Reviewerly/exHarmony huggingface dataset:
CoviBlock: A Secure Blockchain-Based Smart Healthcare Assisting System

The recent COVID-19 pandemic has underlined the significance of digital health record management systems for pandemic mitigation. Existing smart healthcare systems (SHSs) fail to preserve system-level medical record openness and privacy while including mitigating measures such as testing, tracking, and treating (3T). In addition, current centralised compute architectures are susceptible to denial of service assaults because of DDoS or bottleneck difficulties. In addition, these current SHSs are susceptible to leakage of sensitive data, unauthorised data modification, and non-repudiation. In centralised models of the current system, a third party controls the data, and data owners may not have total control over their data. The Coviblock, a novel, decentralised, blockchain-based smart healthcare assistance system, is proposed in this study to support medical record privacy and security in the pandemic mitigation process without sacrificing system usability. The Coviblock ensures system-level openness and trustworthiness in the administration and use of medical records. Edge computing and the InterPlanetary File System (IPFS) are recommended as part of a decentralised distributed storage system (DDSS) to reduce the latency and the cost of data operations on the blockchain (IPFS). Using blockchain ledgers, the DDSS ensures system-level transparency and event traceability in the administration of medical records. A distributed, decentralised resource access control mechanism (DDRAC) is also proposed to guarantee the secrecy and privacy of DDSS data. To confirm the Coviblock’s real-time behaviour on an Ethereum test network, a prototype of the technology is constructed and examined. To demonstrate the benefits of the proposed system, we compare it to current cloud-based health cyber–physical systems (H-CPSs) with blockchain. According to the experimental research, the Coviblock maintains the same level of security and privacy as existing H-CPSs while performing considerably better. Lastly, the suggested system greatly reduces latency in operations, such as 32 milliseconds (ms) to produce a new record, 29 ms to update vaccination data, and 27 ms to validate a given certificate through the DDSS.
Boundary-Aware Salient Object Detection in Optical Remote-Sensing Images

Different from the traditional natural scene images, optical remote-sensing images (RSIs) suffer from diverse imaging orientations, cluttered backgrounds, and various scene types. Therefore, the object-detection methods salient to optical RSIs require effective localization and segmentation to deal with complex scenarios, especially small targets, serious occlusion, and multiple targets. However, the existing models’ experimental results are incapable of distinguishing salient objects and backgrounds using clear boundaries. To tackle this problem, we introduce boundary information to perform salient object detection in optical RSIs. Specifically, we first combine the encoder’s low-level and high-level features (i.e., abundant local spatial and semantic information) via a feature-interaction operation, yielding boundary information. Then, the boundary cues are introduced into each decoder block, where the decoder features are directed to focus more on the boundary details and objects simultaneously. In this way, we can generate high-quality saliency maps which can highlight salient objects from optical RSIs completely and accurately. Extensive experiments are performed on a public dataset (i.e., ORSSD dataset), and the experimental results demonstrate the effectiveness of our model when compared with the cutting-edge saliency models.
Privacy-Preserving and Verifiable Federated Learning Framework for Edge Computing

In federated learning (FL), each client collaboratively trains the global model through the cloud server (CS) without sharing its original dataset in edge computing. However, CS can analyze and forge the uploaded parameters and infer the privacy of clients, which calls for the necessity of verifying the integrity and protecting the privacy for aggregation. Although there are some works to ensure the verifiability of aggregation results, there is still a lack of work on analyzing the relationship between verification and dropout rate for edge computing. In this work, we propose privacy-preserving and verifiable federated learning (PVFL) with low communication and computation overhead for verification. We theoretically demonstrate that PVFL has three properties: 1) the communication overhead for verification is independent of the dropouts and the dimension of the parameter vector; 2) the computation overhead for verification is independent of the dropouts; 3) the value of the loss function is negatively correlated with the number of dropouts. Experimental results demonstrate the correctness of our theoretical results and practical performance with a high dropout rate, thereby facilitating the design of privacy-preserving and verifiable FL algorithms for edge computing with a high dimension of parameter vectors and a high dropout rate.
Are we Cyber aware? A cross sectional study on the prevailing Cyber practices among adults from Thiruvalla, Kerala.

Background: Cyber-crime is described as any unlawful activity which is committed using any computing devices, like computer/smartphone and which is a part of internet. There are different methods by which cyber-crime is committed which include 1) attacks on computer systems, 2) cyber-bullying, 3) email spam, 4) phishing, 5) identity theft. Breaches in cyber security have become a severe danger to world security and the economy, compromising essential infrastructure and wreaking havoc on company performance, resulting in significant cognitive property loss. It is a sad reality that cybercrime cases have witnessed a steady spike. India has witnessed a significant increase in cases of cyber fraud and various cyber-related incidents in the last three years. The present study was done to assess the prevailing cyber practices among adults from Thiruvalla, Kerala.Methods: The present cross-sectional study was conducted among 340 adults in Thiruvalla, Kerala from January to June, and 2022. A semi-structured questionnaire was used to elicit information from the study participants after obtaining consent. The quantitative data collected was analysed using the software statistical package for social sciences. The results have been presented as tables and charts showing frequencies and percentages.Results: 133 out of the 350 study participants (38%) believed that it was important to be aware of cyber security risks in general while 52% (182) of the study participants considered cyber security awareness to be the only solution to the existing online scams.Conclusions: Widespread awareness campaigns are necessary to improve the cyber awareness of the community and thereby improve their cyber practices.
Generation of Creative Game Scene Patterns by the Neutrosophic Genetic CoCoSo Method

Creative procedural generation requires, by its nature, a nondeterministic result.Each set of outputs has to satisfy the required criteria and, at the same time, has to show unique results to be considered creative.One of the keys to increasing nondeterminism is to use fuzzy neutrosophic sets.A combination of a CoCoSo multi-criteria decision algorithm with a genetic algorithm to generate game scenes was proposed.The algorithm satisfies the required game rules and improves the aesthetics value of the visual game scene.The proposed approach seeks to produce a creative solution which satisfies the replayability principle, when the solution is not optimal in the optimization procedure sense.In this paper, improvements of the CoCoSo method were proposed in order to work with iterative genetic algorithm.
Leadership Styles, Organizational Climate, and School Climate Openness from the Perspective of Slovak Vocational School Teachers

The aim of the proposed study is to present the partial results of a research study on the organizational climate in vocational schools as perceived by teachers. Special attention is paid to the applied leadership style by school leaders, and the existence of associations between school leaders’ and teachers’ behaviour in schools. Organizational climate was measured by the standardized OCDQ-RS adapted to the conditions of the Slovak educational environment on the sample of 474 vocational schoolteachers. The scale measures five dimensions: Supportive principal behaviour; Directive principal behaviour; Engaged teacher behaviour; Frustrated teacher behaviour; and Intimate teacher behaviour, allowing for calculation of the Index of school climate openness, which is an indicator of the quality of the organizational climate. The obtained results confirmed the existence of associations between teacher and principal behaviour and school climate openness. It can be assumed that the applied leadership style in an institution can affect the quality of interpersonal relationships and teacher behaviour both in positive and negative directions. Therefore, it is important to pay attention to building favourable organizational climate in schools, which represents a challenge for school leaders and educational systems that should provide school leaders with sufficient learning opportunities in the field of school leadership.
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection.
Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving

A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improvement in accuracy and performance metrics, such as precision and recall, and refining the dataset's validation set. Data distribution and instances included in test and validation-set play a significant role in improving the generalization of neural networks. For producing an unbiased AI model, it should not only be trained to achieve high accuracy and minimize false positives. The goal should be to prevent the dominance of one class/feature over the other class/feature while calculating weights. This paper investigates state-of-art object detection/classification on AI models using metrics such as selectivity score and cosine similarity. We focus on perception tasks for vehicular edge scenarios, which generally include collaborative tasks and model updates based on weights. The analysis is performed using cases that include the difference in data diversity, the viewpoint of the input class and combinations. Our results show the potential of using cosine similarity, selectivity score and invariance for measuring the training bias, which sheds light on developing unbiased AI models for future vehicular edge services.
Towards green machine learning: challenges, opportunities, and developments

Machine Learning has assumed a prominent position in the plethora of design and analysis of intelligent systems. Learning is the holy grail of Machine Learning, and with rapidly growing complexity and the size of the constructed networks (the trend which is profoundly visible in deep learning architectures), the overwhelming computing is staggering. The return on investment clearly diminishes: even a very limited improvement in performance (commonly expressed as a classification rate or prediction error) does call for intensive computing because of learning a large number of parameters. The recent developments in green Artificial Intelligence (or better to say, green Machine Learning) has identified and emphasized a genuine need for a holistic multicriteria assessment of the design practices of Machine Learning architectures by involving computing overhead, interpretability, robustness, and identifying sound trade-offs present in these problems. We discuss a realization of green Machine Learning and advocate how Granular Computing contributes to the augmentation of the existing technology. In particular, some paradigms that exhibit a sound potential to support the sustainability of Machine Learning such as federated learning and transfer learning, are identified, critically evaluated, and cast into some general perspective.
High-Accuracy Three-Dimensional Deformation Measurement System Based on Fringe Projection and Speckle Correlation

Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, which transforms speckle-embedded fringe images into speckle-free fringe images. The principle of the proposed method, 3D coordinate calculation, and deformation measurements are introduced. Compared with the traditional 3D-DIC method, the experimental results show that this method is effective and precise.
E-mail Security System Using El-Gamal Hybrid Algorithm and AES (Advanced Encryption Standard) Algorithm

E-mail is a medium of long-distance communication via the internet, which is currently often used for message exchange needs. But the use of e-mail has security problems, especially regarding data leakage when sending messages via e-mail. One of the efforts to improve the security of data and information is the application of cryptographic techniques and methods, namely end-to-end encryption. Cryptography is the science of reducing the risk of security threats by encrypting and decrypting data and information. In the implementation of the e-mail system, at least 2 (two) suitable encryption techniques are needed, namely symmetric encryption techniques to encrypt messages and data to be sent via e-mail effectively and efficiently, and asymmetric encryption techniques used to distribute keys used by symmetric encryption. Therefore, in this study, we use the El-Gamal encryption model to distribute the symmetric key, and the AES encryption model is a fairly secure algorithm to protect message data or confidential information.
Density-based clustering with fully-convolutional networks for crowd flow detection from drones

Crowd analysis from drones has attracted increasing attention in recent times due to the ease of use and affordable cost of these devices. However, how this technology can provide a solution to crowd flow detection is still an unexplored research question. To this end, we propose a crowd flow detection method for video sequences shot by a drone. The method is based on a fully-convolutional network that learns to perform crowd clustering in order to detect the centroids of crowd-dense areas and track their movement in consecutive frames. The proposed method proved effective and efficient when tested on the Crowd Counting datasets of the VisDrone challenge, characterized by video sequences rather than still images. The encouraging results show that the proposed method could open up new ways of analyzing high-level crowd behavior from drones.
Why NASA and federal agencies are declaring this the Year of Open Science

Here's how NASA is incentivizing open science, and how you can too. Here's how NASA is incentivizing open science, and how you can too.
Bidirectional Multiple Object Tracking Based on Trajectory Criteria in Satellite Videos

Multiple object tracking (MOT) in satellite videos requires to detect all objects belonging to specified categories and identify each object, which plays a basic and necessary role in automatic driving, traffic surveillance, and smart city. The traditional MOT methods in satellite videos mostly follow the detection–association framework. However, the detection–association framework works under a strict assumption that all objects are correctly localized by the detector. In practice, MOT in satellite videos faces challenges such as low resolution, tiny objects, and the wide field of view, which leads to the degradation of detector performance. In order to reduce the impact of detector degradation, we propose a bidirectional MOT framework based on trajectory criteria (BMTC) in satellite videos. In BMTC, the single object tracking (SOT) tracker carries out locating the objects between consecutive frames and the detector is just used for finding new objects. Therefore, it is less dependent on the detector performance. According to the characteristics of satellite videos, the trajectory criteria are designed to control the state of the tracker, which includes trajectory density, the limit of consecutive virtual motion predictions, and trajectory similarity measurement. Invalid fragment trajectory backtracking is implemented to alleviate the misalignment caused by the above subsection trajectory criteria. The method is validated on the VISO benchmark and SkySat-1 dataset. The experimental results show the improvement of completeness and accuracy, and the proposed tracker achieves the state-of-the-art performance.
Application of Mobile Learning Methods in the 21st Century

Education through the Internet or network Access to learning materials through mobile applications, social networks and online education centers; encouraging knowledge retention, lower cost, higher engagement, higher access rates and continued learning.One of the primary factors preventing employees from learning on the job is the lack of time to do so.Low transaction distances include socialized M-learning and short transaction distances.It includes four types of mobile learning environment High transaction distance transfer distance Customized Socialized M-Learning, Advanced M-learning and distance education.They keep learners that are; their learning will be in the right place at the right time a real experience joy.Key features of mobile learning ubiquitous, small-scale mobile devices, hybrid, personal, interactive, collaborative and instant information.Mobile learning enhances learner motivation and, in turn, increases the desire to take courses.According to the findings of this study, 70 percent of respondents reported an enhanced motivation to learn when they successfully complete their studies their mobile devices are used.Mobile learning ability to achieve or deliver a person's educational content.Mobile phones such as PDAs, smart phones and pocket devices.Environment refers to digital learning resources that can be accessed on any personal electronic device.
A noise tolerant parameter-variable zeroing neural network and its applications

Time-varying problems frequently arise in the territories of science and engineering, and most of the time-varying problems can be described by dynamic matrix equations. As a powerful tool for solving dynamic matrix equations, the zeroing neural network (ZNN) develops fast in recent years. Convergence and robustness are two main performance indicators of the ZNN model. However, the development of the ZNN is focused on the improvement of its convergence in the past, and its robustness to noises is rarely considered. In order to achieve fast convergence and robustness of the ZNN model, a novel activation function (NAF) is presented in this paper. Based on the NAF, a noise-tolerant parameter-variable ZNN (NTPVZNN) model for solving dynamic Sylvester matrix equations (DSME) is realized, and its fixed-time convergence and robustness to noises are verified by rigorous mathematical analysis and numerical simulation results. Besides, two examples of electrical circuit currents computing and robotic manipulator trajectory tracking using the proposed NTPVZNN model in noisy environment further demonstrates its practical application ability.
A New Unsupervised Video Anomaly Detection Using Multi-Scale Feature Memorization and Multipath Temporal Information Prediction

Anomaly detection in video is an advanced computer vision challenge that recognizes video segments containing out-of-the-ordinary motions or objects. Most recent techniques in video anomaly detection have focused on reconstruction and prediction methods; however, in practice, frame reconstruction methods deliver suboptimal results due to the outstanding generalization abilities of convolutional neural networks when reconstructing abnormal frames. Meanwhile, frame prediction methods have drawn much attention and are a powerful way of simulating the dynamics of natural scenes. This paper provides a new unsupervised frame prediction-based algorithm for anomaly detection that improves overall performance. Our suggested strategy follows a U-Net-like architecture that employs a Time-distributed 2D CNN-based encoder and 2D CNN-based decoder. A memory module is used in the design to retrieve and store the most relevant prototypical pattern of the normal scenario in the memory slots during training giving our model the capacity to produce poor predictions in the case of unusual input. For the memory module to fully retain normal semantic patterns on multiple scales, we propose an upstream multi-branch structure composed of dilated convolutions to extract contextual information. We also provide a multi-path structure that, as a great substitute for the optical flow loss function, directly includes temporal information into the network design. Experiments on the UCSD Ped1, UCSD Ped2, and CUHK Avenue benchmark datasets revealed that our design outperforms most competing models.
CIDOC-CRM and Machine Learning: A Survey and Future Research

The CIDOC Conceptual Reference Model (CIDOC-CRM) is an ISO Standard ontology for the cultural domain that is used for enabling semantic interoperability between museums, libraries, archives and other cultural institutions. For leveraging CIDOC-CRM, several processes and tasks have to be carried out. It is therefore important to investigate to what extent we can automate these processes in order to facilitate interoperability. For this reason, in this paper, we describe the related tasks, and we survey recent works that apply machine learning (ML) techniques for reducing the costs related to CIDOC-CRM-based compliance and interoperability. In particular, we (a) analyze the main processes and tasks, (b) identify tasks where the recent advances of ML (including Deep Learning) would be beneficial, (c) identify cases where ML has been applied (and the results are successful/promising) and (d) suggest tasks that can benefit from applying ML. Finally, since the approaches that leverage both CIDOC-CRM data and ML are few in number, (e) we introduce our vision for the given topic, and (f) we provide a list of open CIDOC-CRM datasets that can be potentially used for ML tasks.