Reviewerly/exHarmony huggingface dataset:
A Study of the Management of Early Childhood Education in the Nalgonda District of Telangana

The management of early childhood education (ECE) in Nalgonda District as part of the Integrated Child Development Scheme (ICDS) in Nalgonda is highlighted in this study. The program was first implemented in 1975. It was launched on a holy day, the birthday of Mahatma Gandhi, the founding father of our country. With its need-based initiatives, the program has gained widespread acceptance across the nation, which is in the best interests of the millions of children who make up the nation's true assets. The investigation looks into several ECE-related issues, including organizational structure, learning environment, health and hygiene, monitoring and supervision systems, and Anganwadi Center infrastructure. The researcher analyses the system's assets and weaknesses and offers recommendations for improved performance.
Rainfall Mapping in Medan Selayang Sub-District using Geographic Information System

Indonesia is a tropical country which has two seasons, namely the rainy season and the dry season. Rainfall that occurs will vary in each area depending on the altitude of the area, climate, season, and other factors. Medan Selayang Subdistrict is the Subdistrict with the closest distance to the rain station. Where the closer the location of the study area to the rain station, the more accurate the rainfall data obtained will be. One way to make it easier to record and process data is to create a rainfall map by utilizing spatial analysis through a Geographic Information System (GIS). The data in this study are rainfall data and geographic coordinate data obtained from the Meteorology, Climatology and Geophysics Agency in the Medan City area. After data collection, data processing was carried out using the QGIS 3.10 application. with the Inverse Distance Weighted (IDW) interpolation method. From the results of processing rainfall maps in the Medan Selayang sub-district using Inverse Distance Weighted (IDW) interpolation, the rainfall values (rainfall) ranged from 3029.74 mm/year – 3164.20 mm/year.&#x0D;
Metaverse : chances and challenges in my generation Z

The Successor of the internet": Well yes this is what many people believe about the Metaverse.These days everyone appears to be talking about this new technological paradigm called "Metaverse".Since the inception of Covid-19, many people have started to hear and read about this new buzz-word Metaverse.It has a huge hype attached to it because it holds the potential to revolutionize the internet space in such a manner that we have never witnessed before.
Weighted Prototype Network for Few-Shot Learning Via Graph Structure

Few-shot learning is attracting extensive research because of its ability to classify only a few co-trainable samples. Current few-shot learning approaches focus on learning class prototypes representation to solve problems by a simple averaging approach, but this approach ignores intra-class differences. In this paper, we propose a new weighted prototype network for few-shot learning. Our model consists of two modules, feature extraction and graph neural networks. We first construct graphs from the embeddings obtained from the feature extraction module. Then we fed these graphs into graph neural networks in order to model the similarity between samples using the graph structure, so as to obtain more reasonable class prototypes by assigning different weights to the embeddings of samples. The experimental results on two benchmark datasets show that our proposed model is comparable to the state-of-the-art few-shot learning approach.
"HOPE YOU HAVE A SHIT BIRTHDAY YOU FAT CUNT”– cognitive strategies, rhetorical figures and linguistic means used in insulting Tweets

The paper deals with offensive language gleaned from Twitter, encoded by (abusive) insults and verbal reactions to them. The study aims to analyse how they are constructed in terms of (i) the cognitive strategies employed by the insultees, (ii) rhetorical figures and (iii) linguistic devices used by both the insulters and insultees. The tweets are illustrative of the following shifts occurring in responses relative to insults: register clash, changing explicit insult into implicit, figure/ground reversal, syntactic echoing, changing abusive language into jocular or into an ironic insult, etc. The cognitive strategies employed by the insultees comprise: ignoring the insulting content, agreeing with the insult, and attacking the insulter with an explicit or implicit insult.
Quantum Correlation Dynamics of Three Qubits in Non-Markovian Environments

We investigate the quantum correlation dynamics of three independent qubits each locally interacting with a zero temperature non-Markovian reservoir by using the Geometric measure of quantum discord (GQD). The dependence of quantum correlation dynamicson amount of non-Markovian, the degree of initial quantum correlation and purity of the initial states are studied in detail. It is found that the quantum correlation of such three qubits system revives after instantaneous disappearance period when a proper amount of non-Markovian is present. A comparison to the pairwise quantum discord and entanglement dynamics in three qubits system is also made.
Introduction to the Special Section on Recent Advances in Networks and Distributed Systems

introduction Share on Introduction to the Special Section on Recent Advances in Networks and Distributed Systems Authors: Mathias Fischer Universität Hamburg, Hamburg, Germany Universität Hamburg, Hamburg, GermanySearch about this author , Winfried Lamersdorf Universität Hamburg, Hamburg, Germany Universität Hamburg, Hamburg, GermanySearch about this author , Jörg Liebeherr University of Toronto, Toronto, Ontario, Canada University of Toronto, Toronto, Ontario, CanadaSearch about this author , Max Mühlhäuser TU Darmstadt, Darmstadt, Germany TU Darmstadt, Darmstadt, GermanySearch about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 4November 2022 Article No.: 93pp 1–3https://doi.org/10.1145/3584743Published:15 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Topicalization out of English and Mandarin if-clauses and that-clauses

There are three main approaches to the (un)acceptability of long-distance dependencies (LDD): syntactic theories, processing theories, and discourse theories.Syntactic theories argue constraints on LDD are universal and purely syntactic, predicting these constraints should hold across languages and constructions.This study probes into the acceptability of topicalization from English and Mandarin if-clauses and that-clauses through contextualized acceptability judgment experiment, finding no adjunct island effect in either language, which supports the discourse approach, arguing that the discourse status of the extracted element in the construction is at play (focus status of the extracted element depends on the construction).If there is a focus status conflict between the element and construction, the penalty will appear.
Classifier versus taxonomic relations in a similarity judgement task

Compared with non-classifier speakers, classifier speakers often showed more sensitivity to classifier-oriented parameters (also referred to as conceptual saliency) such as animacy, shape and function -the purported "classifier effect".This study thus further testified the classifier effect through a similarity judgement task.Chinese speakers (N=41) rated significantly lower than English speakers (N=41) in taxonomic pairs, but not in thematic, classifier or filler pairs.However, both groups consistently rated highest in the thematic pairs, followed by the taxonomic, classifier and filler pairs.Chinese speakers also spent longer time than their English counterparts in each pair condition.A subset analysis of taxonomic pairs indicated a varying degree of disparity on conceptual saliency.Findings together suggested an implicit classifier effect from Chinese speakers, but classifier relation itself was not employed as a predominant parameter for object perception.
Comparing number and gender agreement processing in Russian: an experimental study

We conducted a self-paced reading experiment comparing attraction effects in number and gender agreement in Russian.Only one previous comprehension study (Tucker et al., 2021) compared them in Standard Arabic, getting distinct profiles.In Russian, larger reading time delays were associated with gender errors compared to number errors, but attraction effects with both features were similar.
Effectiveness of Chemscrabb Media Games on Fundamental Laws of Chemistry Towards for Students' Learning Outcomes

The research aims to determine the effectiveness of Chemistry Scrabble (Chemscrabb) games as Instructional Media on Fundamental Laws of Chemistry to Students Learning Outcomes. This research is a research and development (RD) with designed by Posttest Only design, in which the design is based on the Thiagarajan technique. The research held on SMAN 10 Padang. Sampling selection was done by Random Cluster Sampling with the result that students of X-Science 2 as the experiment class and students of X-Science 4 as the control class. The data instruments used were a Post-test, questionnaire, and interview of two classes of the sample. The data analysis technique was a Hypothesis test by an Independent T-test sample. Based on Independent T-Test, the obtained tcount is 1.833, where the score is in the curve area, which means that accept H0 (there is no significant difference between the two samples). From the data, the research inference is that the utilization of Chemistry Scrabble (Chemscrabb) games as instructional media on Fundamental Laws of Chemistry for 10th-class sciences students is not effective for 10th-class sciences students' learning outcomes. However, students' activeness in the experiment class in the strengthening process (exercise) is higher than in the control class.
VR-based product development process: opportunities and challenges in the automotive industry

In the product development, the success of a manufacturing strategy is achieved by specifying a manufacturing process that it can be produced with minimal impact. In the automotive industry, to detect late, i.e., already during manufacturing phase, issues on customers preferences on new vehicles, physical ergonomic issues, or problems on usability of a product has a high cost of correction and long the time to launch a new product. Thus, given the complexity of products that are under development, designers must perform a number of procedures to guarantee that the finished product fits customer demands and is accepted by the market. With the emergence of low-cost virtual reality devices, the automotive industry is adopting virtual reality for several engineering processes and support everyday engineering. The patent filings have boomed since 2016, in a technology space that is rapidly evolving, offering opportunities to enter an area while it is still young. Virtual reality benefits the product development process by saving costs and time, closing customers, improving interactions, avoiding prototype transportation, optimizing the assembly line, improving the performance and well-being of the user, mitigating hidden dangers, stimulating novel insights and increasing team collaboration, and feeling of engagement, among others. However, certain constraints must be overcome before the technology’s possibilities can be fully harnessed. The challenges faced by VR-based processes are the lack of realism due to unnatural tactile and visual interactions, latency and registration issues, communication difficulties between teams, unpleasant symptoms, issues on depth, haptic, motion and movement perceptions, cybersecurity, cybersickness, lack of intuitiveness, and partial control over the user’s moving around, among others. While there are still challenges to be addressed until industries can fully benefit from the technology’s potential and these constraints prevent virtual reality from fully replacing conventional automotive product development in the near future, it is a valuable contribution to the process. In this context, this work discusses the opportunities and challenges of virtual reality-based product development process in the automotive industry. A better knowledge on this subject may provide a reference for decision-makers, practitioners and researchers as they continue to develop innovative immersive solutions in the product development in the automotive industry.
Global Optimization Based on Hybrid Adaptive Differential Evolution Algorithm and Sooty Tern Optimization Algorithm

This paper proposes a hybrid algorithm combining STOA and DE, called STOA-ADE, for optimization problems. In STOA-ADE. Firstly, a mechanism of selecting mutation operators according to the diversity of the population is proposed to produce higher quality solutions. Further, based on the fitness value and diversity of the population, STOA is applied to the excellent individuals to improve the quality of the solution. The algorithm is tested on cec2015 benchmark function problems. The experiment proves that the strategy mechanism and algorithm proposed in this paper are effective and competitive.
Driver Distracted Behavior Detection Based on Deep Learning

In the traffic control system, the driver's distracted behavior detection is particularly important for traffic safety. Traffic accidents caused by distracted behavior of motor vehicle drivers are a common problem in traffic control systems. Focusing on the driver's distracted behavior detection, based on the original YOLOv4 framework, this paper uses the lightweight network mobilenet as the backbone network for real-time requirements of detection, and uses Depthwise Separable Convolution (DSC) to reduce model calculation and improve detection speed; increases SE module to improve the detection accuracy. The actual surveillance video data shows that the detection speed is 27fps faster than YOLOv4, and the recognition accuracy is increased by 2%.
News classifications based on CBA-PreambleCNN Model

In order to solve the problems of insufficient information extraction and poor classification effect of a single deep learning model, this paper proposes a hybrid multi-neural network CBOW-BiLSTM-Attention-PreambleCNN model(The CBA-PreambleCNN for short, PreambleCNN is the name of the improved TextCNN). The model uses Word2Vec as the word embedding layer to obtain the vector representation of the word, and then feeds the Bidirectional Long Short-Term Memory (BI-LSTM) network to capture the global information of the text, and then uses the Attention mechanism to make the word get different weights. Finally, it is fed into the improved Text Convolutional Neural Network (TextCNN fused with previous information, named PreambleCNN) to obtain the topic features of the Text, and the obtained feature vectors are fed into the softmax function for classification. After comparison with other models, this model has achieved good classification effect, and achieved 93.47% and 84.65% accuracy on Sina news dataset and Sohu news dataset, respectively. To some extent, this model solves the problem of insufficient information captured by a single model.
PEMANFAATAN KOMPOSIT NATURAL BENTONITE DAN AMPAS TEBU SEBAGAI PENJERNIH AIR LIMBAH INDUSTRI TAHU HARAPAN MAJU

&#x0D; &#x0D; &#x0D; &#x0D; Tofu industry is an industry dominated by small-scale businesses with limited capital. In terms of location, this business is also very spread throughout Indonesia. One of them is the Harapan Maju (HS) tofu industry in Cimanggis, Depok, West Java. This industry still uses the traditional method by disposing of waste into water bodies. Tofu liquid waste has the characteristics of containing high organic matter and the levels of BOD, COD, TDS are quite high, and and the pH value is low so that if it is directly discharged into the river it can pollute the environment. Therefore, bentonite and bagasse activated carbon are used to treat the waste water of the Harapan Maju tofu industry so that it is safe to be discharged into water bodies. Bentonite has a good ability to absorb organic matter and binds relatively small solids in water. Meanwhile, bagasse activated carbon functions as a purifier and absorbs heavy metals in wastewater. The process carried out is the absorption process or the process of separating solids from the fluid by passing the fluid through a filter medium or septum, where solids will be left in the medium. With this simple technology, it will be very effective when applied to the tofu industrial wastewater treatment, so that the waste that is disposed of will not disturb the ecosystem of water bodies. Before testing the BOD value was 714 ppm, TDS 1129 ppm, COD 1152 ppm and the pH of the wastewater was 4 . After testing, the BOD values were 142.8 ppm, TDS 407 ppm, COD 195.84 ppm and pH 6.&#x0D; &#x0D; &#x0D; &#x0D;
Investigation of Fake Insider Threats on Private Cloud Computing Services

Cloud-based services are service system mechanisms used by companies or organizations in conducting computerized and integrated transactions in a computer network. A service system must of course be balanced with a level of security. This is used to anticipate cyber crimes that have the potential to occur. Cloud-based services themselves are usually offered by a Cloud Service Provider (CSP). CSPs are generally configured so that they are accessible on the public internet for their services. Companies that prioritize data security want a system that is safe from a series of cyber crimes. Private cloud computing scheme is a solution that can be implemented as an alternative. The problem that occurs is the possibility of MITC (Man in the Cloud) attacks that infiltrate and manipulate identities so that they are detected as fake insider threats on cloud systems. This thesis aims to carry out threat analysis with the Man in the Cloud attack technique on private cloud computing services based on a study of the ISO 27032 standard. Reports and documentation of the results of the analysis are expected to become recommendations for the cybersecurity investigation and management process related to threats to cloud services with private schemes cloud computing.
Learning With Asymmetric Kernels: Least Squares and Feature Interpretation

Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels to be symmetric, which prevents the use of asymmetric kernels. This paper addresses the asymmetric kernel-based learning in the framework of the least squares support vector machine named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AsK-LS</i> , resulting in the first classification method that can utilize asymmetric kernels directly. We will show that AsK-LS can learn with asymmetric features, namely source and target features, while the kernel trick remains applicable, i.e., the source and target features exist but are not necessarily known. Besides, the computational burden of AsK-LS is as cheap as dealing with symmetric kernels. Experimental results on various tasks, including Corel, PASCAL VOC, Satellite, directed graphs, and UCI database, all show that in the case asymmetric information is crucial, the proposed AsK-LS can learn with asymmetric kernels and performs much better than the existing kernel methods that rely on symmetrization to accommodate asymmetric kernels.
Video Relationship Detection Using Mixture of Experts

Machine comprehension of visual information from images and videos by neural networks suffers from two limitations: (1) the computational and inference gap in vision and language to accurately determine which object a given agent acts on and then to represent it by language, and (2) the shortcoming in stability and generalization of the classifier trained by a single, monolithic neural network. To address these limitations, we propose MoE-VRD, a novel approach to visual relationship detection via a mixture of experts. MoE-VRD recognizes language triplets in the form of a < subject,predicate,object > tuple to extract the relationship between subject, predicate, and object from visual processing. Since detecting a relationship between a subject (acting) and the object(s) (being acted upon) requires that the action be recognized, we base our network on recent work in visual relationship detection. To address the limitations associated with single monolithic networks, our mixture of experts is based on multiple small models, whose outputs are aggregated. That is, each expert in MoE-VRD is a visual relationship learner capable of detecting and tagging objects. MoE-VRD employs an ensemble of networks while preserving the complexity and computational cost of the original underlying visual relationship model by applying a sparsely-gated mixture of experts, which allows for conditional computation and a significant gain in neural network capacity. We show that the conditional computation capabilities and massive ability to scale the mixture-of-experts leads to an approach to the visual relationship detection problem which outperforms the state-of-the-art.
PATS: Patch Area Transportation with Subdivision for Local Feature Matching

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at \url{https://zju3dv.github.io/pats/}.