latent growth modeling approaches to study T2D. Methods: MEDLINE (Ovid), EMBASE, CINAHL and Web of Science were searched through August 25 th , 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies.Results: From the 4,694 citations screened, a total of 38 studies were included. The studies were published between 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification. Conclusion: Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification. Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interests: There are no competing interests specific to the study under review.
An enhanced Siamese Recurrent Model with Word Importance for Learning Sentence Similarity

We propose an enhanced Siamese Long Short-Term Memory (LSTM) model with word importance for learning sentence similarity. The framework inherits from the MaLSTM model in [10], which employs LSTM to the word vectors learned separately from a large corpus. Additionally, our framework learns an importance weight for each word in the corpus, along with the LSTM procedure. The importance weight assigned to each word measures its influence on the semantic meaning of the whole sentence. The experimental results demonstrate the addition of the word importance factor to the original MaLSTM model in [10] brings near 0.5&#x0025; performance enhancement of the test accuracy.
Investigating Active Positive-Unlabeled Learning with Deep Networks

Positive-unlabeled (PU) learning deals with the binary classification problem when only positive (P) and unlabeled (U) data are available. Recently, many PU learning models have been proposed based on deep networks and become the SOTA of PU learning. Despite the achievements on the model aspect, theoretical analysis and empirical results have shown that the number and quality of positive data can significantly impact learning performance. Active learning is classically used in machine learning to acquire additional high-quality labelled data, however, there are only a few studies on using active learning in deep PU models. This paper investigates the use of active learning in deep PU models. Specifically, this paper studies the uncertainty query strategy for pool-based active learning and show that due to the “large-small-loss” property of deep networks, the query strategy based purely on uncertainty can achieve diversity simultaneously. Empirical results also illustrate the effectiveness of uncertainty-based queries on active PU learning with deep networks.
Velocity control method of water supply and drainage for multi-functional stadium based on predictive function algorithm

In order to solve the problems of low calculation accuracy and poor control accuracy of the traditional velocity control methods for water supply and drainage in gymnasiums, a new speed control method for water supply and drainage of multi-functional gymnasiums based on predictive function algorithm is proposed in this paper. In this method, the basis function of water supply and drainage velocity control of multi-functional stadium is constructed, and the reference trajectory of water control is drawn by using predictive function algorithm. The multi-function water supply and drainage model is built to predict and optimise the water supply and drainage capacity of the stadium. The experimental results show that the proposed water supply and drainage velocity control method has a high accuracy of water supply and drainage velocity control, and can accurately calculate the water supply and drainage velocity.
Semantic-Based Few-Shot Classification by Psychometric Learning

Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a single and unique class association. Under these assumptions, these algorithms may not be able to identify the proper class assignment when there is no exact matching between support and query classes. For example, given a few images of lions, bikes, and apples to classify a tiger. However, in a more general setting, we could consider the higher-level concept, the large carnivores, to match the tiger to the lion for semantic classification. Existing studies rarely considered this situation due to the incompatibility of label-based supervision with complex conception relationships. In this work, we advance the few-shot learning towards this more challenging scenario, the semantic-based few-shot learning, and propose a method to address the paradigm by capturing the inner semantic relationships using psychometric learning. The experiment results on the CIFAR-100 dataset show the superiority of our method for the semantic-based few-shot learning compared to the baseline.
Semantic-Aware Multi-modal Sensor Fusion for Motion Planning in Autonomous Driving

Multi-modal sensor fusion is important in the perception module of autonomous driving. Fusion methods on geometry-based and vision-based sensors, e.g. LiDAR and camera, have shown great performance. Existing fusion approaches focus on comprehensive scene understanding, integrating multi-modal representations to capture global semantics. However, we demonstrate that global semantics is insufficient in complex scenarios. For example, state representations with global semantics do not perceive emerging vehicles quite well, especially when they are only partially observed by sensors. Shared semantics, namely, the common information between multi-modal sensors, should also be considered. Therefore, we proposed SASF, a Semantic Aware multi-modal Sensor Fusion method, to enhance the effect of shared semantics on perception. We use Transformer to capture global semantics and adopt Mutual Information Maximization to enhance the shared semantics. Based on the enhanced representation, we develop a GRU waypoint prediction network for motion planning. We validate the efficacy of our model in urban settings using the CARLA driving simulator. Experimental results demonstrate that our approach achieves better driving performance in complex scenarios.
BDI for Autonomous Mobile Robot Navigation

We explored how mobile Belief-Desire-Intention (BDI) agents could navigate using path plans that are automatically generated in AgentSpeak, asking if there could be any performance advantages gained by having an agent’s path be automatically generated as a BDI plan that can be monitored, suspended and resumed in case of contingencies. To do the exploration, we used Jason BDI to design a framework to test this premise with simulated mobile robots. We further explored the navigation of mobile agents to see if such functionality should be implemented within the agent in either AgentSpeak or as an internal action, or externally in an environmental module. These agents navigated through three environments of varying complexity: a simple synchronized grid, an asynchronous grid connected via Robot Operating System (ROS), and an autonomous car simulated with AirSim connected using ROS. We demonstrated that our framework handles plan interruptions, such as preventing collisions, managing consumable resources, and updating a map when necessary while moving through an environment; that Jason BDI agents are capable of controlling autonomous mobile robots; and that the AgentSpeak language provides advantages for implementing the navigation search behaviours.
On the Sum-of-Squares of Differential Distribution Table for (&lt;i&gt;n&lt;/i&gt;, &lt;i&gt;n&lt;/i&gt;)-Functions

S-box is one of the core components of symmetric cryptographic algorithms, but differential distribution table (DDT) is an important tool to research some properties of S-boxes to resist differential attacks. In this paper, we give a relationship between the sum-of-squares of DDT and the sum-of-squares indicator of (n, m)-functions based on the autocorrelation coefficients. We also get some upper and lower bounds on the sum-of-squares of DDT of balanced (n, m)-functions, and prove that the sum-of-squares of DDT of (n, m)-functions is affine invariant under affine affine equivalent. Furthermore, we obtain a relationship between the sum-of-squares of DDT and the signal-to-noise ratio of (n, m)-functions. In addition, we calculate the distributions of the sum-of-squares of DDT for all 3-bit S-boxes, the 4-bit optimal S-boxes and all 302 balanced S-boxes (up to affine equivalence), data experiments verify our results.