Reviewerly/exHarmony huggingface dataset:
Influence of Using Rubrics in Higher Education on Students’ Performance and Academic Motivation

Introduction. The relevance of the chosen topic is justified by the need for further discussion on the problem of ensuring an objective and transparent assessment of students’ work. It is also necessary to develop and test formative assessment tools that allow students to improve their academic performance and interest in learning. Rubrics are offered as one such tool. They are a matrix of criteria or attributes for evaluating a specific task and compliance levels. Purpose . The study aimed to determine how the application of rubrics to assess the tasks performed by students affects their academic performance and motivation to study. Materials and Methods. The methodology of the study is based on a questioning of students and an experiment involving the joint development and further application of rubrics with students to evaluate the completed task on the subject. Results. The results obtained confirm the hypothesis that rubrics application facilitates an increase in students’ motivation to study and academic performance. In addition, the assessment using rubrics allows students to receive feedback from the teacher in the form of comments on the achievement of compliance levels for the assessment attributes used. The use of rubrics helped to increase the activity of students in the classroom and to show more interest in scientific literature, including in a foreign language. Discussion and Conclusion. Comparison of the results obtained with existing works in the field of application of rubrics confirms their effectiveness, achieved using understandable assessment criteria. Rubrics seem to be a promising tool for assessing the most significant and complex tasks in the subject since their application facilitates an increase in the interest and satisfaction of students, together with the subsequence and impartiality of the assessment. One of the rubrics’ limitations is some suppression of the students’ pursuit to fulfil their creative potential, which needs to be worked out in further research.
A two-phase heuristic algorithm for power-aware offline scheduling in IaaS clouds

The paper aims at mitigating hot-spots during Offline Scheduling in IaaS (Infrastructure-as-a-Service) cloud systems. Unlike previous studies, the research focuses on identifying and resolving hot-spots not at servers, but at server racks. A two-phase algorithm for performing power-aware offline scheduling is proposed. The first phase aims at identifying and mitigating hot-spots at racks, while the second phase performs VM consolidation, i.e. minimization of the number of occupied servers while maintaining a feasible VM mapping and low migration costs. The proposed algorithm takes into account the dynamic nature of VM's resource consumption: it does not only resolve detected hot-spots, but also tries to avoid hot-spots in a reasonable future time period. The algorithm was tested with the data from a real IaaS cloud with different sets of algorithm's parameters. Experimental evaluation showed that the statistical estimates of the future VM's resource consumption provide the most reliable mapping, which is a result of minimization of the number of new hot-spot occurrences.
Intentions to continue using agile methods: The case of the Greek banking sector

The purpose of this study is to examine the factors that influence team members of software development projects to continue using agile methodologies after their initial adoption. The research focuses on large-scale projects in the banking sector and uses the expectation-confirmation model (ECM) as a conceptual framework. The research model is tested by employing partial least square structural equation modeling (PLS-SEM). The findings validate the model and report statistically significant positive associations between all constructs. The results suggest that confirmation of expectations regarding perceived usefulness and satisfaction are key determinants of agile continuance intentions in software development large-scale projects. The study has both theoretical and practical implications and calls for further research in the field of agile post-adoption.
Attention Augmented Pedestrian Detection on Fisheye Cameras

Recently, fisheye cameras have demonstrated to play a crucial rule in autonomous driving. However, mainstream is inclined to employ rectified images conducting researches instead of using raw fisheye images. Hence, we tackle pedestrian detection task on fisheye cameras, with a effective attention module (Augmented Self-Attention) inserted and domain adaption method utilized. We validate our proposed methods through extensive experiments on PASCAL VOC 2007, 2012 and WoodScape detection datasets using YOLOv4 series. Our experiments show that mutual attributes between fisheye and rectified images can be learned by convolutional kernels. In particular, We achieve 63.6% mAP and 225 FPS on YOLOv4-tiny-mish.
Bayesian Optimization Machine Learning Models for True and Fake News Classification

The performance of a machine learning algorithm depends largely on determining a set of hyperparameters. These hyperparameters have a significant influence on the accuracy of the algorithm. With the increase in algorithm complexity, there are more and more candidates for hyperparameters. How to quickly and accurately select the right hyperparameters for a given problem has become a popular area of research. This paper is based on a Bayesian optimization approach to assist machine learning for hyperparameter extraction. It is also fully validated based on the task of dichotomous classification of true and false news. This paper analyses the principles of the Bayesian optimization approach and how it can be applied to machine learning model parameter selection. The machine learning models to be used in this paper include K-Nearest Neighbour (KNN), Random Forest as well as Gradient Boosted Decision Trees (GBDT). These three are commonly used machine learning models for binary classification problems, with different numbers and classes of hyperparameters. The results of the experiments show that adjusting the original hyperparameters of machine learning using Bayesian optimization can substantially improve classification accuracy. The research in this paper can also provide ideas for other similar work of super parameter selection.
Research on Chinese named Entity Recognition based on RoBERTa and word fusion

With the rapid progress of Internet medicine, a good deal of data is generated every day, which is of great significance to clinical decision-making system and medical entity research. For electronic medical records in Chinese(CEMR) named entity recognition(NER) task of long entity, the entity chaos, border demarcation difficulties and other issues, this paper proposes a fusion based on RoBERTa, and words of Chinese named entity recognition method. This method uses the joint feature representation of characters and entity words depended on the pre-training model RoBERTa and the medical field lexicon created by ourselves, which can precisely spilt entity boundaries, so as to solve the influence of unregistered words. The experimental results show that our model has preferable performance and significant improvement compared with the baseline model. Specifically, the F1 value of CCKS2020 CEMR dataset reached 88.71%, 1.16% higher than all the baseline models.
Adaptively Weighted Balanced Feature Pyramid for Object Detection

Object detection is a common computer vision task.The task of object detection is to find all instances of a given category in an image, classify and locate them.Most of the current models will densely sample the input image when detecting. Therefore, the quality requirements for feature maps are very high. In many object detection models, the performance is not good due to insufficient feature extraction. Especially for datasets with variable object sizes.Aiming at the problem of imbalanced information contained in different levels of the feature pyramid, we propose an adaptive balanced feature pyramid that fuses global information.Our pyramid will adaptively weight and fuse the features of each level, and then rebuild the feature pyramid so that each level has the same features.Our proposed adaptively weighted balanced feature pyramid achieves 36.8%in mAP on FCOS with ResNeXt-64x4d-101.
Research on optimization of K-means Algorithm Based on Spark

This paper studies the principle and implementation of the commonly used clustering algorithm K-means and K-means ++, analyzes the advantages and disadvantages of the algorithm, and improves the K-means ++ algorithm based on the idea of dynamic adjustment of the clustering center for the problem that the initialization center may be uneven. The improved algorithm improves the clustering effect by removing the worst cluster center and splitting the cluster center to be split. And this paper implements the parallelization of the improved algorithm based on Spark. Experiments on clustering effect, running time and acceleration ratio show that compared with the traditional K-means ++ algorithm, the improved K-means ++ algorithm not only improves the quality of clustering and reduces the computing time, but also shows good parallel performance in the multi-node cluster environment. The experimental results show that the proposed algorithm can effectively improve the efficiency of algorithm execution and parallel computing ability.
Improved military equipment identification algorithm based on YOLOv5 framework

In the field of military equipment target recognition, in order to solve the problems of small target detection, such as easy loss and slow detection speed, this paper proposes a target recognition and detection algorithm YOLO-M, which not only keeps high accuracy, but also reduces the number of parameters. Firstly, C3CMix module is used to replace part of the structure of head in YOLO v5, which reduces the number of parameters and effectively reduces the model size. Then, the detection accuracy is further improved by replacing the activation function in the structure. The experimental results show that the average accuracy of YOLO-M is 95.2%, the parameters are reduced by 18.8%, and the calculation is reduced by 14.5%. Therefore, the improved algorithm in this paper is more suitable for deployment in the field of military equipment target recognition.
Dual channel Chinese sentiment analysis of characters and words based on deep learning

In recent years, sentiment analysis has been a hot research topic in the field of natural language processing. In this field, in view of the complex Chinese semantics in Chinese sentiment analysis tasks, the traditional sentiment analysis methods have insufficient effective information extraction and low classification accuracy, and there is room for further improvement in disambiguation. This paper proposes a sentiment analysis model (CWBCNN-Att) based on the fusion of Chinese character vectors and word vectors and an attention mechanism. Among them, the dimension of characters is added to the traditional word-based analysis method, which can extract richer local information, alleviate the problem of unregistered words in the vocabulary, and reduce the problem of ambiguity in Chinese texts to a certain extent; Secondly, this paper uses two parallel and independent bidirectional LSTM and GRU layers to extract the feature information of words respectively to speed up the processing; Furthermore, an attention mechanism is applied to the output of the bidirectional layers of our model to emphasize different weights for different words. To reduce the dimension of features and extract location-invariant local features, we utilize convolution and pooling mechanisms. The experimental results show that the method has excellent performance in terms of accuracy and recall.
Classification of Galaxy Morphology Based on Multi-Channel Deep Residual Networks

In the process of studying the aggregation and evolution of cosmic galaxies, galaxy morphology is an important parameter to be considered. With the rapid development of deep learning and artificial intelligence technology, galaxy morphology classification based on deep learning framework convolutional neural network emerges. In this paper, three typical deep learning frameworks, AlexNet, ResNet50 and VGGNet-E, are used to classify the galaxy images of Galaxy Zoo 2. The performance of the three deep learning frameworks is evaluated by accuracy and loss rate, and it is found that ResNet50 works best. In this paper, a multi-channel depth residual network framework ResNet-Core is designed based on the ResNet50 network structure. This structure can deepen the extraction of detailed features through the control of convolution kernel. The experimental results show that compared with AlexNet,ResNet50,VGGNet-E deep learning neural network models,ResNet-Core model has better classification performance and better robustness.
High-performance Network Traffic Classification Based on Graph Neural Network

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.
Contribution to the construction of an ontology for the phytosanitary surveillance of cotton in Côte d'Ivoire

Thanks to the efforts of agronomic research and cotton sector actors, Côte d'Ivoire ranks second among African cotton producing countries behind Benin. Despite these efforts, cotton cultivation continues to face repeated attacks by pests. To limit crop losses due to these attacks, systematic plant health surveillance has been put in place. The problems encountered in this systematic surveillance led to the development of a mobile application to collect and transfer data in real time. Unfortunately, the mobile solution implemented does not allow knowledge sharing or semantic search for the discovery of new knowledge. To enable and facilitate semantic reasoning in this system, we chose to couple the mobile version with a semantic web platform based on a domain ontology for plant health surveillance of cotton. This article presents the construction of this ontology that we will call ontoSYSPARCOTCI in what follows. The methodology used to build this ontology is NeO <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> . We started by listing all the ontological and non-ontological resources useful and necessary for the construction of the ontology. We then used scenarios 1,2,3 5 and 7 for its construction. Concerning our results, extracts from conceptual model of the ontology, in the graphic formalisms UP/UML, are presented. Based on this model, we obtained a hierarchy of concepts in OWL via protege tool.
Trajectory Angles Evaluation for the Flight Vehicle via Wavelet Transforms and Similarity Measures

In the flight vehicle test, the evaluation of trajectory parameters, such as trajectory inclination angles and trajectory deflection angles, is sometimes required. To do that, we propose an evaluation method based on the wavelet transform and similarity measure. Concretely, since the wavelet transform possesses both smoothing and derivation operations in certain cases, Gaussian and Gaussian-1 wavelet functions are respectively used for wavelet transforms on the non-stationary position components series of the flight vehicle’s trajectory that is reconstructed using noisy radar measurements. As a result, the smoothed position components series and their first derivative series are respectively obtained, wherein the latter can be viewed as the flight velocity components series, and further utilized to calculate the two types of real trajectory angle series. Then, by multi-point matching and comparisons, the average time bias between the flight vehicle’s trajectory series in reality and that in theory is approximately estimated, such that the two types of trajectory angle series in reality and those in theory can be respectively aligned over time via the Lagrange polynomial interpolation. Finally, the correlation coefficients are employed to measure and evaluate the similarities of these time-aligned trajectory angle series. A simulation experiment is provided and carried out to demonstrate the feasibility of the proposed method.
Path Planning of Mobile Robots Based on the Fusion of an Improved A* Algorithm and a Dynamic Window Approach

Aiming at the needs of global optimal path planning and real-time obstacle avoidance for mobile robots, a path planning method based on the fusion of an improved A* algorithm and dynamic window approach (DWA) is proposed. First, based on the traditional A* algorithm, a heuristic function is designed to reduce the number of search nodes and the turns of the planned path. Then, the node neighborhood search strategy is improved to optimize the selection of nodes and reduce the probability of collision obstacles. In addition, a key points selection strategy is used to remove redundant nodes and unnecessary turning nodes in the path. Finally, the evaluation function of the dynamic window approach is improved, and the local obstacle avoidance ability and the smoothness of the path are improved on the basis of ensuring the global optimality of the planned path. The simulation experiment results show that compared with the traditional A* algorithm, the above algorithm reduces the number of turns and the turning angle by 57% and 80%, respectively, and can stay away from obstacles in real time. Moreover, the planned path is smoother and safer. Compared with the dynamic window approach, the above algorithm can obtain the globally optimal path, and the local obstacle avoidance ability is more robust.
A Kind of Syntax Parsing Algorithm Based on The Recursive Subroutines

If the Context free grammar is belong to LL(1) grammars, the recursive subroutine should be established for each non-terminal of the grammar, the funtion of which is to recogonize the sting which can be deduced from the non-terminal. Based on the recursive subroutines, the syntax parsing algorithm can be established. Using the syntax parsing algorithm based on the recursive subroutines, the syntax analysis can be accomplished.
<i>Breath of Light</i>: Reclaiming Shared Breathing Through a Meditative Installation

Abstract Breath of Light is an immersive breath-responsive installation aiming to reclaim the connective act of sharing breath in public spaces. During the exhibition at the 13th Shanghai Biennale in March 2021, the lead author interviewed and observed participants to better understand their experience. A follow-up interview conducted in January 2023 illustrated the work’s long-term effect on participants. This technological mediation of breathing explores its transformative potential to revive connective connotations of shared breathing and cultivate interoceptive awareness, reflection, and interhuman connection during the pandemic and beyond with the use of breathing interaction, metaphors, symbols, and ambiguous instructions.
A probabilistic approach to provenance studies using whole object elemental composition: Chert (flint) as a case study

Provenancing archeological artefacts and “whole rock” fragments, such as cobbles and pebbles, is important both for studying past societies and reconstructing geomorphological and sedimentary processes. Determining the provenance of archaeological artefacts, for example, is helpful in the study of various aspects of human society such as mobility, trade routes, raw material procurement strategies and societal development, whereas whole rock fragments are useful indicators of sediment transportation routes and landscape evolution. In this paper, we introduce a novel probabilistic approach for exploring the limitations and potential of provenance studies utilizing trace element composition of archaeological artefacts and bulk rock fragments through a set of statistical analyses, demonstrated using a dataset of chert (flint) samples from the eastern Mediterranean as a case study. Because our general goal is to identify sediment provenance at the highest possible resolution, and attribute it to specific outcrops, the robustness of such classifications must consider the inherent variability of the studied samples, which poses a complex problem when an entire suite of elements is considered. In the current study, a scheme for evaluating the possible resolution and potential of provenance studies of flint artefacts is illustrated by applying a set of statistical techniques. The data is investigated across the different labelling hierarchy using Spearman correlation and the Kruskal-Wallis test, and a synthetic dataset that follows the statistical properties of the original data is generated using multidimensional copulas that mimic the correlation of the different elements within each subset. The synthetic dataset is then iteratively sampled, filtered and passed into a classifier that is evaluated using a set of classification metrics (e.g., accuracy, recall, etc.). Using this probabilistic approach, the power of supervised classification is determined for the different hierarchy levels and classes based on their distributions, and the number of specimens required for distinguishing sources at different levels (e.g., era, age, formation and site) is evaluated. We show that 10–20 specimens are needed to determine the geological era and age of the strata from which flint was collected with reasonable certainty (accuracy >0.9), whereas specific outcrops cannot be reliably identified even when a large number of specimens are considered. This approach has the potential to enable the classification of items of unknown origin and the associated uncertainty by comparing them against a dataset of possible sources that can be expanded over time.
Secure channel free public key authenticated encryption with multi-keyword search on healthcare systems

A significant amount of electronic health records (EHRs) have been kept in the cloud due to the quick development of healthcare information systems. However, storing EHRs in the cloud may raise security and data privacy issues. Because of the privacy of a patient's medical data, storing it on a cloud server requires encryption. Public-key encryption with keyword search (PEKS) solves the problem of retrieval on ciphertext, thus avoiding the problem of plaintext information leakage. However, most PEKS schemes are susceptible to inside keyword guessing attacks (KGA). Besides, transmitting the trapdoor to the cloud server requires a secure channel, which is impractical in healthcare information systems. Roughly speaking, PEKS does not protect the privacy of trapdoor and it is expensive to establish secure channels. In this paper, we propose a secure-channel free public key authenticated encryption with multi-keyword search (SCF-PAEMKS) scheme. The proposed SCF-PAEMKS scheme supports conjunctive keyword search, meanwhile satisfying multi-ciphertext indistinguishability (MCI) and multi-trapdoor privacy (MTP) simultaneously. The efficiency analysis and experimental results show that our SCF-PAEMKS scheme enjoys a better performance compared with related schemes.