Reviewerly/exHarmony huggingface dataset:
Input and Understanding of Chemical Symbols in Multimedia Education Software

People’s demand for knowledge and skills in work and life will increase day by day. Because multimedia education software has many advantages, such as large information content, diverse forms and strong interactive ability, it is a new teaching medium loved by home. The premise of studying chemical intelligent problem-solving is to study the input, recognition and understanding of various symbols, which is a technical difficulty in multimedia education software. This paper discusses the input of special symbols in chemistry, and designs some algorithms. These algorithms can identify all reactant molecular formulas, product molecular formulas and reaction conditions from the data structure recording a molecular formula, so as to compare and intelligently balance the answers entered by users with the standard answers.
Metaheuristic Optimized Ensemble Model for Classification of SMS Spam in Computer Networks

By use of electronic communication, we are able to communicate a message to the recipient. In this digital age, a collaboration between several people is possible thanks to a variety of digital technologies. This interaction may take place in a variety of media formats, including but not limited to text, images, sound, and language. Today, a person's primary means of communication is their smart gadget, most commonly a cell phone. Spam is another side effect of our increasingly text-based modes of communication. We received a bunch of spam texts on our phones, and we know they're not from anyone we know. The vast majority of businesses nowadays use spam texts to advertise their wares, even when recipients have explicitly requested not to receive such messages. As a rule, there are many more spam emails than genuine ones. We apply text classification approaches to define short messaging service (SMS) and spam filtering in this study, which effectively categorizes messages. In this paper, we use machine learning algorithms and metaheuristic optimization to determine what percentage of incoming SMS messages are spam. This is why we used the optimized models to evaluate and contrast many classification strategies for gathering data.
Construction and Application of Computer Network Experimental Teaching Platform Based on Big Data

With the development of computer science and the popularization of computer application, the social demand for computer talents is increasing day by day. This paper takes “wisdom” as the teaching material as the carrier, we should focus on serving teachers and students in higher vocational colleges. We should not only give play to the leading role of teachers in guiding, enlightening and monitoring the teaching process, but also fully reflect the initiative, creativity and enthusiasm of students as the main body of the learning process. Colleges and universities have successively set up computer related majors, and even training institutions have participated in the training of computer professionals. However, with the enrollment regulations in recent years With the continuous expansion of the model, the construction speed of some school laboratories is far from keeping up with the expansion of enrollment scale. Combined with the reality of our school, this paper analyzes that when the conditions of computer network laboratory can not meet the computer network experimental teaching temporarily, the computer network experimental teaching platform is constructed by using packet tracer, VMware, pdffactory and other virtualization software to ensure the computer network The experimental course went smoothly.
Correction to: Meso-Scale Electrodynamic Coupling of the Earth Magnetosphere-Ionosphere System

Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic relationships between objects, to construct contrastive views. However, most of them ignore the rich meta-path context information that describes how two objects are connected by meta-paths. Further, they fail to distinguish negative samples, which could adversely affect the model performance. To address the problems, we propose MEOW, which considers both meta-path contexts and weighted negative samples. Specifically, MEOW constructs a coarse view and a fine-grained view for contrast. The former reflects which objects are connected by meta-paths, while the latter uses meta-path contexts and characterizes details on how the objects are connected. Then, we theoretically analyze the InfoNCE loss and recognize its limitations for computing gradients of negative samples. To better distinguish negative samples, we learn hard-valued weights for them based on node clustering and use prototypical contrastive learning to pull close embeddings of nodes in the same cluster. In addition, we propose a variant model AdaMEOW that adaptively learns soft-valued weights of negative samples to further improve node representation. Finally, we conduct extensive experiments to show the superiority of MEOW and AdaMEOW against other state-of-the-art methods.
Product Recommendation Using Sentiment Analysis

The corporate environment has gotten more competitive in recent years. Customer pleasure has shifted to the forefront of company strategy. Businesses spend a lot of money and human resources on different approaches to understand and satisfy their consumers’ needs. However, many firms fail to attain customer satisfaction as a result of poor manual analysis of a wide range of consumer demands. Because of this, they are shedding consumer commitment and incurring additional marketing costs. Sentiment Analysis may be used to solve the challenges. It combines Natural Language Processing (NLP) and Machine Learning techniques (ML). Sentiment Analysis is a technique for extracting information from public opinion on a wide range of subjects, goods, and services. We can accomplish that using any publicly available data online. On a look for a powerful strategy for Sentiment Analysis on a big, unbalanced, and multi-class dataset, we used two NLP approaches (Bag-of-Words and TF-IDF) and multiple ML classification algorithms (Support Vector Machine, Logistic Regression, Multinomial Naive Bayes, Random Forest). Sentimental analysis, or SA, is a continuing branch of research that examines customer feelings when purchasing things. This research report looks at a comprehensive mobile phone reviews study. Customer segmentation is an important part of every company's marketing strategy. In this machine learning project, we'll utilize nltk to cluster and manage unlabeled datasets. The findings reveal that the method works, with high accuracy in both mobile classification and user segmentation.
Selected papers from the 15th international symposium on Theoretical Aspects of Software Engineering (TASE 2021)

Global types are at the core of communication-based programming. They allow a high-level specification of protocols involving many participants and enforce good safety and liveness properties, such as deadlock freedom, and the absence of locked participants and orphan messages. The present software provides an implementation in co-logic programming of a novel formalism of global types for sessions with asynchronous communications, where we use coinduction to properly handle the coinductive syntax of global types and processes. It also offers a simple query language to write sessions and global types, providing primitives for type checking.
Estimating the Effective Support Size in Constant Query Complexity

Previous chapter Next chapter Full AccessProceedings Symposium on Simplicity in Algorithms (SOSA)Estimating the Effective Support Size in Constant Query ComplexityShyam Narayanan and Jakub TětekShyam Narayanan and Jakub Tětekpp.242 - 252Chapter DOI:https://doi.org/10.1137/1.9781611977585.ch22PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Estimating the support size of a distribution is a well-studied problem in statistics. Motivated by the fact that this problem is highly non-robust (as small perturbations in the distributions can drastically affect the support size) and thus hard to estimate, Goldreich [ECCC 2019] studied the query complexity of estimating the ε-effective support size Essε of a distribution P, which is equal to the smallest support size of a distribution that is ε-far in total variation distance from P. In his paper, he shows an algorithm in the dual access setting (where we may both receive random samples and query the sampling probability p(x) for any x) for a bicriteria approximation, giving an answer in [Ess(1+β)ε, (1 + γ) Essε] for some values β, γ > 0. However, his algorithm has either super-constant query complexity in the support size or super-constant approximation ratio 1 + γ = ω(1). He then asked if this is necessary, or if it is possible to get a constant-factor approximation in a number of queries independent of the support size. We answer his question by showing that not only is complexity independent of n possible for γ > 0, but also for γ = 0, that is, that the bicriteria relaxation is not necessary. Specifically, we show an algorithm with query complexity . That is, for any 0 < ε, β < 1, we output in this complexity a number ñ ∊ [Ess(1+β)ε, Essε]. We also show that it is possible to solve the approximate version with approximation ratio 1 + γ in complexity . Our algorithm is very simple, and has 4 short lines of pseudocode. Previous chapter Next chapter RelatedDetails Published:2023eISBN:978-1-61197-758-5 https://doi.org/10.1137/1.9781611977585Book Series Name:ProceedingsBook Code:SOSA23Book Pages:vi + 389
Simple Random Order Contention Resolution for Graphic Matroids with Almost no Prior Information

Random order online contention resolution schemes (ROCRS) are structured online rounding algorithms with numerous applications and links to other well-known online selection problems, like the matroid secretary conjecture. We are interested in ROCRS subject to a matroid constraint, which is among the most studied constraint families. Previous ROCRS required to know upfront the full fractional point to be rounded as well as the matroid. It is unclear to what extent this is necessary. Fu, Lu, Tang, Turkieltaub, Wu, Wu, and Zhang (SOSA 2022) shed some light on this question by proving that no strong (constant-selectable) online or even offline contention resolution scheme exists if the fractional point is unknown, not even for graphic matroids.In contrast, we show, in a setting with slightly more knowledge and where the fractional point reveals one by one, that there is hope to obtain strong ROCRS by providing a simple constant-selectable ROCRS for graphic matroids that only requires to know the size of the ground set in advance. Moreover, our procedure holds in the more general adversarial order with a sample setting, where, after sampling a random constant fraction of the elements, all remaining (non-sampled) elements may come in adversarial order.* This project received funding from Swiss National Science Foundation grant 200021 184622 and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 817750).
Review: A descriptive study of karst conditions and problems in Indonesia and the role of karst for flora, fauna, and humans

Abstract. Aprilia D, Arifiani KN, Sani MF, Jumari, Wijayanti F, Setyawan AD. 2021. Review: A descriptive study of karst conditions and problems in Indonesia and the role of karst for flora, fauna, and humans. Intl J Trop Drylands 5: 61-74. The karst area in Indonesia covers an area of about 15.4 million hectares and is spread almost throughout Indonesia. It is estimated the age of karst in Indonesia started from 470 million years ago to the most recent about 700,000 years. The existence of this area shows that many of the Indonesian islands were once seabed but were later uplifted and hardened. Most of the karst areas in Indonesia are composed of carbonate rocks, and almost none are composed of other rocks such as gypsum, salt rock, and evaporite rocks. Karst in Indonesia can be classified based on its development and climate. The amount of water available in the karst area plays an important role in human life and so do flora and fauna around the karst area. The karst area functions as an ecosystem for the habitat of various animals and plants. The richness of flora and fauna of this karst area is extraordinary. Karst area plays an important role in economy, science, and human culture. In addition, karst areas have an important role in the ecosystem, such as providing clean water, limestone-based natural materials, and controlling climate change. Its role in ecological function is that karst areas can also be a source of CO2 gas absorption. About 9.5% (155,000 km2) of the total karst area of Indonesia was damaged due to limestone mining activities, logging of vegetation, and land conversion. Given the importance of karst and limestone ecosystems as non-renewable natural resources, it is necessary to do conservation to maintain the ecological function. Steps that can be taken for conservation efforts in karst areas include limiting the sale of raw limestone to outside the area, clarifying protected areas and cultivation areas, socializing the importance of preserving karst areas, providing skills or developing other business opportunities, and reclaiming used land and mining according to the level and type of damage. This study aims to describe the geographical conditions and karst problems in Indonesia so that the conservation measures taken are known and identify the role of the karst area for flora, fauna, and humans.
Evaluation of semi-supervised data augmentation in sentence sentiment analysis

Data augmentation by using Semi-Supervised Learning is an important research direction currently. Usually, this method applies the possible score of prediction as the confidence degree for the next training iteration so that Semi-Supervised Learning may gradually enhance the model's accuracy. The principle of Semi-Supervised Learning is using the original model to predict the unlabeled data to produce pseudo labels, then choosing the high confidence pseudo label to train the model iteratively. However, this method may not be helpful due to the iterative steps. This paper uses the model in this iteration to predict the data labeled in the last iteration and compares the pseudo label identity with the result produced by the previous iteration model. The evaluation shows that the consistency between models in two consecutive iterations is not high, which explains that Semi-Supervised Learning does not eventually enhance the model's accuracy. This research indicates that applying consistency and accuracy jointly as the standard for Semi-Supervised Learning in sentence sentiment analysis is a more reasonable method.
The Implementation of Snake and Ladder Game in Teaching Speaking: The Advantages and The Disadvantages

Nina Hayuningtyas, 2022: The Implementation of Snake and Ladder Game in Teaching Speaking: The Advantages and The Disadvantages.&#x0D; Speaking is one of the critical communication skills that students should be able to acquire. By speaking, they can convey thoughts, feelings, arguments, and ideas as well as emotions and other mental states. Yet, there are many problems faced by students in learning speaking. Due to those problems, choosing an appropriate media for teaching learning is an ability that every teacher must have. One of the entertaining and intriguing games that may be used to teach speaking is snake and ladder game.&#x0D; This study reports the advantages and the disadvantages of snake and ladder game in teaching speaking. While, the outcome can be summed up as follows: The Snake and Ladder game's design has both advantages and disadvantages. The advantages of the Snake and Ladder game is that it encourages students to practice speaking since it creates an enjoyable and relaxed learning environment. By forcing students to practice speaking since they must do so while playing, the snake and ladder game may help students indirectly enhance their English-speaking abilities. One of the drawbacks of the Snake and Ladder game is that it takes a lot of planning and practice time.&#x0D;
Efficiency for data parallel computation in deep neural networks

The data parallelism provides greater efficiency under multiple-node systems. Under these circumstances, there is more and more utilisation on multiple GPUs for better efficiency for computation and lower cost of time for accomplishing the project. However, some typical, ordinary, and common circumstances in which using multiple GPUs is worse than using one GPU in a machine. This paper aims to prove that using various GPUs is not omnipotent in efficiency and cost of time when running a program. This paper represents the limitations of computation with CPU, GPU, and multiple GPUs. Two main parts represent the limitations of the experiment. They are the comparison between one running CPU and one running GPU and the comparison between one running GPU and running multiple GPUs with the cost of time after running a program by numerous times and different data sizes
Comparative analysis of different scene for face mask detection based on YOLOv4

The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement.
Optimization research of UAV target detection algorithm based on deep learning

With the rapid development of deep learning, UAV target detection technology based on computer vision and artificial intelligence has been widely used in practice. However, due to the instability of UAV movement, limited by load and endurance, the development of UAV target detection is slow, and there are challenges such as significant changes in target scale, occlusion between objects, and changes in target density. This paper builds on the network model structure of YOLOv5 to address these challenges. It adds a detection head generated from low-level feature layers and high-resolution combined feature maps to detect tiny objects. We utilize the Bifpn network structure and a weighted fusion splicing approach to fuse more features and introduce an improved Coordinate Attention to obtain location information for feature enhancement accurately. Extensive experiments on the Visdrone2021 dataset show that the model achieves good results in UAV target detection and is helpful for tiny and occluded target detection.
Transforming images into paintings in the style of Van Gogh based on CycleGAN

Nowadays, with the rapid development of entertainment industry, filter has almost become an indispensable function in photography. However, most of the filters today are not directly change the style of an image, and even those techniques that can achieve image style transfer are difficult to be applied by users of other disciplines because of the complexity of their algorithms. In this study, a Generative Adversarial Network named CycleGAN, is used to transfer normal landscape images to images with the style of Van Gogh’s paintings. By using this model, two generators and discriminators will be formed. For the generator, they both generate a domain of images to the other domain, transfer them back to the original domain, and to make sure the generated images will not be differed so much with the original images. In this way, the generator can learn the important features of a specific style of images and apply them to another set of images with a different style. Experimental results show that the model does well in transferring normal landscape images to images with the style of Van Gogh’s paintings, but for the transformation from the style of Van Gogh’s paintings to landscape images, the effect of the model is not very ideal.
Development of Media Pembebas (Adobe Flash-Based Learning) on eclipse theme

The 21st Century learning paradigm is required to be based on critical thinking, connecting knowledge with the real world, communicating and collaborating, and mastering technology as well. Interactive learning media can be a link between environmental-based knowledge and technology. Eclipse theme is an abstract one as the object cannot be sensed directly. Learning media is needed so that abstract concepts can be actualized and facilitate students to understand the theme. This study aims to develop Media Pembebas on eclipse theme validly and feasibly. This Research and Development uses the ADDIE research model (Analyze, Design, Development, Implementation, and Evaluation). Validation by media and material experts shows the result by percentage respectively 91.4% and 100% with a very valid category. The result of this research is a product of Adobe Flash-Based Learning Media on eclipse theme. The topics presented in the Media Pembebas include the types of shadows in the eclipse, the process of the occurrence of a Solar eclipse, and the process of the occurrence of a Lunar eclipse. Assessment by Junior High School’s Science Teachers and 7th Grade Students shows the result by a percentage of 86% and 84.8%, respectively, with a very feasible category.
Implementation of augmented reality technology-based learning media in molecular hybridization concept

Augmented Reality (AR) is a technology that can be used as a learning media. AR can help students in describing 3D structures in chemical compounds. This study aimed to apply AR learning media to improve the ability of submicroscopic representation in the molecular hybridization concept. Determination of the sample used the pre-experimental method. This research was conducted in the fourth semester using a scientific learning model based on submicroscopic representation. Data collection techniques used the pretest and posttest questions with 20 multiple choice questions through the LMS (Learning Management System) application, observation sheets and student worksheets. The results of the data analysis showed an increase in learning using AR media. The t-test results of pretest and posttest obtained t-test signification of 0.000 <0.05 showed that Ho was rejected and Ha was accepted and could be categorized as moderate with the N-gain value of 0.45.
Sinkless Orientation Made Simple

The sinkless orientation problem plays a key role in understanding the foundations of distributed computing. The problem can be used to separate two fundamental models of distributed graph algorithms, LOCAL and SLOCAL: the locality of sinkless orientation is Ω(log n) in the deterministic LOCAL model and O(log log n) in the deterministic SLOCAL model. Both of these results are known by prior work, but here we give new simple, self-contained proofs for them.