Reviewerly/exHarmony huggingface dataset:
Edubot

Recently, chatbots have been used in various domains including health care and entertainment. Despite the impact of using chatbots on student engagement, there is little investment in how to develop and use chatbots in education. Such use of advanced technologies supports student learning, both individually and collaboratively. The effective use of chatbots in education depends on different factors, including the learning process, teaching methods, communications, etc. In this paper, the authors focus on the systematic utilization of chatbots in education. A proof of concept has been developed and tested using two MSc module, i.e., cloud computing and software engineering. The authors have used AWS Services to build the backend of Chatbot and integrate it with Facebook Messenger to allow students to learn via an additional venue, i.e., social media. The use of EDUBOT proved that chatbot can improve student learning and engagement especially at the time of COVID-19 where higher education is moving towards online teaching. Extending EDUBOT framework will help to support students' admin and other queries.
Incorporating Self-attention Mechanism and Multi-task Learning into Scene Text Detection

In recent years, Mask R-CNN based methods have achieved promising performance on scene text detection tasks. This paper proposes to incorporate self-attention mechanism and multi-task learning into Mask R-CNN based scene text detection frameworks. For the backbone, self-attention-based Swin Transformer is adopted to replace the original backbone of ResNet, and a composite network scheme is further utilized to combine two Swin Transformer networks as a backbone. For the detection heads, a multi-task learning method by using cascade refinement structure for text/non-text classification, bounding box regression, mask prediction and text line recognition is proposed. Experiments are carried out on the ICDAR MLT 2017 & 2019 datasets, which show that the proposed method has achieved improved performance.
Towards Autonomous Grading in the Real World

Surface grading is an integral part of the construction pipeline. Here, a dozer, which is a key machinery tool in any construction site, is required to level an uneven area containing pre-dumped sand piles. In this work, we aim to tackle the problem of autonomous surface grading on real-world scenarios. We design both a realistic physical simulation and a scaled real-world prototype environment mimicking the real dozer dynamics and sensory information. We establish heuristics and learning strategies in order to solve the problem. Through extensive experimentation, we show that although heuristics are capable of tackling the problem in a clean and noise-free simulated environment, they fail catastrophically when facing real-world scenarios. However, we show that the simulation can be leveraged to guide a learning agent, which can generalize and solve the task both in simulation and in a scaled prototype environment.
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning

Learning effective motion features is an essential pursuit of video representation learning. This paper presents a simple yet effective sample construction strategy to boost the learning of motion features in video contrastive learning. The proposed method, dubbed Motion-focused Quadruple Construction (MoQuad), augments the instance discrimination by meticulously disturbing the appearance and motion of both the positive and negative samples to create a quadruple for each video instance, such that the model is encouraged to exploit motion information. Unlike recent approaches that create extra auxiliary tasks for learning motion features or apply explicit temporal modelling, our method keeps the simple and clean contrastive learning paradigm (i.e., SimCLR) without multi-task learning or extra modelling. In addition, we design two extra training strategies by analyzing initial MoQuad experiments. By simply applying MoQuad to SimCLR, extensive experiments show that we achieve superior performance on downstream tasks compared to the state of the arts. Notably, on the UCF-101 action recognition task, we achieve 93.7% accuracy after pre-training the model on Kinetics-400 for only 200 epochs, surpassing various previous methods.
Categorical computation

In quantum computing, the computation is achieved by linear operators in or between Hilbert spaces. In this work, we explore a new computation scheme, in which the linear operators in quantum computing are replaced by (higher) functors between two (higher) categories. If from Turing computing to quantum computing is the first quantization of computation, then this new scheme can be viewed as the second quantization of computation. The fundamental problem in realizing this idea is how to realize a (higher) functor physically. We provide a theoretical idea of realizing (higher) functors physically based on the physics of topological orders.
Presyllabic Method to Correct Dysorthography in Elementary School Students

The process of teaching writing was impacted during virtual education by the Covid 19 pandemic in the first years of study, due to the inadequate use of methodological strategies. Dysorthography is a disorder that affects between 10 to 15% of the population in Latin America and is considered one of the first factors that affect the student's school performance, so we sought to implement a methodological guide of phonemes to contribute positively to learning in students in the third year of basic education. The research was field, descriptive, and bibliographic documentation was applied. Through the application of the methodological guide of phonemes, it was possible to observe the continuous improvement of the students in the learning process. The development of the research was necessary, since it was possible to determine in the first instance that the students do not have a correct spatial orientation, there is confusion with the letters b and d, because they do not recognize phonemes, so their writing is not legible, it is evident that dysorthography affects 70% of the academic performance in the area of language and literature. After four months of application of this phoneme guide, the results were excellent, with an 80% improvement in the teaching and learning process. It is considered that 52% of the students recognize the phonemes, 30% correctly apply punctuation marks and a 40% improvement in performance has been observed.
Virtual Physics Learning for Basic Education

This project shows an interactive virtual laboratory for teaching Physics, in the first year of BGU (Bachillerato General Unificado), adapted to the new Ecuadorian National Curriculum. The virtual laboratory was developed using JAVA language and was implemented under an opened software system. Additionally, it used a pedagogical work proposal for the integration of a virtual laboratory as a complementary activity to teach Physics. Finally, teachers and students were able to work online or offline, so that it can be used anywhere and anytime. The main result after implementing the interactive virtual laboratory was an increment in performance of students that domain the learning from 2% to 18% and the students that achieved the learning increased from 68% to 80%. Also, a pedagogical and didactic reinforcement for the students of physics in the first year of the New Curriculum at the BGU level was achieved. Finally, a technical procedure to implement complete courses for physics at all levels for BGU was established. The interactive virtual laboratory to teach Physics has been a pedagogical and didactic tool for students in the BGU, adapting to the new curriculum issued by MINEDUC.
Rotating Dual Antennas Based Detection Method For Distributed GNSS Spoofing Attacks

Global Navigation Satellite System (GNSS) spoofing is one of the most dangerous attacks, with the purpose of inducing false information and maliciously manipulating the positioning and timing results of GNSS applications. To protect the safety of the GNSS, a lot of anti-spoofing methods have been well-researched. Most of them are based on the single-antenna assumption, where multiple spoofing signals are transmitted from the same spoofing antenna. However, these methods would fail when confronted with distributed spoofing attacks. This type of spoofing attack simulates a scenario where authentic signals are coming from satellites in different directions by utilizing multiple transmitting antennas, more covert and more threatening. The calibrated antenna array can be utilized to cope with such threats. It compares whether the signal direction estimated by the antenna array and the expected signal direction obtained from the ephemeris message is consistent to detect spoofing signals. However, utilizing an antenna array is of high complexity both in the aspects of hardware and software. Besides, the antenna array needs to be calibrated in advance to compensate relative phase and gain of the antenna elements, which is inconvenient in practical use. The idea presented in this paper intends to overcome such limitations, based on the rotating dual antennas. Our method is able to detect spoofing attacks both utilizing single or multiple antennas. Furthermore, compared with the methods with the calibrated antenna array, the number of antenna elements is reduced by utilizing the rotating characteristics of antennas and no calibration process should be conducted. The performance analysis is supported by several simulation tests and field experiments, to prove the effectiveness of the proposed method.
The rational and artin characters of the finite group D2m × CP when P is a prime number

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Intissar Abd AL-Hur Kadum, Azal Taha Abdulwahab; The rational and artin characters of the finite group D2m × CP when P is a prime number. AIP Conference Proceedings 13 February 2023; 2414 (1): 040020. https://doi.org/10.1063/5.0114614 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
An Analysis of Agile Coaching Competency Among Practitioners

A key approach used by improvement specialists is that of Coaching. Coaching is described as a power tool for organisations looking to transition towards, and sustain, a new way of working. That new way of working, is often described as an Agile or Lean mode of work, with Agile being the progenitor of Lean. Agile is not alone in promoting the role of the coach, with Lean having similar roles identified as a cornerstone of mindset immersion. However, there is no accepted industry definition of what a process improvement Coach is, with a leading reference mode adopted informally. This outlines a core Competency Framework to capture the holistic skills required to become a competent Coach and is not specific about levels of mastery to attain the various competencies outlined. The model, being imprecise, is not backed by academic literature and aligns with noted observations about competency modelling being typically derived by less methodologically rigorous consultants who were not researchers. Despite this, the role of the Agile Coach has become an accepted and formative piece of a teams strategy and can derive success when deployed appropriately. In this paper, we will explore the deficiencies in the established competency model and propose a theoretically grounded competency model for Agile Coaches to act as a reference for Agile Coaches.
Development of Fundamental Tournament Learning Model for Elementary School Children in Limited Face-to-face Learning

The implementation of learning with a limited face-to-face meeting system.After online learning since the covid-19 pandemic, children are again getting a little bit of happiness in being able to interact and socialize at school.Therefore, the development of motion learning models through group competition games aims to be able to restore a positive attitude, cooperation, self-confidence, independence and responsibility in children and children's movement skills are more optimally developed in limited face-to-face meetings at this time.This development research uses the Research and Development (R&D) Borg and Gall development model.The small group trial in this development research was carried out at Kalangsurya III Elementary School, Rengasdengklok District, Karawang Regency with 30 students and the large group test was carried out at Kalangsari III Elementary School and Salman Al-Farisy Elementary School Rengasdengklok totaling 20 students, which then resulted in a motion learning model " Fundamental Tournament" which is declared feasible to be applied to elementary school children.The results of this study can be useful for teachers of physical education, sports and elementary school health in providing basic movement learning materials based on competition games that can give students a sense of enthusiasm, confidence, and cooperation in learning motion.
Proof of Concepts of Corda Blockchain Technology Applied on the Supply Chain Area

Currently, it is necessary a view from origin, movement, and destiny of products or goods in a supply chain system to avoid losses and risks of both time and money. Without a mechanism to synchronize data into multiple entities in a supply chain system, each entity can record its version of the data, producing errors and economic losses. In this scenario, blockchain technology has emerged as a solution for producer, buyers, sellers, suppliers, conveyors, and other entities in a supply chain system which can transact in a secured and trustworthy network that keeps synchronization in real-time. Thus, in this paper, we propose the employ of blockchain Corda as well we develop the CorDapps as a proof of concept for its use in supply chain sector. It is demostrated that Corda allows to transact in a secure way over shared data to increase efficiency, transparency and visibility. Further, it was evaluated the CorDapps benchmark performance in terms of throughput, latency, CPU and memory usage, where satisfactory results were obtained by using Corda DLT in the supply chain area.
Mining Frequent Patterns with Counting Quantifiers

Frequent Pattern Mining (FPM) is a classical graph mining task. In recent years, we have witnessed wide applications of FPM. However, emerging applications keep calling for more expressive patterns to capture more complex structures in a large graph. In light of this, we investigate the problem of mining frequent patterns with counting quantifiers. We first introduce quantified graph patterns (QGPs), which are the patterns incorporated with counting quantifiers. We then develop an algorithm along with an optimization technique to mine QGPs in a single large graph. On real-life graphs, we verified the performances of our (optimization) techniques.
Lightweight Model Inference on Resource-Constrained Computing Nodes in Intelligent Surveillance Systems

Intelligent Surveillance System (ISS) is an important application combining deep learning with IoT technologies. Meanwhile, multiple targets multiple camera tracking (MTMCT) has been widely recognized as a promising solution for ISS. However, current terminal devices have limited memory, power and computing power. Deep learning models deployed on these resource-constrained devices need to maintain a low-level number of parameters while ensuring inference delay and accuracy as much as possible. In this paper, we propose a lightweight model inference approach for resource-constrained edge computing nodes, which leverages the limited computing capability of edge devices for collaborative processing of model inference tasks. We consider a system model that includes a combination of both horizontal and vertical methods to dynamically partition deep neural networks. Further, we propose an adaptive strategy that dynamically controls the execution part in the terminal devices. In addition, a learning-based algorithm is used to obtain the near-optimal solutions for dynamic resource allocation decisions. Finally, we evaluate the system in terms of two metrics, i.e., computational latency and network throughput, based on a real-world dataset of campus surveillance. The experimental results reveal the superior effectiveness of the proposed scheme.
Proximity Preserving Graph Convolutional Networks

Graph Neural Networks (GNNs) have achieved promising performance in a wide range of graph-based tasks. Their key is to capture the smoothness of labels or features over nodes manifested in graph structure when stacking multiple layers. However, as the number of layers increases, most GNNs suffer from the over-smoothing issue. To solve this issue, we propose proximity preserving graph convolutional networks (PPGCNs) in this paper. Different from previous methods finetuning graph convolutional operator, we resort to modified graph Laplacian regularization in loss function. Specially, a novel optimization objective function with multi-order proximity preserving regularization (PP-Reg) is designed to guide an end-to-end framework. The theoretical evidence w.r.t. such optimization objective function verifies it can further smooth node features but inhibit over-smoothness. Moreover, empirical evidence verifies 1st-order PP-Reg is more suitable for assortative graphs, and 2nd-order PP-Reg is more suitable for disassortative graphs. Empirical evidence verifies PPGCNs achieve state-of-the-art results in the task of semi-supervised node classification on 8 assortative or disassortative benchmark datasets. The code is available on Github ( https://github.com/GNN-zl/PPGCNs ).
A Social-Aware Deep Learning Approach for Hate-Speech Detection

Despite considerable efforts to automatically identify hate-speech in online social networks, users still face an uphill battle with toxic posts that seek to sow hatred. In this paper, we initially observe that there is a great deal of social properties transcending both hateful passages and respective authors. We then exploit this observation by i) developing deep learning neural networks that classify online posts as either hate or non-hate based on their content, and ii) proposing an architecture that may invigorate any such text-based classifier with the use of additional social features. Our combined approach considerably enhances the classification accuracy of previously proposed state-of-the-art models and our evaluation reveals social attributes that are the most helpful in our classification effort. We also contribute the first publicly-available dataset for hate-speech detection that features social properties.
Machine Learning KNN Classifier for Forecasting Hourly Global Solar Irradiance over Adrar City

In this investigation, simulations based on the K-Nearest Neighbor (KNN) classifier were performed to examine the performance metric characteristics in estimating the hourly global solar irradiance received at Adrar city/Algeria. The system is implemented and simulated in Anaconda, and its performance is evaluated using real unsupervised dataset with seven (07) features and 44872 instances for classifying the hourly global solar irradiance. For the classification, four (04) classes (4 target name labels) were created based on the captured global irradiance magnitude. The simulation results show that the performance metrics depends on both the test size and the number of neighbors k. The model perform very well in predicting the class label of hourly global irradiance magnitude when k is in the range of [7–11] and the training size is less than 25%. The model prediction accuracy is about 86%.
Sustainability, Digital Security, and Cyber Hygiene During the Covid-19 Pandemic

The COVID-19 Pandemic has affected and transformed the world since the end of 2019. In the pandemic process, individuals, along with health concerns, also carried concerns about getting true information and news or protecting their digital footprints and cyber security. So much so that the internet is in almost every stage of life. With the pandemic, there has been a transition to sustainable practices such as online shopping, banking, trade, business, and education models. At this point, the concept of cyber hygiene, which provides digital security as well as maintaining it, is important. It is also aimed to stay away from bad and dirty content and to be prepared against cyber attacks. Therefore, cyber hygiene is in a key position in digital security. Therefore, in this study, the concept of cyber hygiene and sustainable models and practices are discussed within the framework of the increasing use of digital, during the pandemic process.
Visual Question Answering reasoning with external knowledge based on bimodal graph neural network

&lt;abstract&gt;&lt;p&gt;Visual Question Answering (VQA) with external knowledge requires external knowledge and visual content to answer questions about images. The defect of existing VQA solutions is that they need to identify task-related information in the obtained pictures, questions, and knowledge graphs. It is necessary to properly fuse and embed the information between different modes identified, to reduce the noise and difficulty in cross-modality reasoning of VQA models. However, this process of rationally integrating information between different modes and joint reasoning to find relevant evidence to correctly predict the answer to the question still deserves further study. This paper proposes a bimodal Graph Neural Network model combining pre-trained Language Models and Knowledge Graphs (BIGNN-LM-KG). Researchers built the concepts graph by the images and questions concepts separately. In constructing the concept graph, we used the combined reasoning advantages of LM+KG. Specifically, use KG to jointly infer the images and question entity concepts to build a concept graph. Use LM to calculate the correlation score to screen the nodes and paths of the concept graph. Then, we form a visual graph from the visual and spatial features of the filtered image entities. We use the improved GNN to learn the representation of the two graphs and to predict the most likely answer by fusing the information of two different modality graphs using a modality fusion GNN. On the common dataset of VQA, the model we proposed obtains good experiment results. It also verifies the validity of each component in the model and the interpretability of the model.&lt;/p&gt;&lt;/abstract&gt;
The Impact of Social media on family communication from the parents' point of view A field study in Mosul

The aim of the research is to know the impact of social networking sites on family communication from the parents' point of view, and to achieve the goals of the research, a questionnaire was designed consisting of (13) paragraphs. After confirming the honesty and consistency, it was applied to a sample consisting of (60) respondents from individuals representing heads of families, male and female. (Father. Mother) in the city of Mosul, with its left and right sides, they were randomly selected, and after statistically processing the data, the research reached a number of results, the most important of which is that social media in the city of Mosul is a method that has reduced family communication within the family and that it is a direct cause in multiplying social family relationships , And there were many differences in the relationship between the spouses within the family, as it did not meaningful dialogue and caused freezing of feelings and emotions and contributed to widening the gap between the children.