Reviewerly/exHarmony huggingface dataset:
Self‐supervised monocular depth estimation via asymmetric convolution block

Abstract Without the dependence of depth ground truth, self‐supervised learning is a promising alternative to train monocular depth estimation. It builds its own supervision signal with the help of other tools, such as view synthesis and pose networks. However, more training parameters and time consumption may be involved. This paper proposes a monocular depth prediction framework that can jointly learn the depth value and pose transformation between images in an end‐to‐end manner. The depth network creatively employs an asymmetric convolution block instead of every square kernel layer to strengthen the learning ability of extracting image features when training. During inference time, the asymmetric kernels are fused and converted to the original network to predict more accurate image depth, thus bringing no extra computations anymore. The network is trained and tested on the KITTI monocular dataset. The evaluated results demonstrate that the depth model outperforms some State of the Arts (SOTA) approaches and can reduce the inference time of depth prediction. Additionally, the proposed model performs great adaptability on the Make3D dataset.
ARA- A Voice Assistant for Disabled Personalities

This paper illustrates the implementation of our proposed voice assistant that provides assistance to disabled personalities over the website. ARA-the voice assistant is a software agent that can interpret human speech and respond via synthesized voices. The most popular voice assistants are Siri, Alexa, Cortana, and Google Assistant, which can all be found on smart phones and dedicated speakers. As voice assistants become more widely used, but have complex functionality and only open when we click the mic option, this will start on voice command, easier for the people who are blind or have cognitive disabilities. ARA will read out the content of the website and then using speech to text and text to speech modules along with selenium, our software can automate any website. The designed voice assistance connects with the intended applications to provide results that the user has demanded. The objective of this paper is to illustrate how voice assistants are used in everyday life and to explore whether there is potential for making them accessible for people with disabilities.
Assistive Systems for Special Mobility Needs in the Coastal Smart City

The Smart City is an important evolution for cities of different sizes and contexts. Multifaceted proposals are made all around the world. Mobility support and its evolution is an important part of this issue. In this paper we propose to study a particular context, which is devoted to showing how a Coastal city can become a Coastal Smart City. We present our study and proposals based mainly on special mobility configurations, the role and functionalities of corresponding assistive systems, and the HCIs implemented in practice. Finally, we integrate all these aspects in the architecture of the assistive system managing them.
Design of an Incremental Music Teaching and Assisted Therapy System Based on Artificial Intelligence Attention Mechanism

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task.
Research of the Extreme Learning Machine as Incremental Learning

The external environment of the world is dynamically changing, which requires the ability of continuous learning and memorization from intelligent systems. Iterative learning methods neural networks have smooth convergence, so they do not allow achieving such an effect. An exception is the Extreme Learning Machine (ELM), which allows for incremental learning. In the ELM, the input weights are randomly generated. The weights between hidden and output layers are computed in one step, resulting in a faster learning rate compared to traditional SLFN learning algorithms. Incremental training allows the neural network to retain existing data while at the same time adapting to new data. This article presents the research and assessment of the ELM as incremental learning. In particular, research of the sample size effect for different numbers of neurons in the hidden layer on the ELM performance is presented.
Comparison Study on Convolution Neural Network (CNN) Techniques for Image Classification

Deep Learning is an Artificial Intelligence (AI) function which can imitate the human brain to process data and deciding.It has networks that able to learn the unsupervised data that unlabeled or unstructured.It also identified as Deep Neural Network or Deep Neural Learning.Convolutional Neural Network (CNN) is a subset of Deep Neural Network which frequently used to analyse images.CNN also called as ConvNet which can be trained using an existing model that has been finetuned or trained from zero by using a large data set.CNN was often used in image classification due to its effectiveness and accuracy.However, there are several CNN architectures such as AlexNet, GoogleNet and ResNet-50.To select the appropriate architecture for our research in agriculture, a preliminary study to evaluate the architecture were conducted by using five different types of flower datasets that obtained from Matlab and Kaggle database.The three types of CNN architecture were compared in terms of accuracy in classifying the flowers.Result of this study indicated that the optimal configuration is by setting the number of epochs at 30, with the learning rate at 0.0005, to obtain the highest accuracy at 99.82%.
Blind deblurring with patch-wise second-order gradient prior

The patch-based image priors have been successfully applied to blind image deblurring algorithms. But those priors are time-consuming since too many non-linear operators are involved. However, the solving of these prior needs to use nonlinear operators that greatly decrease the computational efficiency. Proposed in this study is a simply patch-wise image prior that uses non-overlapped local patches to compute the local maximal second-order gradient of an image. We find that the values of the patch-wise second-order gradient (PSG) of an image decrease with the motion blur process. A new optimization algorithm is proposed by combining L1 regularized PSG with the maximum posterior probability. Besides, kernel similarity constraint is employed to control the iteration times to reduce the computational costs. Comparative experiments on mainstream datasets show that the results of the presented algorithm are generally better than those of other algorithms on both quantitative contrast and visual contrast.
Book Review of Ringrow and Pihlaja’s Contemporary Media Stylistics

Contemporary Media Stylistics permits readers to explore a variety of methodological applications to data analysis. Recent developments in technology and media communication provide linguists and stylisticians with authentic data for analysis based on real-life communication. .
Matrix Power Function Based Block Cipher Operating in CBC Mode

In our previous study, we proposed a perfectly secure Shannon cipher based on the so-called matrix power function. There we also introduced a concept of single round symmetric encryption, i.e., we used the matrix power function together with some rather simple operations to define a three-step encryption algorithm that needs no additional rounds. Interestingly enough, the newly proposed Shannon cipher possesses the option of parallelization—an important property of efficiently performing calculations using several processors. Relying on our previous proposal, in this study we introduce a concept of a one round block cipher, which can be used to encrypt an arbitrary large message by dividing it into several blocks. In other words, we construct a block cipher operating in cipher block chaining mode on the basis of the previously defined Shannon cipher. Moreover, due to the perfect secrecy property of the original algorithm, we show that our proposal is able to withstand the chosen plaintext attack.
The Relationship Between Performance and Trust in AI in E-Finance

Artificial intelligence (AI) is fundamentally changing how people work in nearly every field, including online finance. However, our ability to interact with AI is moderated by factors such as performance, complexity, and trust. The work presented in this study analyzes the effect of performance on trust in a robo-advisor (AI which assists in managing investments) through an empirical investment simulation. Results show that for applications where humans and AI have comparable capabilities, the difference in performance (between the human and AI) is a moderate indicator of change in trust; however, human or AI performance individually were weak indicators. Additionally, results indicate that biases typically seen in human-human interactions may also occur in human-AI interactions when AI transparency is low.
Group Activity Recognition based on Temporal Semantic Sub-Graph Network

Group Activity Recognition is a very important and challenging task in the field of computer vision. Most of the proposed methods only extract the semantic or temporal information of video respectively, while ignoring the important relationship between temporal information and semantic information. In this paper, a more flexible and effective Spatial-Temporal Sub-Graph Network was proposed, which regards the features of each video frame thand e relationship between frames as nodes and edges. respectively. It uses Mixed Pooling Module (MPM)to pool and modify the basic features of video frames. Frame Feature Extraction Module (FFEM) learns node features by integrating context and updating relationship edges frequently, and the Frame Relationship Graph Module (FRGM) localizes each relationship sub-graph and maps each sub-graph into Euclidean space. In order to evaluate the performance of the Network, experiments on two public datasets in group activity recognition field have been conducted.
Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose

Since the passive sensor has the property that it does not radiate signals, the use of passive sensors for target tracking is beneficial to improve the low probability of intercept (LPI) performance of the combat platform. However, for the high-maneuvering targets, its motion mode is unknown in advance, so the passive target tracking algorithm using a fixed motion model or interactive multi-model cannot match the actual motion mode of the maneuvering target. In order to solve the problem of low tracking accuracy caused by the unknown motion model of high-maneuvering targets, this paper firstly proposes a state transition matrix update-based extended Kalman filter (STMU-EKF) passive tracking algorithm. In this algorithm, the multi-feature fusion-based trajectory clustering is proposed to estimate the target state, and the state transition matrix is updated according to the estimated value of the motion model and the observation value of multi-station passive sensors. On this basis, considering that only using passive sensors for target tracking cannot often meet the requirements of high target tracking accuracy, this paper introduces active radar for indirect radiation and proposes a multi-sensor collaborative management model based on trajectory clustering. The model performs the optimal allocation of active radar and passive sensors by judging the accumulated errors of the eigenvalue of the error covariance matrix and makes the decision to update the state transition matrix according to the magnitude of the fluctuation parameter of the error difference between the prediction value and the observation value. The simulation results verify that the proposed multi-sensor collaborative target tracking algorithm can effectively improve the high-maneuvering target tracking accuracy to satisfy the radar's LPI performance.
On solvability of systems of partial fuzzy relational equations

Systems of fuzzy relational equations belong to key mathematical problems related to the correct behavior of fuzzy systems, especially of fuzzy inference systems. Indeed, if we find a fuzzy relation that solves a given system of fuzzy relational equations, we found a model of a fuzzy rule base related to the given system that will behave correctly in the sense that it will preserve the fundamental modus ponens property. This problem with numerous results becomes absolutely unexplored as soon as we allow partiality in the system. Partial fuzzy sets have only partially defined membership degrees, i.e., for some elements, the membership degree is undefined, i.e., partial propositions have only partially defined truth-values. There are many origins from which we may obtain propositions with undefined truth-values, e.g., non-denoting or irrelevant terms, inconsistent (both true and false) propositions, or truth-values are simply unknown. Partial logics belong to classical topics in logic so, the extensions to partial fuzzy logic in recent years were not surprising. However, we should not dare to deal with partial fuzzy sets in fuzzy systems without any guarantee of the preservation of fundamental properties such as modus ponens. This article is focusing on filling this gap.
Dynamic graph embedding‐based anomaly detection on internet of things time series

Abstract Anomaly detection is critical in the internet of things (IoT) environment. To this issue, this study provides a novel approach for detecting anomalies in multivariate IoT time series. The proposed approach identified relationships between IoT time series to establish a dynamic graph and estimated the graph entropy to detect anomalies. The presented approach was applied to industrial IoT datasets. The results have shown that the presented method outperformed other models by 0.21 with respect to F1‐score. In addition, we used three distinct algorithms to detect the anomalies from the multivariate IoT time series. According to the results, the local outlier factor approach outperformed the others by 0.18 with respect to F1‐score.
Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.
Designing Physical Objects for Young Children's Magnitude Understanding: A TUI Research Through Design Journey

Magnitude understanding, an understudied topic in Child-Computer Interaction, entails making nonsymbolic ‘more-less’ comparisons that influence young children's later math and academic achievements. To support this ability, designing tangible user interfaces (TUIs) demands considering many facets, ranging from elements within the physical world to the digital design components. This multifaceted activity brings many design decisions often not reflected in research. Therefore, we present this reflection via our research through design process in developing a vital design element, the physical form. We share our (i) physical object design criteria elicitation for magnitude understanding, (ii) hands-on making process, and (iii) preliminary studies with children engaging with objects. With our insights obtained through these steps, we project how this physical object-initiated research inspires the TUI in the upcoming steps and present design takeaways for CCI researchers.
A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning

One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic.
College English Reading Teaching Integrating Production Oriented Approach from the Perspective of Artificial Intelligence

The objectives are to solve many problems in traditional English reading teaching, such as the passive acceptance of students’ learning situation, the rigid teaching mode of teachers and the difficulty in taking into account the individual needs of each student, and the forced averaging of students’ English learning ability and level. Firstly, a detection method of English reading level based on the Backpropagation neural network (BPNN) is designed. Secondly, the teaching process and specific teaching plan are designed with the Production Oriented Approach (POA). Finally, the detection method of English reading level is verified by simulation experiments, and the effect of the teaching method integrating POA is analyzed. The results show that (1) when the learning rate is 0.26 and the training time is 1000 times, the mean square error (MSE) of the model is the smallest. (2) The results of the practical experiment indicate that the number of people who sign up for the college English proficiency test, the number of actual exams, and the pass rate of the test in the experimental group using the teaching method are higher than those in the control group. Moreover, the number of people in the experimental group increased more than that in the control group after the experiment. There is no significant difference in the test results of the two groups before the experiment, and the average test scores of the experimental group are obviously higher than those of the control group after the experiment. The <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> value of the two groups of results is less than 0.05 in the t-test, indicating that there is a significant difference in the English reading level between the two groups after the experiment. Simultaneously, the difference between the results of class A before and after the experiment is larger than that of class B, showing that the teaching method integrating POA is more effective for students with higher English levels. Therefore, compared with the traditional teaching method, the teaching mode integrating POA is more effective in improving the English reading level of college students. It aims to transform POA theory into practice and provide a great reference for college English reading teaching.
Artificial Intelligence Research During COVID-19 Pandemic: Contributed to Future Education

Besides being able to cause death, it turns out that the COVID-19 pandemic has caused problems in various sectors, including education, health, and Social Life.In addition, the COVID-19 pandemic also has an impact on the field of research, including the field of Artificial Intelligence (AI) which has become an interesting issue in the era of the Industrial Revolution 4.0.Based on Scopus data (search by article title, abstract, and keywords), it shows that there are 381,691 documents.Results of Bibliometric through VOSViewer found some parameters or interrelationships among variables to capture the trend and novelty of researching on AI During COVID-19 Pandemic, such as researching on AI During COVID-19 Pandemic and technology, detection, patient, diagnosis, performance, radiologist, feature, education, and image.Implication research in general evidence, (1) AI researchers have bright career opportunities in the present and the future.This is evident from the condition of the central role of AI in all lines of human life, (2) The trend of AI research is also classified as having a very large contribution to have entered various fields during the COVID-19 pandemic.(3) Including the prevention of COVID-19 transmission with AI, various tools and media have emerged in the medical world that provides many benefits.(4) AI needs more exploration in education field research.In the last five years, AI has made a major contribution advancing the field of education.