Reviewerly/exHarmony huggingface dataset:
ETHEREUM PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES – A COMPARATIVE STUDY

In recent years, popularity and use of cryptocurrencies has been rising along with their prices and Ethereum is the second most famous cryptocurrency after Bitcoin. Cryptocurrencies are based on blockchain, which is a distributed and empowered technology that has the power to transform any banking systems. It has become an attractive investment for traders as well as individuals looking to invest. The price of Ethereum varies and is controlled by different factors, such as the crypto market in which it is sold, supply and demand. Ethereum is so valuable because it could be used as cash, we could also pay a portion or part of Ethereum to someone in exchange and it is easily guaranteed by the blockchain. Unlike stocks, Ethereum price is much more variable, as it has a trading time of 24-hours a day without any close time. The paper compares the results of three different models, namely Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs) and Bi-directional Long Short-Term Memory (Bi-LSTMs). The dataset consists of the closing price for the last 2000 days that is used to predict both short-term (30 days) and long-term (90 days) Ethereum prices. These prices are being fetched from an API which is in JSON format and are updated every day.
Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture Search

Recommendation systems are now widely used on the Internet. In recommendation systems, user preferences are predicted by the interaction of users with products, such as clicks or purchases. Usually, the heterogeneous information network is used to capture heterogeneous semantic information in data, which can be used to solve the sparsity problem and the cold-start problem. In a more complex heterogeneous information network, the types of nodes and edges are very large, so there are lots of types of metagraphs in a complex heterogeneous information network. At the same time, machine learning tasks on heterogeneous information networks have a large number of parameters and neural network architectures that need to be set artificially. The main goal is to find the optimal hyperparameter settings and neural network architectures for the performance of a task in the set of hyperparameter space. To address this problem, we propose a metapath search method for heterogeneous information networks based on a network architecture search, which can search for metapaths that are more suitable for different heterogeneous information networks and recommendation tasks. We conducted experiments on Amazon and Yelp datasets and compared the architecture settings obtained from an automatic search with manually set structures to verify the effectiveness of the algorithm.
TWEANN Approach to the Double Pole Balancing Problem

We discuss the effect of mutation probabilities on the performance of Mutation-Based Evolving Artificial Neural Networks (MBEANN), which is one of the methods of Topology and Weight Evolving Artificial Neural Networks (TWEANN). TWEANN is an approach for evolving both structures and weights of artificial neural networks. TWEANN is expected to perform well than an approach using a fixed-topology neural network with only evolving weight values. The phenotype of MBEANN consists of sub-networks, and the topology of the neural network grows independently within them. Moreover, the structural mutations of MBEANN are designed to reduce the influence on the fitness value. In this study, we focus on the effect of structural mutation probabilities on performance by using a double pole balancing problem without velocity inputs. The performance of MBEANN is compared with NeuroEvolution of Augumenting Topologies (NEAT), which is a typical method of TWEANN. The results show that MBEANN has a higher task achievement rate regardless of the mutation probabilities and task difficulty. From the comparison with NEAT, MBEANN shows a higher performance even with a larger network structure due to the phenotype that consists of sub-networks.
Parallel Distributed Genetic Programming using Long-term Memory for Dynamic Scheduling Problems

Genetic Programming (GP) is an evolutionary computation method that optimizes the rules defining the relationship between environmental states and system output. GP is an effective method for dynamic environments in which the information repeatedly changes multiple times. On the other hand, in GP, the rules are evaluved as the environmental state changes, so the rules acquired in the distant past disappear in time, and re-learning is required in a dynamic environment. This paper proposes an optimization method for the dynamic scheduling problem where new jobs arrive intermittently. Specifically, a method to improve the learning efficiency of GP in such a periodic dynamic environment by dividing the population into several subpopulations and recording the environmental states or their characteristics. This paper conducts some numerical experiments on the dynamic scheduling problems in which new jobs arrive irregularly to verify the usefulness of the proposed method.
The Meaning-Shift Units of Modern Chinese Verbs: Case Studies of Delexicalized Verb “jiayi (加以)”

Considering meaning-shift unit model as the theoretical framework, this study investigates the Chinese delexicalized verb “jiayi” in Beijing Language and Culture University Modern Chinese Corpus (BCC). It is found that three meaning units involving “applying an action”, “reserving unfavorable situations” and “psychological activity” are mainly presented by co-selection patterns of “jiayi”. When expressing the meaning of “applying an action”, there is one canonical form and seven variations. When the meaning of the pattern is “reversing the unfavorable situations”, one canonical form and two variations are recognized. When the pattern conveys the meaning of “psychological activity”, one canonical form and four variations are summarized. The meaning-shift unit model provides a novel perspective and method for language research, which is of great significance to the research of phraseology.
Routines and Challenges: the Curriculum Setting of Mixed-age Education in Chinese Village-level Kindergartens

A village-level kindergarten, called L-village kindergarten, was established in Jiangxi province, China in 2016. It formed mixed-age education due to the limited number of students of different ages. However, teachers lack relevant professional training and educational knowledge. The education in L-village kindergarten performs many issues. The one-day activity arrangement, activity space, area division, indoor and outdoor environment setup, and material provision are not suitable and professional designed for mixed-age education. The evaluation of the development and application of mixed-age children are inadequate. In this paper, we propose a solution to activate teachers’ idea and design the advanced labor education curriculum. We aim to develop a mixed-age education curriculum with the local characteristics for mixed-age education in village-level public kindergartens. We formulate mixed-age education curriculum implementation plans, dividing activity spaces and regions, creating indoor and outdoor environments, providing play teaching aids, and one-day activities. In our observation, children learn better than the original education in L-village kindergarten.
Influencing Factors of Student's Learning Self-efficacy and Learning Attitude in Higher Vocational Colleges in China

This empirical study investigates the influencing factors of students learning self-efficacy and learning attitude in Chinese higher vocational colleges. Survey is used to collect data, and the partial least squares path modeling (PLSPM) method based on R language is applied to analysis data. Results indicate that, in China's higher vocational education (1) student's learning self-efficacy exerts significantly positive influence on his or her learning attitude; (2) student's learning self-efficacy is positively and significantly affected by his or her Awareness of the Major studied and learning purpose. Furthermore, student's gender and grade level, to some extent, exert moderating effects on the relationship between variables. Results of this study provide empirical evidences for the theory of self-efficacy in the field of vocational education. Meanwhile, this study gives suggestions on improving the learning attitude of students in higher vocational colleges in China.
Real-time Trajectory Replanning for Dynamic Obstacles Avoidance for Robotics Manipulators

Collaborative robots are being used in various industrial tasks and need to collaborate with other robots or humans. An unforeseen obstacle may appear in their workspace at any time. Therefore, they must execute their motions without colliding with obstacles that are in the workspace. This paper proposes an algorithm for real-time dynamic obstacles avoidance for robotic manipulators. The workspace of the robot can be modeled as a graph with nodes corresponding to every conceivable set of joint coordinates matching to the workspace position of the end-effector. The obstacles are monitored during the trajectory execution. If there is an obstacle that could cause a collision with the robot, the proposed approach uses the generated graph and a breadth first search algorithm to replan the blocked part of the trajectory. The applicability of the algorithm is evaluated by experiments with planar type of robotic manipulators.
Green Inspires Students (GIS.edu): A STEM educational project combining a Mobile Data Collection application with ArcGIS Online platform, towards carbon neutrality in school.

Our paper presents the educational project “Green Inspires Students” (GIS.edu). It is a project combining a Mobile Data Collection application that sends data to a cloud SQL Server. Data is manipulated using ArcGIS Pro and results are illustrated through ArcGIS Online with webmaps and dashboards. GIS.edu project is educationally designed according to STEM. Each student collects data with a mobile phone on his way to school, the data is analyzed so that each student understands the total CO2 produced by the type of vehicle used for educational purposes. Finally, webmaps are produced with all the movements and routes of the students, transportation type percentages and mileage, plus a tree indicator that is focused on the trees that should be planted to absorb the CO2 produced, towards carbon neutrality.
Extrapolating the Digital Readiness – Are Turkmen Undergraduate Students Competent for Online Learning?

Every institution has to take challenges in order to obtain its own place in technological progress competition. Current technological progress for Higher Educational Institutions is to develop new forms of teaching and learning which require IT integration with ordinary education processes (methodology). To complete these requirements of the era, every institution has to know the conditions of the auditorium. This study evaluates students of the two public universities of Turkmenistan on digital readiness for online learning by adopting commonly recognized “Digital Competency Profiler” tool and the “General Technological Competency and Use” framework. The findings of this study might be a reference for further enhancement of digital readiness for online learning and stress the urgency of digital transformation in order to succeed in technological progress competition.
An Improved Mobilenetv2 for Rust Detection of Angle Steel Tower Bolts Based on Small Sample Transfer Learning

Rust detection of diagonal steel tower bolts is dangerous and heavy, so it needs to be assisted by intelligent technology. In order to be applied in the equipment with limited resources such as mobile terminal or embedded terminal, based on the method of deep learning, this paper proposes a new lightweight neural network to solve the classification problem of rusty or non-rusty angle steel tower bolts. The network is improved on the basis of mobilenetv2. In order to be suitable for the training environment with small samples, the strategy of transfer learning is used. In order to make up for the loss of characteristic information caused by convolution, a double branch structure is used to better obtain characteristic information. Using the attention mechanism, the network can automatically obtain the important weight of features according to the training process, so as to better improve the separation performance. In the task of bolt rust detection, the proposed network obtains an accuracy of 97.6% and an F1 score of 96.3% while ensuring small parameters. Compared with other excellent neural networks, the proposed method has better classification performance and is suitable for outdoor environment detection.
Clustering Analysis in the Student Academic Activities on COVID-19 Pandemic in Mexico

The pandemic caused by the COVID-19 disease has affected all aspects of the life of the people in every region of the world. The academic activities at universities in Mexico have been particularly disturbed by two years of confinement; all activities were migrated to an online modality where improvised actions and prolonged isolation have implied a significant threat to the educational institutions. Amid this pandemic, some opportunities to use Artificial Intelligence tools for understanding the associated phenomena have been raised. In this sense, we use the K-means algorithm, a well-known unsupervised machine learning technique, to analyze the data obtained from questionaries applied to students in a Mexican university to understand their perception of how the confinement and online academic activities have affected their lives and their learning. Results indicate that the K-means algorithm has better results when the number of groups is bigger, leading to a lower error in the model. Also, the analysis helps to make evident that the lack of adequate computing equipment, internet connectivity, and suitable study spaces impact the quality of the education that students receive, causing other problems, including communication troubles with teachers and classmates, unproductive classes, and even accentuate psychological issues such as anxiety and depression.
A Novel Gaze Detection Method Based on Local Feature Fusion

As the important research field in computer vision, gaze detection has attracted the attention of researchers. When dealing with head pose changes and incomplete head images, some existing gaze detection methods directly extract head global features and ignore some local features. Moreover, the feature extraction network neglects small objects or objects with insignificant features in the scene as the network layer deepens. Regarding the above issue, we propose a gaze detection model based on local feature fusion (Local-GazeNet). First, attention mechanisms is adopted to the scene feature extraction network for enhancing the significance of objects of interest and suppressing redundant information in the image. Secondly, a local feature extractor is employed to the gaze direction detection module, and the extracted local features are supplemented into the global features in the form of residuals, so that the model can obtain more abundant head features to deal with the complex changes of head poses. We validate the performance of the Local-GazeNet model on the GazeFollow dataset. The experimental results illustrate that our proposed method can achieve satisfactory performance and outperform some existing state-of-the art gaze detection methods.
Cross Distance Minimization for Solving the Nearest Point Problem Based on Scaled Convex Hull

In pattern classification, the geometric method often provides a simple and intuitive solution. In the case of linear separability, solving the optimization hyperplane problem can be transformed into solving the nearest point problem of the convex hulls between classes. In the case of nonlinear separability, the notion of scaled convex hull (SCH) is employed to reduce the initially overlapping convex hulls to become separable. Two classic nearest point algorithms, GSK and MDM, have been used as effective solvers for SCHs. However, their problem-solving speed is still a bit underperforming. This paper proposes a new solver called SCH-CDM, in which the CDM (cross distance minimization) algorithm is employed to calculate the nearest point pair of between-class SCHs. Experimental results indicate that the SCH-CDM algorithm can achieve faster convergence than the SCH-GSK algorithm and the SCH-MDM algorithm. In terms of accuracy, it also shows good competitiveness compared to the baseline methods.
Deep Spatio-Temporal Attention Network for Click-Through Rate Prediction

In online advertising systems, predicting the click-through rate (CTR) is an important task. Many studies only consider targeted advertisements in isolation, but do not focus on its relationship with other ads that may affect the CTR. We look at a variety of additional elements that can help with CTR prediction for tailored advertisements. We consider supplementary ads from two different angles: 1) the spatial domain, where contextual adverts on the same page as the target advertisements are considered and 2) from the perspective of the temporal component, where we assume people have previously clicked unclicked advertisements. The intuition is that contextual ads shown with targeted ads may influence each other. Also, advertisements that are connected reflect user preferences. Ads that are not connected may indicate to some extent what the user is not interested in. We propose a deep spatio-temporal neural network (DSTAN) for CTR prediction to use these auxiliary data effectively. Our model can decrease the noise in new data, learn the interaction between varied extra data and targeted advertisements, and fuse heterogeneous data into a coherent framework, highlighting important hidden information. Offline experiments on two public datasets show that DSTAN outperforms several of the most common methods in CTR prediction.
Inverse Sparse Object Tracking via Adaptive Representation

Sparse representation has been widely applied in object tracking, which is challenging due to various factors, such as occlusion, illumination variation, and background clutter. In this paper, we propose an inverse sparse tracking algorithm based on adaptive representation. Unlike the conventional tracking methods, we explore the low-rank property from the group structure of the candidate particles and calculate their similarity between foreground and background, which makes our representations more discriminative and robust. Specifically, we employed the candidate particles as the dictionary atoms to sparse encode the templates, which makes less cost on optimization. The adaptive regularization based on the rank of each group of candidate sub-matrix is employed in our method to adapt to appearance changes. Moreover, foreground templates and background templates are collected as positive and negative dictionaries, respectively, to make the tracker more discriminate from the complex environment. The experiment is conducted on a public dataset OTB100, and the quantitative results are reported, which demonstrate the favorable performance of the proposed method over some state-of-the-art trackers.
Nut Recognition and Positioning Based on YOLOv5 and RealSense

After analyzing the ranging principle of structured light depth camera and the conversion relationship between pixel coordinate system and camera coordinate system. Combine YOLOv5 and Hough circle detection to obtain the pixel coordinates of the center of the nut. A nut identification and positioning system is designed, which converts the pixel coordinates into the real-scale three-dimensional coordinates of the nut relative to the camera through the Camera Intrinsic Matrix Camera Intrinsic Matrix Camera Intrinsic Matrix. In many experiments, the maximum error in X and Y directions is less than 2 mm, and the error in Z direction is less than 1 mm, which verifies that the system has certain accuracy and stability.
A Clustering Method Based on Improved Density Estimation and Shared Nearest Neighbors

Density-based clustering methods can detect clusters of arbitrary shapes. Most traditional clustering methods need the number of clusters to be given as a parameter, but this information is usually not available. And some density-based clustering methods cannot estimate local density accurately. When estimating the density of a given point, each neighbor of the point should have different importance. To solve these problems, based on the K-nearest neighbor density estimation and shared nearest neighbors, a new density-based clustering method is proposed, which assigns different weights to k-nearest neighbors of the given point and redefines the local density. In addition, a new clustering process is introduced: the number of shared nearest neighbors between the given point and the higher-density points is calculated first, the cluster that the given point belongs to can be identified, and the remaining points are allocated according to the distance between them and the nearest higher-density point. Using this clustering process, the proposed method can automatically discover the number of clusters. Experimental results on synthetic and real-world datasets show that the proposed method has the best performance compared with K-means, DBSCAN, CSPV, DPC, and SNN-DPC.
Blockchain Adoption in University Archives Data Management

Archives data management is an important link in the process of talent training in universities, and the informatization of archives data management has played a great role in promoting education. The existing university archives management generally adopts the centralized management mode, which leads to the low efficiency of archives service and insufficient protection measures for archives data. At the same time, the emergence of advanced technologies, such as quantum computers, has increased the probability of password cracking and information leakage, which has brought new challenges to archives information security. This paper analyzes the feasibility and application advantages of blockchain technology in university archives data management by summarizing the practical difficulties faced by university archives data management, and then a new model of university archive data management based on blockchain technology is proposed. On the basis of ensuring the efficient completion of the archives data management work, it can ensure the safety and reliability of the data in the process of archives data management and comprehensive sharing.
Image Dehazing Based on Deep Multiscale Fusion Network and Continuous Memory Mechanism

This paper proposed a multi-scale feature fusion image dehazing network by incorporating a contiguous memory mechanism (MFFDN-CM). Specifically, the pixel attention mechanism, continuous memory strategy and residual dense blocks are integrated into the dehazing model with a prevalent encoder-decoder structure (U-Net). Firstly, our model obtains multiscale feature maps by subsampling operations, and further employs skip connections between the corresponding network layers to connect the feature maps between the encoder and the decoder for good feature fusion. Then, we introduce a continuous memory residual block to strengthen the information flows for feature reuse. Moreover, to leverage detail representation and accomplish adaptive dehazing according to the haze density, MFFDN-CM adopts a pixel attention module on the skip connections to combine the residual dense block module of the corresponding decoding layers. Finally, multiple residual blocks are exploited on the bottleneck in encoder-decoder structure to prevent network performance degradation due to vanishing gradients. Experimental results demonstrate the proposed model can achieve better hazing performance than the state-of-the-art methods based on deep neural network.