Reviewerly/exHarmony huggingface dataset:
Recursive Euclidean Distance-based Robust Aggregation Technique for Federated Learning

Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by training the local model with malicious data. This paper proposes a novel robust aggregation approach based on recursive Euclidean distance calculation. Our approach measures the Euclidean distance from the most recent global to local models and assigns weights accordingly. Local models which are far away from the most recent global model are assigned smaller weights to minimize the data poisoning effect during aggregation. Our experiments indicate that the proposed algorithm surpasses the latest algorithms by at least 5% in accuracy while reducing time complexity by less than 55%. Our contribution is significant as it addresses the critical issue of malicious attacks in federated learning while reducing aggregation time and improving the accuracy of the global model.
The effects of blended learning activities based on the ASSURE model in teaching on students and teachers in music lessons

This study, carried out during COVID-19, aimed at evaluating the effects of music lesson activities prepared with blended learning and the ASSURE instructional design model on both students and music teachers. 10th grade students (n=30) in a public school participated in the study. An action research design was prepared with a combination of learning at stations method, mobile games (Rhythm Cat, NoteWorks) and Web 2.0 tools (Thinglink, Plickers, Kahoot). “Teacher diary” and “semi-structured interview protocol” were used as data collection tools. The data were analyzed by content analysis with Maxqda software. Interrater reliability of the two experts asked to code the qualitative data was calculated to increase the reliability of the study. Following the implementation, the findings showed that the students’ independent learning, motivation, collaboration, making learning fun, interaction, communication, competition, socialization and productivity improved intensively. It was understood that the emerged negativities were not related to the teaching tools used in the action research procedure but were generally related to the negative learning conditions due to the pandemic. The most notable negative aspects were difficulty in technological competence, difficulties in self-regulation, temporary anxiety, digital access and some short-term technological malfunctions. The study concludes with several recommendations and highlights the points that need further attention in such innovative research.
Health Code as ‘access infrastructure’: Innovative practices and concerns of mediated governance

This study explores the Health Code (Chinese: 健康码), a digital contact tracing application used in Mainland China during the COVID-19 pandemic, and examines the process and impact of its entry into users’ lives and gradual infrastructuralisation within the field of internet infrastructure research. Results show that techno-nationalism was the basis for the inception, implementation, and development of the Health Code. Furthermore, the Health Code has gradually transformed from an early digital health credential into a type of ‘access infrastructure’ by linking physical, information, and communication infrastructure. As access infrastructure, the relationship between the Health Code and users was a tense one, with users actively engaging in ‘micro-help’ on social media platforms and performing ‘soft-resistance’ on psychological and operational levels. Concurrently, the Health Code was embedded into users’ usage habits and social culture, becoming a shared memory and potential digital infrastructure for the future.
Analogical inference from distributional structure: What recurrent neural networks can tell us about word learning

One proposal that can explain the remarkable pace of word learning in young children is that they leverage the language-internal distributional similarity of familiar and novel words to make analogical inferences about possible meanings of novel words (Lany and Gómez, 2008; Lany and Saffran, 2011; Savic et al., 2022b; Unger and Fisher, 2021; Wojcik and Saffran, 2015). However, a cognitively and developmentally plausible computational account of how language-internal lexical representations are acquired to enable this kind of analogical inference has not been previously investigated. In this work, we tested the feasibility of using the SRN (Elman, 1990) as the supplier of language-internal representations for use in analogical inference. While the SRN is in many ways well suited to this task, we discuss several theoretical challenges that might limit its success. In a series of simulations with controlled artificial languages and the CHILDES corpus, we show that Recurrent Neural Networks (RNNs) are prone to acquiring ‘entangled’ lexical semantic representations, where some features of a word are partially encoded in the representations of other frequently co-occurring words. However, we also show that this problem is mitigated when RNNs are first trained on language input to young children, due to the fact that its distributional structure more reliably predicts semantic category membership of individual words. Overall, our work sheds light on the conditions under which RNNs organize their learned knowledge so that word-level information can be more easily extracted and used in downstream processes, such as word learning.
Learnable Nonlocal Contrastive Network for Single Image Super-Resolution

Single image super-resolution (SISR) aims to recover a high-resolution image from a single low-resolution image. In recent years, SISR methods based on deep convolutional neural networks have achieved remarkable success, and some methods further improve the performance of the SISR model by introducing nonlocal attention into the model. However, most SISR methods that introduce nonlocal attention focus on more complex attention mechanisms and only use fixed functions for measurement when exploring image similarity. In addition, the model penalizes the algorithm in terms of loss when the output predicted by the model does not match the target data, even if this output is a potentially valid solution. To this end, we propose learnable nonlocal contrastive attention (LNLCA), which flexibly aggregates image features while maintaining linear computational complexity. Then, we introduce the adaptive target generator (ATG) model to address the problem of the single model training mode. Based on LNLCA, we construct a learnable nonlocal contrastive network (LNLCN). The experimental results demonstrate the effectiveness of the algorithm, which produces reconstructed images with more natural texture details.
Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features

The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recognition on the unannotated statements. However, due to the uncertainty of NLP (natural language processing) in short scenes and the complexity of Chinese text, existing methods have some limitations in accuracy and application scenarios. In this paper, we propose an opinion tendency recognition model HGAT (heterogeneous graph attention network) that integrates text vector and context structure methods to address the above problems. This method first trains a text vectorization model based on annotation text content, then constructs an isomorphic graph with annotation, news, and theme as its apex, and then optimizes the feature vectors of all nodes using an isomorphic graph neural network model with attention mechanism. In addition, this article collected 1,684,318 news items and 57,845,091 comments based on Toutiao, sifted through 511 of those stories and their corresponding 103,787 comments, and tested the impact of HGAT on this dataset. Experiments show that this method has stable improvement effect on different NLP methods, increasing accuracy by 2–10%, and provides a new perspective for opinion tendency recognition.
Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the patient’s X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence.
YOLO-MFE: towards more accurate object detection using multiscale feature extraction

YOLOv3, as a multi-scale object detection algorithm, has a simple structure and can be detected quickly. However, during the training process, as the bottom convolutional layer contains more object detail, and the information in the underlying layer will be gradually faded out through continuous convolution. To address this, this paper adds a normalized attention mechanism behind the feature layers at different scales to further suppress insignificant features and enhance the discriminability and robustness of the features. Moreover, an improved pyramid structure is leveraged to fuse the detail of the shallow feature maps with the semantic feature information of the higher layers, aiming to improve the model performance. In particular, a YOLOv3 object detection algorithm named YOLO-MFE based on improved multi-scale feature extraction is proposed. Experimental results on the Pascal VOC dataset demonstrate that our algorithm outperforms the original YOLOv3 algorithm in terms of object detection accuracy, with a mAP on the Pascal VOC 2007 test set that is 1.66% higher.
Software-Defined Networking Orchestration for Interoperable Key Management of Quantum Key Distribution Networks

This paper demonstrates the use of software-defined networking (SDN) orchestration to integrate regionally separated networks in which different network parts use incompatible key management systems (KMSs) managed by different SDN controllers to ensure end-to-end QKD service provisioning to deliver the QKD keys between geographically different QKD networks. The study focuses on scenarios in which different parts of the network are managed separately by different SDN controllers, requiring an SDN orchestrator to coordinate and manage these controllers. In practical network deployments, operators often utilize multiple vendors for their network equipment. This practice also enables the expansion of the QKD network's coverage by interconnecting various QKD networks equipped with devices from different vendors. However, as coordinating different parts of the QKD network is a complex task, this paper proposes the implementation of an SDN orchestrator which acts as a central entity to manage multiple SDN controllers, ensuring end-to-end QKD service provisioning to address this challenge. For instance, when there are multiple border nodes to interconnect different networks, the SDN orchestrator calculates the path in advance for the end-to-end delivery of keys between initiating and target applications belonging to different networks. This path selection requires the SDN orchestrator to gather information from each SDN controller managing the respective parts of the QKD network. This work shows the practical implementation of SDN orchestration for interoperable KMS in commercial QKD networks in South Korea. By employing an SDN orchestrator, it becomes possible to coordinate multiple SDN controllers and ensure the efficient and secure delivery of QKD keys between different QKD networks with varying vendor equipment.
Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis

The purpose of this study is to explore how machine learning technologies can improve healthcare operations management. A machine learning-based model to solve a specific medical problem is developed to achieve this research purpose. Specifically, this study presents an AI solution for malaria infection diagnosis by applying the CNN (convolutional neural network) algorithm. Based on malaria microscopy image data from the NIH National Library of Medicine, a total of 24,958 images were used for deep learning training, and 2600 images were selected for final testing of the proposed diagnostic architecture. The empirical results indicate that the CNN diagnostic model correctly classified most malaria-infected and non-infected cases with minimal misclassification, with performance metrics of precision (0.97), recall (0.99), and f1-score (0.98) for uninfected cells, and precision (0.99), recall (0.97), and f1-score (0.98) for parasite cells. The CNN diagnostic solution rapidly processed a large number of cases with a high reliable accuracy of 97.81%. The performance of this CNN model was further validated through the k-fold cross-validation test. These results suggest the advantage of machine learning-based diagnostic methods over conventional manual diagnostic methods in improving healthcare operational capabilities in terms of diagnostic quality, processing costs, lead time, and productivity. In addition, a machine learning diagnosis system is more likely to enhance the financial profitability of healthcare operations by reducing the risk of unnecessary medical disputes related to diagnostic errors. As an extension for future research, propositions with a research framework are presented to examine the impacts of machine learning on healthcare operations management for safety and quality of life in global communities.
Adaptive navigation assistance based on eye movement features in virtual reality

Navigation assistance is very important for users when roaming in virtual reality scenes, however, the traditional navigation method requires users to manually request a map for viewing, which leads to low immersion and poor user experience. To address this issue, first, we collected data when users need navigation assistance in a virtual reality environment, including various eye movement features such as gaze fixation, pupil size, and gaze angle, etc. After that, we used the Boostingbased XGBoost algorithm to train a prediction model, and finally used it to predict whether users need navigation assistance in a roaming task. After evaluating the performance of the model, the accuracy, precision, recall, and F1-score of our model reached about 95%. In addition, by applying the model to a virtual reality scene, an adaptive navigation assistance system based on the user’s real-time eye movement data was implemented. Compared with traditional navigation assistance methods, our new adaptive navigation assistance could enable the user to be more immersive and effective during roaming in VR environment.
Machine learning for semi-automated scoping reviews

Scoping reviews are a type of research synthesis that aim to map the literature on a particular topic or research area. Though originally intended to provide a quick overview of a field of research, scoping review teams have been overwhelmed in recent years by a deluge of available research literature. This work presents the interdisciplinary development of a semi-automated scoping review methodology aimed at increasing the objectivity and speed of discovery in scoping reviews as well as the scalability of the scoping review process to datasets with tens of thousands of publications. To this end we leverage modern representation learning algorithms based on transformer models and established clustering methods to discover evidence maps, key themes within the data, knowledge gaps within the literature, and assess the feasibility of follow-on systematic reviews within a certain topic. To demonstrate the wide applicability of this methodology, we apply the here proposed semi-automated method to two separate datasets, a Virtual Human dataset with more than 30,000 peer-reviewed academic articles and a smaller Self-Avatar dataset with less than 500 peer-reviewed articles. To enable collaboration, we provide full access to analyzed datasets, keyword and author word clouds, as well as interactive evidence maps.
Stan Is THE Van: Mobile Makerspace a Hit in Ohio

Stan the Tech Van is an experience on wheels belonging to Madison (OH) Public Library (MPL). It’s not just a 2022 Ram Promaster 2500 cargo van; it can hold an FDM 3D printer along with other tech on carts in the back. We chose a high roof delivery van to allow any staff member the ability to drive Stan for a program or event since it does not require a commercial driver’s license. Stan is outfitted with delivery shelving, a roof rack, and an awning package as well as a powered Gramps Aluminum Gated Commercial Van Ramp System. It’s more than a vehicle that holds enough LEGO Robotics that a staff member doesn’t have to clean out his or her car to get a classroom set out to a school. It’s a mobile makerspace that holds all types of machines that anyone can use to create all sorts of projects.
Prolog in Automated Reasoning in Geometry

In this paper a brief overview of tools for automated reasoning in geometry developed in Prolog is given. We argue that Prolog is as a good choice for automated reasoning applications and this argument is justified by the example of the tool ArgoTriCS for automated solving of geometry construction problems, developed by the author of the paper. We point out features which made Prolog suitable for development of the tool ArgoTriCS, and illustrate the important aspects of the tool: specification of the underlying knowledge base and the search procedure. The system ArgoTriCS can solve many different triangle construction problems and output formal specification of construction in GCLC language, as well as construction in JSON format which enables generation of dynamic illustrations.
DANet: Temporal Action Localization with Double Attention

Temporal action localization (TAL) aims to predict action instance categories in videos and identify their start and end times. However, existing Transformer-based backbones focus only on global or local features, resulting in the loss of information. In addition, both global and local self-attention mechanisms tend to average embeddings, thereby reducing the preservation of critical features. To solve these two problems better, we propose two kinds of attention mechanisms, namely multi-headed local self-attention (MLSA) and max-average pooling attention (MA) to extract simultaneously local and global features. In MA, max-pooling is used to select the most critical information from local clip embeddings instead of averaging embeddings, and average-pooling is used to aggregate global features. We use MLSA for modeling local temporal context. In addition, to enhance collaboration between MA and MLSA, we propose the double attention block (DABlock), comprising MA and MLSA. Finally, we propose the final network double attention network (DANet), composed of DABlocks and other advanced blocks. To evaluate DANet’s performance, we conduct extensive experiments for the TAL task. Experimental results demonstrate that DANet outperforms the other state-of-the-art models on all datasets. Finally, ablation studies demonstrate the effectiveness of the proposed MLSA and MA. Compared with structures using backbone with convolution and global Transformer, DABlock consisting of MLSA and MA has a superior performance, achieving an 8% and 0.5% improvement on overall average mAP, respectively.
Space-time video super-resolution using long-term temporal feature aggregation

Abstract Space-time video super-resolution (STVSR) serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts. Recent approaches utilize end-to-end deep learning models to achieve STVSR. They first interpolate intermediate frame features between given frames, then perform local and global refinement among the feature sequence, and finally increase the spatial resolutions of these features. However, in the most important feature interpolation phase, they only capture spatial-temporal information from the most adjacent frame features, ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity. In this paper, we propose a novel long-term temporal feature aggregation network (LTFA-Net) for STVSR. Specifically, we design a long-term mixture of experts (LTMoE) module for feature interpolation. LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features, which are then combined with different weights to obtain interpolation results using several gating nets. Next, we perform local and global feature refinement using the Locally-temporal Feature Comparison (LFC) module and bidirectional deformable ConvLSTM layer, respectively. Experimental results on two standard benchmarks, Adobe240 and GoPro, indicate the effectiveness and superiority of our approach over state of the art.
AutoTag: automated metadata tagging for film post-production

Abstract Film post-production can be time- and money-inefficient. The reason is that a lot of the work involves a person or group of people, called metadata taggers, going through each individual piece of media and marking it up with relevant tags, such as the scene number, transcripts, and the type of shot for video footage. Such a task is particularly time-consuming for films with high shooting ratios (i.e., footage shot/footage shown). AutoTag automates much of the tagging process across 16 languages, saving both time and money. We describe the algorithms and implementation of AutoTag and report on some case studies.