Reviewerly/exHarmony huggingface dataset:
Investigation and benchmarking of U-Nets on prostate segmentation tasks

In healthcare, a growing number of physicians and support staff are striving to facilitate personalized radiotherapy regimens for patients with prostate cancer. This is because individual patient biology is unique, and employing a single approach for all is inefficient. A crucial step for customizing radiotherapy planning and gaining fundamental information about the disease, is the identification and delineation of targeted structures. However, accurate biomedical image segmentation is time-consuming, requires considerable experience and is prone to observer variability. In the past decade, the use of deep learning models has significantly increased in the field of medical image segmentation. At present, a vast number of anatomical structures can be demarcated on a clinician's level with deep learning models. These models would not only unload work, but they can offer unbiased characterization of the disease. The main architectures used in segmentation are the U-Net and its variants, that exhibit outstanding performances. However, reproducing results or directly comparing methods is often limited by closed source of data and the large heterogeneity among medical images. With this in mind, our intention is to provide a reliable source for assessing deep learning models. As an example, we chose the challenging task of delineating the prostate gland in multi-modal images. First, this paper provides a comprehensive review of current state-of-the-art convolutional neural networks for 3D prostate segmentation. Second, utilizing public and in-house CT and MR datasets of varying properties, we created a framework for an objective comparison of automatic prostate segmentation algorithms. The framework was used for rigorous evaluations of the models, highlighting their strengths and weaknesses.
A Natural Language Processing Algorithm to Improve Completeness of ECOG Performance Status in Real-World Data

Our goal was to develop and characterize a Natural Language Processing (NLP) algorithm to extract Eastern Cooperative Oncology Group Performance Status (ECOG PS) from unstructured electronic health record (EHR) sources to enhance observational datasets. By scanning unstructured EHR-derived documents from a real-world database, the NLP algorithm assigned ECOG PS scores to patients diagnosed with one of 21 cancer types who lacked structured ECOG PS numerical scores, anchored to the initiation of treatment lines. Manually abstracted ECOG PS scores were used as a source of truth to both develop the algorithm and evaluate accuracy, sensitivity, and positive predictive value (PPV). Algorithm performance was further characterized by investigating the prognostic value of composite ECOG PS scores in patients with advanced non-small cell lung cancer receiving first line treatment. Of N = 480,825 patient-lines, structured ECOG PS scores were available for 290,343 (60.4%). After applying NLP-extraction, the availability increased to 73.2%. The algorithm’s overall accuracy, sensitivity, and PPV were 93% (95% CI: 92–94%), 88% (95% CI: 87–89%), and 88% (95% CI: 87–89%), respectively across all cancer types. In a cohort of N = 51,948 aNSCLC patients receiving 1L therapy, the algorithm improved ECOG PS completeness from 61.5% to 75.6%. Stratification by ECOG PS showed worse real-world overall survival (rwOS) for patients with worse ECOG PS scores. We developed an NLP algorithm to extract ECOG PS scores from unstructured EHR documents with high accuracy, improving data completeness for EHR-derived oncology cohorts.
Synthesizing credit data using autoencoders and generative adversarial networks

Data quality is an essential element necessary for the development of a successful machine-learning project. One of the biggest challenges in various real-world application domains is class imbalance. This paper proposes a new framework for oversampling credit data by combining two deep learning techniques: autoencoders and generative adversarial networks. A trivial autoencoder (TAE) is used to change data representation, and modified generative adversarial networks (GAN) are used to create new instances from random noise. The experiment on three different datasets demonstrates that the same classifier achieves a better area under the receiver operating characteristic curve (AUC) on datasets augmented by the proposed framework compared to datasets oversampled by other techniques. Additionally, the results show that datasets balanced by the new framework influence the classifier to change the prediction error types, significantly reducing false negatives; more expensive misclassification case in the imbalance learning. The improvements are significant, and considering the change in error distribution, the proposed technique is an excellent complement to existing oversampling techniques.
DCARS: Deep context-aware recommendation system based on session latent context

Recommendation systems (RSs) usually create suggestions based on users’ prior intentions. Users’ interests may evolve due to context change or user-mode change. Discovering such a change is crucial for producing personalized suggestions. Traditional approaches assume that each user has a fixed preference. On the contrary, context-aware recommendation systems (CARSs) use contextual information to detect user intention changes. However, applying contextual information is the main challenge in CARSs, because it is not always feasible to achieve all the users’ contextual information. Furthermore, adding different contexts to RSs grows its dimensionality in multiple applications. Besides, existing CARSs cannot precisely obtain the hierarchical relationships between items and contexts items that influence users’ intentions. They often use short-term interest with either static long-term preference in the recommendation process. To alleviate the mentioned challenges, we propose a novel deep context-aware recommendation system (DCARS) to capture and incorporate user preferences changes in the recommendation process. The proposed method models the latent context among selected items in each session throughout users’ historical interactions and combines users’ short-term and long-term preferences to generate recommendations. Specifically, we suggest a DCARS based on latent representations of sessions derived from users’ activities. The experiment results on benchmark context-aware data sets show that the proposed DCARS model surpasses state-of-the-art approaches.
Effective Feature Selection Strategy for Supervised Classification based on an Improved Binary Aquila Optimization Algorithm

Feature Selection (FS) is considered a crucial step in machine learning and data mining tasks, which facilitates minimizing the direct consequence of redundant and irrelevant attributes on the model's accuracy. Hence, the researchers developed various algorithms to choose the most appropriate features for improving the accuracy rate of the presented dataset. Nevertheless, these algorithms may fall into local optima problems when applied to substantial feature sizes' datasets. In this paper, for handling the FS strategy through dimensionality-lessening while improving the classification accuracy, an effective Aquila Optimization (AO) algorithm is introduced. AO has stable exploration and exploitation capabilities. It is enhanced by integrating a random position amendment approach with Local Search (LS) strategy to avoid local optima and then cloned into a binary version called Improved Binary AO (IBAO). Additionally, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) are quality estimators. On 18 multi-scale benchmarks, the IBAO algorithm is compared with the original BAO algorithm and twelve recent algorithms, such as Binary Artificial Bee Colony (BABC), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Whale Optimization Algorithm (BWOA), Binary Grey Wolf Optimization (BGWO), Binary Sailfish Optimizer (BSFO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO). According to Wilcoxon's rank-sum test (α=0.05), the dominance and significant influence of IBAO are apparent on both small and large dimensional benchmarks and obtained classification accuracy up to 100% in some of these benchmarks integrated with a feature reduction size down to 92%.
A unified framework of medical information annotation and extraction for Chinese clinical text

Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. This is a critical step to exploit electronic medical records (EMRs). Given the recent thriving NLP technologies, model implementation and performance seem no longer an obstacle, whereas the bottleneck locates on a high-quality annotated corpus and the whole engineering workflow. This study presents an engineering framework consisting of three tasks, i.e., medical entity recognition, relation extraction and attribute extraction. Within this framework, the whole workflow is demonstrated from EMR data collection through model performance evaluation. Our annotation scheme is designed to be comprehensive and compatible between the multiple tasks. With the EMRs from a general hospital in Ningbo, China, and the manual annotation by experienced physicians, our corpus is of large scale and high quality. Built upon this Chinese clinical corpus, the medical information extraction system show performance that approaches human annotation. The annotation scheme, (a subset of) the annotated corpus, and the code are all publicly released, to facilitate further research.
How features and affordances of a metaverse portal engage users? Evidence from exergames

Purpose Building on the “needs–affordances–features” framework, the authors explored how users are motivated by their needs to actualize the feature-enabled affordances and engage in the metaverse. Design/methodology/approach The data were collected through semi-structured and in-depth interviews with 35 participants. The authors applied thematic analysis to summarize the key features and affordances, supplemented by frequency analysis to explore the significance of the features. Sentiment analysis was employed to explicate the relationship between user affordance sentiments and engagement. Findings The key features of the metaverse portal components—hardware, software and content—afford user behaviors. The features of mechanics and physics engines are important for user engagement in the metaverse. The affordances are related to needs satisfaction and user engagement. Mental immersion was frequently mentioned by the participants, implying that it is significant to afford mental immersion in the metaverse. Practical implications The findings of the study provide a rich understanding for practitioners in the metaverse on how to use the features to afford user behaviors and engage them. The authors identified the key elements of user engagement that can be used to guide metaverse game designers. Originality/value This study provides a rich and systematic understanding of features, affordances, needs satisfaction and engagement in the metaverse. Going beyond a fragmented view, the findings conclude a research framework that weaves features, affordances, needs and engagement together.
High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm

With the development of automation technologies, autonomous robots are increasingly used in many important applications. However, precise self-navigation and accurate path planning remain a significant challenge, particularly for the robots operating in complex circumstances such as city centers. In this paper, a nonholonomically constrained robot with high-precision navigation and path planning capability was designed based on the Robot Operating System (ROS), and an improved hybrid A* algorithm was developed to increase the processing efficiency and accuracy of the global path planning and navigation of the robot. The performance and effectiveness of the algorithm were evaluated by using randomly constructed maps in MATLAB and validated in a practical circumstance. Local path planning and obstacle avoidance were carried out based on the model predictive control (MPC) theory. Compared with the conventional A* + DWA (dynamic window approach) method, the average searching time was reduced by 12.62~24.5%, and the average search length was reduced by 9.25~9.5%. In practical navigating tests, the average search time was reduced by 18~24%, and the average search length was reduced by 10.3~12%, while the overall path was smoother. The results demonstrate that the improved algorithm can enable precise and efficient navigation and path planning of the robot in complex circumstances.
M-Learning adoption in higher education towards SDG4

Mobile learning (M-Learning) is under-researched in Zimbabwe although it has the potential to improve learning and teaching in the Zimbabwe higher education sector. It is a tool that can help to achieve the United Nation’s Sustainable Development Goal 4 (SDG4) which is to “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”. This study identifies the benefits and challenges of transitioning from a traditional approach to M-Learning from the perspective of Zimbabwean students, and how these challenges can be mitigated or overcome in tertiary institutions to achieve the SDG4. This qualitative study utilises focus group discussions involving twenty undergraduate students from three higher education institutions, and conducts thematic analysis of the data. This paper identifies the benefits of M-Learning in terms of SDG4 and the challenges of M-Learning in attaining SDG4, showing the complexity involved in introducing digital learning on a larger scale. The study offers suggestions to mitigate the challenges, and recommends ways to implement M-Learning effectively to ensure the provision of quality education. Finally, the paper concludes with an acknowledgement of the study’s limitations and suggestions for future research.
Exploring Factors and Impact of Blockchain Technology in the Food Supply Chains: An Exploratory Study

Blockchain technology (BCT) has been proven to have the potential to transform food supply chains (FSCs) based on its potential benefits. BCT promises to improve food supply chain processes. Despite its several benefits, little is known about the factors that drive blockchain adoption within the food supply chain and the impact of blockchain technology on the food supply chain, as empirical evidence is scarce. This study, therefore, explores factors, impacts and challenges of blockchain adoption in the FSC. The study adopts an exploratory qualitative interview approach. The data consist of Twenty-one interviews which were analyzed using thematic analysis techniques in NVivo (v12), resulting in identifying nine factors classified under three broad categories (Technology-complexity, compatibility, cost; Organization-organization size, knowledge; Environment-government support, competitive pressure, standardization, and compliance) as the most significant factors driving blockchain adoption in the FSC. In addition, five impacts were identified (visibility, performance, efficiency, trust, and value creation) to blockchain technology adoption. This study also identifies significant challenges of blockchain technology (interoperability, privacy, infrastructure conditions, and lack of knowledge). Based on the findings, the study developed a conceptual framework for blockchain adoption in food supply chains. The study adds to the corpus of knowledge by illuminating the adoption of blockchain technology and its effects on food supply chains and by giving the industry evidence-based guidance for developing its blockchain plans. The study provides full insights and awareness of blockchain adoption challenges among executives, supply chain organizations, and governmental agencies.
Developing an Interactive VR CAVE for Immersive Shared Gaming Experiences

The popularity of VR technology has led to the development of public VR setups in entertainment venues, museums, and exhibitions. Interactive VR CAVEs can create compelling gaming experiences for both players and the spectators, with a strong sense of presence and emotional engagement. This paper presents the design and development processes of a VR interactive environment called MobiCave (in room-scale size), that uses motion-tracking systems for an immersive experience. A user study was conducted in the MobiCave, aimed to gather feedback regarding their experience with a demo game. The study researched factors such as immersion, presence, flow, perceived usability, and motivation regarding players and the bystanders. Results showed promising findings for both fun and learning purposes while the experience was found highly immersive. This study suggests that interactive VR setups for public usage could be a motivating opportunity for creating new forms of social interaction and collaboration in gaming.
Boosting Adversarial Training Using Robust Selective Data Augmentation

Abstract Artificial neural networks are currently applied in a wide variety of fields, and they are near to achieving performance similar to humans in many tasks. Nevertheless, they are vulnerable to adversarial attacks in the form of a small intentionally designed perturbation, which could lead to misclassifications, making these models unusable, especially in applications where security is critical. The best defense against these attacks, so far, is adversarial training (AT), which improves the model’s robustness by augmenting the training data with adversarial examples. In this work, we show that the performance of AT can be further improved by employing the neighborhood of each adversarial example in the latent space to make additional targeted augmentations to the training data. More specifically, we propose a robust selective data augmentation (RSDA) approach to enhance the performance of AT. RSDA complements AT by inspecting the quality of the data from a robustness perspective and performing data transformation operations on specific neighboring samples of each adversarial sample in the latent space. We evaluate RSDA on MNIST and CIFAR-10 datasets with multiple adversarial attacks. Our experiments show that RSDA gives significantly better results than just AT on both adversarial and clean samples.
Foreign direct investment in the context of rising populism: The role of institutions and firm‐level internationalization

Abstract Research Summary We investigate how the presence of populist rulers affects foreign direct investment in democratic countries. Our analysis sheds light on the mechanisms by which this political force impacts firms' decisions, considering the effect of institutions and internationalization strategies at the firm level in the global arena. We test our theory using instrumental variables with a panel dataset of US multinationals in 37 democratic countries between 1999 and 2020. Our findings suggest that the presence of a populist ruler at the helm of government seems to undermine firms' foreign investment and it is potentially moderated by country‐level institutions and firm‐level internationalization. Collectively, our results offer new insights on how populism affects multinationals' investment decisions, contributing to the literature that examines the relationship between political pressure and investment decisions. Managerial Summary Investing in countries with populist rulers poses a challenge for multinationals. Populist leaders constantly threaten to abruptly change the institutions that provide certainty for foreign investments. This study argues that the presence of a populist negatively affects firms' foreign investment, moderated by institutions and internationalization strategies. The adverse impacts of populism on investment are mitigated in countries with robust institutional frameworks. In such cases, the credibility of populists' promises to alter the established “rules of the game” is lower than in countries with weaker institutions. Additionally, by expanding their degree of internationalization, firms are better equipped to navigate the lack of information generated by populist rulers. Our research provides implications for managers in assessing the risks and opportunities associated with investing in such contexts.
Performance and Economic Analysis of Designed Different Solar Tracking Systems for Mediterranean Climate

Solar power occupies a significant position among global renewable energy sources due to its abundant energy potential. Consequently, its contribution to electricity generation is steadily increasing. However, obtaining peak efficiency from fixed solar photovoltaic (PV) panels is a formidable task due to their limited ability to consistently tap into solar energy. To tackle this issue and mitigate energy efficiency losses, the utilization of solar tracking systems has emerged as an exceptionally effective solution. These systems enable continuous adjustment of the panels’ position to align with the sun’s trajectory, optimizing energy absorption and enhancing overall performance. This paper presents the performance and cost analysis of three distinct solar panel tracking systems, namely, a fixed system, a single-axis system, and a dual-axis system. The systems are operated under identical coordinates and conditions. The production data are collected over a period of 15 days for comparative analysis. The tracking movements of the systems are controlled using Arduino. The mechanical components are specifically designed for the establishment of each system. The findings of this study indicate that both single-axis and dual-axis solar tracking systems outperformed fixed systems in terms of power generation. The single-axis system demonstrated a 24.367% increase in power production, while the dual-axis system showed a 32.247% increase compared to the fixed system. Moreover, a cost analysis was carried out considering the installation expenses and power production data of the three systems. It was determined that the single-axis tracking system achieved payback in 0.39 years less compared to the fixed system, while the dual-axis system achieved payback in 1.48 years less compared to the fixed system. Overall, this study underscores the advantages of implementing solar tracking systems, particularly in the single-axis and dual-axis configurations, as they contribute to higher power generation and cost-effectiveness compared to fixed systems.
OTP-NMS: Toward Optimal Threshold Prediction of NMS for Crowded Pedestrian Detection

Pedestrian detection is still a challenging task for computer vision, especially in crowded scenes where the overlaps between pedestrians tend to be large. The non-maximum suppression (NMS) plays an important role in removing the redundant false positive detection proposals while retaining the true positive detection proposals. However, the highly overlapped results may be suppressed if the threshold of NMS is lower. Meanwhile, a higher threshold of NMS will introduce a larger number of false positive results. To solve this problem, we propose an optimal threshold prediction (OTP) based NMS method that predicts a suitable threshold of NMS for each human instance. First, a visibility estimation module is designed to obtain the visibility ratio. Then, we propose a threshold prediction subnet to determine the optimal threshold of NMS automatically according to the visibility ratio and classification score. Finally, we re-formulate the objective function of the subnet and utilize the reward-guided gradient estimation algorithm to update the subnet. Comprehensive experiments on CrowdHuman and CityPersons show the superior performance of the proposed method in pedestrian detection, especially in crowded scenes.