Reviewerly/exHarmony huggingface dataset:
Mixture of Graph Enhanced Expert Networks for Multi-task Recommendation

Multi-task learning (MTL), which jointly tackles multiple tasks through information sharing, has been widely applied to many recommendation applications. Recently, current efforts targeted for recommendation focus on learning task relationships based on the Multi-gate Mixture-of-Experts (MMoE) architecture with shared input features (i.e., subtle feature engineering for user-item interaction). Recent evidences suggest the Graph Neural Network (GNN) as a powerful component in characterizing deep interaction context for recommendation, greatly contributing to easing the data sparseness issue in online advertising services. Hence, we make the first attempt to explore the GNN towards multi-task recommendation, by designing Mixture of Graph enhanced Expert Networks (MoGENet). Specifically, we propose a novel multi-channel graph neural network to jointly model high-order information with the user-item bipartite graph as well as derived collaborative similarity graphs for users and items. On the top of the learned deep interaction context, a group of graph enhanced expert networks are introduced for contributing to the multi-task recommendation in a cooperative manner. Experimental results on three real-world datasets show that MoGENet consistently and significantly outperforms state-of-the-art baselines across all target tasks.
Medication Adherence of Tuberculosis Patients in Yogyakarta: A Cross Sectional Study

Background: Tuberculosis (TBC) is one of the infectious diseases that is a major health problem in the world. The disease usually affects the lungs but can also affect other sites. Treatment adherence is the most important component in achieving treatment success. Medication compliance can also be interpreted as the extent to which the patient consumes the drug in accordance with the provisions that have been given by the doctor. This study was conducted to determine the factors associated with adherence to treatment of TBC patients in the city of Yogyakarta. Methods: This research is an analytic observational study with a cross sectional approach. The population in this study included all pulmonary TBC patients in 18 health center in Yogyakarta and still undergoing treatment, a sample of 75 people was obtained. The data was collected using questionnaires. Then data were analyzed using chi-square test with a significance value of £0,05. Result: The results of this study indicate that factors related to medication adherence in tuberculosis patient are knowledge (sig=0.016) and attitude (sig=0.038), while the factors that are not related to medication adherence are self-efficacy (sig=1.000), motivation (sig=0.375), family support (sig=0.700), support for health workers (sig=0.353) and stigma (sig=0.754) with adherence to treatment of TBC patients in Yogyakarta City. Conclusions: Knowledge and attitude are risk factors for medication adherence in tuberculosis patients in Yogyakarta City
Scope and Challenges of Blockchain in Indian Supply Chain Transformation for MSMEs

Micro, Small, and Medium Enterprises (MSMEs) contribution is around 29% of India’s GDP through national as well as international trade. They are not only providing employment on a larger scale but they are also contributing to the economic development of the underdeveloped and rural areas of India. MSME's old approach is unable to keep up with today's global business operations. Numerous studies have highlighted several reasons why the operations of MSMEs are insufficient: To begin with, MSMEs' transactions are typically kept in sync with a centralized system provided by third-party software vendors. Second, there is a lot of information scattered throughout the lifespan, such as an updated bill, real-time efficiency assessments, and changing maintenance requirements. Third, conventional information frameworks lack an effective method for exchanging expertise and resources. Due to this crucial contribution, MSMEs must adopt a new technological advancement and need to drive the business efficiently and effectively for India’s growth. This article focuses on use cases and research is done on one of the technological advancements, Blockchain technology. Through cryptocurrencies, Blockchain technology has disrupted the FinTech industry with humongous recognition from the Public as well as the Private Sector. Likewise, Blockchain technology has significant importance in the supply chain management of products or services. This article contains the inferences from the primary research on the adoption of Blockchain technology in MSMEs’ supply chain, various models, challenges, and the overall scope of transforming the supply chain of Indian MSMEs. The article has also covered the study of Global MSMEs. The objective of this study is to reduce the gap in the literature and to help MSMEs to adopt Blockchain technology in their supply chain management functions. The findings imply that by using BT, MSMEs can grow their operations and streamline their supply chains.
Students Views on the Benefits and Drawbacks of Using Tablets: A South African Case Study

The aim of the research was to explore the views of students on the benefits and drawbacks of using tablets at a rural university located in the Eastern Cape province of South Africa.The study adopted qualitative method and employed a case study research design.A total of 155 students from a population of 254 voluntarily partook in the study.The instrument used to collect the data was an open-ended questionnaire.Thematic analysis was used to analyse qualitative data.Findings showed that while students had benefits in the case of learning, doing research and user-friendliness, they also had drawbacks such as distractions, taking photos and watching videos.Overall, it should be understood that every technological device has benefits and drawbacks, so in tablets as well.Therefore, if the students can behave maturely and avoid the off-task behaviours, tablets can be considered as a very effective tool for the learning purpose.
Pengembangan Aplikasi Android Komik Interaktif Berbasis STEM-PjBL Sebagai Media Pembelajaran pada Materi Sistem Pernapasan pada Manusia

In order to increase the active role and interest in student learning, creativity is one of the things that teachers must have both in choosing methods and teaching materials, one of which is in biology subjects. One way to increase the active role and interest in student learning, teachers can use multimedia that has been integrated with the learning model, one of which is the STEM-PjBL learning model. This statement was strengthened by the results of the initial survey on 25 students of class XI IPA at SMA Islam NU Pujon which showed that 86% of students stated that learning biology by utilizing multimedia teaching materials based on learning models could attract them to learn the concept of biology, as many as 88% of students stated that they wanted biology learning. which is supported by the existence of multimedia teaching materials in order to bring abstract concepts into reality and as many as 85% of students think learning using multimedia can make it easier to learn abstract concepts about biology. Based on the survey results, the researcher wants to develop an interactive comic android application based on STEM-PjBL as a learning media on the respiratory system in humans.
Research on the improvement of English cognitive linguistics under computer artificial intelligence technology

In the spoken dialogue scene of English cognitive linguistics under artificial intelligence technology, we use artificial intelligence technology to construct different forms of virtual reality scenes. In the artificial intelligence English learning scenario, students can obtain real-life English cognitive linguistics oral dialogue training. During the training process, the artificial intelligence technology can record the students’ English cognitive linguistics oral dialogue voice information and play back the dialogue voice information to the students. The artificial intelligence technology enables the students to know whether their pronunciation in the English cognitive linguistics oral dialogue is not correct.
CSR Disclosures and Knowledge-Sharing Behavior: The Impacts of CSR Altruistic Attribution and CSR Identification

Journal of Management Sciences (JMS) is a biannual HEC Pakistan indexed research journal. JMS is a scholarly journal that publishes scientific research on the practice of management and its application in business. It is multidisciplinary in scope and interdisciplinary
Detection of Adversarial Examples Based on the Neurons Distribution

Deep neural network has very good achievements in the detection, identification, and prediction of various fields because deep learning models can efficiently learn the features of various fields. However, a recent attack called adversarial examples can interfere with feature extract of deep learning models by producing data that is similar to real data but completely different. We found that although the output examples are indistinguishable to the naked eye, the activation state of neurons is completely different. Based on this observation, we used the probability distribution model to count the activation of neurons and successfully detected the attack of adversarial examples.
Content Consistency Preserving Style Transfer Network

For the problem that it will generate errors in the process of content feature transfer and cross-domain feature fusion caused by encoder-decoder style transfer networks, a content consistency preserving style transfer network is proposed. Source and target images are input to the encoding module for extracting content and style features respectively. Dual-chain feature transfer module constructs the same depth feature mapping with content encoder and decoder, and processes content feature parallelly by feature enhancement and feed-forward reference chains. Content and style features are fused in decoder and output style transfer result. With the simulation test based on multimodal unsupervised image-to-image translation (MUNIT), the style transfer effect of proposed network on vague objects and low contrast between the objects and background in low light environment is significantly increased. The experiment results on BDD100K dataset show that FID and IS are decreased by 3.2% and increased by 8.6% on average than MUNIT respectively, the proposed network can achieve content consistently and style accurately image trans-formation, which is able to application autonomous vehicle systems.
Research on Fault Diagnosis Method for Reactor Primary Circuit System Based on multi-source information fusion

Reactor primary circuit system is a complex dynamic system, variable parameter coupling, operation safety problems are prominent. In order to reduce the risk, a multi-source information fusion diagnosis system based on signed directed graph (SDG) and particle swarm optimization BP neural network (PSO-BP) is proposed. Utilizing D-S evidence theory for neural network diagnostic information fusion, logic inference combining SDG model, to determine potential failure. Simulation test shows that the intelligent diagnosis model could estimate the faults effectively, and provides the fault alarm transmission path.
Synthesis of Stochastic Functions in Presence of Varied Degrees of Correlation

Correlation is an intriguing topic in stochastic computing and often leads to efficient solutions when applied to certain class of problems. There are various works to formalize the description of Stochastic Function (SF) implemented using correlation in numbers. In the present work, a heuristic mathematical study has been conducted using the properties of correlation interaction in Stochastic Numbers (SNs). Relevant theorems are developed based on the findings with a motto to illustrate the necessary mathematical background on correlation injection in SNs. We further conceptualized the idea to synthesize complex Boolean functions consisting of n variables using simplistic approach.
Comparative Study of Horse Swarm Algorithm and Classical Algorithm

At present, optimization algorithms have been widely used in various scientific fields.These optimization algorithms are usually stimulated by the natural behavior of human, animal, plant, physical or chemical reagents.Most of the algorithms proposed in the past decade are inspired by animal behavior.Based on the horse swarm optimization algorithm, a horse swarm algorithm (WHO), which simulates the social life behavior of horses, is proposed in this paper.The new algorithm integrates the golden sinusoidal guiding mechanism as local operators into WHO algorithm, which improves the accuracy and convergence speed of the algorithm; It avoids the early over convergence of the algorithm.On the challenging CEC2019 test set, the WHO algorithm is comprehensively compared with other improved algorithms.Simulation results show that WHO algorithm has better performance in search efficiency, convergence accuracy and avoiding local optimum for both high-dimensional and fixed-dimensional problems.The results show that compared with other algorithms, this algorithm has strong competitiveness.
New Collaborative Environment Enables a Holistic Strategy to Revitalize Mature Field Development Plans

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Events

The support for events is considered one of the most exciting features in C#. It is useful because you can send notifications from one code segment to another code segment by using events. These are common in GUI applications such as when you fill in an online form or click the submit button and then notice some interesting change in the UI (for example, you may see a pop-up message saying that the data was submitted properly). The Visual Studio IDE provides a lot of support when your code deals with events. Since these concepts are at the core of C#, it’s good to learn them well. So, before we jump into the Q&A sessions, I suggest you read the following points:
A Graph-Based Approach to the Disambiguation of Multiple Languages

There is a growing need for sophisticated free text filtering in today's world, and this demand is reflected in the abstract.Consequently, it is preferable to exclude instances when the phrase or words are used in an incorrect meaning when doing a search using those terms.Internet browsers, resource finding tools, relational databases with free-text fields, electronic document management, data warehousing, data mining, etc. might all benefit from this work.In this paper, we propose a collaborative method for resolving such ambiguities in words, called Word Sense Disambiguation (WSD).In order to implement graph-based WSD across several Indian languages, our technique makes use of IndoWordNet, a connected lexical knowledge base comprising word nets of 18 scheduled languages of India, a very big knowledge base.Therefore, the results demonstrate that we may attain state-of-the-art in WSD contexts by combining the extensive lexical knowledge with strong graph-based algorithms and combination approaches.However, no manual training, lexical entry hand-coding, or textual entity labelling is required.
Improving Deepfake Video Detection with Comprehensive Self-consistency Learning

Abstract Deepfake videos created by generative-base models have become a serious societal problem recently as been hardly distinguishable by human eyes, which has aroused a lot of academic attention. Previous researches have made effort to address this problem by various schemes to extract visual artifacts of non-pristine frames or discrepancy between real and fake videos, where the patch-based approaches are shown to be promising but mostly used in frame-level prediction. In this paper, we propose a method that leverages comprehensive consistency learning in both spatial and temporal relation with patch-based feature extraction. Extensive experiments on multiple datasets demonstrate the effectiveness and robustness of our approach by combines all consistency cue together.
Multi-feature Fusion Network for Single Image Dehazing

Existing image dehazing methods consider the learning-based methods as the mainstream. Most of them are trained on synthetic dataset, and may not be able to efficiently transfer to real outdoor scenes. In order to further improve the dehazing effect of the model in real outdoor scenes, this paper proposes an end-to-end Multi-Feature Fusion Network for Single Image Dehazing (MFFN). The proposed network combines the prior-based methods and learning-based methods. This paper first uses the method of supporting backpropagation in order to directly extract the dark channel prior and color attenuation prior features. It then designs a Multi-Feature Adaptive Fusion Module (MFAFM) which can adaptively fuse and enhance the two prior features. Finally, the prior features are added to the decoding stage of the backbone network in a multi-scale manner. The experimental results on the synthetic dataset and real-world dataset demonstrate that the proposed model performs favorably against the state-of-the-art dehazing algorithms.
Application of Double Sensitive Cost Random Forest in Heart Disease Detection

Traditional feature selection algorithms simply compute a feature cost vector to make the random process more tendentious, but do not consider the relative relationship between features, and degenerate into ordinary random forest algorithms when feature differentiation is not significant. In view of this, we propose the dual cost-sensitive random forest algorithm. The algorithm introduces two improvements. 1) Introducing sequential analysis in generating feature vectors, giving dynamic weights to different categories in classification. 2) Introducing cost sensitivity in the decision tree generation stage with the goal of minimum average error. After comparing with logistic regression, random forest, support vector machine and other algorithms, the experimental results show that the method has a lower misclassification rate in heart disease detection, which makes the result classification more reliable and more suitable for practical applications.
Smart Contracts: Violations, Applications and Comparisons

Blockchain is a rising technology and can revolutionize businesses, governments, organizations, and individuals. It helps to facilitate the transfer of money, products, and sensitive information throughout the world by providing a simple, safe means to create a trust for nearly any type of transaction. In this paper, we first introduce blockchain, its benefits, current developments and trends. Then we move on to Smart Contracts, discuss its working and lifecycle. Smart contract enforces behavior between parties that can be violated. This paper also describes ways of dealing and recovering from these errors. Finally, we provide an overview of different applications of smart contracts and a comparison between smart contract development platforms.
PENGEMBANGAN APLIKASI MEDIA PEMBELAJARAN TEBAK GAMBAR FLORA DAN FAUNA BERBASIS ANDROID PADA SDN 01 TIAS BANGUN

The quick advancement of innovation in the current period of globalization has given many advantages underway in different social angles. The utilization of innovation by people in assisting with finishing work is an unquestionable requirement throughout everyday life. This innovative improvement should likewise be trailed by advancements in Human Resources (HR).Humans as technology users must be able to take advantage of current technology, as well as develop the technology in the future. How to make an android-based guessing game application, with the guessing game using the can increase the intelligence of the students of SDN 1 Tias Bangun, with the research method of observation, documentation, the Android-based Guessing Game application was built using the Embarcadero Delphi 10.4 software and using a programming language java scripts. Based on the testing of the Android-based Guess Picture application, it can be concluded that the learning media is in the form of an Android-Based Educational Game to Improve Student Learning Outcomes at SDN 1 Tias Bangun.
A Non-Local Fuzzy C-Means Clustering Segmentation Algorithm Based on Comentropy and Between-Cluster Scatter Matrix to Overcome the Inherent Coherence Speckles of SAR Images

The fuzzy c-means (FCM) algorithm and many of its variations have been widely adopted for image segmentation tasks. However, these methods are unable to present satisfactory segmentation results when dealing with synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. In order to achieve the effective segmentation of SAR images, a robust FCM algorithm, namely NCBS_FCM, is proposed. The nonlocal spatial information is utilized to reduce the effect of speckle noise. Furthermore, NCBS_FCM takes advantage of the comentropy based on local gray histogram to acquire the adaptive weighting parameter for nonlocal spatial information term, which can achieve a better balance between speckle suppression and edge detail preservation. In addition, this paper incorporates the between-cluster scatter term into the objective function to adjust the distance between the cluster centers accordingly. Therefore, NCBS_FCM is more robust to various images and achieves satisfactory segmentation accuracy. Experiments on simulated and real SAR images show that NCBS_FCM outperforms other proposed variations of FCM by a significant margin.
Research on the Strategy of Chinese Language Teaching System in Colleges and Universities based on the Background of VR Technology

The article takes VR technology overview as the starting point, analyzes that VR technology can give the real experience to the experiencer, manipulate the virtual environment in real time, and solve the teaching problems. Based on this, the article combines the current situation of teaching reform of Chinese language and literature majors in colleges and universities under the background of VR technology, determines that teaching objectives, teaching resources and teaching methods are affected by VR technology, and proposes corresponding teaching reform strategies, so as to provide reference for related workers.
Test Users

When a scratch org is created, a user called User User is automatically added that has the System Administrator profile assigned to it. If your application or customization is only used by system administrators, you can perform testing with this user. Having said that, you will normally want to perform testing with different end users in mind, such as service agents and account executives; therefore, it makes much more sense to test with a user that has the same permissions as the end users in your production org.