Reviewerly/exHarmony huggingface dataset:
Mobile Educational Resources in the Teaching–Learning Process of Inorganic Chemistry Nomenclature

The starting point of the present research work is the lack of interest shown by students of high school in learning inorganic chemical nomenclature, mainly due to the rote learning of the contents. From this scenario, the main objective is to analyze how mobile learning application can help students in the learning of inorganic chemical nomenclature, supporting the idea that mobile educational resources effectively influence students’ learning. The research assumes a qualitative-quantitative approach applying objective tests to the study population composed of 27 students in the experimental group and 24 in the control group. The results of the pretest objective tests compared with those of the posttests show a positive change in the academic performance of the students after the application of a set of didactic strategies that integrate active methodologies with mobile applications. Additionally, students are interviewed to know the level of academic satisfaction achieved after the use of mobile applications for their learning. The research concludes that students are more motivated to learn, they strengthen cooperative learning because they learn socially, they begin to make better use of mobile devices especially at home to improve their autonomous learning, they find a practical sense to the knowledge they acquire in each class, in short they are the essence of the teaching–learning process.
Blockchain in the Smart City and Its Financial Sustainability from a Stakeholder’s Perspective

In this paper, we take a city’s budget, which represents the resources that need to be allocated, and test how many blockchain users need to join a voting process of how the city’s resources should be allocated in order to best represent their preferences. This voting process can be tracked very well through the utilization of IoT and smart technology in a smart city. Therefore, we showed that the budget resource allocation of a smart city can be significantly optimized through the utilization of blockchain technology. We found that just a tiny fraction of 0.12% of the population of blockchain participants is needed to significantly represent the spending behavior of the total population. This has significant implications as it shows the strength and importance of a required blockchain in a smart city and its minimal energy consumption requirements.
Asgard: Are NoSQL databases suitable for ephemeral data in serverless workloads?

Serverless computing platforms are becoming increasingly popular for data analytics applications due to their low management overhead and granular billing strategies. Such analytics frameworks use a Directed Acyclic Graph (DAG) structure, in which serverless functions, which are fine-grained tasks, are represented as nodes and data-dependencies between the functions are represented as edges. Passing intermediate (ephemeral) data from one function to another has been receiving attention of late, with works proposing various storage systems and methods of optimization for them. The state-of-practice method is to pass the ephemeral data through remote storage, either disk-based (e.g., Amazon S3), which is slow, or memory-based (e.g., ElastiCache Redis), which is expensive. Despite the potential of some prominent NoSQL databases, like Apache Cassandra and ScyllaDB, which utilize both memory and disk, prevailing opinions suggest they are ill-suited for ephemeral data, being tailored more for long-term storage. In our study, titled Asgard, we rigorously examine this assumption. Using Amazon Web Services (AWS) as a testbed with two popular serverless applications, we explore scenarios like fanout and varying workloads, gauging the performance benefits of configuring NoSQL databases in a DAG-aware way. Surprisingly, we found that, per end-to-end latency normalized by $ cost, Apache Cassandra's default setup surpassed Redis by up to 326% and S3 by up to 189%. When optimized with Asgard, Cassandra outdid its own default configuration by up to 47%. This underscores specific instances where NoSQL databases can outshine the current state-of-practice.
Asymmetric gradient penalty based on power exponential function for imbalanced data classification

Abstract Model bias is a tricky problem in imbalanced data classification. An asymmetric gradient penalty method is proposed based on the power exponential function to alleviate this. The methodology integrates a power exponential function as a moderator into the cross-entropy loss of the negative samples, driving the model to focus on hesitant samples while ignoring easy and singular samples. The rationality of the algorithm is explored from the gradient point of view, and it is demonstrated that the approach improves focal loss and asymmetric focal loss. Then, the imbalanced data classification experiments were deployed on MNIST, CIFAR10, CIFAR100, and Caltech101, respectively. For binary classification, datasets with several imbalance ratios constituted by varying the sample size of the majority class and minority class are included in the experiments. In the multi-category classification experiments, we discuss imbalanced datasets with only a single majority category and those with several majority categories and examine step-imbalance datasets and linear-imbalance datasets. The results reveal that the proposed method exhibits competitiveness on various imbalanced datasets and better robustness on high imbalance ratio datasets. Finally, the approach is deployed on the pulsar candidate dataset HTRU, and the state-of-the-art results are yielded. Our code is available at https://github.com/gzmtzly/GPPE .
Virtual Tours of Museums to Promote Tourism

Museums represent a collection of ancient and modern knowledge which must be protected, disseminated, and transmitted in the best viable way. Digital development and technology in recent years have made it possible to transform the way of interacting with museums through virtual tours (Palmieri et al in Springer Series in Design and Innovation, pp 71–82, 2023 [1]), which gained strength in times of pandemic, the products generated for virtual tours range from physical reproductions/faithful virtual images of historical artifacts to digital libraries that can be used by experts and others, in addition to 360º technology which allows a 360° viewing angle to be covered in two axes. That is, we can see everything around the point where it was made the photo or video. In this article, the virtual tours offered by some museums are analyzed, as well as the use of these tours as a tourism marketing strategy, giving the user the opportunity to interact with the place they plan to visit in a new way (Cóndor-Herrera and Ramos-Galarza in Marketing and smart technologies. Smart innovation, systems and technologies, pp 443–449, 2022 [2]).
Visualising flight regimes using self-organising maps

Abstract The purpose of this paper is to group the flight data phases based on the sensor readings that are most distinctive and to create a representation of the higher-dimensional input space as a two-dimensional cluster map. The research design includes a self-organising map framework that provides spatially organised representations of flight signal features and abstractions. Flight data are mapped on a topology-preserving organisation that describes the similarity of their content. The findings reveal that there is a significant correlation between monitored flight data signals and given flight data phases. In addition, the clusters of flight regimes can be determined and observed on the maps. This suggests that further flight data processing schemes can use the same data marking and mapping themes regarding flight phases when working on a regime basis. The contribution of the research is the grouping of real data flows produced by in-flight sensors for aircraft monitoring purposes, thus visualising the evolution of the signal monitored on a real aircraft.
Turizm Sektöründe Blockchain Kullanımı ve Güncel Eğilimler: Kavramsal Bir Araştırma

Today, with changes and developments in technology, there are changes in concepts, uses, and even in daily life, and it is seen that this situation permeates all areas of life. In this context, many new types of technology have emerged. Blockchain is one of these technologies and is one of the applications in the tourism sector in human life. Therefore, it is necessary to examine this phenomenon by focusing on the studies carried out to perceive the possible results of both software and hardware applications realized with technologies developed on blockchain, and to understand the opportunities that may arise in the tourism sector. Accordingly, in this study, particularly studies on the areas where tourism and blockchain intersect are examined and a conceptual analysis is conducted on these topics by determining the topics of blockchain use in Europe and the USA from the perspective of basic accommodation services, tourist data, medical tourism, and global application areas. Based on this academic framework, an application suggestion for Türkiye is also provided.
A Web3 secure platform for assessments and educational resources based on blockchain

Abstract A rising number of educational solutions based on blockchain technology have been designed. Students and other authorities responsible for verifying certificates are very concerned about the authenticity of academic credentials, either because the institutions that issue them no longer exist or because they do not keep adequate records. Blockchain technology has much to offer in education, including its high level of security, improved data access control, low cost, improved accountability, identity authentication, transparency, increased trust, and improved efficiency in managing student records. We proposed a decentralized blockchain‐based secure platform for the storage of academic certificates and student assessments with double encryption. Apart from speeding up the verification process, it will increase the safety of personal education data and assessments of all kinds of misuse. The data would only be accessed using a stakeholder's private key, and storing documents on a blockchain would increase security. We proposed a framework to offer a secure channel for educational resources. We are storing documents over blockchain utilizing an IPFS distributed data server. We give a thorough explanation of the system development, design, and evaluation of the suggested solution in terms of security and cost. Finally, we put the proposed framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the proposed method is effective and feasible.
Research on the Diversification of Language Ability in Applied Linguistics and Foreign Linguistics Based on New Media

In order to improve the research effect of the diversification of language ability in applied linguistics and foreign linguistics, this paper conducts research on the diversification of language ability in applied linguistics and foreign linguistics based on new media and intelligent data processing technology. In the process of language learning data processing, combining the adjacency degree and the network structure entropy, this paper proposes the adjacency structure entropy based on the super network to identify the key nodes in the super network. Moreover, this paper applies this indicator to the competitive hypernetwork, and accurately obtains the key nodes of the hypernetwork. In addition, this paper constructs a scientific research cooperation hypernetwork, and applies the adjacency structure entropy in the hypernetwork to this dataset. From the experimental research, it can be seen that the algorithm proposed in this paper can play a certain role in the diversification analysis of linguistic ability in linguistics and foreign linguistics. At the same time, the model proposed in this paper has a certain effect on the diversification of language ability in applied linguistics and foreign linguistics.
Providing Realtime Support for Containerized Edge Services

Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, providing real-time support at the containerized edge systems (CESs) remains a challenge. In this paper, we propose EdgeMan , a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Additionally, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of time-sensitive services run with EdgeMan is reduced by 85.9% on average compared with that of existing methods in both industry and academia.
Sentiment Analysis of Code-Mixed Telugu-English Data Leveraging Syllable and Word Embeddings

Learning the inherent meaning of a word in Natural Language Processing (NLP) has motivated researchers to represent a word at various levels of abstraction, namely character-level, morpheme-level, and subword-level vector representations. Syllable-Aware Word Embedding (SAWE) can effectively handle agglutinative and fusion-based NLP tasks. However, research attempts on assessing the SAWE on such extrinsic NLP tasks has been scanty, especially for low-resource languages in the context of code-mixing with English. A model to learn SAWE to extract semantics at fine-grained subunits of a word is proposed in this article, and the representative ability of the embeddings is assessed through sentiment analysis of code-mixed Telugu-English review corpora. Multilingual societies and advancements in communication technologies have accounted for the prolific usage of mixed data, which renders the State-of-the-Art (SOTA) sentiment analysis models developed based on monolingual data ineffective. Social media users in the Indian subcontinent exhibit a tendency to mix English and their respective native language (using the phonetic form of English) in expressing their opinions or sentiments. A code-mixing scenario provides flexibility to borrow words from a foreign language, usage of shorthand notations, elongation of vowels, and usage of words without following syntactic/grammatical rules, which renders the sentiment analysis of code-mixed data challenging to perform. Deep neural architectures like Long Short-Term Memory and Gated Recurrent Unit networks have been shown to be effective in solving several NLP tasks, such as sequence labeling, named entity recognition, and machine translation. In this article, a framework to perform sentiment analysis on a code-mixed Telugu-English review corpus is implemented. Both word embedding and SAWE are input to a unified deep neural network that contains a two-level Bidirectional Long Short-Term Memory/Gated Recurrent Unit network with Softmax as the output layer. The proposed model leverages the advantages of both word embedding and SAWE, which enable the proposed model to outperform existing SOTA code-mixed sentiment analysis models on a Telugu-English code-mixed dataset of the International Institute of Information Technology–Hyderabad and a dataset curated by the authors. The improvement realized by the proposed model on these datasets is [3% increase in F1-score and 2% increase in accuracy] and [7% increase in F1-score and 5% in accuracy], respectively, in comparison with the best-performing SOTA model.
Traffic Light Detection by Integrating Feature Fusion and Attention Mechanism

Path planning is a key problem in the design of autonomous driving systems, and accurate traffic light detection is very important for robust routing. In this paper, we devise an object detection model, which mainly focuses on traffic light classification at a distance. In the past, most techniques employed in this field were dominated by high-intensity convolutional neural networks (CNN), and many advances have been achieved. However, the size of traffic lights may be small, and how to detect them accurately still deserves further study. In the object detection domain, the scheme of feature fusion and transformer-based methods have obtained good performance, showing their excellent feature extraction capability. Given this, we propose an object detection model combining both the pyramidal feature fusion and self-attention mechanism. Specifically, we use the backbone of the mainstream one-stage object detection model consisting of a parallel residual bi-fusion (PRB) feature pyramid network and attention modules, coupling with architectural tuning and optimizer selection. Our network architecture and module design aim to effectively derive useful features aimed at detecting small objects. Experimental results reveal that the proposed method exhibits noticeable improvement in many performance indicators: precision, recall, F1 score, and mAP, compared to the vanilla models. In consequence, the proposed method obtains good results in traffic light detection.
Skilling communication: The discourse and metadiscourse of communication in self-help books

In the past few decades, self-help books on communication have ranked among the top titles on bestseller lists. Offering advice about improving communication skills in a variety of contexts, they both reflect and promote a widespread discourse about the importance of good communication in everyday life, in what is in fact a paradoxical endeavor – solving flawed communication with more communication. Based on an analysis of 18 bestselling self-help books, the paper examines the meaning of three recurring themes – “listening,” “awareness” and “practice” – and analyzes the paradoxical relationship between what the books say about communication and how they say it. The findings serve to illuminate the relationship between communication and metacommunication more broadly, which, in turn, helps to explain the conditions by which authors express their ideas – their selection of textuality, despite, and precisely because of, its difference from oral talk.
Improved TPH for object detection in aerial images

ABSTRACTDeep learning has greatly enhanced the general object detection capability. However, when directly applied to aerial images, performance drops significantly due to: (1) Most objects in aerial images are dense and small; (2) UAV altitude variations cause diverse object scales. In this paper, we improve TPH algorithm for exceptional aerial object detection performance. Specifically, we introduce the SPD module to replace the strided convolution layers and pooling layers. And we improve the TPH backbone and neck networks so that large and small objects can be detected accurately. Experiments on VisDrone2019 and DOTA datasets validate the effectiveness of our method.KEYWORDS: General object detectionsmall object detectionaerial imagesconvolution layers Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementVisdrone2019 and DOTA datasets are freely available at https://github.com/VisDrone/VisDrone-Dataset. The DOTA dataset of this study are available at https://captain-whu.github.io/DOTA/dataset.html.Additional informationFundingThis research received no external funding.
Sexism in Focus: An Annotated Dataset of YouTube Comments for Gender Bias Research

This paper presents a novel dataset of 200k YouTube comments from 468 videos across 109 channels in four content categories: Entertainment, Gaming, People & Blogs, and Science & Technology. We applied state-of-the-art NLP methods to augment the dataset with sexism-related features such as sentiment, toxicity, offensiveness, and hate speech. These features can assist manual content analyses and enable automated analysis of sexism in online platforms. Furthermore, we develop an annotation framework inspired by the Ambivalent Sexism Theory to promote a nuanced understanding of how comments relate to the gender of content creators. We release a small sample of comments annotated using this framework. Our dataset analysis confirms that female content creators receive more sexist and hateful comments than their male counterparts, underscoring the need for further research and intervention in addressing online sexism.
Semi‐automatic fine delineation scheme for pancreatic cancer

Pancreatic cancer fine delineation in medical images by physicians is a major challenge due to the vast volume of medical images and the variability of patients.A semi-automatic fine delineation scheme was designed to assist doctors in accurately and quickly delineating the cancer target region to improve the delineation accuracy of pancreatic cancer in computed tomography (CT) images and effectively reduce the workload of doctors.A target delineation scheme in image blocks was also designed to provide more information for the deep learning delineation model. The start and end slices of the image block were manually delineated by physicians, and the cancer in the middle slices were accurately segmented using a three-dimensional Res U-Net model. Specifically, the input of the network is the CT image of the image block and the delineation of the cancer in the start and end slices, while the output of the network is the cancer area in the middle slices of the image block. Meanwhile, the model performance of pancreatic cancer delineation and the workload of doctors in different image block sizes were studied.We used 37 3D CT volumes for training, 11 volumes for validating and 11 volumes for testing. The influence of different image block sizes on doctors' workload was compared quantitatively. Experimental results showed that the physician's workload was minimal when the image block size was 5, and all cancer could be accurately delineated. The Dice similarity coefficient was 0.894 ± 0.029, the 95% Hausdorff distance was 3.465 ± 0.710 mm, the normalized surface Dice was 0.969 ± 0.019. By completing the accurate delineation of all the CT images, the speed of the new method is 2.16 times faster than that of manual sketching.Our proposed 3D semi-automatic delineative method based on the idea of block prediction could accurately delineate CT images of pancreatic cancer and effectively deal with the challenges of class imbalance, background distractions, and non-rigid geometrical features. This study had a significant advantage in reducing doctors' workload, and was expected to help doctors improve their work efficiency in clinical application.