Reviewerly/exHarmony huggingface dataset:
Deep-Learning-Based Sequence Causal Long-Term Recurrent Convolutional Network for Data Fusion Using Video Data

The purpose of AI-Based schemes in intelligent systems is to advance and optimize system performance. Most intelligent systems adopt sequential data types derived from such systems. Realtime video data, for example, are continuously updated as a sequence to make necessary predictions for efficient system performance. The majority of deep-learning-based network architectures such as long short-term memory (LSTM), data fusion, two streams, and temporal convolutional network (TCN) for sequence data fusion are generally used to enhance robust system efficiency. In this paper, we propose a deep-learning-based neural network architecture for non-fix data that uses both a causal convolutional neural network (CNN) and a long-term recurrent convolutional network (LRCN). Causal CNNs and LRCNs use incorporated convolutional layers for feature extraction, so both architectures are capable of processing sequential data such as time series or video data that can be used in a variety of applications. Both architectures also have extracted features from the input sequence data to reduce the dimensionality of the data and capture the important information, and learn hierarchical representations for effective sequence processing tasks. We have also adopted a concept of series compact convolutional recurrent neural network (SCCRNN), which is a type of neural network architecture designed for processing sequential data combined by both convolutional and recurrent layers compactly, reducing the number of parameters and memory usage to maintain high accuracy. The architecture is challenge-able and suitable for continuously incoming sequence video data, and doing so allowed us to bring advantages to both LSTM-based networks and CNNbased networks. To verify this method, we evaluated it through a sequence learning model with network parameters and memory that are required in real environments based on the UCF-101 dataset, which is an action recognition data set of realistic action videos, collected from YouTube with 101 action categories. The results show that the proposed model in a sequence causal long-term recurrent convolutional network (SCLRCN) provides a performance improvement of at least 12% approximately or more to be compared with the existing models (LRCN and TCN).
Joint Video Super-Resolution and Frame Interpolation via Permutation Invariance

We propose a joint super resolution (SR) and frame interpolation framework that can perform both spatial and temporal super resolution. We identify performance variation according to permutation of inputs in video super-resolution and video frame interpolation. We postulate that favorable features extracted from multiple frames should be consistent regardless of input order if the features are optimally complementary for respective frames. With this motivation, we propose a permutation invariant deep architecture that makes use of the multi-frame SR principles by virtue of our order (permutation) invariant network. Specifically, given two adjacent frames, our model employs a permutation invariant convolutional neural network module to extract "complementary" feature representations facilitating both the SR and temporal interpolation tasks. We demonstrate the effectiveness of our end-to-end joint method against various combinations of the competing SR and frame interpolation methods on challenging video datasets, and thereby we verify our hypothesis.
Research on Automatic Error Data Recognition Method for Structured Light System Based on Residual Neural Network

In a structured light system, the positioning accuracy of the stripe is one of the determinants of measurement accuracy. However, the quality of the structured light stripe is reduced by noise, object shape, color, etc. The positioning accuracy of the low-quality stripe center will be decreased, and the large error will be introduced into measurement results, which can only be recognized by a human. To address this problem, this paper proposes a method to identify data with relatively large errors in 3D measurement results by evaluating the quality of the grayscale distribution of stripes. In this method, the undegraded and degraded stripe images are captured. Then, the residual neural network is trained using the grayscale distribution of the two types of stripes. The captured stripes are classified by the trained model. Finally, the data corresponding to the degraded stripes, which correspond to large errors in the data, can be identified according to the classified results. The experiment shows that the algorithm proposed in this paper can effectively identify the data with large errors automatically.
Automatic Code Review by Learning the Structure Information of Code Graph

At present, the explosive growth of software code volume and quantity makes the code review process very labor-intensive and time-consuming. An automated code review model can assist in improving the efficiency of the process. Tufano et al., designed two automated tasks to help improve the efficiency of code review based on the deep learning approach, from two different perspectives, namely, the developer submitting the code and the code reviewer. However, they only used code sequence information and did not explore the logical structure information with a richer meaning of the code. To improve the learning of code structure information, a program dependency graph serialization algorithm PDG2Seq algorithm is proposed, which converts the program dependency graph into a unique graph code sequence in a lossless manner, while retaining the program structure information and semantic information. We then designed an automated code review model based on the pre-trained model CodeBERT architecture, which strengthens the learning of code information by fusing program structure information and code sequence information, and then fine-tuned the model according to the code review activity scene to complete the automatic modification of the code. To verify the efficiency of the algorithm, the two tasks in the experiment were compared with the best Algorithm 1-encoder/2-encoder. The experimental results show that the model we proposed has a significant improvement under the BLEU, Lewinshtein distance and ROUGE-L metrics.
Improving Deep Echo State Network with Neuronal Similarity-Based Iterative Pruning Merging Algorithm

Recently, a layer-stacked ESN model named deep echo state network (DeepESN) has been established. As an interactional model of a recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms the unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN. The newly proposed method has broad application prospects in real-time systems.
HAAN: Learning a Hierarchical Adaptive Alignment Network for Image-Text Retrieval

Image-text retrieval aims to search related results of one modality by querying another modality. As a fundamental and key problem in cross-modal retrieval, image-text retrieval is still a challenging problem owing to the complementary and imbalanced relationship between different modalities (i.e., Image and Text) and different granularities (i.e., Global-level and Local-level). However, existing works have not fully considered how to effectively mine and fuse the complementarities between images and texts at different granularities. Therefore, in this paper, we propose a hierarchical adaptive alignment network, whose contributions are as follows: (1) We propose a multi-level alignment network, which simultaneously mines global-level and local-level data, thereby enhancing the semantic association between images and texts. (2) We propose an adaptive weighted loss to flexibly optimize the image-text similarity with two stages in a unified framework. (3) We conduct extensive experiments on three public benchmark datasets (Corel 5K, Pascal Sentence, and Wiki) and compare them with eleven state-of-the-art methods. The experimental results thoroughly verify the effectiveness of our proposed method.
Innovative Educational Technologies and Competencies in Higher Education

The relevance of the research topic is that in order to effectively ensure the professional training of highly qualified personnel in higher education institutions, priority is given to professionally competent teachers, researchers and innovators who are able to implement modern methodological approaches to scientific knowledge and student-centred learning during a lifetime with prolonged employment. The purpose of this article is to study the innovative educational technologies and competencies in higher education. The leading research methods are survey, questionnaire, interviewing, testing and the method of statistical analysis. The authors formed experimental and control groups of future teachers in order to measure the level of readiness of philology students to use technological innovations in future professional and pedagogical activities. It was found that positive changes in the identification of students’ interest in these activities occurred only in those students who observed innovative approaches of teachers to teaching foreign languages. Also, it was determined that the increased interest of students—future teachers of philological specializations of the control and experimental groups in multimedia and electronic learning tools is due to the global digitalization of the educational process, as well as the school reformation processes, which provide for the active introduction and use of multimedia teaching tools. The principal research question for this research is as follows: Is it necessary for philolosophy students to use technological innovations in future professional and pedagogical activities? It was concluded that the developed and implemented system of teaching foreign languages to students of philological specializations using technological innovations is effective.
SimGRL: a simple self-supervised graph representation learning framework via triplets

Abstract Recently, graph contrastive learning (GCL) has achieved remarkable performance in graph representation learning. However, existing GCL methods usually follow a dual-channel encoder network (i.e., Siamese networks), which adds to the complexity of the network architecture. Additionally, these methods overly depend on varied data augmentation techniques, corrupting graph information. Furthermore, they are heavily reliant on large quantities of negative nodes for each object node, which requires tremendous memory costs. To address these issues, we propose a novel and simple graph representation learning framework, named SimGRL. Firstly, our proposed network architecture only contains one encoder based on a graph neural network instead of a dual-channel encoder, which simplifies the network architecture. Then we introduce a distributor to generate triplets to obtain the contrastive views between nodes and their neighbors, avoiding the need for data augmentations. Finally, we design a triplet loss based on adjacency information in graphs that utilizes only one negative node for each object node, reducing memory overhead significantly. Extensive experiments demonstrate that SimGRL achieves competitive performance on node classification and graph classification tasks, especially in terms of running time and memory overhead.
Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

Purpose Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides. Design/methodology/approach Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system. Findings This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately. Originality/value The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F 1-score of 99.20 per cent.
Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81 works; those were analyzed for qualitative study. The in-depth study of the literature shows the relevance and efficacy of the deep learning (DL) approach, context-aware feature extraction techniques, and their relevance in BTS. Biomedical question answering (BQA) system is one of the most popular applications of text summarizations for building self-sufficient healthcare systems and are pointing to future research directions. The review culminates in realization of a proposed framework for the BQA system MEDIQA with design of better heuristics for content screening, document screening, and relevance ranking. The presented framework provides an evidence-based biomedical question answering model and text summarizer that can lead to real-time evidence-based clinical support system to healthcare practitioners.
Scholarly Infrastructure: a Latin American perspective

Infrastructure: what’s at stake Infrastructure often is perceived as a “given”, as something that was always there, as “natural”. In the digital age, infrastructure seems more “natural” than ever (it's hard to imagine there was a time without internet connectivity in our mobile phones or even a time when phone lines were a luxury item) and the social and economic dimension of infrastructure tend to be invisibilized and are left out of
Readiness for Implementing an E-Voting System in Ethiopia

Extant literature revealed that elections conducted in traditional ways mostly result in conflicts. Intending to address such challenges, e-voting technology is being used in some countries to conduct a transparent election. However, the application of this new system encountered different challenges due to a lack of readiness to exploit its value, especially in developing countries contexts. To that end, the readiness of government, citizens, and political parties needs to be assessed before using e-voting as an electoral system. The main purpose of this study is to assess the gaps in the readiness of Ethiopia for e-voting system implementation. A qualitative research method is employed, and a thematic analysis was used to analyze the data. The finding revealed that Ethiopia is not ready in terms of information communication technology (ICT) infrastructure, human resources, and legality measures for e-voting technology. Finally, recommendations are forwarded for policymakers and practitioners for action.
A model-driven approach to adopt good practices for agile process configuration and certification

The agile development paradigm has been extensively adopted in the industry. This adoption is highly dependent on the knowledge and good practices applied by most experienced practitioners in organizations. Hence, it would be valuable to count on appropriate support to preserve and systematically use this expert knowledge in configuring agile development processes aligned with organizational standards. This paper presents a model-driven approach for representing and selecting good practices to configure agile practices in development processes aligned with organizational development practices and quality standards. We define a conceptual approach for configuring agile development processes that fulfills enterprise good practices and external quality standards. This approach was implemented in a tool suite and applied to an industrial development scenario related to ISO 9001 certification. The approach was implemented in a model-driven tool that provides automatic support for identifying good practices when configuring agile development processes. The tool also verifies consistency with development methods and quality standards, such as ISO 9001. The results obtained from the industrial application indicate that practitioners can reuse expert knowledge to configure agile development processes aligned with quality certifications. Moreover, the approach also facilitates the tailoring of agile practices into concrete development processes that take advantage of organizational good practices.
A machine learning approach for hierarchical classification of software requirements

Classification of software requirements into different categories is a critically important task in requirements engineering (RE). Developing machine learning (ML) approaches for requirements classification has attracted great interest in the RE community since the 2000s. This paper aims to address two related problems that have been challenging real-world applications of ML approaches: the problems of class imbalance and high dimensionality with low sample size data (HDLSS). These problems can greatly degrade the classification performance of ML methods. The paper proposes HC4RC, a novel ML approach for multiclass classification of requirements. HC4RC solves the aforementioned problems through semantic-role based feature selection, dataset decomposition and hierarchical classification. We experimentally compare the effectiveness of HC4RC with three closely related approaches — two of which are based on a traditional statistical classification model whereas one using an advanced deep learning model. Our experiment shows: (1) The class imbalance and HDLSS problems present a challenge to both traditional and advanced ML approaches. (2) The HC4RC approach is simple to use and can effectively address the class imbalance and HDLSS problems compared to similar approaches. This paper makes an important practical contribution to addressing the class imbalance and HDLSS problems in multiclass classification of software requirements.
Mispronunciation Detection Using Neural Networks for Second Language Learners

Speaking a second language fluently is the aim of any language learner. Computer-aided language learning (CALL) systems help learners achieve this goal. Mispronunciation detection can be considered the most helpful component in CALL systems. For this reason, the focus is currently on research in mispronunciation detection systems. There are different methods for mispronunciation detection, such as posterior probability-based methods and classifier-based methods. Recently, deep-learning-based methods have also attracted great interest and are being studied. This chapter reviews the research that proposed neural network methods for mispronunciation detection conducted between 2014 and 2021 for second language learners. The results obtained from studies in the literature and comparisons between different techniques are also discussed.
Estimation of the Historical and Future Renewable Energy Potential with RegCM4 over the Region of Southeastern Europe

Wind and solar radiation are sources of renewable energy, which is no harmful for the ecosystems, and according to the directive 2018/2001/EU, renewable energy should reach 32% of all the energy sources by 2030. These technologies are under constant development, and their use implies an improvement of air quality and a decrease in adverse health effects. There are already build a lot of wind and solar power plants and generators at many locations in Southeastern Europe. However, there are still territories, such as the marine ones, that might be suitable for building wind turbines and solar panels. We study the renewable energy potential in Southeastern Europe by regional climate modelling with the RegCM4 model. The aim of the research is to study the possibilities for energy production in a historical manner and its change under different climate scenarios in the future. The research deals with one historical – 1975–2004 and two future periods – 2021–2050, 2070–2099 using the Representative Concentration Pathways RCP2.6, RCP4.5, and RCP8.5. The main findings of the study suggest that the potential for energy production from renewable sources as well as its temporal change are characterized with spatial heterogeneity. This is especially true for the wind energy.