Reviewerly/exHarmony huggingface dataset:
Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as 'tumor' or 'normal'. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images.
The impact of key indicators on the overall performance of Saudi Arabian telecommunication companies

Performance management helps organizations to ensure that they are on the right path. Thus, this requires increasing the ability of organizations to understand their own key indicators to manage and measure their performance. The purpose of this study is to determine the key performance indicators used in Saudi Arabian telecommunication companies. Moreover, it examines whether these indicators impact the overall performance of Saudi Arabian telecommunication companies. This study adopted a quantitative method based on a survey questionnaire. Participants were reached through human resources officers in the telecommunication companies. Questionnaires were distributed to 247 employees at middle and top management levels in Saudi Arabian telecommunication companies using a convenience sampling technique. However, 212 responses were returned completely filled with a response rate of 85.8%. This study used statistical software of SPSS and SmartPLS for data analysis. The results revealed that customer satisfaction, delivery reliability, learning and growth, employee satisfaction, cost, financial performance, flexibility, and quality are the key indicators used in Saudi Arabian telecommunication companies to measure performance. The results also revealed that customer satisfaction, delivery reliability, learning and growth, employee satisfaction, cost, financial performance, flexibility, and quality have an impact on the overall performance of Saudi Arabian telecommunication companies. These indicators can be used to determine the state of organizations, help measure the implementation of strategies, evaluate the organization’s current performance, design strategies for improvement, and evaluate organization’s departments and employees. AcknowledgmentThis paper was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [GRANT348].
Integrating Lean Six Sigma with blockchain technology for quality management – a scoping review of current trends and future prospects

Purpose The purpose of this research is to study and analyze the literature that integrates Lean Six Sigma (LSS) approach with blockchain technology in different sectors for improved quality management. Design/methodology/approach This study presents a scoping review on the application of integrated LSS and blockchain technology in the manufacturing and healthcare sector. Further, the authors examined existing blockchain-based solutions on a variety of dimensions, including application area, technical approach, methodology, application scenario, various blockchain platforms, purpose, and monitoring parameters. The authors study LSS approaches in detail, as well as the key benefits that blockchain technology can enable. Finally, the authors discuss significant research problems to be addressed in order to develop a highly efficient, resilient, and secure quality management framework using blockchain technology. Findings It has been observed that the adoption of blockchain technology for quality management and assurance is influenced by several factors such as transaction execution speed, throughput, latency. Also, prior blockchain-based solutions have neglected to leverage the benefits of LSS methodologies for effective quality management. Originality/value This is the first study to explores the influence of blockchain technology on quality management and assurance in manufacturing and healthcare industry. Furthermore, prior research has not examined how integrating the LSS methodology with blockchain technology can aid in the control of product quality management.
Heuristic Application System on Pose Detection of Elderly Activity Using Machine Learning in Real-Time

Many developed countries have a population of elderly people that is greater than the population of young workers. So. To meet the demand for labor there is a decline because many are retiring. To meet the future demand that many elderly people need a quality care service so that the elderly who experience physical and cognitive decline can be well protected. Great potential study and evaluation of elderly movement activity for healthcare. The algorithm of pose estimation takes advantage of recording video have tracked elderly movement automatically using camera devices and computer vision. Monitoring and measuring elderly movement activity in real-time more easily accessible with this view of technology offers a clear and exciting potential as motor assessment by the doctor at the patient at home. The perpetrator can send video recording directly in the field by combining expertise and perspective as from physical therapy insight into the application of pose estimation in human health, especially the elderly. This is focusing in a safe and comfortable way. These models use CNN and LSTM for classified labeling landmark point detection results with high performance 97,3 accuracy average and in real-time have range 30 FPS. So the heuristic application system can be recommended for monitoring the use of the camera with all its limitations
CortexVR: Immersive analysis and training of cognitive executive functions of soccer players using virtual reality and machine learning

This paper presents an immersive Virtual Reality (VR) system to analyze and train Executive Functions (EFs) of soccer players. EFs are important cognitive functions for athletes. They are a relevant quality that distinguishes amateurs from professionals.The system is based on immersive technology, hence, the user interacts naturally and experiences a training session in a virtual world. The proposed system has a modular design supporting the extension of various so-called game modes. Game modes combine selected game mechanics with specific simulation content to target particular training aspects. The system architecture decouples selection/parameterization and analysis of training sessions via a coaching app from an Unity3D-based VR simulation core. Monitoring of user performance and progress is recorded by a database that sends the necessary feedback to the coaching app for analysis.The system is tested for VR-critical performance criteria to reveal the usefulness of a new interaction paradigm in the cognitive training and analysis of EFs. Subjective ratings for overall usability show that the design as VR application enhances the user experience compared to a traditional desktop app; whereas the new, unfamiliar interaction paradigm does not negatively impact the effort for using the application.The system can provide immersive training of EF in a fully virtual environment, eliminating potential distraction. It further provides an easy-to-use analyzes tool to compare user but also an automatic, adaptive training mode.
Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm

Abstract Sleeping postures are one of the indicators for judging sleep quality and preventing sudden diseases. The sleeping postures not only affect people’s sleep quality but also has great significance for the diagnosis of apnea syndrome and bedsores. To realize and recognize the laying positions, this paper researches the regularized extreme learning (RELM) algorithm to analyze the pressure due to lying positions. Based on this algorithm first, the array pressure sensor is used to obtain the back lying posture pressure image, and the image is pre-processed to complete the extraction of multiple feature values (Geometric features, Energy features, and Colour features). Second, the multi-feature values are normalized and finally, these multi-feature values are trained and predicted by the RELM algorithm. In concluding this, the accuracy of lying posture recognition was the highest, achieving 98.75 percent, this is when 1120 datasets of feature values were used as training data and 160 sets as test data while the hidden nodes were 80. RELM algorithm can overcome the problems of extreme learning (ELM) algorithm, such as slow learning speed and local minimum value, and so on. Therefore, this method can be applied in the scenarios of lying posture recognition.
Automating hardware security property generation

Security verification is an important part of the hardware design process. Security verification teams can uncover weaknesses, vulnerabilities, and flaws. Unfortunately, the verification process involves substantial manual analysis to create the threat model, identify important security assets, articulate weaknesses, define security requirements, and specify security properties that formally describe security requirements upon the hardware. This work describes current hardware security verification practices. Many of these rely on manual analysis. We argue that the property generation process is a first step towards scalable and reproducible hardware security verification.
Sign Language Detection using Convolutional Neural Network (CNN)

Communication is important to express feelings of oneself. Effective communication helps in personal and professional growth. Communicating with a person having some disabilities, such as speech and hearing impairment, is always a major challenge. Deaf And Dumb people cannot communicate with person with no disability because of communication barrier between them and the other person not knowing the sign language. As Per India’s Census 2011, At all India levels, disabled people constitute 2.21% of the total population. In India, about 19% of disabled people have hearing disabilities and 7% in Speech impairment [1]. Sign language gestures are not always enough for communication of people with hearing disability or people with impairment of speech. The gestures/signs made by the people having disabilities often get mixed or difficult to understand for someone who does not understand the language. Thus, we have implemented two models to convert sign gestures to text using Convolutional Neural Network (CNN) in Python and AlexNet in MATLAB.
VIRTUAL REALITY: SUSTAINABLE TECHNOLOGIES

The development of virtual reality (VR) technologies offers high expectations for growth in the coming years. The massive increase in the use of virtual environments will lead to a high consumption of resources, making it necessary to ensure their sustainability. Taking this scenario into account, this paper focuses on analyzing and comparing the sustainability of the two variants of VR, immersive virtual reality (IVR) and non-immersive virtual reality (NIVR). To achieve this goal, the different areas of sustainable development that are influenced by these technologies have been identified (economy, society and environment). From the results obtained, it can be stated that VR technology is advantageous for all three areas of sustainable development analyzed from three main points of view: (i) as an emerging technology that allows energy costs to be reduced and encourage economic activity with low environmental impact; and (ii) as a teaching resource in higher education, such as engineering, because it allows the design of dynamic and interactive learning environments, which facilitate the understanding of certain concepts. In the case of economic and social areas, especially those related to education, it is the areas of sustainable development in which these differences are most noticeable. In this research, an experience is carried out with engineering students where it is shown that students perceive the RVI as more interactive than the RVNI, but that this perception does not lead to an increase in the academic performance of the students. On the contrary, in the professors' opinion, the impression caused by the IVR on the students could lead them to divert their attention to certain recreational or interactive aspects of the activity that do not have an impact on their learning. Keywords: immersion, virtual reality, development, sustainable technologies, virtual environments.
Semi‐supervised breast histopathological image classification with self‐training based on non‐linear distance metric

Histopathological analysis requires a lot of clinical experience and time for pathologists. Artificial intelligence (AI) may have an important role in assisting pathologists and leading to more efficient and effective histopathological diagnoses. To address the challenge of requiring a large number of labelled images to train deep learning models in breast cancer histopathological image classification, a self-training semi-supervised learning method consisting three components is proposed: Firstly, a pre-trained ResNet-18 was used to extract features and generate pseudo-labels for unlabelled data; secondly, a relational weight network based on the squeeze-and-excitation network (SENet) was trained to calculate the non-linear distance metrices between labelled and unlabelled samples, in order to improve the accuracy of pseudo-labelling; lastly, a consistency loss—maximum mean difference (MMD)—was added into the model to minimize the divergence between distributions of unlabelled and labelled samples. Extensive experiments were conducted on the open access BreakHis dataset. The proposed method outperformed the state-of-the-art semi-supervised methods at all tested annotated percentages (10–70%), and also achieved comparable performance with supervised methods at higher annotated percentages (50%, 70%).
Learning Implicit and Explicit Multi-task Interactions for Information Extraction

Information extraction aims at extracting entities, relations, and so on, in text to support information retrieval systems. To extract information, researchers have considered multitask learning (ML) approaches. The conventional ML approach learns shared features across tasks, with the assumption that these features capture sufficient task interactions to learn expressive shared representations for task classification. However, such an assumption is flawed in different perspectives. First, the shared representation may contain noise introduced by another task; tasks coupled for multitask learning may have different complexities but this approach treats all tasks equally; the conventional approach has a flat structure that hinders the learning of explicit interactions. This approach, however, learns implicit interactions across tasks and often has a generalization ability that has benefited the learning of multitasks. In this article, we take advantage of implicit interactions learned by conventional approaches while alleviating the issues mentioned above by developing a Recurrent Interaction Network with an effective Early Prediction Integration (RIN-EPI) for multitask learning. Specifically, RIN-EPI learns implicit and explicit interactions across two different but related tasks. To effectively learn explicit interactions across tasks, we consider the correlations among the outputs of related tasks. It is, however, obvious that task outputs are unobservable during training, so we leverage the predictions at intermediate layers (referred to as early predictions) as proxies as well as shared features across tasks to learn explicit interactions through attention mechanisms and sequence learning models. By recurrently learning explicit interactions, we gradually improve predictions for the individual tasks in the multitask learning. We demonstrate the effectiveness of RIN-EPI on the learning of two mainstream multitasks for information extraction: (1) entity recognition and relation classification and (2) aspect and opinion term co-extraction. Extensive experiments demonstrate the effectiveness of the RIN-EPI architecture, where we achieve state-of-the-art results on several benchmark datasets.
Optimus: An Operator Fusion Framework for Deep Neural Networks

The reduction of neural parameters and operations for the applications on embedded and IoT platforms in current deep neural network (DNN) architectures has received increasing attention. Relatively, the intermediate feature maps of such lightweight neural networks begin to grow and usually outsize the on-chip memory as the new bottleneck, which introduces considerable power-consuming off-chip memory accesses. To reduce the feature-induced memory accesses, operator fusion has been proposed to parallelize the execution of multiple convolutional layers and shown significant reduction of off-chip memory accesses. However, how to fuse the neural operators is still a challenging issue that heavily depends on both the neural network (NN) topology and the specific DNN accelerator configuration. In this work, we observed prior operator fusion approaches fail to guarantee memory-level optimality as they search in the constrained operator fusion design space. Considering the complexity of the NN topologies and the constrained resources of the DNN accelerators, we develop a novel operator fusion framework, Optimus. Optimus includes an accurate memory cost model dedicated to the scheduler to evaluate the potential operator-fusion schemes and a directed acyclic graph-based operator fusion algorithm for both off-line and on-line workload deployment scenarios, which altogether generates high-efficiency operator-fusion solutions for arbitrary network models running on DNN accelerators. The experimental results show that Optimus reduces 17–75% off-chip memory accesses and obtains 1.86×–3.66× energy efficiency on state-of-the-art DNN workloads when compared to the baselines and brings significant power-efficiency boost to the DNN accelerators of different architectures and dataflows.
Evolutionary Optimization for the Belief-Rule-Based System: Method and Applications

Evolutionary optimization (EO) has been proven to be highly effective computation means in solving asymmetry problems in engineering practices. In this study, a novel evolutionary optimization approach for the belief rule base (BRB) system is proposed, namely EO-BRB, by constructing an optimization model and employing the Differential Evolutionary (DE) algorithm as its optimization engine due to its ability to locate an optimal solution for problems with nonlinear complexity. In the EO-BRB approach, the most representative referenced values of the attributes which are pre-determined in traditional learning approaches are to be optimized. In the optimization model, the mean squared error (MSE) between the actual and observed data is taken as the objective, while the initial weights of all the rules, the beliefs of the scales in the conclusion part, and the referenced values of the attributes are taken as the restraints. Compared with the traditional learning approaches for the BRB system, the EO-BRB approach (1) does not require transforming the numerical referenced values of the attributes into linguistic terms; (2) does not require identifying any initial solution; (3) does not require any mathematical deduction and/or case-specific information which verifies it as a general approach; and (4) can help downsize the BRB system while producing superior performances. Thus, the proposed EO-BRB approach can make the best use of the nonlinear modeling ability of BRB and the optimization superiority of the EO algorithms. Three asymmetry numerical and practical cases are studied to validate the efficiency of the proposed EO-BRB approach.
Microstructure segmentation using multi-angle polarized light microscopy

Grain size and shape are important microstructure attributes affected by material processing and contributing to material properties. We develop automated tools to segment multi-angle polarized light microscopy (PLM) images of low symmetry metals such that statistics of grain size and shape can be automatically measured. To demonstrate the tools, we apply PLM to alpha‑titanium, an hcp crystal structure metal with axisymmetric optical properties and a good candidate for the technique. The segmentation routine uses a region growing algorithm based on similarity of the intensity variance of neighboring pixels for three sample rotations, and grain dilation that checks the proximity of pixels to grain boundaries and triple points. For relatively fine-grained Ti, the segmentation is accurate for 97.5% of the grain boundaries in the microstructure when compared to EBSD results, and the accuracy is expected to improve for larger grained materials. The technique does a good job of defining twin features in deformed materials that, due to twin size and optical limitations, exhibit substantial intensity gradients making segmentation challenging.
Formation of professional competency in life saving appliances operation of future seafarers by means of online and simulation VR technologies

Nowadays simulation training technology is a priority method of maritime specialists’ practical training in the world. The main purpose of using VR simulators within an educational process is to simulate work on real equipment in order to form professional competencies of seafarers. The article describes system of blended learning on the basis of Kherson State Maritime Academy, that includes alternation of traditional and online learning, virtual training by means of the VR technology, training on simulators. In accordance with the principles of blended learning in Academy, there was developed an author's course ``Rescue boats and life rafts specialist'', which aims at providing theoretical and practical training of seafarers on launching and handling the lifeboats and liferafts and, as a result, ensures seafarers’ formation of professional competency ``life-saving appliances operation''. The article also reveals the results of an experiment with implementation of VR technologies in forming the professional competency ``life-saving appliances operation''. The deviation of the results in control and experimental groups was 9,8\%. The effectiveness of our research was manifested in the fact that students have gained experience of practical skills before coming to the vessel and showed higher level of educational achievements in professional competency ``life-saving appliances operation''.