Reviewerly/exHarmony huggingface dataset:
Ticket automation: An insight into current research with applications to multi-level classification scenarios

Modern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1-score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy.
Multi-relation graph convolutional network for Alzheimer’s disease diagnosis using structural MRI

Structural magnetic resonance imaging (sMRI) is widely applied in Alzheimer’s disease (AD) diagnosis tasks by reflecting structural anomalies of the brain. Currently, most existing methods solely focus on pathological changes in disease-affected brain regions and ignore their potential associations and interactions, which provide valuable information for brain investigation. Meanwhile, how to construct effective structural brain graphs composed of nodes and edges remains appealing. To tackle these issues, in this paper, we propose a novel multi-relation reasoning network (MRN) to learn multi-relation-aware representations of brain regions in sMRI data for AD diagnosis, including spatial correlations and topological information. We frame distinguishing different disease statuses as the graph classification problem. Each scan is regarded as a graph, where nodes represent brain regions with abnormal changes selected by group-wise comparison, and edges denote semantic or spatial relations between them. Specifically, the dilated convolution module learns informative features to provide discriminative node representations for constructing brain graphs. Multi-type inter-region relations are then captured by the local reasoning module based on the graph convolutional network to provide a reliable basis for AD diagnosis, including geometric correlations and semantic interactions. Moreover, global reasoning is employed on the learned graph structure to achieve information aggregation and gradually generate the subject-level representation for AD diagnosis. We evaluate the effectiveness of our proposed method on the ADNI dataset, and extensive experiments demonstrate that our MRN achieves competitive performance for multiple AD-related classification tasks, compared to several state-of-the-art methods.
A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies

Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3+, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic necrosis including validation and test, augmented with 500 image tiles of 448 × 448 pixels. Precision, recall, and accuracy were calculated for each algorithm based on the prediction results of 60 test images of 2688 × 2688 pixels. The two segmentation algorithms, DeepLabV3+ and Mask R-CNN, showed over 90% of accuracy (0.94 and 0.92, respectively), whereas SSD, an object detection algorithm, showed lower accuracy. The trained DeepLabV3+ outperformed all others in recall while also successfully separating hepatic necrosis from other features in the test images. It is important to localize and separate the abnormal lesion of interest from other features to investigate it on a slide level. Therefore, we suggest that segmentation algorithms are more appropriate than object detection algorithms for use in the pathological analysis of images in non-clinical studies.The online version contains supplementary material available at 10.1007/s43188-023-00173-5.
Flood-Related Multimedia Benchmark Evaluation: Challenges, Results and a Novel GNN Approach

This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data.
How is Linguistic Accommodation Perceived in Instant Messaging? A Survey on Teenagers’ Evaluations and Perceptions

This study examines Flemish teenagers’ perceptions and evaluations of linguistic accommodation in instant messaging: people adapting their language use to (that of) their interlocutor. We conducted a survey among 254 pupils in Belgian secondary education, and compare the results to teenagers’ online writing practices in a reference corpus. Our findings yield insight in the indexicality of accommodation, with (more) mirroring evoking (more) positive evaluations of the relationship between interlocutors. As for self-reported mirroring, the socio-demographic variable of education stands out: while the participants report a frequent online contact with people who differ from them regarding age, gender, or educational track, they only report to accommodate in the first two situations. Furthermore, they perform quite poorly at recognizing accommodative adjustments made by their peers, especially for mixed-education interactions. Finally, while teenagers appear to hold largely the same opinions on accommodation, the analyses reveal some socio-demographic variation.
Classification of Spam E-mail based on Naïve Bayes Classification Model

With the rising number of spam email, the need of more sufficient antispam filter is surging. Phishing attack can lead to extremely large losses of companies and individual, even more than 1 billion dollars in one year. This paper investigates and combines Naïve Bayes Classification and clustering algorithm in the application of identifying spam emails. With sample emails to create a dynamic dictionary containing most frequent words in spam and normal emails, this distribution of spam filter will provide a stricter method to prevent spam emails than those methods used in mail companies, e.g., Google, Yahoo, and Outlook.com. Besides, this paper also compares several algorithms used today in classifying spams and the future techniques of deep learning and machine learning’s application in classifying spam emails. According to the analysis, Google’s algorithm has the most comprehensive function, but such algorithm has less strict rule than Yahoo’s. Outlook.com, as a combination of Microsoft application, it has a unique algorithm for encrypting and filtering spams. Overall, these results shed light on guiding further exploration of both comprehensive and strict rule for classifying spams.
Analysis of POW in Bitcoin and POS in Peercoin

The technology of blockchain, which is the underlying technology of cryptocurrency like Bitcoin, has changed the world dramatically and it has fascinated researchers from all over the world. Originating from the Byzantine General Problem, the consensus mechanism is utilized to reach an agreement in a group where the channel for communication could be unreliable. Among all the consensus mechanisms in the blockchain, proof of stake (POS) and proof of work (POW) are the most widely used. POW was first used in the Bitcoin blockchain and soon became its central consensus mechanism of it and the earliest implementation of POS for cryptocurrency was Peercoin. Numerous research has been done in this field, but they failed to explain the features of the two mechanisms comprehensively. In this paper, the author did a brief introduction to the hash function and consensus mechanism as well as how POW and POS work, then made detailed analysis and comparisons of a POW in Bitcoin and POS in Peercoin in terms of fairness, security, environmental influence, penalty, negotiability and sustainability, and future trend, and declared that both of POS and POS had some advantages and drawbacks. The author also listed some risks and potential problems of POW and POS and then gave possible solutions for them.
Comparison of Machine Learning Strategies in Hazardous Asteroids Prediction

The purpose of this study is to use multiple classification algorithm from machine learning to predict hazardous asteroids that orbit Earth. Seven models are Logistic Regression, K-Nearest Neighbor Classifier, Random Forest Classifier, Decision Tree Classifier, Multinomial Naïve Bayes Classifier, Gradient Boosting Classifier, and Voting Classifier. Confusion matrix is used to evaluate those models. Evaluation metrics include accuracy, precision, recall, and f1-score. The result shows that random forest classifier has the greatest overall performance with highest accuracy. Decision Tree classifier, Gradient Boosting classifier, and Voting classifier also perform well. Gradient Boosting classifier is capable of greatly reducing the risk of hazardous asteroid, which is, reduce the number of hazardous asteroids that is predicted as non-hazardous. Because of assumptions of some models like Logistic Regression, data used in the experiment do not follow them, so the overall performance of those models are not well. It would be better to select data for fitting the model. The result shows that combined classifiers perform better. Voting Classifier can be used to assemble those accurate models and get a more accurate result by offsetting disadvantages of each model.
Classification of Research Papers on Radio Frequency Electromagnetic Field (RF-EMF) Using Graph Neural Networks (GNN)

This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameter λ, which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value of λ increases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification.
Finding Wordle Strategies That Can be Mastered by Humans

Wordle is a popular web game. Players use strategies and hints to figure out the correct answer of a daily puzzle. In general, there are several strategies to solve it efficiently, but which is the best depends on the game’s current states. This paper designed 4 individual strategies which is learnable for both computers and human players, and trained an AI based on Q-learning to solve the game automatically with a win rate of over 80%. The decision this AI made is only based on the word list, not including other information (e.g., frequency of appearance on the Internet). Based on the analysis, it can give players advices in various kinds of situations and offer useful combos. By using this AI players, it is feasible to figure out what is the best choice and improve their skills. However, it still cannot solve some rare situations efficiently, which may require more training or a new way of setting the game states.
An Abstractive Summarization Model Based on Joint-Attention Mechanism and a Priori Knowledge

An abstractive summarization model based on the joint-attention mechanism and a priori knowledge is proposed to address the problems of the inadequate semantic understanding of text and summaries that do not conform to human language habits in abstractive summary models. Word vectors that are most relevant to the original text should be selected first. Second, the original text is represented in two dimensions—word-level and sentence-level, as word vectors and sentence vectors, respectively. After this processing, there will be not only a relationship between word-level vectors but also a relationship between sentence-level vectors, and the decoder discriminates between word-level and sentence-level vectors based on their relationship with the hidden state of the decoder. Then, the pointer generation network is improved using a priori knowledge. Finally, reinforcement learning is used to improve the quality of the generated summaries. Experiments on two classical datasets, CNN/DailyMail and DUC 2004, show that the model has good performance and effectively improves the quality of generated summaries.
Uniform distribution elephant herding optimization (UDEHO) based virtual machine consolidation for energy-efficient cloud data centres

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
High performance HITA based Binary Edward Curve Crypto processor for FPGA platforms

In an embedded and resource-constrained environment, Elliptic Curve Cryptography (ECC) has been noted as an efficient and suitable methodology for achieving information security via public-key cryptography. However, the drawback of ECC is its lack of unifiedness in point operation that makes it prone to side-channel attack. Also, ECC does not satisfy the completeness property due to which the addition formula is not defined for all the pairs of input points. Edward curve, with its unified addition law and completeness property, proved to be the answer to aforementioned flaws. High throughput while maintaining low resource is a key issue for elliptic curve cryptography (ECC) hardware implementations in many applications. This paper presents the implementation of a Binary Edward curve Crypto processor over GF(2233) for FPGA platforms. The architecture is modified to perform scalar multiplication in a parallel manner using two hybrid Karatsuba field multipliers. Field inversion being one of the most tedious operations while reconversion, is also performed in a parallel manner using an efficient Hex Itoh-Tsujii inversion algorithm. The hardware resources are shared for performing point operations and inversion. Exploiting parallelism in point and inversion operations has resulted in reduction of the clock cycles consumed and the resultant architecture is more efficient in terms of throughput over area. The design takes 0.038 ms on Xilinx Virtex-4 and 0.031 ms on Virtex-7 FPGA platforms to perform a 233-bit point multiplication operation. It takes 73.57%, 13.71%, 14.76% and 48.76% more efficient than existing scalar multiplication with BEC. This proposed scalable, side-channel attack resilient design outperforms the existing techniques with respect to throughput over area.
An Improved Quantum-Behaved Particle Swarm Optimization Algorithm Combined with Reinforcement Learning for AUV Path Planning

In order to solve the problem of fast path planning and effective obstacle avoidance for autonomous underwater vehicles (AUVs) in two-dimensional underwater environment, a path planning algorithm based on deep Q-network and Quantum particle swarm optimization (DQN-QPSO) was proposed. Five actions are defined first: normal, exploration, particle explode, random mutation, and fine-tuning operation. After that, the five actions are selected by DQN decision thinking, and the position information of particles is dynamically updated in each iteration according to the selected actions. Finally, considering the complexity of underwater environment, the fitness function is designed, and the route length, deflection angle, and the influence of ocean current are considered comprehensively, so that the algorithm can find the solution path with the shortest energy consumption in underwater environment. Experimental results show that DQN-QPSO algorithm is an effective algorithm, and its performance is better than traditional methods.
An extended technology acceptance model on immersive virtual reality use with primary school students

ABSTRACTABSTRACTVirtual Reality (VR) is an emerging technology with an increasing number of studies assessing its benefits in educational settings. However, there is a shortage of empirical studies aimed at evaluating the acceptance of this technology in primary education. The authors propose an extended version of the Technological Acceptance Model to examine students' intention to use immersive VR. To this end, 111 fourth-graders completed a history teaching unit using immersive VR. Structural equation modelling was employed to assess causal relationships between six variables: prior knowledge, perceived attention, perceived utility, perceived ease of use, perceived enjoyment and attitude towards the use of immersive VR. The results indicate that students' prior knowledge was not significant in explaining their intention to use immersive VR. However, perceived attention had a significant effect on this variable mediated through perceived utility. These results have important implications regarding the design and use of VR tools for educational purposes.KEYWORDS: Virtual realityprimary educationhistory teachingtechnology acceptance model Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the University of Castilla-La Mancha and by the European Regional Development Fund (ERDF) [grant number 2022-GRIN-34039], by the Castilla-La Mancha Regional Administration [grant number SBPLY/19/180501/000278], and by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR [grant number TED2021-131557B-100].Notes on contributorsRafael Villena-TaranillaRafael Villena-Taranilla is a primary teacher in San Cristóbal school (Albacete), where he is the Coordinator of the Longlife Learning Programme and digital transformation. He is also a part-time lecturer at University of Camilo José Cela (Spain). His research interests include virtual reality, augmented reality, gamification and educational robotics.Ramón Cózar-GutiérrezRamón Cózar-Gutiérrez is a lecturer of the Department of History at the University of Castilla-La Mancha. He is co-leader of the research group LabinTic at the Faculty of Education of Albacete. His research fields in education address the teaching and learning of social sciences, teacher training and educational technology.José Antonio González-CaleroJosé Antonio González-Calero is currently a lecturer at the University of Castilla-La Mancha. He is co-leader of the research group LabinTic at the Faculty of Education of Albacete. His research interests include educational technology and the development and implementation of interactive learning environments.Pascual D. DiagoPascual D. Diago is a lecturer at the University of València. He is currently teaching on the degree courses of Early Childhood Education and Primary Education and on the master's degree in research in Specific Didactics. His research focuses on problem-solving and the use of new technologies in the teaching-learning processes of school mathematics.
The role of distance learning technologies in the organization of the educational process in Higher Educational Institutions of Ukraine in the conditions of the large-scale armed aggression russian federation against Ukraine

2022 has been a year of huge challenge for all Educational Institutions of Ukraine in terms of their ability to quickly organization of distance version of the educational process in connection with large-scale armed aggression russian federation against Ukraine with goal to destruction of Ukraine and its people. After February 24, 2022 when Russia-Ukraine war that began back in 2014 became the large-scale war and President of Ukraine announced of martial law, the Ministry of Defense of Ukraine (MD) and the Ministry of Education and Science (MES) of Ukraine recommended of all Educational Institutions of the country to use the distance learning technologies (DLT) for further continuation educational process. That’s why Institutions Higher Education (IHE) and Higher Military Educational Institutions (HMEI) of Ukraine actively use own developments and the best developments of the world’s leading companies which work in this direction (Google, Microsoft, ZOOM, Coursera, Udemy, edX, SpaceX, hp, Vodafone, Lifecell, Київстар, etc.) to improve the quality of distance learning in the conditions of the large-scale armed aggression russian federation against Ukraine. The wide opportunities of modern information technologies for the creation of simulation models of objects and processes (flash-animation, 3-D models, educational computer games etc.) allow visualization of information and make the content of the distance courses the most understandable and interesting for students and cadets. That’s why the task of researching the role of distance learning technologies in the organization of the educational process in Higher Educational Institutions of Ukraine in the conditions of the large-scale armed aggression russian federation against Ukraine is more relevant than ever [1-7].