Reviewerly/exHarmony huggingface dataset:
Block-chain enabled secure smart IoT healthcare monitoring for COVID-19 patients

During these pandemic days, the situation is hazardous to move physically for treatment. Also, the monitoring of health with wearable devices becomes easier with attachment of IoT smart-health facilities. The established hospitals are enabled to provide smart-health care. The wearable devices sense different heath index parameters and sends to the health monitoring agencies including their legal guardians. During such communication, data can be theft or changed, which may increase unwanted risks for the patients. Sometimes, that harms the mental health by passing miss information to patients and their concern guardians too. The proposed approach helps to reduce such chances of data theft with a simple implementation of hyper ledger fabric block chain with the associated devices meant for smart health care monitoring. For the scattered and predominantly adhoc deployment of IoT devices, we use block-chain consortium to secure the communication. From the obtained results, we found that by leveraging block-chain hyper ledger, such issues with the association of limited capacity IoT devices for smart-health care, likely preferable to solve.
Comparative Analysis of Research Papers Categorization using LDA and NMF Approaches

In the digital world, the research papers are growing exponentially with time, and it is essential to cluster the documents under their respective categories for easier identification and access. However, researchers find it relatively challenging to recognize and categorize their favorite research articles. Though this task can be achieved by putting in the human work, it would be tedious and exhaustively time-consuming. Henceforth, much research has been done in the field of topic modelling to yield accurate results with a good computation time. The main objective of this paper is to compare the two distinct yet vastly used topic modelling approaches for research paper classification, which can further group the research papers into their respective classes. The two chosen topic modeling methodologies are Non-Negative Matrix Factorization (NMF) and Latent Dirichlet allocation (LDA). This paper introduces a comparison between LDA model's performance with a relatively efficient generative model (NMF) and analyzes its performance on the dataset that consists of 1740 papers extracted from the NYC university website. In comparison, the average coherence score for the LDA method was 0.5282, with its optimal choice of topics being 22, which was slightly higher than the NMF model as it yielded a coherence score of 0.4937 with its optimal topics being 9. To enhance the categorization of LDA, clustering the optimal topics of LDA from 22 to 10 using pyLDAvis has been done. On closely comparing both the models, LDA performs slightly better than NMF with a higher confidence score.
Credit Card Fraud Detection Using LSTM Algorithm

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.
Machine Learning Empowered Software Prediction System

Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by identifying and discovering faults before or during the software enhancement process. Defect prediction will play an increasingly important role in the design process as the scope of software projects grows. Bugs or the number of bugs used to measure the performance of a defect prediction procedure are referred to as "bugs" in this context. Defect prediction models can incorporate a wide range of metrics, including source code and process measurements. Defects are determined using a variety of models. Using machine learning, the defect prediction model may be developed. Machine inclining in the second and third levels is dependent on the preparation and assessment of data (to break down model execution). Defect prediction models typically use 90 percent preparation information and 10 percent testing information. Improve prediction performance with the use of dynamic/semi-directed taking in, a machine learning approach. So that the results and conclusion may be sharply defined under many circumstances and factors, it is possible to establish a recreated domain to house the entire method. Computer-aided engineering (CAE) is being used to identify software defects in the context of neural networks. Neural network-based software fault prediction is compared to fuzzy logic fundamental results in this research paper. On numerous parameters, neural network training provides better and more effective outcomes, according to the recommended findings and outputs.
Using Flexible Memories to Reduce Catastrophic Forgetting

In continual learning, a primary factor of catastrophic forgetting is task-recency bias, which arises when a model is trained on an imbalanced set of new and old task instances. Recent studies have shown the effectiveness of rehearsal-based continual learning methods; however, a major drawback of these methods is the loss of accuracy on older tasks when training is biased towards newer tasks. To bridge this gap, we propose a $$\lambda $$ Stability Wrapper ( $$\lambda $$ SW), where the learner uses a task-based policy to adjust the probability of when instances are replaced in memory to account for task-recency bias to alleviate catastrophic forgetting. The policy results in an increased number of instances seen from older tasks. By construction, $$\lambda $$ SW can be applied with other rehearsal-based continual learning algorithms. We validate the effectiveness of $$\lambda $$ SW with three well known baseline methods: Gradient-based Sample Selection, Experience Replay, and Maximally Interfered Retrieval. Our experimental results show significant gains in accuracy on eleven out of twelve of our experiments across four datasets.
Improving Knowledge Graph Entity Alignment with Graph Augmentation

Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. However, existing GNN-based methods either suffer from the structural heterogeneity issue that especially appears in the real KG distributions or ignore the heterogeneous representation learning for unseen (unlabeled) entities, which would lead the model to overfit on few alignment seeds (i.e., training data) and thus cause unsatisfactory alignment performance. To enhance the EA ability, we propose GAEA, a novel EA approach based on graph augmentation. In this model, we design a simple Entity-Relation (ER) Encoder to generate latent representations for entities via jointly modeling comprehensive structural information and rich relation semantics. Moreover, we use graph augmentation to create two graph views for margin-based alignment learning and contrastive entity representation learning, thus mitigating the negative influence caused by structural heterogeneity and sparse seeds. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our method. Our codes are available at https://github.com/Xiefeng69/GAEA .
Transfer Reinforcement Learning Based Negotiating Agent Framework

While achieving tremendous success, there is still a major issue standing out in the domain of automated negotiation: it is inefficient for a negotiating agent to learn a strategy from scratch when being faced with an unknown opponent. Transfer learning can alleviate this problem by utilizing the knowledge of previously learned policies to accelerate the current task learning. This work presents a novel Transfer Learning based Negotiating Agent (TLNAgent) framework that allows a negotiating agent to transfer previous knowledge from source strategies optimized by deep reinforcement learning, to boost its performance in new tasks. TLNAgent comprises three key components: the negotiation module, the adaptation module and the transfer module. To be specific, the negotiation module is responsible for interacting with the other agent during negotiation. The adaptation module measures the helpfulness of each source policy based on a fusion of two selection mechanisms. The transfer module is based on lateral connections between source and target networks and accelerates the agent’s training by transferring knowledge from the selected source strategy. Our comprehensive experiments clearly demonstrate that TL is effective in the context of automated negotiation, and TLNAgent outperforms state-of-the-art Automated Negotiating Agents Competition (ANAC) negotiating agents in various domains.
The implementation of learning apps in biological education: a quantitative study of the current situation in Austria

Purpose This research paper aims to provide information about certified learning apps for biological education and gave an ordered list of all learning apps currently used by Austrian biology teachers in the classroom, which should serve as an overview for all biology teachers. In addition, the (currently little known) certification process of learning apps (seal of quality for educational applications) is described. Design/methodology/approach Online questionnaire for all biology teachers throughout Austria, on the one hand to find out the apps, and on the other hand to research how Austrian teachers find suitable apps. The data were evaluated using descriptive statistics. Findings A total of84 different learning apps are currently used by biology teachers in Austria. There are two certified lernapps in Austria, both are used. The most common app in biology lessons is “Anton”. The teachers find the information about apps throughout their own research or through colleagues. There are regional and school-specific differences in regards of usage and knowledge about seal of quality. It needs its own teacher training (TT) via suitable learning apps, because problems (data protection, advertising) are sometimes not taken into account during use. Research limitations/implications Limitations of this paper are that some of the teachers indicated the apps from other subjects (mathematics) to use this learning app, although this is not possible for biology lessons. Data protection was stated to the best of the authors’ knowledge by the authors, if the authors were not sure it says “unsure”. The participants are mainly women, but this corresponds to the gender ratio, which is typical of the Austrian teaching profession. Practical implications The overview of the apps, compiled by this Austria-wide research, can be taken over into the biology lessons of all teachers. In addition, on the basis of this study, a TT at the University of Education 2023 in Linz was created. In addition, the (currently little known) certification process of learning apps is described. Social implications The TT and the overview of the learning apps used serve as guidelines for teachers as to which apps they can use in biology lessons without hesitation. Above all, the aspect of the follow-up of digital media/apps will be emphasized. Data backup, inappropriate advertising must be processed in class or completely omitted. Biology teachers need the right training (TT) and appropriate materials and tools (apps) to reduce problems (cybercrimes). Originality/value Currently, there is no prepared list of suitable (certified and uncertified) learning apps for biology lessons. There are isolated recommendations and individual apps, but the selection criteria and backgrounds of the authors are not clear. This list shows which apps (how often) are used by which teachers. In addition, the (currently little known) certification process of learning apps is described.
Multi-Encoder Transformer for Korean Abstractive Text Summarization

In this paper, we propose a Korean abstractive text summarization approach that uses a multi-encoder transformer.Recently, in many natural language processing (NLP) tasks, the use of the pre-trained language models (PLMs) for transfer learning has achieved remarkable performance.In particular, transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) are used for pre-training and applied to downstream tasks, showing state-of-the-art performance including abstractive text summarization.However, existing text summarization models usually use one pre-trained model per model architecture, meaning that it becomes necessary to choose one PLM at a time.For PLMs applicable to Korean abstractive text summarization, there are publicly available BERT-based pretrained Korean models that offer different advantages such as Multilingual BERT, KoBERT, HanBERT, and KorBERT.We assume that if these PLMs could be leveraged simultaneously, better performance would be obtained.We propose a model that uses multiple encoders which are capable of leveraging multiple pre-trained models to create an abstractive summary.We evaluate our method using three benchmark Korean abstractive summarization datasets, each named Law (AI-Hub), News (AI-Hub), and News (NIKL) datasets.Experimental results show that the proposed multi-encoder model variations outperform single-encoder models.We find the empirically best summarization model by determining the optimal input combination when leveraging multiple PLMs with the multi-encoder method.
Thirteen ways of looking at a license

This column describes how one library seeks to mitigate harms or to optimize protections and advantages resulting from journal licenses. Progress toward improved licensing outcomes is described with examples or with qualitative data, from a comprehensive overview of one library's journal licenses. Discussion includes so-called transformative agreements, a variety of rights, non-disclosure and data privacy clauses, term and termination, and transfer obligations.
On Toeplitz codes of index t and isometry codes

In this paper, we introduce Toeplitz codes of index t, which are a generalization of quasi-cyclic codes of index t. We show that such codes meet the asymptotic Gilbert-Varshamov bound on the relative distance with asymptotic rate 1t over Fq, so they are asymptotically good for any t. We characterize some special classes of Toeplitz codes as LCD codes (resp. linear codes with one-dimensional hull or self-orthogonal codes). It is worth mentioning that we determine the hull dimension of the isometric image of a linear code. Some optimal or almost optimal binary linear codes are obtained as a result. Most importantly, we obtain some optimal or almost optimal LCD codes (resp. linear codes with one-dimensional hull) from quasi-cyclic codes. In particular, we construct binary LCD [32,16,8], [33,11,11] and [34,17,8] codes with improved parameters directly.
An analytical study on the low-pass filtering effect of digital image correlation caused by under-matched shape functions

The low-pass filtering effect, which stems from undermatching of shape function, sincerely affects the metrological performance of digital image correlation, especially for localized deformations. Here, we build a theoretical model characterizing the low-pass filtering effect, which explicitly expresses the measured displacement in terms of subset size, shape function order, and underlying deformation field. Based on the model, we derive formulae for transfer function of digital image correlation and study the attenuation of Gaussian displacement fields. These results provide a deep insight into shape function undermatching and open up new possibilities for compensating the low-pass filtering effect.
Adaptive decoder-block selection with filter reweighting for medical image segmentation

Image segmentation models are mostly designed for specific tasks and trained on large-scale datasets. Medical image segmentation tasks, however, are highly diverse and usually have a small training dataset, making it difficult for a segmentation model to generalize well on other tasks. This architecture bias motivates a lot of research on designing a task-adaptive segmentation network with the most suitable settings including the network’s depth and width and feature fusion strategy. In this paper, we propose the adaptive Decoder-block Selection with Filter Reweighting (DeSFiR) algorithm to generate an efficient and effective medical image segmentation model for each specific dataset. This algorithm is a two-step iterative process. In the adaptive decoder-block selection step, we develop an auxiliary indicator function layer to bypass the decoder block whose individual responses are lower than a pre-defined threshold, aiming to generate a lightweight model. In the filter reweighing step, we replace invalid layers in the segmentation network with the external information from a simultaneously trained twin network. To this end, we utilize an entropy-based criterion to measure the informativeness of each layer and an adaptive weighting strategy to balance the replaced information between two networks. We have evaluated our algorithm on three tasks, including the segmentation of neural structures, nuclei, and liver. Our results indicate that the medical image segmentation model generated by the proposed DeSFiR algorithm has a significantly reduced complexity and comparable accuracy.
Hybrid Data Augmentation and Dual-Stream Spatiotemporal Fusion Neural Network for Automatic Modulation Classification in Drone Communications

Automatic modulation classification (AMC) is one of the most important technologies in various communication systems, including drone communications. It can be applied to confirm the legitimacy of access devices, help drone systems better identify and track signals from other communication devices, and prevent drone interference to ensure the safety and reliability of communication. However, the classification performance of previously proposed AMC approaches still needs to be improved. In this study, a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC approach is proposed to enhance the classification accuracy for the purpose of aiding drone communication because SDDFNN can effectively mine spatiotemporal features from modulation signals through residual modules, long-short term memory (LSTM) modules, and attention mechanisms. In addition, a novel hybrid data augmentation method based on phase shift and self-perturbation is introduced to further improve performance and avoid overfitting. The experimental results demonstrate that the proposed AMC approach can achieve an average classification accuracy of 63.44%, and the maximum accuracy can reach 95.01% at SNR = 10 dB, which outperforms the previously proposed methods.
Evaluating the Applicability of Existing Lexicon-Based Sentiment Analysis Techniques on Family Medicine Resident Feedback Field Notes: Retrospective Cohort Study

Field notes, a form for resident-preceptor clinical encounter feedback, are widely adopted across Canadian medical residency training programs for documenting residents' performance. This process generates a sizeable cumulative collection of feedback text, which is difficult for medical education faculty to navigate. As sentiment analysis is a subfield of text mining that can efficiently synthesize the polarity of a text collection, sentiment analysis may serve as an innovative solution.This study aimed to examine the feasibility and utility of sentiment analysis using 3 popular sentiment lexicons on medical resident field notes.We used a retrospective cohort design, curating text data from University of Toronto medical resident field notes gathered over 2 years (from July 2019 to June 2021). Lexicon-based sentiment analysis was applied using 3 standardized dictionaries, modified by removing ambiguous words as determined by a medical subject matter expert. Our modified lexicons assigned words from the text data a sentiment score, and we aggregated the word-level scores to a document-level polarity score. Agreement between dictionaries was assessed, and the document-level polarity was correlated with the overall preceptor rating of the clinical encounter under assessment.Across the 3 original dictionaries, approximately a third of labeled words in our field note corpus were deemed ambiguous and were removed to create modified dictionaries. Across the 3 modified dictionaries, the mean sentiment for the "Strengths" section of the field notes was mildly positive, while it was slightly less positive in the "Areas of Improvement" section. We observed reasonable agreement between dictionaries for sentiment scores in both field note sections. Overall, the proportion of positively labeled documents increased with the overall preceptor rating, and the proportion of negatively labeled documents decreased with the overall preceptor rating.Applying sentiment analysis to systematically analyze field notes is feasible. However, the applicability of existing lexicons is limited in the medical setting, even after the removal of ambiguous words. Limited applicability warrants the need to generate new dictionaries specific to the medical education context. Additionally, aspect-based sentiment analysis may be applied to navigate the more nuanced structure of texts when identifying sentiments. Ultimately, this will allow for more robust inferences to discover opportunities for improving resident teaching curriculums.