Reviewerly/exHarmony huggingface dataset:
Deep semi-supervised learning with contrastive learning and partial label propagation for image data

Deep semi-supervised learning is becoming an active research topic because it jointly utilizes labeled and unlabeled samples in training deep neural networks. Recent advances are mainly focused on inductive semi-supervised learning which generally extends supervised algorithms to include unlabeled data. In this paper, we propose CL_PLP, a new transductive deep semi-supervised learning algorithm based on contrastive self-supervised learning and partial label propagation. The proposed method consists of two modules, contrastive self-supervised learning module extracting features from labeled and unlabeled data and partial label propagation module generating confident pseudo-labels through label propagation. For contrastive learning, we propose an improved twins network model by adding multiple projector layers and the contrastive loss term. Meanwhile, we adopt strong and weak data augmentation to increase the diversity of the dataset and the robustness of the model. For the partial label propagation module, we interrupt the label propagation process according to the quality of pseudo-labels and improve the impact of high-quality pseudo-labels. The performance of our algorithm on three standard baseline datasets CIFAR-10, CIFAR-100 and miniImageNet is better than previous state-of-the-art transductive deep semi-supervised learning methods. By transferring our model to the medical COVID19-Xray dataset, it also achieves good performance. Finally, we propose a strategy to integrate our partial label propagation module with inductive semi-supervised learning method, and the results prove that it can further improve their performance and obtain additional high-quality pseudo-labels for the unlabeled data.
Occupational Disease Risk Assessment System Using Artificial Intelligence System and Chatbot

This research aimed to study and develop a chatbot to assess the risk from occupational diseases among informal workers and to provide knowledge on disease prevention. There is a problem of accessing health services among informal workers or independent professional groups who do not have health benefit, lack professional healthcare, and lack knowledge about risk factors arising from their work. In addition, visiting a doctor in person takes time and informal workers may lose benefit from the work they do when doing so. Therefore, the researcher applied artificial intelligence to seek answers according to word groups based on word weight that classified symptoms, disease risk, and disease prevention guidelines for each occupation from diagnostic manuals. The system was developed to be compatible with the LINE application that is easily accessible. The evaluation of the system accuracy by experts had a total value of 86%. The evaluation of the overall system quality in 3 aspects had an average value of 4.24. It can be concluded that the developed system can be used in actual practice.
A selective image encryption algorithm based on a chaotic model using modular sine arithmetic

In this paper, an effective sine modular arithmetic chaotic model is proposed, and applied to image encryption. The new system is a combination of module operation, Sine map and other traditional one-dimensional chaotic maps. The performance analysis demonstrates that the proposed chaotic model has larger chaotic ranges and much better chaotic behaviors than other existing one-dimensional chaotic maps. We apply one of the chaotic maps to the image encryption algorithm. The encryption algorithm first extracts the face part of the image using a face recognition algorithm, and encrypts the information of the face part by combining random cyclic scrambling and forward diffusion techniques. Another encryption algorithm is used to encrypt the whole image. Results of simulations and security analyses show that the proposed encryption algorithm exhibits an excellent security performance and high efficiency in practical applications.
Based on billions of words on the internet,<scp>people</scp>=<scp>men</scp>

Recent advances have made it possible to precisely measure the extent to which any two words are used in similar contexts. In turn, this measure of similarity in linguistic context also captures the extent to which the concepts being denoted are similar. When extracted from massive corpora of text written by millions of individuals, this measure of linguistic similarity can provide insight into the collective concepts of a linguistic community, concepts that both reflect and reinforce widespread ways of thinking. Using this approach, we investigated the collective concept person/people, which forms the basis for nearly all societal decision- and policy-making. In three studies and three preregistered replications with similarity metrics extracted from a corpus of over 630 billion English words, we found that the collective concept person/people is not gender-neutral but rather prioritizes men over women-a fundamental bias in our species' collective view of itself.
Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.
Benefits and concerns associated with blockchain-based health information exchange (HIE): a qualitative study from physicians' perspectives

Blockchain technology has the potential to revolutionize information sharing in companies. Many studies suggest using blockchain-powered platforms to replace existing mechanisms for health information exchange (HIE) across healthcare organizations. However, very few blockchain-based projects have been implemented in the healthcare sector. This study takes a qualitative approach to explore benefits, concerns, and barriers to the rollout of blockchain in HIE projects from physicians' perspectives.The Promoting Action on Research Implementation in Health Services (PARIHS) framework was used to help us better understand root causes, existing problems, perceived risks, perceived benefits, and suggestions. In-depth interviews have been conducted with 38 physicians in six months. The data were analyzed and coded using NVIVO to classify conceptually similar themes mentioned by the interviewees.In total, seven themes have been identified. The key benefits are categorized into three themes: innovative technological features, collaborative ecosystem, and system performance. The main concerns and risks are categorized into four themes: individual, organizational, technological, and market-related issues. The findings can contribute to knowledge by highlighting key values expected from blockchain technology in HIEs. The results also explore obstacles to leveraging the blockchain in healthcare from the perspectives of an important stakeholder (physicians).The results show that although blockchain technology may create several benefits (e.g., innovative technological features, collaborative ecosystem, and system performance), its applications in healthcare are still in their early stages. The perceptions of the individual issues (e.g., lack of knowledge), organizational issues (e.g., implementation issues), technological issues (e.g., blockchain model types), and market-related issues (e.g., regulatory concerns) indicate that blockchain-based applications in healthcare continue to be an emerging field. This study has practical implications as understanding these concerns can help developers and healthcare managers identify potential issues in the planning, developing, and implementing blockchain-based HIE systems. Addressing these barriers would support the widespread use of blockchain-based HIEs in different healthcare settings and facilitate interoperability and connectivity in regional and community health information networks.
Stick or Twist—The Rise of Blockchain Applications in Marketing Management

The adoption of blockchain technology by companies can change the way they interact with stakeholders, redefining communication strategies and other marketing processes. In this study, we investigated the relevance of blockchain applications for marketing management from the perspective of marketing-related professionals. Answers about blockchain technology application in the marketing arena were collected from the social platform Quora. The data were analyzed through text mining and Spearman’s correlation coefficient to assess the degree of association, inherent intensity, and the association significance between the variables payments, supply chain, loyalty programs, digital marketing, credential management, and marketing management, using Quora-specific metrics, namely, upvotes, shares, and views. The results posit blockchain technology as being an asset for marketing, with greater relevance in supply chain and internal management among marketing operations. Professionals will be able to potentially improve internal management systems and marketing campaigns, which will enhance companies’ competitive advantage.
A survey of automated International Classification of Diseases coding: development, challenges, and applications

The International Classification of Diseases (ICD) is an international standard and tool for epidemiological investigation, health management, and clinical diagnosis with a fundamental role in intelligent medical care. The assignment of ICD codes to health-related documents has become a focus of academic research, and numerous studies have developed the process of ICD coding from manual to automated work. In this survey, we review the developmental history of this task in recent decades in depth, from the rules-based stage, through the traditional machine learning stage, to the neural-network-based stage. Various methods have been introduced to solve this problem by using different techniques, and we report a performance comparison of different methods on the publicly available Medical Information Mart for Intensive Care dataset. Next, we summarize four major challenges of this task: (1) the large label space, (2) the unbalanced label distribution, (3) the long text of documents, and (4) the interpretability of coding. Various solutions that have been proposed to solve these problems are analyzed. Further, we discuss the applications of ICD coding, from mortality statistics to payments based on disease-related groups and hospital performance management. In addition, we discuss different ways of considering and evaluating this task, and how it has been transformed into a learnable problem. We also provide details of the commonly used datasets. Overall, this survey aims to provide a reference and possible prospective directions for follow-up research work.
Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

As multimedia technology is developing and growing these days, the use of an enormous number of images and its datasets is likewise expanding at a quick rate. Such datasets can be utilized for the purpose of image retrieval. This research focuses on extraction of similar images established on its different features for the image retrieval purpose from huge dataset of images. In this paper initially, the query image is searched within the available dataset and, then, the color difference histogram (CDH) descriptor is employed to retrieve the images from database. The basic characteristic of CDH is that it counts the color difference stuck among two distinct labels in the L∗a∗b∗ color space. This method is experimented on random images used for various medical purposes. Various unlike features of an image are extracted via different distance methods. The precision rate, recall rate, and F-measure are all used to evaluate the system's performance. Comparative analysis in terms of F-measure is also made to check for the best distance method used for retrieval of images.
A Secure Two-Factor Authentication Framework in Cloud Computing

Cloud computing technology has brought tremendous evaluation in the arena of IT (information technology). This technology paves the path of starting business with lowest investment by availing infrastructure as a service (IAAS), platform as a service (PAAS) and software as a service (SAAS) pay per uses model. Cloud computing services can be quickly and easily provisioned and discharged with minimum efforts and service provider (SP) relationship. Cloud computing characteristics such as on demand self-service, broad network access, resource pooling, and rapid elasticity lead the demand of computing. Despite these features, this platform is free to security issues and attacks specifically in terms of communication because of unsecure authentication and privacy. However, strong user authentication procedure impedes illegal access to the SP which is the principal requirement for securing cloud computing ecosystem. In this regard, we attempt to propose possible counter measures for the cloud ecosystem. Hence, this paper presented a novel one way hash and nonce-based two-factor secure authentication scheme with traditional user IDs, password, and OTP verification procedure that resist brute force attack, session and account hijacking attack, MITM attacks, and replay attacks.
Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries

While research continues to investigate and improve the accuracy, fairness, and normative appropriateness of content moderation processes on large social media platforms, even the best process cannot be effective if users reject its authority as illegitimate. We present a survey experiment comparing the perceived institutional legitimacy of four popular content moderation processes. We conducted a within-subjects experiment in which we showed US Facebook users moderation decisions and randomized the description of whether those decisions were made by paid contractors, algorithms, expert panels, or juries of users. Prior work suggests that juries will have the highest perceived legitimacy due to the benefits of judicial independence and democratic representation. However, expert panels had greater perceived legitimacy than algorithms or juries. Moreover, outcome alignment -agreement with the decision - played a larger role than process in determining perceived legitimacy. These results suggest benefits to incorporating expert oversight in content moderation and underscore that any process will face legitimacy challenges derived from disagreement about outcomes.
An evolutionary block based network for medical image denoising using Differential Evolution

Image denoising is the key component in several computer vision and image processing operations due to unavoidable noise in the image generation process. For medical image processing, deep convolutional neural networks (CNN) gives a state-of-the-art performance. However, network structures are manually constructed for specific tasks and require several trials to tune a large number of hyperparameters, which can take a long time to construct a network. Additionally, the fittest hyperparameters which may be suitable for source data properties like noisy features cannot be easily found to target data. The realistic noise is generally mixed, complex, and unpredictable in medical images, which makes it difficult to design an efficient denoising network. We developed a Differential Evolution (DE) based automatic network evolution model in this paper to optimize the network architectures and hyperparameters by exploring the fittest parameters. Furthermore, we adopted a transfer learning technique to accelerate the training process. The proposed evolutionary algorithm is flexible and finds optimistic network architectures using well-known methods including residual and dense blocks. Finally, the proposed model was evaluated on four different medical image datasets. The obtained results at different noise levels show the potentiality of the proposed model named DEvoNet for identifying the optimal parameters to develop a high-performance denoising network structure.
Echo state network with logistic mapping and bias dropout for time series prediction

An echo state network (ESN) is a special structure of a recurrent neural network in which the recurrent neurons are randomly connected. ESN models that have achieved high accuracy on time series prediction tasks can be utilized as time series prediction models in many fields. Nevertheless, in most ESN models, the input weights are irregularly generated and the reservoir layer units are generally redundant, which cannot guarantee that the ESN models will always be optimal for a given task. In this paper, a novel ESN model that combines logistic mapping (LM) and bias dropout (BD) algorithms is proposed to optimize the irregular input weight matrix and generate a superior and simpler reservoir. Initially, the initial input weight matrix of ESN is replaced by an LM input weight matrix that is generated by the recurrent LM algorithm. Meanwhile, the reservoir, which can convert the input space into a high-dimensional feature space, is formed by the input signals and the LM input weight matrix. Then, the units with low activation values that are determined by a reservoir dropout probability are discarded through the BD algorithm. The dropout probability is determined by the contributions of the reservoir units to the training performance. Three multivariable benchmark tasks and four univariate real-world time series tasks indicate that the proposed LM-BD-ESN model is effective in reducing the testing time, reservoir size, and model complexity while improving the performance of the traditional ESN.
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling

Weakly-supervised semantic segmentation (WSSS) methods with image-level labels generally train a classification network to generate the Class Activation Maps (CAMs) as the initial coarse segmentation labels. However, current WSSS methods still perform far from satisfactorily because their adopted CAMs (1) typically focus on partial discriminative object regions and (2) usually contain useless background regions. These two problems are attributed to the sole image-level supervision and aggregation of global information when training the classification networks. In this work, we propose the visual words learning module and hybrid pooling approach, and incorporate them in classification network to mitigate the above problems. In visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered. Specifically, the visual words are learned with a codebook, which could be updated via two proposed strategies, i.e. learning-based strategy and memory-bank strategy. The second drawback of CAMs is alleviated with the proposed hybrid pooling, which incorporates the global average and local discriminative information to simultaneously ensure object completeness and reduce background regions. We evaluated our methods on PASCAL VOC 2012 and MS COCO 2014 datasets. Without any extra saliency prior, our method achieved 70.6% and 70.7% mIoU on the val and test set of PASCAL VOC dataset, respectively, and 36.2% mIoU on the val set of MS COCO dataset, which significantly surpassed the performance of state-of-the-art WSSS methods.
Securing the access control policies to the Internet of Things resources through permissioned blockchain

Abstract Security and privacy of information transmitted among the devices involved in an Internet of Things (IoT) network represent relevant issues in IoT contexts. Guaranteeing effective control and supervising access permissions to IoT applications is a complex task, mainly due to resources' heterogeneity and scalability requirements. The design and development of highly customizable access control policies, along with an efficient mechanism for ensuring that the rules applied by the IoT platform are not tampered with or violated, will undoubtedly have a significant impact on the diffusion of IoT‐based solutions. In such a direction, the article proposes the integration of a permissioned blockchain within an honest‐but‐curious (i.e., not trusted) IoT distributed middleware layer, which aims to guarantee the correct management of access to resources by the interested parties. The result is a robust and lightweight system, able to manage the data produced by IoT devices, support relevant security features, such as integrity and confidentiality, and resist different kinds of attacks. The use of blockchain will ensure the tamper‐resistance and synchronization of the distributed system, where various stakeholders own applications and IoT platforms. The methodology and the proposed architecture are validated employing a test‐bed.
A Deep Motion Sickness Predictor Induced by Visual Stimuli in Virtual Reality

In a virtual reality (VR) environment, where visual stimuli predominate over other stimuli, the user experiences cybersickness because the balance of the body collapses due to self-motion. Accordingly, the VR experience is accompanied by unavoidable sickness referred to as visually induced motion sickness (VIMS). In this article, our primary purpose is to simultaneously estimate the VIMS score by referring to the content and calculate the temporally induced VIMS sensitivity. To seek our goals, we propose a novel architecture composed of two consecutive networks: 1) neurological representation and 2) spatiotemporal representation. In the first stage, the network imitates and learns the neurological mechanism of motion sickness. In the second stage, the significant feature of the spatial and temporal domains is expressed over the generated frames. After the training procedure, our model can calculate VIMS sensitivity for each frame of the VR content by using the weakly supervised approach for unannotated temporal VIMS scores. Furthermore, we release a massive VR content database. In the experiments, the proposed framework demonstrates excellent performance for VIMS score prediction compared with existing methods, including feature engineering and deep learning-based approaches. Furthermore, we propose a way to visualize the cognitive response to visual stimuli and demonstrate that the induced sickness tends to be activated in a similar tendency, as done in clinical studies.
A Coarse-to-Fine Framework for the 2021 Kidney and Kidney Tumor Segmentation Challenge

Kidney cancer is one of the most common malignant tumors in the world. Automatic segmentation of kidney, kidney tumor, and kidney cyst is a essential tool for kidney cancer surgery. In this paper, we use a coarse-to-fine framework which is based on the nnU-Net and achieve accurate and fast segmentation of the kidney and kidney mass. The average Dice and surface Dice of segmentation predicted by our method on the test are 0.9077 and 0.8262, respectively. Our method outperformed all other teams and achieved $$1^{st}$$ in the KITS2021 challenge.