Reviewerly/exHarmony huggingface dataset:
Chinese Comments Classification and Hotspot Mining Algorithm based on Natural Language Processing

With the popularity of the Internet, more and more people start to leave comments in the comment sections of major platforms to reflect their thoughts, and these comments are characterized by large amounts of information and uneven information quality. In order to distinguish the comment categories more effectively and obtain the hotspot information of comments, this paper adopts the TF-IDF method to vectorize the comment text, and uses the SVD algorithm to reduce dimension of the TF-IDF matrix. We use the LinearSVC model to classify comments, and construct a comment hotspot mining algorithm, which is based on the K-Means algorithm to cluster the comments. The topic words of the clustering results are extracted using the LDA topic model, and the hot words are analyzed by TOPSIS algorithm based on hotspot evaluation indicators. The experimental results show that the accuracy of comment classification can reach more than 90% and can identify the hotspot information of comments.
Design of driving circuit for chaotic lasers with different thermoelectric coolers

In order to effectively regulate and control the temperature and the driving current of chaotic lasers with different thermoelectric coolers (TECs), we optimize the circuit on the basis of the previous circuit design. The new circuit achieves effective control of the temperature and current of chaotic lasers with different TEC models. The range of the maximum output voltage at the TEC end is from 0 to 3.3 V. The output current stability is controlled within 0.02%, and the output wavelength fluctuation of the laser is stabilized within 9 pm. Compared with the control of the Newport driver, the normalized mean square error of the output wavelength of transmitter optical subassembly (TOSA) laser is decreased from 0.004 4 to 0.002 0. The stability of control has been improved.
Image Enhancement Processing Based on Fast Chord Intersection

According to relevant statistics, 80% of the information received by human comes from vision, then, high-quality images are crucial in human life. Therefore, it is of great significance to develop an effective image enhancement method. This paper mainly takes the fast chord-intercept iterative method as the model, and proposes the principle and implementation method of the center-boundary filter and the boundary-boundary filter, and uses MATLAB to process ordinary grayscale images, Gaussian noise images and salt-and-pepper noise images. Three traditional image enhancement methods are compared and analyzed, and the final conclusion is drawn: center-boundary filtering, boundary-boundary filtering, center-boundary filtering and boundary-boundary filtering are proposed based on the fast chord truncation method for images with Gaussian noise and salt and pepper noise It has better noise reduction effect, the image is effectively enhanced, and the noise reduction ability of center-boundary filtering and boundary-boundary filtering is stronger than that of center-boundary filtering and boundary-boundary filtering.
Development of Interactive Learning Model Teams Games Tournament Assisted Media Rolling Ball Game in Elementary School

This research aims to improve students' morale and learning outcomes by using an interactive learning model of teams games tournaments assisted by rolling ball game media. This type of research uses Research and Development (R&amp;D) development research. The study used the Dick &amp; Carey development model, consisting of 10 stages, but researchers only used five. The analysis stage is carried out to analyze learning needs consisting of analysis of learning objectives, analysis of characteristics of learners, developing research instruments, developing learning strategies, and developing teaching materials. Three validators further validate the model that has been created in the course: validator design, language validator, and material validator. Validation results are then analyzed to find out the validity of the product that has been stored and is very valid value with a percentage of 85.7%. The results are evidenced by the value of the material expert 80%, the value of the linguist 90%, and the design expert 87%. From the results of practicality data that have been obtained, it is known that 7 points score researchers there are six good with a percentage of 75%, and there is an excellent 1 with a percentage of 100%. Therefore, the total is very practical value with a percentage of 90%. From this research, it can be concluded that this learning model can improve learners' spirit and learning outcomes.
LD-IDH-Clu: A New Clustering Algorithm Based on the Local Density Estimation and an Improved Density Hierarchy Strategy

Clustering is an important branch of data mining. In this study, we proposed a new clustering method, named LD-IDH-Clu, to overcome the problems such as sensitivity to noises, and unreasonable distribution of clustering results which exist in present hierarchical clustering algorithms. First, to solve the noise problem, a simple but effective local density estimation strategy is introduced to determine the representative points of clusters; and then the similarity between clusters is measured by calculating the distance between the representative points of clusters. Then, an improved density hierarchy scheme, IDH, is proposed to address the issue of unreasonable distribution. Compared with the existing density hierarchy (DH), the IDH can significantly reduce the running time while maintaining the advantage of DH. Finally, the performance of the LD-IDH-Clu was compared with some existing algorithms on eight UCI datasets, five 2-D synthetic datasets, and a Chinese wine market dataset. Experimental results show that the LD-IDH-Clu has better performance than the previous models in terms of Calinski-Harabasz (CH) Index, Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI), V-measure, runtime, and distribution. Particularly, by applying the LD-IDH-Clu to segment the Chinese wine market, it is found that for an unfamiliar dataset, the LD-IDH-Clu algorithm can always yield some segments that are worthy of further analysis, regardless of the need to segment it into several sub-markets in a real-world scenario.
Comparative Analysis of Deep Learning Models for Malaria Detection

The blood morphology of a human provides information on changes in the human body and underlying maladies. Disease like malaria, caused by parasites of the genus Plasmodium transmitted through infected Anopheles mosquitoes is an endemic across large areas of Asia, Africa, Central and South America, Eastern Europe and the South Pacific. Conventional diagnosis of malaria involves microscopic examination of the blood smear of a patient which is stained with the nucleic acid. Recent decades have experienced rapid development of Artificial Intelligence in the healthcare industry due to the influx of large datasets provided by healthcare practitioners and the advancement of powerful computer hardware. In particular, the development of deep learning and machine learning is considered to be effective in computer-aided detection and diagnosis, image analysis, drug discovery and health monitoring. This work analyses and provides a detailed review of the implementation of state-of-the-art Deep learning networks in detecting malaria infections. In the present work, the CNN, transfer learning, CNN-KNN and Vision Transformer networks are being analyzed and evaluated against various metrics like accuracy and precision. The network experiments for malaria detection using deep learning on real-world datasets have proven to show significant results despite the limit on training data.
Defining an A/B Testing Process for Usability and User Experience Evaluation Through the Analysis of the Results of a Literature Review

Usability and User Experience are high-impact quality factors for the design of interactive systems, since they impact how we use them in daily life activities. Despite the importance of these quality attributes, several users report problems when interacting with information systems. Considering the need to meet different quality standards, software testing is an important area for identifying software defects. Despite the proposal of different approaches for A/B testing in the context of Usability and UX evaluation, there are still some improvement opportunities. For instance, after carrying out a literature review, we did not identify an A/B testing process that could be applied for identifying both usability and user experience problems. Furthermore, most of the A/B testing processes in the context of human computer interaction do not gather qualitative data, which makes it difficult to identify what to improve in the selected version of a software to meet the users’ needs. Considering the above, this paper proposes a new A/B testing process that allows the evaluation of both usability and user experience attributes. For each of the A/B testing processes found in our literature review, we gathered data on the categorization of the process, its activities, the roles within the process, and the artifacts that were employed during the planning, conducting and reporting of the test. We then organized our results creating a new process that would consider all relevant activities and artifacts from previous work.
Multi-Phase Image Segmentation Based on Low-Rank Prior Decomposition

Natural images usually contain structure component and texture component. The existing image segmentation models based on piecewise smooth cannot handle such natural images containing texture well. For this reason, in this paper, a multi-region image segmentation model based on low-rank prior decomposition is proposed. We use the low-rank prior to characterize the texture component, and the total variational norm to characterize the piecewise smooth structure component so that the proposed method can perform image decomposition and image segmentation tasks simultaneously. Then, the decomposed structure image can be used for image segmentation, and thus we can improve the accuracy of segmentation. To solve the new model, the alternating direction multiplier method is designed. The experimental results show that compared with the related classical models, the new model can significantly improve the subjective visual effect of image segmentation and the mean values of the precision rate, F1-measure, and Jaccard similarity coefficient for the new model on the test images are at least 3.29%, 1.74%, and 3.13% higher, respectively.
Extreme Learning Machine to Graph Convolutional Networks

Graph Convolutional Network (GCN) is a powerful model to deal with data arranged as a graph, a structured non-euclidian domain. It is known that GCN reaches high accuracy even when operating with just 2 layers. Another well-known result shows that Extreme Learning Machine (ELM) is an efficient analytic learning technique to train 2 layers Multi-Layer Perceptron (MLP). In this work, we extend ELM theory to operate in the context of GCN, giving rise to ELM-GCN, a novel learning mechanism to train GCN that turns out to be faster than baseline techniques while maintaining prediction capability. We also show a theoretical upper bound in the number of hidden units required to guarantee the GCN performance. To the best of our knowledge, our approach is the first to provide such theoretical guarantees while proposing a non-iterative learning algorithm to train graph convolutional networks.
CAVAN: Commonsense Knowledge Anchored Video Captioning

It is not merely an aggregation of static entities that a video clip carries, but also a variety of interactions and relations among these entities. Challenges still remain for a video captioning system to generate descriptions focusing on the prominent interest and aligning with the latent aspects beyond observations. In this work, we present a Commonsense knowledge Anchored Video cAptioNing(dubbed as CAVAN) approach. CAVAN exploits inferential commonsense knowledge to assist the training of video captioning model with a novel paradigm for sentence-level semantic alignment. Specifically, we acquire commonsense knowledge complementing per training caption by querying a generic knowledge atlas (ATOMIC [1]), and form the commonsense-caption entailment corpus. A BERT [2] based language entailment model trained from this corpus then serves as a commonsense discriminator for the training of video captioning model, and penalizes the model from generating semantically misaligned captions. Experimental results with ablations on MSRVTT [3], V2C [4] and VATEX [5] datasets validate the effectiveness of CAVAN and reveal that the use of commonsense knowledge benefits video caption generation.
Dynamics-Aligned Transfer Reinforcement Learning For Autonomous Underwater Vehicle Control

Autonomous Underwater Vehicles (AUVs) in shallow waters usually are subject to model uncertainties from loading and unloading objects, thruster malfunction due to thruster caught by waterweed, etc. Learning-based controllers are suitable for uncertainty attenuation control. But excessive uncertainties heavily affect the state transition in Markov Decision Process (MDP) or Partially Observable Markov Decision Process (POMDP). In addition, learning procedures on real AUV system requires a large number of training samples, which may damage systems. Therefore, a policy trained exclusively on a simulated model usually results in unsatisfactory performance on a real AUV system due to dynamics model mismatch (transition model mismatch). In this paper, we propose a transfer reinforcement learning approach that adapts a control policy to an underwater robot under dynamics model mismatch, for the purpose of uncertainty rejection. In particular, A policy trained on a source dynamics model is transferred to target dynamics models via dynamics alignment, by proposing a distance metric between two dynamics models. We have tested the proposed approach on a pose regulation task through numerical simulations of three scenarios, mainly including changes in thruster characteristics. The results have demonstrated the effectiveness of the proposed dynamics-aligned transfer reinforcement learning approach.
High-dimensional inference: searching for principal components

Abstract We have focused so far on how to infer simple models (defined by one or few parameters) from few or many data. In this chapter we are going to address a more complex situation, where the number of unknown parameters is comparable to the number of available data. This situation happens in many practical applications of statistical inference methods. We here concentrate on one of them, principal component analysis (PCA), that is, the inference of the most relevant direction in the high-dimensional space of data. Based on random matrix theory we will see how the inference of this direction is characterized by a phase transition, called retarded learning, which defines a minimum value of the number of data per parameter below which successful inference is not possible.
Enhancing action maze video games using Shuffled Frog Leaping Algorithm

Shuffled Frog Leaping Algorithm(SFLA) is a very popular population-based meta-heuristic algorithm, created by Eusuff and Lansey in 2006. Because of its limited variables and easiness in implementation, it is used in memetics and particle swarm optimization to give several use cases. We believe one unexplored application of this algorithm is in video games, primarily in the genre of action based video games. They involve optimizations in path planning and navigation where SFLA can be utilized. We also believe these games can serve as simulations for real-life scenarios of navigation, thereby giving a virtual environment to enhance this algorithm further. Finally, we compare this method with popular nature based ones such as BFA and SPPSO algorithms and conclude on their optimization efficiency for action maze video games like Pac-Man.
Standards - The World’s Common Language

On the 14th of October each year, the world is celebrating Standards Day as a way of honoring the efforts of those who developed Standards within Standards Development Organizations. It’s difficult to imagine how the world would be without common standards. Customers of the particular product from different parts of the world would find it difficult to transact with other suppliers of a similar product due to the difference in taste, size, and use of such a product. This essay shows how standards are world's common language.
QUARRY: A Graph Model for Queryable Association Rules

Association rule mining is a pivotal technique for knowledge discovery, but often involves time-intensive manual labour when performed on large datasets. In this paper we propose a solution for this problem: QUARRY, a graph model that enables consumable and queryable insights from association rules. In contrast to existing systems which take a list of rules and display them in a purpose-built visualisation, our graph-based model enables association rules to be queried directly via graph queries. Through a case study on maintenance data we show how this model enhances knowledge discovery by eliminating the need for domain experts to trawl through large lists of rules to find useful information. QUARRY, which is designed for compatibility with existing knowledge graphs, provides users with the means to easily search for rules pertaining to specific items as well as roll up and drill down on their searches using the concept hierarchy. Domain experts may also query for association rules based on transaction properties such as costs and dates, enabling critical insights into their data.
Qualitative Analysis for Discrete Time Model

In this section, some mathematical equipments are introduced for the qualitative analysis on the one dimensional discrete time model, which are closely related to the contents of Chap. 2 .
Grid-Search Enhanced Tree-Based Machine Learning for Traffic IoT Data Anomaly Detection

Anomaly detection is an important part of machine learning. Detection of outliers in the field of transportation can provide valid data for future traffic predictions or traffic flow analysis. This paper builds a model based on XGBoost to detect outliers in IoT data. The data is preprocessed first, followed by model building. Then we use the grid search to adjust the parameters and substitute the optimal parameters into the building model. To validate the model, we cross-checked it with two benchmark models, iFroset and Random Forest. The final experimental results show that the model constructed in this paper can accurately detect outliers in traffic flow and the accuracy is better than that of the baseline model.
Structural properties of skew-cyclic codes over a finite ring

In this article we consider the notion of skew-cyclic codes over finite rings with derivations, as introduced by Boucher and Ulmer (2014). We investigate the structural properties of skew-cyclic codes over the finite ring ℤ4+vℤ4, where v2=v, with a derivation on ℤ4+vℤ4. As an application, we provide several optimal linear codes over ℤ4 constructed or derived from the skew-cyclic codes mentioned above.
ColorNet: A Deep Learning-Based Approach to Colorize the Historical Documentaries of Bangladesh

Image colorization is a process where each pixel in images or videos is given a suitable amount of red, green, and blue (RGB) main color components in order to make it appear more vivid. The process of colorization has significant values while preserving historical documentaries or investigating cosmological photography. However, the colorization of historically significant documentaries requires real witnesses, and a significant amount of human labor is needed to colorize even a single image frame. Recently, deep learning-based approaches have become one of the most reliable techniques to automate challenging computer vision tasks. Various deep learning methods are applied to automate the colorization process. However, we have found that most of the colorization models show poor performance while colorizing Bangladeshi historical documentaries. In our work, we developed ColorNet: a deep learning-based model using an encoder and a pre-trained feature extractor named InceptionV3, also, a fusion layer and a decoder. We have built a new dataset from Bangladeshi historical documentaries. After training our colorization model, it shows a significant result while colorizing Bangladeshi historical documentaries.
Exploring Geospatial Data Visualization Based on Python

OpenStreetMap (OSM), as a typical representative of crowdsourced geographic information data, covers a large amount of open source geographic information data, which provides freely editable world-class geographic data for realizing information mining, analysis and visualization of geospatial data. Based on the geographic information data from the official website of OSM, this paper uses Python's geographic information data processing technology, geospatial data visualization technology and layer overlay method based on vector data analysis algorithm to realize visualization and information annotation of various types of geospatial data, providing reliable data processing, visualization and analysis methods for domestic resource exploration, transportation, epidemic prevention and control and other fields.
High-Resolution Stereo Matching Network Based on Attention Mechanism and Intelligent Computing

To solve the problem that the stereo matching algorithm loses the highest resolution feature level information in the process of feature extraction, a high resolution unit is used to maintain the highest level feature information and fuse the feature information of each resolution level. Due to the lack of semantic correlation and the measurement of the relationship between each cost feature in convolution process, the mismatch rate, especially in the foreground region, is too high. This paper uses the attention mechanism module which combines channel attention mechanism and spatial attention mechanism. By comparing Scene Flow and KITTI datasets, the proposed algorithm is compared with GC-net and other commonly used algorithms. On KITTI datasets, the proposed algorithm reduces the false matching rate by 0.26 percentage points compared with GC-net, and reduces the false matching rate by 0.1 and 1.5 percentage points in background and foreground regions respectively.
An Improved YOLOv3 Airplane Target Detection Algorithm Based on Multi-Scale Feature Fusion

The detection and identification of airplane targets images have essential application value in remote sensing. Being aimed at the problem of unbalanced accuracy in the current detection of small-scale airplane targets, an improved airplane target detection algorithm based on YOLOv3 is proposed. First, with the feature pyramid network of the YOLOv3 model redesigned, a prediction feature layer for small targets was added, and the features of the external network and the deep network were fused to improve the model’s detection ability small targets. Then, the K-means clustering algorithm is utilized to adjust the a priori box scale of the prediction feature layer. Lastly, experimental works based on the airplane target detection show: that the average precision of the YOLOv3 algorithm of the proposed method achieves 95.3%, which is 22.9% higher than the original algorithm. The precision and recall rates are improved, and the PFS is 30.86. These results prove the effectiveness of the method proposed in this letter.