Reviewerly/exHarmony huggingface dataset:
An Improved DBSCAN Algorithm for Adaptively Determining Parameters in Multi-density Environment

The density-based spatial clustering DBSCAN algorithm can realize clustering of arbitrary shapes in noisy data sets. The DBSCAN algorithm needs to manually determine the Eps and Min Pts parameters. As a result, the accuracy of the clustering results directly depends on the user's choice of parameters. In the face of large-scale irregular data, fixed parameters lead to ineffective multi-density data clustering. ideal. In response to the above situation, an improved DBSCAN algorithm for adaptively determining parameters in a multi-density environment is proposed. The non-parametric kernel density estimation theory is used to analyze the distribution characteristics of data samples to automatically determine the Eps parameters, preprocess the data, and identify each Based on the density around the data object, the density threshold MinPts suitable for the density of the area is automatically generated. The manual intervention of the clustering process is avoided, and the automation of the clustering process is realized. This method can select reasonable Eps and MinPts parameters, and obtains clustering results with higher accuracy.
Deep Transfer Learning for Successive POI Recommendation

Personalized POI recommendation attracts more and more attention from both industrial and research fields. Due to data collection mechanism, it is common to see data collection with the unbalanced spatial distribution. For example, some cities may release check-ins for multiple years while others only release a few days of data. In this paper, we tackle the problem of successive POI recommendation for the cities with only a short period of data samples. We aim to leverage the transfer learning technique to utilize the knowledge from long-period data to enhance the POI recommendation process in target cities. Different from existing methods that transfer knowledge from one single city to a target city, we utilize the knowledge from multiple source cities to increase the stability of transfer. Specifically, our proposed model is designed as the spatio-temporal attentive recurrent neural network, MetaGRU, with a meta-learning paradigm. The spatio-temporal attentive mechanism leverage an external memory layer to store processed user historical preference information, and use spatio-temporal attention to capture the different correlations between user current status with past check-in behaviors. In addition, the meta-learning paradigm learns a well-generalized initialization of the spatio-temporal neural network, which can be effectively adapted to target cities. Extensive experiments show that our method is able to outperform state-of-the-art successive POI recommendation models in multiple tasks.
Research on Android Architecture and Application Development

Abstract With the continuous development of the electronic industry, mobile phones are also developing towards convenient and intelligent direction. The system of mobile devices is constantly expanding, and Android has certain advantages over the system of wire transmission. In this paper, the architecture of The Android platform and the composition and working mechanism of Android applications are first described. Meanwhile, the architecture of the Android platform is illustrated with examples for readers’ reference.
Web based Information system for selling spare parts and custom bicycle frames (Case Study Of The Surabaya Minion Shop)

The purpose of this research is to design and produce an Online Shop application that is a Web that can provide information on goods data related to the sale of spare parts and custom frames that they are looking for. The research method used is the waterfall method, while the techniques for data collection use observation and interview techniques. The spare part data is obtained from visiting from shop to shop and the shop itself. The result of this research is to make it easier for customers to find the spare parts they want and to make more and more bicycle enthusiasts, especially onthel mini bikes, use them.
Obstacle Avoidance Algorithm for Mobile Robot Based on ROS and Machine Vision

With the robot gradually stepping into our daily life, more and more attention has been paid to the mobile ability of robot, and obstacle avoidance is a key problem. In this paper, the indoor environment is taken as the application scene, and the visual detection and local dynamic obstacle avoidance are studied respectively. Aiming at the shortcomings of the single external sensor lidar’s incomplete perception of obstacle information, this paper proposes an obstacle avoidance method based on machine vision for mobile obstacle detection under the ROS operating system. The method is improved based on YOLO-v4 in terms of vision, which can meet the real-time requirements of mobile terminal. Combined with dynamic window approach (DWA), the local obstacle avoidance algorithm is improved. In the process of local obstacle avoidance, visual detection information is integrated to increase the ability of local dynamic obstacle avoidance and improve the performance of robot local obstacle avoidance. Finally, the feasibility and validity of the algorithm are verified in the actual environment.
Face Image Compression and Reconstruction Based on Improved PCA

Face recognition technology has many usages in the real-world applications, and it has generated extensive interest in recent years. However, the amount of data in a digital image is growing explosively, taking up a lot of storage and transmission resources. There is a lot of redundancy in an image data representation. Thus, image compression has become a hot topic. The principal component analysis (PCA) can effectively remove the correlation of an image and condense the image information into a characteristic image with several main components. At the same time, it can restore different data images according to their principal components and meet the needs of image compression and reconstruction at diverse levels. This paper introduces an improved PCA algorithms. The covariance matrix, calculated according to a batch of training samples, is an approximation of the real covariance matrix. The matrix is relatively to the dimension of the covariance matrix, and the number of training samples is often too small. Therefore, it difficult to accurately obtain the covariance matrix. This improved PCA algorithm called 2DPCA can solve this problem effectively. By comparing it with several discrete PCA improvement algorithms, we show that the 2DPCA has a better dimensionality reduction effect. Compared with the PCA algorithm, the 2DPCA has a lower root-mean-square error under the constant noise condition.
Bangla Handwritten Digit Recognition Based on Different Pixel Matrices

Handwritten digit or character recognition is the primary task of researching in the field of OCR system. But handwritten digit recognition is a complex task due to the variation of training image size and handwritten style. This paper presents how the nature of the motion of the digit recognition model changes based on different pixel images. We have proposed a 12-layer Convolutional Neural Network (CNN) using four different pixel handwritten images such as 24 × 24, 28 × 28, 32 × 32, and 36 × 36. All fundamental steps such as dataset collection, dataset preparation, detail architecture of CNN, and hyperparameter optimization are described briefly in this paper. The goal of this study is to provide a clear insight into CNN hyperparameters, CNN architecture, images of different pixels on which the effectiveness of the model depends. In this research, we have used two publicly available datasets named CMATERDB 3.1.1 and BanglaLekha-Isolated. The proposed method has achieved a validation accuracy of 99.50% for the CMATERDB 3.1.1 dataset and 98.85% for the BanglaLekha-Isolated dataset.
The Role of Untangled Latent Spaces in Unsupervised Learning Applied to Condition-Based Maintenance

Advances in signal processing are complemented by advances in machine and deep learning and vice versa. In general, machine and deep learning are employed as discriminative models within a supervised setting. Progress in unsupervised generative modelling allows for generative models to be employed in discriminative (discrete classes) and deviation (continuous deviation from a baseline) tasks. This only requires the samples to be chronological. Discriminative and deviation analysis is usually based on the reconstruction loss. However, this is limited as it offers only a single scalar from which inference can be made. Generative models do, however, learn a latent representation of the data from which additional scalars can be derived. Whether these derived scalars are informative depends on the quality of the latent representations. Most learning algorithms derive a latent representation that efficiently explains the variance in the data, which can be informative when the property of interest is well explained by variance. Alternatively, a lesser known class of learning algorithms aim to learn a latent representation that aims to identify sources in the data. Hence, given the same data, an infinite number of latent representations are possible, of which only a fraction are informative. We consider three classes of latent spaces that are stochastic, entangled and untangled. Furthermore, we highlight the importance of untangled latent spaces to obtain informative signals for condition monitoring.
FINKI scholar, a publications database for Faculty of Computer Science and Engineering scholars

The aim of this paper is to develop a Web application where scholars of the Faculty of Computer Science and Engineering (FINKI) at the University of Ss. Cyril and Methodius can display and share their projects and publications. Visitors can view, search through, and filter the authors, projects and publications that can be added and edited by the administrators via the administrator panel. In this paper, we first explain the type of system we are building and go through similar existing systems explaining how they work and what they offer. Then, we go through the programming languages and technologies we decided to use to develop this Web application. After that, the development phase follows, where we describe each of the features we implemented. In The Final Product section we finally show images where you can see how the Web application works and what it looks like. We finish the paper with a conclusion, briefly summarizing what we have achieved.
Serf: Towards better training of deep neural networks using log-Softplus ERror activation Function

Activation functions play a pivotal role in determining the training dynamics and neural network performance. The widely adopted activation function ReLU despite being simple and effective has few disadvantages including the Dying ReLU problem. In order to tackle such problems, we propose a novel activation function called Serf which is self-regularized and non-monotonic in nature. Like Mish, Serf also belongs to the Swish family of functions. Based on several experiments on computer vision (image classification and object detection) and natural language processing (machine translation, sentiment classification and multi-modal entailment) tasks with different state-of-the-art architectures, it is observed that Serf vastly outperforms ReLU (baseline) and other activation functions including both Swish and Mish, with a markedly bigger margin on deeper architectures. Ablation studies further demonstrate that Serf based architectures perform better than those of Swish and Mish in varying scenarios, validating the effectiveness and compatibility of Serf with varying depth, complexity, optimizers, learning rates, batch sizes, initializers and dropout rates. Finally, we investigate the mathematical relation between Swish and Serf, thereby showing the impact of pre-conditioner function ingrained in the first derivative of Serf which provides a regularization effect making gradients smoother and optimization faster.
Underwater target detection system based on YOLO v4

research-article Underwater target detection system based on YOLO v4 Share on Authors: Weiru Wang China Shipbuilding Industry Corporation, China China Shipbuilding Industry Corporation, ChinaView Profile , Yunlong Wang Harbin Engineering University, China Harbin Engineering University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 107Pages 1–5https://doi.org/10.1145/3469213.3470310Online:18 August 2021Publication History 0citation31DownloadsMetricsTotal Citations0Total Downloads31Last 12 Months31Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Analysis of Bank Competiveness, Financial Intermediation and Entrepreneurship Development in African Economies

Given probable nexus between financial intermediation and entrepreneurship development, this paper uses multivariate regression techniques to test the impact of bank competitiveness on the nexus in forty-two African economies. With data obtained from the World Development Indicators, the results reveal that lending rates, the proxy for financial intermediation positively impacts business start-up-cost, the proxy for entrepreneurship, while bank competitiveness negatively impacts business start-up-cost, which suggests that banking competitiveness has the capacity to improve entrepreneurship in African economies. The region’s economies should reduce monetary base rates to single digit; advanced liberalization of the financial industry; encourage stronger competition through digital finance; lessen the requirements for new conventional bank entry; provide fiscal support for increased bank branching
Research on Hybrid Teaching Mode Based on Digital Teaching Resource Computer Platform

Abstract It is certain that digital teaching is conducive to the improvement of teaching quality. As can be seen from the national efforts to promote educational information, today’s educational information has entered the 2.0 era, and many teachers and students have enjoyed the convenience brought by educational technology. Information-based teaching can be said to be a part of the process of educational information, which is common in real teaching, especially in online education. The application of educational technology in teaching can not only attract students’ attention, activate the atmosphere and inspire students’ thinking, but also promote the development of teachers’ own specialization and improve the teaching quality and efficiency. Educational technology has inserted wings for teaching to take off, and has brought great convenience to both teachers and students. In this context, this paper discusses the hybrid teaching mode based on the digital teaching resources computer platform.
BYANJON: A Ground Truth Preparation System for Online Handwritten Bangla Documents

The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that future researchers can use the database to do research in this field. Secondly, the ground truth information will help other researchers to evaluate the performance of their algorithms developed for text-line extraction, word extraction, word segmentation, stroke recognition, and word recognition. The reported ground truth generation scheme starts with text-line extraction from the online handwritten Bangla documents, then words extraction from the text-lines, and finally segmentation of those words into basic strokes. After word segmentation, the basic strokes are assigned appropriate class labels by using modified distance-based feature extraction procedure and the MLP ( Multi-layer Perceptron ) classifier. The Unicode for the words are then generated from the sequence of stroke labels. XML files are used to store the stroke, word, and text-line levels ground truth information for the corresponding documents. The proposed system is semi-automatic and each step such as text-line extraction, word extraction, word segmentation, and stroke recognition has been implemented by using different algorithms. Thus, the proposed ground truth generation procedure minimizes huge manual intervention by reducing the number of mouse clicks required to extract text-lines, words from the document, and segment the words into basic strokes. The integrated stroke recognition module also helps to minimize the manual labor needed to assign appropriate stroke labels. The freely available and can be accessed at https://byanjon.herokuapp.com/ .
Research on the Construction of Teaching Staff for Innovative Experimental Class in Applied Undergraduate Universities

To cultivate application oriented innovative talents, it is necessary to have a team of teachers with application-oriented qualities, so that the school's mission of running a school can be implemented, the school's positioning can be reflected, and students can acquire knowledge and abilities. The school's ability to run a school benefits from the quality of teachers to a certain extent. Therefore, this article discusses the construction of the teaching team of innovative experimental classes in applied undergraduate universities from the aspects of guiding ideology, basic requirements of teacher quality, teacher selection and training mechanism, and teacher appointment and management of applied undergraduate colleges and universities.
Implementation of Grover’s Algorithm to Solve the Maximum Clique Problem

The maximum clique of an undirected graph is the largest subgraph in which an edge exists between every vertex. The maximum clique problem presents itself in various fields and finding a tractable algorithm to solve the problem is important. This paper introduces an oracle to solve the maximum clique problem using Grover’s quantum algorithm. This approach potentially solves the maximum clique problem in $O\left( {\left| V \right|\sqrt {{2^{\left| V \right|}}} } \right)$ time complexity rather than the current classical time complexity of O(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">|V|</sup> ) of the best known algorithm, where |V| is the number of vertices in the graph. The full circuit implementation is presented using the open-source Qiskit environment [1]. The paper also analyzes the growth of the hardware resources requirements with the number of vertices in the targeted graph.
A Personalized Recommendation Method of Restaurants based on Analytic Hierarchy Process

The purpose of this article is to build up a recommendation method for the restaurants in Hong Kong. A method with customized percentages on multi factors based on Analytic Hierarchy Process (AHP) is proposed. It would provide the individuals with more dynamic and personalized restaurants recommendations according to their own demand rankings from several dimensions. Utilizing this model, we get a list of restaurant rankings in Hong Kong. The advantages of the method are analyzed through the experimental results.
Distributed Computing in Blockchain Technology

Appropriated computing in blockchain technology distributed innovation stage for creating decentralized applications and information stockpiling. A disseminated record is database that is con exotically shared and inner associated with arrange hubs. It permits exchanges data and to have open record database in the executive framework. The ledger can be described as a ledger of any transactions or contracts kept up in decentralized form across different geolocation connection which are made up with network nodes. The contestant at each node of the network can access the registered data shared across that network and can own an identical copy of it. Public ledger have any changes or update is made it will reflected and copied to all participants. Underlying distributed ledgers is the same technology that is used by blockchain which is the technology that is used to cryptocurrencies. Distributed computing in blockchain is working under decentralized public ledger; distributed computing blockchain technology consists of two different methodologies: public blockchain and private blockchain. This chapter explains the importance of distributed computing in blockchain, platforms, barriers, and privacy challenges of distributed computing.
Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation

Posture estimation using a single depth camera has become a useful tool for analyzing movements in rehabilitation. Recent advances in posture estimation in computer vision research have been possible due to the availability of large-scale pose datasets. However, the complex postures involved in rehabilitation exercises are not represented in the existing benchmark depth datasets. To address this limitation, we propose two rehabilitation-specific pose datasets containing depth images and 2D pose information of patients, both adult and children, performing rehab exercises. We use a state-of-the-art marker-less posture estimation model which is trained on a non-rehab benchmark dataset. We evaluate it on our rehab datasets, and observe that the performance degrades significantly from non-rehab to rehab, highlighting the need for these datasets. We show that our dataset can be used to train pose models to detect rehab-specific complex postures. The datasets will be released for the benefit of the research community.
Construction and Effect of the Cloud Classroom of College English Based on Blend Mode

Information technology brings motive force of modern education reform. In the context of "internet+", "cloud classroom", which integrates mobile internet, cloud computing with English courses, has become a new development trend. Based on the cloud platform of college English teaching, this study carries out the practical teaching of college English blended teaching mode in the cloud classroom. It is found that this mode has more significant effects on cultivating students' innovation ability, teamwork ability, language application ability, logical thinking ability, cultural literacy ability and so on. In the process of teaching, the awareness of teachers, the participation of students, the monitoring and evaluation system should be enhanced so as to make college English a satisfactory class for teachers and students.
Integrated metasystem for image recognition

With lithographically defined inter-layer alignment, we demonstrate diffractive deep optical network on silicon photonic platform, towards broadband spatial pattern classification and hyperspectral imaging. The high-throughput vector-by-matrix multiplications is enabled by 103 passive subwavelength phase shifters as weight elements.