Reviewerly/exHarmony huggingface dataset:
The digital skills of teachers in the teaching practice with students with specific needs

The use of digital technologies in the educational context has been reinforced by the importance they represent in the teaching-learning process of children with specific needs. In this sense it is important to understand the importance attributed by teachers regarding the use and training in this area. 108 teachers of the group of special education teaching in schools in the North of Portugal participated in this study. The study is quantitative in nature, the statistical analysis was performed using the IBM/SPSS program, version 26.0 with exploratory factor analysis. Ethical issues were taken into account, safeguarding the anonymity of participants throughout the process. The participants were informed about the research objectives and gave their consent for the publication of the results, as well as being free to leave the study whenever they wanted. The questionnaires were sent via email and collected by one of the study authors. The results point to a clear recognition of the advantages of digital technologies in the teaching-learning process of students with Specific Needs, not being so evident with regard to Technologies as a decisive factor in the process of inclusion of these students. It was verified that the teachers' perceptions do not always agree with the usefulness of their practices. An aspect that is not dissociated from the gaps highlighted in terms of specific training in digital technologies.
Development of an Information System for the Monitoring of Physiological Variables and Information Storage of Neonates on Domiciliary Oxygen

The development of information systems has increased over the years, and the health area is no exception given the interest of professionals in the use of information systems that help make decisions based on the data taken. An information system for the management and monitoring of information related to neonates on home oxygen was developed using the Scrum framework and the incremental iterative development model, in addition to different web development technologies. The development of an information system with 12 modules that allow the monitoring of physiological variables of oxygen-dependent neonates as well as the management of related information was achieved. The resulting tool offers important functionalities, including the possibility of monitoring vital signs and issuing alerts based on the values of these signs. For the follow-up in the development process of each of the modules of the information system, the Scrum framework was used, using the incremental iterative software development model. A tool was developed through the integration of development methodologies, languages, and programming techniques that supports the process of health professionals in institutions that monitor the evolution of neonates with home oxygen through the use of telecare.
Industry 4.0 Smart Supply Chain Data Protection Using Block Chain

In recent years, cryptocurrency has made a significant impact on the financial industry, primarily due to its secure and immutable nature enabled by blockchain technology. However, the use of blockchain is not limited to finance and can be applied to various fields, including supply chain management. This article presents an introduction to the topic of blockchain technology and its potential application in supply-chain management. Research conducted in this project employs Hyperledger Fabric to create a fully functional blockchain network capable of managing a large supply chain. The proposed architecture has two channels, three peers, and one orderer organization, each representing different stages of the supply chain. The system was tested by installing various chain codes to evaluate its effectiveness in tracing transactions and recording the product’s life cycle. Nevertheless, the system has some limitations and requires further development to be ready for full-scale deployment. This prototype serves as a proof-of-concept for a blockchain-based supply chain management system.
From theory to practice: Understanding DevOps culture and mindset

DevOps is a potential solution to time and quality restrictions in software development. It aims to increase the frequency, quality, and speed of software delivery using automated procedures. DevOps is a culture transformation, not just a toolchain. DevOps emphasizes cooperation, automation, measurement, information sharing, and web service utilization. It positively impacts IT development, online services, and quality assurance. Before commencing the DevOps journey, it is necessary to understand DevOps principles, practices, tools, benefits, and underlying issues. Such vital parameters are critically reviewed in this article. This systematic review addresses gaps and recommendations related to DevOps, aiming to provide a comprehensive understanding of its culture and mindsets. The article presents an in-depth examination of DevOps, covering topics like architecture, components, tools, principles, and security challenges. It establishes a conceptual framework for practical implementation. Security has also been discussed in the paper, which is one of the difficult problems in DevOps implementations. The research findings aid in a better understanding of the phenomenon from a human factors perspective. The state-of-the-art discussion on several tools covering architectural and networking aspects in DevOps is included in this article to attract practitioners and researchers for DevOps adoption. Our analysis revealed three key themes related to DevOps culture and mindset: collaboration, continuous improvement, and automation. Moreover, DevOps is not immune to challenges. The proposed work presents the existing gaps and future research directions to address the same.
The Role of Mathematics in Data Science: Methods, Algorithms, and Computer Programs

The field of data science relies heavily on mathematical analysis. A solid foundation in certain branches of mathematics is essential for every data scientist already working in the field or planning to enter it in the future. In whatever area we focus on, data science, machine learning engineering, business intelligence development, data architecture, or another area of expertise, it is important to examine the several kinds of mathematical prerequisites and insights and how they're applied in the field of data science. Machine learning algorithms, data analysis and analyzing require mathematics. Mathematics is not the only qualification for a data science education and profession but is often the most significant. Identifying and translating business difficulties into mathematical ones are a crucial phase in a data scientist's workflow. In this study, we describe the different areas of mathematics utilized in data science to understand mathematics and data science together.
StratDef: Strategic defense against adversarial attacks in ML-based malware detection

Over the years, most research towards defenses against adversarial attacks on machine learning models has been in the image recognition domain. The ML-based malware detection domain has received less attention despite its importance. Moreover, most work exploring these defenses has focused on several methods but with no strategy when applying them. In this paper, we introduce StratDef, which is a strategic defense system based on a moving target defense approach. We overcome challenges related to the systematic construction, selection, and strategic use of models to maximize adversarial robustness. StratDef dynamically and strategically chooses the best models to increase the uncertainty for the attacker while minimizing critical aspects in the adversarial ML domain, like attack transferability. We provide the first comprehensive evaluation of defenses against adversarial attacks on machine learning for malware detection, where our threat model explores different levels of threat, attacker knowledge, capabilities, and attack intensities. We show that StratDef performs better than other defenses even when facing the peak adversarial threat. We also show that, of the existing defenses, only a few adversarially-trained models provide substantially better protection than just using vanilla models but are still outperformed by StratDef.
Pose estimation for swimmers in video surveillance

Abstract Traditional models for pose estimation in video surveillance are based on graph structures, in this paper, we propose a method that breaks the limitation of template matching within a range of pose changes to obtain robust results. We implement our swimmer pose estimation method based on deep learning. We take use of High-Resolution Net (HRNet) to extract and fuse visual features of visual object and complete the object detection using the key points of human joint. The proposed model could be applied to all kinds of swimming styles throughout appropriate training. Compared with the methods that require multimodel combinations and training, the proposed method directly achieves the end-to-end prediction, which is easily to be implemented and deployed. In addition, a cross-fusion module is added between parallel networks, which assists the network to make use of the characteristics of multiple resolutions. The proposed network has achieved ideal results in the pose estimation of swimmers by comparing HRNet-W32 and HRNet-W48. In addition, we propose an annotated key point dataset of swimmers which was created from the view of underwater swimmers. Compared with side view, the torso of swimmers collected by the underwater view is much suitable for a broad spectrum of machine vision tasks.
Fast and robust for texture-less feature registration via adaptive heterogeneous kernels

Feature registration is a core problem in computer vision and machine learning techniques. In recent years, significant progress has been made in learning-based feature registration algorithms. However, such methods usually capture image-level features and strive to capture contour information, inadvertently neglecting the exploration of low-texture regions, while the complex model structure brings more parameters. Therefore, it remains a challenging task to effectively address low-texture feature registration under limited sample conditions. In this paper, we propose a fast and robust feature registration algorithm to achieve fast feature registration in the low-texture case. Specifically, we first construct a two-step learning framework and use a faster adaptive heterogeneous filter (AdaH-Filter) to guide coarse-grained and fine-grained feature extraction. Then, we propose registration feature refiners (ReFR) to achieve the fusion of two-stage features in a low-texture environment while guaranteeing the speed of the detector. Finally, to further improve the network performance, a Warp Extractor is proposed to improve the speed and portability of model inference. This strategy allows the network to perform different levels of fine extraction adaptively according to the registration requirements. Moreover, the proposed method is trained in an end-to-end manner, guided by cosine loss, without any additional supervision. Extensive experimental results on several challenging and representative feature registration datasets in the industry show that the proposed method outperforms the existing state-of-the-art methods. More importantly, it has a faster inference speed under different sensor modalities.
Analisis Kesulitan Membaca Permulaan Pada Siswa Dasar Kota Bekasi

This research was motivated by the learning difficulties experienced by class IA students at SDN Teluk Pucung VI Kota Bekasi in the initial reading material. This study is aimed to determine the ability of students for beginning reading and the types of difficulties faced by students in beginning reading. The method used in this research is a qualitative research with a case study approach. The data collection techniques used are field observation, indept interviews, and documentation. The model analysis and interpretation of data using interactive model proposed by Miles and Hubermen, namely: data collection, data reduction, data presentation, and drawing conclusions. The results showed that 6 students were able to read the beginning but still had difficulty, and 1 student could not read the beginning. The types of difficulties experienced by students include (1) difficulty distinguishing similar letters, (3) not being able to understand sound symbols, (3) having a deficiency in visual memory, (4) having a lot of reading errors, and (5) not being able to read fluent reading. Based on these results, it can be concluded that the students of class SDN Teluk Pucung VI Kota Bekasi still have difficulty reading the beginning. Collaboration between teacher and parents are needed for the sake of the students in the future time
Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning

Purpose of research. The purpose of the study is to evaluate certain machine learning models in data processing based on speed and efficiency related to the analysis of sentiment or consumer opinions in business intelligence. To highlight the existing developments, an overview of modern methods and models of sentiment analysis is given, demonstrating their advantages and disadvantages. Materials and methods. In order to improve the semester analysis process, organized using existing methods and models, it is necessary to adjust it in accordance with the growing changes in information flows today. In this case, it is crucial for researchers to explore the possibilities of updating certain tools, either to combine them or to develop them to adapt them to modern tasks in order to provide a clearer understanding of the results of their treatment. We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was conducted using a simple data augmentation approach. This project uses natural language processing (NLP), deep learning, and models such as LSTM, CNN, SVM TF-IDF, Adaboost, Naive Bayes, and then combinations of models. The results of the study allowed us to obtain and verify model results with user reviews and compare model accuracy to see which model had the highest accuracy results from the models and their combination of CNN with LSTM model, but SVM with TF-IDF vectoring was most effective for this unbalanced data set. In the constructed model, the result was the following indexes: ROC AUC - 0.82, precision - 0.92, F1 - 0.82, Precision - 0.82, and Recall - 0.82. More research and model implementation can be done to find a better model. Conclusion. Natural language text analysis has advanced quite a bit in recent years, and it is possible that such problems will be completely solved in the near future. Several different models in ML and CNN with the LSTM model, but SVM with the TF-IDF vectorizer proved most effective for this unbalanced data set. In general, both deep classification algorithm. A combination of both approaches can also learning and feature-based selection methods can be used to solve be used to further improve the efficiency of the algorithm. some of the most pressing problems. Deep learning is useful when the most relevant features are not known in advance, while feature-based
A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved.
Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation

Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we propose a novel approach to enhance HRL performance through topologically sorted potential calculation for reward machines. By leveraging the topological structure of the task hierarchy, our method efficiently determines potentials for different sub-goals. This topological sorting enables the agent to prioritize actions leading to the accomplishment of higher-level goals, enhancing the learning process. To assess the efficacy of our approach, we conducted experiments in the grid-world environment with OpenAI-Gym. The results showcase the superiority of our proposed method over traditional HRL techniques and reward machine-based reinforcement learning approaches in terms of learning efficiency and overall task performance.
Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0-0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.
Fathom: Understanding Datacenter Application Network Performance

We describe our experience with Fathom, a system for identifying the network performance bottlenecks of any service running in the Google fleet. Fathom passively samples RPCs, the principal unit of work for services. It segments the overall latency into host and network components with kernel and RPC stack instrumentation. It records these detailed latency metrics, along with detailed transport connection state, for every sampled RPC. This lets us determine if the completion is constrained by the client, network or server. To scale while enabling analysis, we also aggregate samples into distributions that retain multi-dimensional breakdowns. This provides us with a macroscopic view of individual services. Fathom runs globally in our datacenters for all production traffic, where it monitors billions of TCP connections 24x7. For five years Fathom has been our primary tool for troubleshooting service network issues and assessing network infrastructure changes. We present case studies to show how it has helped us improve our production services.
A graph attention network under probabilistic linguistic environment based on Bi-LSTM applied to film classification

Film reviews contain rich and complex linguistic information that can reflect the opinions and emotions of the reviewers. However, existing methods for emotion classification of film reviews rely on quantifying qualitative evaluations numerically. This approach can lead to difficulties in interpretation, information loss, and performance degradation under massive data. In this paper, we propose a novel method that utilizes a probabilistic linguistic term set (PLTS) and graph attention network (GAT) to classify films based on their emotional content in long reviews. Firstly, the Bi-directional long short-term memory (Bi-LSTM) method is used to convert film reviews into distributed emotional probabilities. This approach not only captures the emotional information in reviews, but also avoids the limitations of numerical quantification. Secondly, using PLTS to represent emotional information not only considers the relationships of linguistic features but also captures multiple emotional information simultaneously. Finally, we utilize multiple GATs to learn and aggregate the distributed emotional probabilities, enabling our method to fully perceive multiple emotional information in the reviews. Experimental results demonstrate that our method outperforms other models in classification accuracy on the IMDB film review dataset. Our method emulates human thinking to analyze emotional information in reviews and uses a human-like attention mechanism to learn the interrelationship between emotions in film reviews. Therefore, our method exhibits significant improvements in both accuracy and interpretability compared to current models, making it applicable to diverse domains that necessitate the analysis of linguistic data. Overall, the proposed method in this paper presents a novel and effective approach to analyzing and classifying films based on linguistic reviews.