Reviewerly/exHarmony huggingface dataset:
Innovative College Students’ Educational Management Mode Based on BP Neural Network

With the advancement of society, the necessities for school graduates are getting increasingly elevated. In this unique situation, the conventional showing the board model in schools and colleges has not been able to adjust to the pattern of present day advancement and should be improved and developed likewise. By advancing the instructive administration model, this paper expects to work on the idea, conduct, and advancement capacity of undergrads to address the issues of social turn of events. In light of this, this paper principally concentrates on the issues existing in the instruction of the board of undergrads and the comparing imaginative models. This paper takes Wenzhou University students as the research object, combining qualitative analysis with quantitative analysis. This paper assembles a general travel industry proficient development and business venture schooling assessment framework based on BP brain organization. It lessens the impedance brought about by human elements in the assessment cycle and guarantees the precision of the assessment results. This paper improves the research and analysis framework of related fields, enriches the research results of innovation and entrepreneurship education evaluation in tourism, and has important theoretical value for improving the evaluation model in this field. The exploratory aftereffects of this paper show that the thorough organization yield esteem is 0.7261 as per the information gathered by Wenzhou University utilizing the poll review. It demonstrates that the advancement and business training of Wenzhou University is at a medium level, and there is still a lot of opportunity to get better.
PageRank Topic Finder based Algorithm for Multimedia Resources in Preschool Education

In traditional preschool education, it is time-consuming and laborious to acquire effective materials by using artificial search method. However, with the development of Internet technology, a variety of preschool education institutions or individuals have released their own preschool education resources on the Internet. At present, multimedia technology has been popularized in many schools, and it plays a more and more significant role in teaching. In preschool education teaching, teachers use multimedia resources not only conducive to improve children’s learning efficiency but also make the teaching quality from the whole to a higher level. However, some kindergarten teachers rely too much on multimedia in teaching and do not effectively combine it with traditional teaching methods. Sometimes they even use video and related multimedia teaching resources throughout the class, which makes preschool children lack knowledge and knowledge. Therefore, this paper designs a multimedia resource retrieval system based on the theme of preschool education, which mainly achieves the extraction of multimedia resources from web pages and the analysis of multimedia-related text information. In order to design a high-performance topic search algorithm, we must first carry out page parsing, Chinese and English word segmentation, and other page preprocessing. The research results show that it is found that the text-based automatic classification of multimedia resources in preschool education and the filtering of multimedia noise in web pages can provide relevant personnel in the field of preschool education with the retrieval service of multimedia resources.
Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention

We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our method explicitly leverages the contents of both the preferred clips and the target videos using pre-trained features for the objects and the human activities. We design a multi-head attention mechanism to adaptively weigh the preferred clips based on their object- and human-activity-based contents, and fuse them using these weights into a single feature representation for each user. We compute similarities between these per-user feature representations and the per-frame features computed from the desired target videos to estimate the user-specific highlight clips from the target videos. We test our method on a large-scale highlight detection dataset containing the annotated highlights of individual users. Compared to current baselines, we observe an absolute improvement of 2-4% in the mean average precision of the detected highlights. We also perform extensive ablation experiments on the number of preferred highlight clips associated with each user as well as on the object- and human-activity-based feature representations to validate that our method is indeed both content-based and user-specific.
DTLMV2—A real-time deep transfer learning mask classifier for overcrowded spaces

Through the commencement of the COVID-19 pandemic, the whole globe is in disarray and debating on unique approaches to stop this viral transmission. Masks are being worn by people all around the world as one of the preventative measures to avoid contracting this sickness. Although some people are following and adopting this precaution, others are not, despite official recommendations from the administration and public health organisations has been announced. In this paper DTLMV2 (Deep Transfer Learning MobileNetV2 for the objective of classification) is proposed - A face mask identification model that can reliably determine whether an individual is wearing a mask or not is suggested and implemented in this work. The model architecture employs the peruse of MobileNetV2, a lightweight Convolutional Neural Network (CNN) that requires less computing power and can be readily integrated into computer vision and mobile systems. The computer vision with MobileNet is required to formulate a low-cost mask detection system for a group of people in open spaces that can assist in determining whether a person is wearing a mask or not, as well as function as a surveillance system since it is effective on both real-time pictures and videos. The face recognition model obtained 97.01% accuracy on validation data, 98% accuracy on training data and 97.45% accuracy on testing data.
Bond graph modeling and trajectory control of H-drone

Four-rotor drones are unmanned aerial vehicles (UAV) which have found usage in varied application like, surveillance, medical field and entertainment due to their simple design and high payload capacity. This paper presents trajectory tracking control of a H-drone using optimized PD control. The realistic dynamic model of H-drone has been derived using bond graph technique. Then the control scheme using PD controller is presented and the parameters of the controller were tuned using Broyden-Fletcher-Goldfarb-Shanno algorithmic optimization approach for circular and smooth fifth order trajectories. To validate the performance of the optimized controller simulations are run using 20-sim software. The simulation results demonstrate that H-drone efficiently tracks both the trajectories.
3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge

We propose a 3D version of the Contextual Multi-scale Multi-level Network (3D CMM-Net) with deeper encoder depth for automated semantic segmentation of different brain tumors in the BraTS2021 challenge. The proposed network has the capability to extract and learn deeper features for the task of multi-class segmentation directly from 3D MRI data. The overall performance of the proposed network gave Dice scores of 0.7557, 0.8060, and 0.8351 for enhancing tumor, tumor core, and whole tumor, respectively on the local-test dataset.
HOFS: Higher order mutual information approximation for feature selection in R

Feature selection is a process of choosing a subset of relevant features so that the quality of predictive models can be improved. An extensive body of work exists on information-theoretic feature selection, based on maximizing Mutual Information (MI) between subsets of features and class labels. The current methods use a lower order approximation, by treating the joint entropy as a summation of several single variable entropies. This leads to locally optimal selections and misses correlated (multi-way) non-local feature combinations. In this article we present a higher order MI-based approximation technique called Higher Order Feature Selection (HOFS) implemented in R software. Instead of producing a single list of features, our method produces a ranked collection of feature subsets that maximizes MI, giving better comprehension (feature ranking) as to which features work best together when selected, due to their underlying interdependence. We demonstrate that the proposed method performs better than existing feature selection approaches while keeping similar running times and computational complexity.
Entanglement Property of Tripartite GHZ State in Different Accelerating Observer Frames

According to the single-mode approximation applied to two different mo des, each associated with different uniformly accelerating reference frames, we present analytical expression of the Minkowski states for both the ground and first excited states. Applying such an approximation, we study the entanglement property of Bell and Greenberger-Horne-Zeilinger (GHZ) states formed by such states. The corresponding entanglement properties are described by studying negativity and von Neumann entropy. The degree of entanglement will be degraded when the acceleration parameters increase. We find that the greater the number of particles in the entangled system, the more stable the system that is studied by the von Neumann entropy. The present results will be reduced to those in the case of the uniformly accelerating reference frame.
MALL Instructional Course Design: Constructing Out-of-Class Experience

Mobile-assisted language learning (MALL) is a new channel of learning languages. The usage of MALL instructional design integrates mobile devices with educational scenarios of teaching foreign languages. The study explores the central issue of how MALL instructional course design could help students to construct an out-of-class MALL experience. The research investigates the practical characteristics of MALL instructional design, which lead to foreign language acquisition. The study had three objectives: to explore the perceptions of students and needs in MALL; to create a conceptual framework of MALL instructional course design; to summarize the results in pedagogical implications to the course. The data came from an exploratory study of employing MALL in instructional course design at the tertiary level within the first semester of 2021. This study analyses the feedback of 32 students about the effectiveness of the MALL activities and students’ satisfaction. The research shows how student feedback stimulates investigation into MALL instruction and evaluation of its design. The researchers used qualitative and quantitative analyses. The results of the study, conducted in three stages, helped to develop a design framework. The research proved that designing the MALL activities supported interaction, communication, access to resources, and people within the network community practice. MALL course design promoted active collaborative learning that helped students to construct the MALL experience in the real-world environment. The researchers provide recommendations on combining various MALL facilities and channels of communication that mobiles afford.
Word Alignment by Fine-tuning Embeddings on Parallel Corpora

Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs. The great majority of past work on word alignment has worked by performing unsupervised learning on parallel texts. Recently, however, other work has demonstrated that pre-trained contextualized word embeddings derived from multilingually trained language models (LMs) prove an attractive alternative, achieving competitive results on the word alignment task even in the absence of explicit training on parallel data. In this paper, we examine methods to marry the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel text with objectives designed to improve alignment quality, and proposing methods to effectively extract alignments from these fine-tuned models. We perform experiments on five language pairs and demonstrate that our model can consistently outperform previous state-of-the-art models of all varieties. In addition, we demonstrate that we are able to train multilingual word aligners that can obtain robust performance on different language pairs. Our aligner, AWESOME (Aligning Word Embedding Spaces of Multilingual Encoders), with pre-trained models is available at https://github.com/neulab/awesome-align
Gesture Interaction System Design for Telerehabilitation Based on Mediapipe

Tele-rehabilitation has gained much attention for patients' rehabilitation treatment. Hand gesture recognition is considered important in tele-rehabilitation area, since its efficiency. In this paper, we design a computer vision-based rehabilitation equipment gesture interaction system, which uses a computer vision-based gesture recognition algorithm to recognize the user's dynamic gestures. It is implemented via MediaPipe, a framework for building cross-platform ML solutions. This paper proved that the system has a promoting effect on hand gesture recognition, and it can assist doctors in rehabilitation training.
SiBo-The Sign Bot, Connected World for Disabled

Sign Language has proven to be a leap forward in enabling a communication medium for mute and hard-of-hearing people, yet it is not a recognized language among others, making the challenged ones feel socially excluded. In this paper, the proposed assistive device - SiBo, brings the power of embedded AI to a subset of people, often sidelined when delivering mainstream technology. This proposed device exploits the capabilities of Infineon's Sense2GoL radar and its MCU portfolio in interpreting and recognizing the visual-manual modality of communication - American Sign Language (ASL) to control the smart home components. This work explores the state-of-the-art radar technology in gesture recognition and proposes an edge deployable deep neural network model with a small memory footprint. The developed model extensively uses ML pipeline services offered by Edge Impulse enabling faster prototyping methods for such edge applications. It is capable of classifying the input ASL gestures in real-time with an accuracy of 89.4% and inference latency of 2 ms. The already existing methods utilize high-end cameras to capture hand gestures. But, they being susceptible to adverse weather conditions and posing an added risk of privacy breaches or data vulnerabilities makes it less favorable in such applications and hence gives an edge to radar technology.
Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring?

Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education.However, these techniques often suffer from their inabilities to explain and justify how a prediction is made, which, unavoidably, decreases their trustworthiness and hinders educators from embracing them in practice.This study aimed to investigate whether (and to what extent) DL-based graders align with human graders regarding the important words they identify when marking short answer questions.To this end, we first conducted a user study to ask human graders to manually annotate important words in assessing answer quality and then measured the overlap between these human-annotated words and those identified by DL-based graders (i.e., those receiving large attention weights).Furthermore, we ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL-based graders on human grading.The results showed that: (i) DL-based graders, to a certain degree, displayed alignment with human graders no matter whether DL-based graders and human graders agreed on the quality of an answer; and (ii) it is possible to facilitate human grading by highlighting those DL-detected important words, though further investigations are necessary to understand how human graders exploit such highlighted words.
Constraint-based Multi-hop Question Answering with Knowledge Graph

The objective of a Question-Answering system over Knowledge Graph (KGQA) is to respond to natural language queries presented over the KG.A complex question answering system typically addresses one of the two categories of complexity: questions with constraints and questions involving multiple hops of relations.Most of the previous works have addressed these complexities separately.Multihop KGQA necessitates reasoning across numerous edges of the KG in order to arrive at the correct answer.Because KGs are frequently sparse, multi-hop KGQA presents extra complications.Recent works have developed KG embedding approaches to reduce KG sparsity by performing missing link prediction.In this paper, we tried to address multi-hop constrainedbased queries using KG embeddings to generate more flexible query graphs.Empirical results indicate that the proposed methodology produces state-of-the-art outcomes on three KGQA datasets.
Assessing the Practical Benefit of Automated Short-Answer Graders

Short-Answer Grading (SAG) is a task where student answers to open questions are automatically graded with the support of Natural Language Processing and Machine Learning (ML), saving manual effort. Two main challenges remain in small-scale testing scenarios: (1) ML models work best given large amounts of manually graded training instances, and (2) published evaluation results for pre-trained models do not translate well to new data sets, making automated grading intransparent for teachers and students. We present a grader evaluation workflow that teachers can use for their individual situation.
A Comprehensive Study on Pathfinding Algorithm for Static 2D Square Grid

Pathfinding is essential and widely used in many fields, like video games, tour guides or escape routes, and artificial transportation. Based on those fundamental algorithms like the DFS and A&#x002A; This paper explores two optimized algorithms, Jump Point Search (JPS) and Subgoal Graph. Other than showing how to implement the basic work of these algorithms, this work also designs some regular grid maps, which vary in terms of obstacle numbers, map size, obstacle shape, aiming to explore which algorithm fits better in which situation. This paper is constructive for the reader who wants to know how single-agent pathfinding problems figure out by using programming skills and the detection of the otherness among those algorithms in the realistic applied range.
Distributed Robust Kalman Filters under Model Uncertainty and Multiplicative Disturbance

This paper considers the problem of distributed robust state estimation for sensor networks in the presence of model uncertainty and multiplicative noise. More precisely, we assume that the modeling uncertainty, i.e., the actual state-space model belongs to an ambiguity set or a set of convex polytopic uncertain parameters. Several robust Kalman filters are proposed based on projection theorem, variance-constrained optimization, and robust mean square error estimation with different types of ambiguity sets. Stability analysis and simulation example verify the presented distributed robust filters.
Building a NoSQL ERP

Enterprise resource planning (ERP) systems are needed in many business activities. Small and medium enterprises (SMEs) are not well-served by current ERPs, as such systems are hard to tailor. This prompts us to experiment with building an ERP on top of a NoSQL database, which intends to be more flexible, as it is based on JSON and not on a relational data model. We present a novel ERP solution specifically designed to grow and evolve as the world changes. The ERP is for a service company which bills for time spent on customer projects. The work involves various challenges: data modelling, query specification, write and read performance analysis, versioning, user interface generation and query optimisation. Here, we report on the performance of a NoSQL ERP using MongoDB and show that writes are fast and queries and reports are fast enough.
Towards Generating Counterfactual Examples as Automatic Short Answer Feedback

Receiving response-specific, individual improvement suggestions is one of the most helpful forms of feedback for students, especially for short answer questions. However, it is also expensive to construct manually. For this reason, we investigate to which extent counterfactual explanation methods can be used to generate feedback from short answer grading models automatically. Given an incorrect student response, counterfactual models suggest small modifications that would have led the response to being graded as correct. Successful modifications can then be displayed to the learner as improvement suggestions formulated in their own words. As not every response can be corrected with only minor modifications, we investigate the percentage of correctable answers in the automatic short answer grading datasets SciEntsBank, Beetle and SAF. In total, we compare three counterfactual explanation models and a paraphrasing approach. On all datasets, roughly a quarter of incorrect responses can be modified to be classified as correct by an automatic grading model without straying too far from the initial response. However, an expert reevaluation of the modified responses shows that nearly all of them remain incorrect, only fooling the grading model into thinking them correct. While one of the counterfactual generation approaches improved student responses at least partially, the results highlight the general weakness of neural networks to adversarial examples. Thus, we recommend further research with more reliable grading models, for example, by including external knowledge sources or training adversarially.
Assessing information security performance of enterprise internal financial sharing in cloud computing environment using analytic hierarchy process

The present work aims to improve the efficiency of enterprises by solving a series of problems such as high financial costs, low financial management efficiency and structural redundancy. The maturity model subdivides the four indicators: information security strategy, information security technology, information security organisation and information security operation. An information security evaluation system is constructed for financial sharing. Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) are combined to evaluate financial sharing capabilities in protecting information security. The proposed model is validated using example data. Results demonstrate that cloud computing-based financial sharing management can effectively reduce financial costs and improve the financial management efficiency of enterprises. The AHP-based model for evaluating information security performances can effectively protect financial information and guarantee enterprises' financial information security. Through experiments, significant and practical references can be provided for promoting the development of large enterprises.