Reviewerly/exHarmony huggingface dataset:
Using latent variable models to make gaming-the-system detection robust to context variations

Abstract Gaming the system, a behavior in which learners exploit a system’s properties to make progress while avoiding learning, has frequently been shown to be associated with lower learning. However, when we applied a previously validated gaming detector across conditions in experiments with an algebra tutor, the detected gaming was not associated with reduced learning, challenging its validity in our study context. Our exploratory data analysis suggested that varying contextual factors across and within conditions contributed to this lack of association. We present a new approach, latent variable-based gaming detection (LV-GD), that controls for contextual factors and more robustly estimates student-level latent gaming tendencies. In LV-GD, a student is estimated as having a high gaming tendency if the student is detected to game more than the expected level of the population given the context. LV-GD applies a statistical model on top of an existing action-level gaming detector developed based on a typical human labeling process, without additional labeling effort. Across three datasets, we find that LV-GD consistently outperformed the original detector in validity measured by association between gaming and learning as well as reliability. LV-GD also afforded high practical utility: it more accurately revealed intervention effects on gaming, revealed a correlation between gaming and perceived competence in math and helped understand productive detected gaming behaviors. Our approach is not only useful for others wanting a cost-effective way to adapt a gaming detector to their context but is also generally applicable in creating robust behavioral measures.
Seq2EG: a novel and effective event graph parsing approach for event extraction

Event extraction is a fundamental task in information extraction. Most previous approaches typically transform event extraction into two subtasks: trigger classification and argument classification, and solve them via classification-based methods, which suffer from some inherent drawbacks. To overcome these issues, in this paper, we propose a novel event extraction model Seq2EG by first formulating event extraction as an event graph parsing problem, and then exploiting a pre-trained sequence-to-sequence (seq2seq) model to transduce an input sentence into an accurate event graph without the need for trigger words. Based on the generative event graph parsing formulation, our model Seq2EG can explicitly model the multiple event correlations and argument sharing and can naturally incorporate some graph-structured features and the rich semantic information conveyed by the labels of event types and argument roles. Extensive experimental results on the public ACE2005 dataset show that our approach outperforms all previous state-of-the-art models for event extraction by a large margin, respectively, obtaining an improvement of 3.4% F1 score for event detection and an improvement of 4.7% F1 score for argument classification over the best baselines.
A Modular Hierarchical Model for Paper Quality Evaluation

Paper quality evaluation is of great significance as it helps to select high quality papers from the massive amount of academic papers. However, existing models needs improvement on the interaction and aggregation of the hierarchical structure. These models also ignore the guiding role of the title and abstract in the paper text. To address above two issues, we propose a well-designed modular hierarchical model (MHM) for paper quality evaluation. Firstly, the input to our model is most of the paper text, and no additional information is needed. Secondly, we fully exploit the inherent hierarchy of the text with three encoders with attention mechanisms: a word-to-sentence(WtoS) encoder, a sentence-to-paragraph(StoP) encoder, and a paper encoder. Specifically, the WtoS encoder uses the pre-trained language model SciBERT to obtain the sentence representation from the word representation. The StoP encoder lets sentences in the same paragraph interact and aggregates them to get paragraph embeddings based on importance scores. The paper encoder does interaction among different hierarchical structures of three modules of a paper text: the paper title, abstract sentences, and body paragraphs. Then this encoder aggregates new representations generated into a compact vector. In addition, the paper encoder models the guiding role of the title and abstract, respectively, generating another two compact vectors. We concatenate the above three compact vectors and additional four manual features to obtain the paper representation. This representation is then fed into a classifier to obtain the acceptance decision, which is a proxy for papersÃ¢â‚¬â„¢ quality. Experimental results on a large-scale dataset built by ourselves show that our model consistently outperforms the previous strong baselines in four evaluation metrics. Quantitative and qualitative analyses further validate the superiority of our model.
Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures

The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol- ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.
Multi-resolution domain adaptation via multiple instance learning for improving the recognition accuracy of Japanese oak wilt in low-resolution satellite imagery

Due to the trade-off between spatial resolution of the imagery and satellite revisit times, the research topics using multiresolution remote sensing data have attracted much attention. In recent years, the techniques for improving the visibility of multi-resolution imagery have been proposed, including pan-sharpening and super-resolution. However, there are relatively few studies on the techniques of improving the model performance on low-resolution imagery by referring to detailed information from the high-resolution imagery during training time. To tackle this type of task, domain adaptation has been proposed in the field of computer vision to adapt a model trained on one dataset to another with different properties. Yet, domain adaptation for multi-resolution data is difficult due to the scale variation in addition to differences from the camera sensors. In this study, we propose a new approach for multi-resolution modeling that combines the major techniques, semi-supervised domain adaptation (SSDA) and multiple instance learning (MIL). Under the MIL framework, a large scene can be regarded as a bag of instances (e.g., image patches), and information from different receptive field sizes can be exploited. We conducted experiments on a dataset of Japanese oak wilt, which is known to have severe forest damage with two different optical satellite imagery, SPOT6&7 satellite imageries (1.5m) and Pleiades-1B satellite imagery (0.5m). The proposed method improves the discrimination accuracy of the low-resolution model compared to the standard SSDA technique. This obtained result reveals the potential usefulness of MIL for effective multi-resolution modeling.
Distance education management system for high-tech agricultural talents based on cloud computing

The scale of school construction is becoming larger and larger, which requires that the traditional information management and processing need to be changed to adapt to the continuous development of society. How to use the means different from the traditional way to make corresponding changes and changes, and establish systematic and convenient information management has become an indispensable requirement. Only by establishing an intelligent management system, can data information be carried out effectively and quickly. This not only reduces the workload but also avoids the occurrence of errors affecting the normal teaching work. In this paper, part of the cloud platform data is obtained through the My SQL database provided. According to the cloud platform database, the information is obtained to help analyze the goal of user behavior. The remaining cloud platform data is obtained through the provided cloud platform API interface, and the data processing is collated and executed for subsequent use. This paper designs the management system of distance education for agricultural talents, and initially constructs a new operation model of the design education platform which adapts to the era of mobile Internet and conforms to educational reform. The experiment tells that the related design of this paper plays a positive role in the reform of design education and teaching in practice.
HUMAN CAPITAL DEVELOPMENT AND NIGERIA’S ECONOMIC GROWTH

The study examined the effect of developing human capital on Nigeria's economic growth.Contextual analysis served as the main information source for this study, in addition to various documentary and electronic sources.The human capital theory is the foundation of this study.The study concludes that there is a clear and evident link between the development of human capital and economic growth in Nigeria and makes several recommendations, including encouraging private sector investment in health and education to increase their involvement in the delivery of human capital services to the populace.This assumes that government spending on the health sector is ineffective in addressing health challenges without private investment in health.Also, an increase in the welfare programs for health care workers in order to lower the misery index in Nigeria and prevent a mass flight of doctors and other health professionals from the country's shores is important as this would improve the quality of life for Nigerians.
Posetal Diagrams for Logically-Structured Semistrict Higher Categories

We now have a wide range of proof assistants available for compositional reasoning in monoidal or higher categories which are free on some generating signature. However, none of these allow us to represent categorical operations such as products, equalizers, and similar logical techniques. Here we show how the foundational mathematical formalism of one such proof assistant can be generalized, replacing the conventional notion of string diagram as a geometrical entity living inside an n-cube with a posetal variant that allows exotic branching structure. We show that these generalized diagrams have richer behaviour with respect to categorical limits, and give an algorithm for computing limits in this setting, with a view towards future application in proof assistants.
Opening doors to the future. Technology and gender equity in learning

This research applied an Interactive Survey is innovative and motivate program. Participants to field of science and technology. Female gender students and undergraduate level. Method.- quasi-experimental design, experimental field for new developments longitudinal group with three measurements (pretest, posttest and follow-up). Population 2,791 students and random sample 930 female students are studying the third year of high school belonging to 9 campuses of Universidad Autónoma del Estado de México. Material.- Platform for Educational Services (SEDUCA3) to implement the intervention of Interactive Survey and Learning Strategies Questionnaire for University (CEA-U). Results.- The Interactive Survey elevates learning strategies in: positive association, applicability, gradual approach, generative design and planning. Increasing enrollment in computer science area to 1.3%.
Construction of intelligent sensing and fault early warning system of large power transformer based on cloud edge cooperation

According to the ubiquitous power Internet of Things strategy, the paper builds an intelligent analysis model for transformer online monitoring based on the power transmission and transformation Internet of Things architecture, summarizes the transformer’s intelligent sensing parameters and their acquisition methods, analyzes the monitoring data transmission method, and then proposes the application The algorithm model of prediction and diagnosis realizes the intelligent evaluation and early warning, and finally builds a cloud-side comprehensive evaluation model to form an efficient cloud-side collaboration system. This combination method efficiently and completely constructs a large-scale power transformer state assessment and fault warning method system based on cloud-side collaboration, which fully embodies the mechanism of rapid and timely warning at the edge, comprehensive assessment of the cloud, and collaborative operation of cloud-side information exchange. It is time-sensitive and comprehensive. It has specific guiding significance for the operation management and maintenance decision-making of the transformer under the new mode.
Occlusion face recognition based on improved attention mechanism

Due to the new crown and other epidemic diseases that make people wear masks to travel, the accuracy of the original face recognition system is affected. To address this challenge, a mask-wearing face recognition system based on an improved attention mechanism is proposed. First, Adding a maximum pooling operation to the CA (Coordinate Attention) attention module, then, placing attention module in the residual unit to form a feature extraction network. LResNet18E-IR is selected as the backbone network. Finally, the ArcFace loss and occlusion probability loss are combined to establish a multi-task network, which further promotes the accuracy of occluded face recognition. The results demonstrate that the system effectively increases the recognition accuracy of masked face and maintains almost the same accuracy as the original model on the unmasked dataset.
Introduction of A Row-Skip Pattern in Complete Coverage Path Planning for Agricultural Fields

Over the past two decades, an evolutionary effort has been established in the agricultural sector to develop efficient autonomous systems that can carry out common in-field operations including harvesting, mowing, and spraying. Increasing production while decreasing costs and environmental damages is one of the main objectives for these autonomous systems. Due to the nature of these tasks, complete coverage path planning techniques are crucial to determining the best path that covers the entire field while accounting for terrain characteristics, operational needs, and robot properties. In this study, we propose a novel complete coverage path planning approach to define the ideal path for a wheeled robot across an agricultural field. To identify all feasible solutions satisfying a set of predefined constraints, a method based on tree exploration is first proposed that examines row-skip patterns. Second, the most optimal solution is selected by a selection method. Maximizing the covered area while minimizing overlaps, non-working path length, number of turns containing reverse moves, and overall travel time are the objectives of the selection method. We showed on 6 real-world fields geometries that the row skip approach offered benefits in terms of reduction of the required headland size, and often helped decreasing the number of necessary reverse moves and the overlaps, while increasing the covered area.
Design of Highly Scalable Graph Database Systems without Exponential Performance Degradation

The main challenge faced by today's graph database systems is sacrificing performance (computation) for scalability (storage). Such systems probably can store a large amount of data across many instances but can't offer adequate graph-computing power to deeply penetrate dynamic graph dataset in real time. A seemingly simple and intuitive graph query like K-hop traversal or finding all shortest paths may lead to deep traversal of large amount of graph data, which tends to cause a typical BSP (Bulky Synchronous Processing) system to exchange heavily amongst its distributed instances, therefore causing significant latencies. This paper proposes three schools of architectural designs for distributed and horizontally scalable graph database while achieving highly performant graph data processing capabilities. The first school, coined HTAP, augments distributed consensus algorithm RAFT paired with vector-based computing acceleration to achieve fast online data ingestion and real-time deep-data traversal in a TP and AP hybrid mode. The second school, named as GRID, leverages human-intelligence for data partitioning, and preserving the HTAP data processing capabilities across all partitioned clusters. The last school incorporates SHARD and advanced GQL optimization techniques to allow data partitioning to be done fully automated yet strive to achieve lower latency via minimum I/O cost data migration model when queries spread across multiple clusters.
Recognizing Command Words using Deep Recurrent Neural Network for Both Acoustic and Throat Speech

The importance of speech command recognition in a human-machine interaction system is increased in recent years. In this study, we propose a deep neural network-based system for acoustic and throat command speech recognition. We apply a preprocessed pipeline to create the input of the deep learning model. Firstly, speech commands are decomposed into components using well-known signal decomposition techniques. The Mel-frequency cepstral coefficients (MFCC) feature extraction method is applied to each component of the speech commands to obtain the feature inputs for the recognition system. At this stage, we apply and compare performance using different speech decomposition techniques such as wavelet packet decomposition (WPD), continuous wavelet transform (CWT), and empirical mode decomposition (EMD) in order to find out the best technique for our model. We observe that WPD shows the best performance in terms of classification accuracy. This paper investigates long short-term memory (LSTM)-based recurrent neural network (RNN), which is trained using the extracted MFCC features. The proposed neural network is trained and tested using acoustic speech commands. Moreover, we also train and test the proposed model using a throat mic. speech commands as well. Lastly, the transfer learning technique is employed to increase the test accuracy for throat speech recognition. The weights of the model train with the acoustic signal are used to initialize the model used for throat speech recognition. Overall, we have found significant classification accuracy for both acoustic and throat command speech. We obtain LSTM is much better than the GMM-HMM model, convolutional neural networks such as CNN-tpool2 and residual networks such as res15 and res26 with an accuracy score of over 97% on Google’s Speech Commands dataset and we achieve 95.35% accuracy on our throat speech data set using the transfer learning technique.
Google Apps® used in teaching practice

This project seeks to promote the use of Google Apps® in teaching teachers and students to improve their teaching - learning. The research employed a reliability of 95% and generated a series of educational tools based on incorporating tools Google® in education through the creation of a Virtual Classroom and culminated in the acceptance of the study population. The project allowed breaking traditional patterns and obtaining improvements in student performance against the knowledge and understanding of their knowledge. In conclusion, the use of Google Apps® helped get savings on average 83.68% of time in their review of tests.
Implementation of Scrum Methodology in the construction of the intelligent system Acerkte of Academic tutorials

This paper describes the automation of processes tutorials based on a feasibility study on the HEIs, just as agile SCRUM methodologies are implemented in the construction of Acerkte system that will strengthen the support of the student during his tutorial session, as well as manage and manage activities to optimize time and resources in the school, tutor-tutored for it, the web system is made up of 10 well defined in the Sprint Backlog and assigned priorities Sprints. For agile methodology which will allow deliveries to functional and developed with cutting-edge technologies applied to development as PHP 5.5 [6] [7], HTML 5, CSS3, jQuery Mobile, Phonegap, Corel Draw, Arrowchat, Bootstrap, Senchatouch.