Reviewerly/exHarmony huggingface dataset:
Google Page Rank Site Structure Strategies for Marketing Web Pages

There are several Search Engines to categorize the web content and show us on base of our search query.These search engines are continuously visiting the pages/sites and gather the information using different techniques called crawling/spidering. On basis of daily content collection all search engines are managing their own indexes for searches.For every business there is a need to make its pages as most top rated/ranked pages by making structurally and content wise batter so that any crawler can easily crawl it and can rank it among the top 10 results.In this thesis only, structural behavior is being discussed in which internal graphical relationships between pages and the loading time of pages in terms of all supportive content .Structural overview includes the HTML tags structure which works as tree flow starting from tag and then moving towards child nodes.Page speed identifies the loading time of a page, it helps search engine to categories pages for mobile devices as well.Sometimes there is a thunder option on mobile search with rank it represents that this page is super-fast in loading. Page loading includes the loading of all content except Ajax base content.According to Google Page, rank is based on page content, page structure and page loading time.As discussion is only Page structure and page loading time so Google already gave some instructions related to Page structure and page loading speed but those instructions are not enough also it need to discover the new dimensions to make business pages among the top-rated results.
The (Moral) Language of Hate

Humans use language toward hateful ends, inciting violence and genocide, intimidating and denigrating others based on their identity. Despite efforts to better address the language of hate in the public sphere, the psychological processes underlying the development of hate remain unclear. In this work, we hypothesize that morality and hate are concomitant in language. In a series of studies, we find evidence in support of this hypothesis using language from a diverse array of contexts, including the use of hateful language in propaganda to inspire genocide (Study 1), hateful slurs as they occur in large text corpora across a multitude of languages (Study 2), and hate speech on social-media platforms (Study 3). In post-hoc analyses focusing on particular moral concerns, we found that language about purity and spiritual degradation were strongly salient in hateful linguistic communication. Our findings provide a new lens for understanding the psychologicalunderpinnings of out-group hate and its associated behaviors, with practical implications for mitigating hateful rhetoric online.
PENERAPAN FUZZY TIME SERIES DALAM MERAMALKAN JUMLAH WISATAWAN DI MASA PANDEMI COVID19

Tourism is one of Indonesia’s assets to promote economic growth. This sector became one of the largest contributors to national foreign exchange. The number of foreign tourist visits is an indicator of the contribution of tourism which has experiencing the upward trend in annually until the Covid19 happened. In this study, forecasting the number of foreign tourists is needed as a plan to improve the quality of tourism during the pandemic. We used Fuzzy Time Series (FTS) Cheng method. The actual data processed comes from the Central Statistics Agency from April 2020 through December 2021. Based on forecasting results, the performance of the forecasting model is in the very good category with Mean Absolute Percentage Error (MAPE) value is 5.06%. It means that our predictions are on average 5.06% away from the actual values they were aiming for. In other side, we have forecasting accuracy value is 94.94% which means that the forecast values were close to the actual.&#x0D; Keywords: Tourist, FTS, Cheng Methods
Academic mobility of students: values, attitudes and social practices in the South of Russia

Relevance: Academic mobility, as a special type of movement for the purpose of ed-ucational and scientific practices, is one of the mechanisms of self-regulation of the learning process of Russian students. In the South of Russia, expanding the prospects for intra-university and intra-Russian academic mobility contributes to improving the quality of education, creating a unified intellectual environment and promoting tour-ism practices. Scientific problem: A critical analysis of the scientific literature has shown that the scientific literature has not yet considered how the prevailing ideas about education and values affect students' attitudes to academic mobility. The pur-pose of this study was to identify the features of attitudes and social practices of ac-ademic mobility of Russian students, limited by formal external barriers existing in universities in the South of Russia. Methods: The theory of mental programs is used to study the concepts of education as a value, attitudes and social practices of aca-demic mobility. The analysis is based on the study of significant relationships of vari-ables through the Chi-square criterion. Research results: Attitudes to intra-university mobility are highest among students with pragmatic views, although, in general, ideas about education have little effect on intra-university mobility. Students with con-servative ideas show a great attitude to the practices of intra-Russian mobility, see them as an educational value. While students with liberal and pragmatic views believe that this may interfere with their studies at their native university or they are not in-terested in it. Most students have an attitude to international mobility, but do not have the financial opportunity for it. The number of implemented social practices is largely due to external factors, that is, the opportunities that exist in the universities of the Rostov region and the Republic of Crimea, as well as the state of academic mobility in Russian higher education in general. Conclusions: Academic mobility of students in Russia is of particular importance for improving the quality of higher edu-cation in the country. The student youth in the South of Russia, showing a high de-gree of self-regulation, demonstrates high attitudes to all types of academic mobility. However, the nature of attitudes is largely determined by the conservative and liberal ideas of students that have developed in the process of social programming.
A-star Optimization with Heap-sort Algorithm on NPC Character

Objectives: To find out whether the A-star algorithm that is optimized with the heap-sort algorithm can reach the target destination faster than the native Astar when applied on NPC to find the path.Methods: Comparisons are made by implementing an optimized A-star algorithm with heap-sort and native Astar on an NPC in a dungeon-crawling game genre by applying five different scenarios, where each scenario has a different start position of the NPC, and the target goal is set.The comparison is made by measuring the time required by both algorithms to reach the target goal in milliseconds with Unity engine software and C# language.Findings: From the results of experiments and calculation, it was found that the A-star algorithm optimized with the heap-sort algorithm was 50%-80% faster than using only the A-star algorithm in cases that was applied alone on NPC in dungeon-crawling game.Novelty: The novelty in this research is the optimization of the A-star algorithm with heap-sort, and then that algorithm is compared with the native A-star algorithm that is applied to NPCs in the dungeon-crawling game genre.
Few-shot object detection model based on transfer learning and convolutional neural network

Abstract Object detection is an important research content in computer vision. Faster R-CNN has been widely used in object detection with large samples. Sample scarcity in practice makes it difficult for Faster R-CNN to extract effective feature and obtain high detection accuracy. In this paper, an improved few-shot object detection model is proposed based on Faster R-CNN. Transfer learning algorithm is applied to transfer trained weights of similar source datasets firstly. Then, attention mechanism is introduced and loss function is improved to construct the few-shot object detection model which has powerful generalization ability. Experimental results on PASCAL VOC dataset show that the proposed model can converge faster and the detection accuracy mAP reaches 79.1% which is 14.4% higher than that before the improvement. Compared with SSD and YOLO v3 network, the detection accuracy of the proposed model is also increased 31.4% and 13.1%, respectively.
An Automatically Verified Prototype of the Android Permissions System

Abstract In a previous work De Luca and Luna presented formal specifications of idealized formulations of the permission model of Android in the Coq proof assistant. This formal development is about 23 KLOC of Coq code, including proofs. This work aims at showing that { log } (‘setlog’)---a satisfiability solver and a constraint logic programming language---can be used as an effective automated prover for the class of proofs that must be discharged in the formal verification of systems such as the one carried out by De Luca and Luna. We show how the Coq model is encoded in { log } and how automated proofs are performed. The resulting { log } model is an automatically verified executable prototype of the Android permissions system. Detailed data on the empirical evaluation resulting after executing all the proofs in { log } is provided. The integration of Coq and { log } as to provide a framework featuring automated proof and prototype generation is discussed.
Bayesian Estimation of Test Engagement Behavior Models with Response Times

Detecting non-engagement test answering behavior is a crucial task in situations where the tests are low-stakes for the individuals but the scores are employed in decision-making. Nagy and Ulitzsch (2022) proposed four test engagement models, but their estimations were conducted using maximum likelihood estimation. This study applied the Bayesian estimation method to the test engagement models. Bayesian formulation of the test engagement models was introduced and estimation was conducted using “just another Gibbs sampler” language. Real data analysis was conducted and problems were discussed regarding the future orientation of the Bayesian framework in test answering behavior modeling.
Implementasi Chatbot Sebagai Virtual Assistant di Universitas Panca Marga Probolinggo menggunakan Metode TF-IDF

This research is to implement a chatbot as a virtual assistant that can be used as an academic information service for the general public as well as for the academic community of the Panca Marga University Probolinggo campus. The stages of developing this chatbot using the waterfall method include analysis, design, code, testing and maintenance. The method used for learning chatbots uses Tf-Idf and VSM for word weighting in documents and queries and Cosine similarity to calculate similarity (similarity) between documents and queries. The final result of this research is a chatbot application that can be used as a virtual assistant as customer service in serving and providing information about academics at Panca Marga University Probolinggo. Based on the results of the accuracy and UAT testing, the accuracy rate obtained by the chatbot reached 85.7% and the UAT test in the first test reached 84.1% with a total of 30 respondents, in the second test it reached 82.1% with a total of 92 respondents.
Learning GFlowNets from partial episodes for improved convergence and stability

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfully used for various probabilistic modeling tasks. Existing training objectives for GFlowNets are either local to states or transitions, or propagate a reward signal over an entire sampling trajectory. We argue that these alternatives represent opposite ends of a gradient bias-variance tradeoff and propose a way to exploit this tradeoff to mitigate its harmful effects. Inspired by the TD($\lambda$) algorithm in reinforcement learning, we introduce subtrajectory balance or SubTB($\lambda$), a GFlowNet training objective that can learn from partial action subsequences of varying lengths. We show that SubTB($\lambda$) accelerates sampler convergence in previously studied and new environments and enables training GFlowNets in environments with longer action sequences and sparser reward landscapes than what was possible before. We also perform a comparative analysis of stochastic gradient dynamics, shedding light on the bias-variance tradeoff in GFlowNet training and the advantages of subtrajectory balance.
Generalized Parametric Contrastive Learning

In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed benchmarks manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models. Moreover, GPaCo can be applied to the semantic segmentation task and obvious improvements are observed on the 4 most popular benchmarks. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
Autonomous Classification and Decision-Making Support of Citizen E-Petitions Based on Bi-LSTM-CNN

The increasing number of e-petition services requires accurate calculation methods to perform rapid and automated delivery. Automated text classification significantly reduces the burden of manual sorting, improving service efficiency. Moreover, existing text classification methods focus on improving sole models with an insufficient exploration of hybrid models. Moreover, existing research lacks combinatorial model selection schemes that yield satisfactory performance for petition classification applications. To address these issues, we propose a hybrid deep-learning classification model that can accurately classify the responsible department of a petition. First, e-petitions were collected from the Chinese bulletin board system and then cleaned, segmented, and tokenized into a sequence of words. Second, we employed the word2vec model to pretrain an embedding matrix based on the e-petition corpus. An embedding matrix maps words into vectors. Finally, a hybridized classifier based on convolutional neural networks (CNN) and bidirectional long short-term memory (Bi-LSTM) is proposed to extract features from the title and body of the petition. Compared with baseline models such as CNN, Bi-LSTM, and Bi-LSTM-CNN, the weighted F1 score of the proposed model is improved by 5.82%, 4.31%, and 1.58%, respectively. Furthermore, the proposed automated petition classification decision support system is available on the e-petition website and can be used to accurately deliver petitions and conduct citizen opinion analysis.
Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning

In MOOC learning, learners' emotions have an important impact on the learning effect. In order to solve the problem that learners' emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities.
Performance Driven Analytical Workload Division Model for the HPC Applications on CPU-GPU Heterogeneous Cluster

Abstract High-Performance Computing is the cornerstone for many scientific and industrial innovations. The demand for high-performance computing power is one of the driving factors for the innovations of computer hardware. In the hybrid system, CPUs and GPUs are combined to produce better performance while executing HPC applications. The critical challenge to achieving better performance in a heterogeneous cluster is the efficient distribution of the workload among the CPUs/GPUs in the nodes. In this work, to address the distribution workload issue, an optimized analytical workload division model for the heterogeneous cluster is developed to efficiently distribute the workload among the nodes of a heterogeneous cluster. The analytical model considers workload, processing capabilities, and the number of CPUs and GPUs on the cluster to effectively distribute the workload. HPL and merge sort benchmark applications are used to test the proposed strategy. Workload division strategy is tested by conducting extensive experiments. To address the inter-node and intra-node communication challenge, pinned memory technique is used along with a single MPI process per node technique and CUDA IPC. The proposed workload division strategy is validated with the HPL application and Merge sort. Experiments show that the proposed workload division strategy performs much better than the existing works.
Designing User Interface of a Mobile Learning Application by Using a Design Thinking Approach: A Case Study on UNI Course

UNI Couse, a mobile learning application product developed by CV Unindo Hestama Kreatif, is a mobile learning application that newly transform its system from web-based e-learning platforms. The incapability of web-based systems to gain a better experience with wider consumers trigger the transformation. Designing a mobile learning application is a strategy to increase the usability of the existing e-learning system in a higher variety of services as well as more users. This study utilizes a design thinking approach to understand what user feels and need in their experience of using a mobile application. This approach consists of five stages and is divided into three processes: understanding, designing, and evaluating. This study illustrates a step-by-step design thinking implementation as well as the result of each stage. User testing assesses the usability of the mobile learning application. Finally, design thinking implementation reveals that more users feel comfortable with the apps because all features are needed and easy to understand. The design thinking approach has successfully helped UNI Course to create mobile learning applications that satisfy the users.
Pairing of applicants’ CJM with university profile in digital environment as a management task

The work is devoted to the application of the Customer Journey Map (CJM) for university applicants and managerial tasks of adapting the profile of the university in the digital environment to the trajectories of the consumer. The author’s definition of the “profile of the university in the digital environment” concept is offered, the analysis of statistical data of university websites is made. The combination of classical studies of applicants’ motivation and factors influencing enrollment in a particular university with modern methods of studying consumer behavior in the digital environment allows to increase the effectiveness of the admission campaign. Customer Journey Map as an analysis tool, in conjunction with the data of network traffic allows to optimize all types of communications of the university and provide its management with necessary information when making managerial decisions. The research revealed an insignificant influence of the university social networks and thematic groups on the decision to enroll to a particular university, which what caused a deeper study of visitors’ behavior on the university websites. Internet traffic activity for a number of universities was assessed as direct requests, through search services, social networks, mail servers, as well as through referral links, and advertising traffic. The study showed that search engines and direct queries gave the maximum value for traffic. But the effectiveness of advertising to attract applicants is rather low, which indicates the insignificant role of university advertising and confirms the data of the survey in the construction of the Customer Journey Map.
Software defect prediction: A study on software metrics using statistical and machine learning methods

Software defect prediction aims at identifying defect prone software modules in order to allocate optimal testing resources.The role of testing in software development life cycle is vital especially when software systems are becoming more and more complex, representing a suitable environment for defects.Several researchers have striven to develop models able to determine defective modules with the aim of reducing time and cost of software testing.Such models are typically trained on software measurements, known as software metrics, which describe the characteristics of a software project in terms of e.g., dimension and complexity.These metrics reduce the subjectivity of software quality assessment and can be relied on for decision making, e.g., to decide where to focus software tests.The aim of our work is to employ both feature selection or construction techniques and machine learning techniques to build software defect prediction models on different kinds of software dataset metrics (derived from various software projects available in the NASA and Eclipse repositories), and assess their performances by considering accuracy, precision, recall and area under the curve.We have used non parametric tests to compute the statistical significance of the obtained results.The collected metrics belong to three main categories: dimension, complexity and object orientation.The involved datasets contain class labels, i.e., information on the defectiveness of the software modules.To make our study available to research community, we have developed an open source and extensible R application that supports researchers to load the selected kinds of datasets, to filter them according to the their features and to apply machine learning techniques.