Reviewerly/exHarmony huggingface dataset:
A Real time Healthcare Tracking System based on Blockchain Application

Smart health systems have the potential to improve the life and the provided health quality. Since the collaborative working is important in healthcare, the integration of Blockchain for the distribution of real-time patient health becomes more and more important because non-authorized access can result in health risks or private information disclosure. Many Blockchain healthcare applications have been proposed to manage the patient information, however, there is still a lack of providing a smart real-time healthcare monitoring and tracking system. This paper therefore proposes a Real-Time Healthcare system (RTH-Care) based on Blockchain technology that supports the life cycle of a patient in the healthcare system. In particular, we improve the quality of the healthcare system by ensuring a consensus around tests and information about the patients and by centralizing the prescription system. Furthermore, we collect patient health data by integrating medical IoT devices. Subsequently, we provide a data access control to restrict access to the granted persons. The performance analysis of the implementation of RTH-Care with the Hyperledger Fabric tool is showing promising results. An analysis of the communication failures is discussed by using Hyperledger Caliper. Indeed, the obtained results show that our solution is reliable and scalable in terms of the number of transactions per second processed by the network, and the average latency measurements.
Evaluation of the Approach for the Identification of Trajectory Anomalies on CCTV Video from Road Intersections

The approach for the detection of vehicle trajectory abnormalities on CCTV video from road intersections was proposed and evaluated. We mainly focused on the trajectory analysis method rather than objects detection and tracking. Two basic challenges have been overcome in the suggested approach—spatial perspective on the image and performance. We used trajectory approximation by polynomials as well as the Ramer-Douglas-Peucker N thinning technique to increase the performance of the trajectory comparison method. Special modification of trajectory similarity metric LCSS was suggested to consider the spatial perspective. We used clustering to discover two types of classes—with normal and abnormal trajectories. The framework, which implements the suggested approach, was developed. A series of experiments were carried out for testing the approach and defining recommendations for using different techniques in the scope of it.
Integrating user experience evaluation in the development of a web-based Community Engagement Toolkit

Current advances in technology have allowed programs to become more innovative and provide web-based services and products for their participants. This requires evaluators to expand their evaluation repertoire to continue to provide useful and actionable information. Our study illustrates the application of user experience evaluation as part of the formative evaluation of the Community Engagement Toolkit (CETK), developed by the Network of the National Library of Medicine. The CETK is a website containing resources that help library staff deliver health-literacy programming more effectively. The overarching objective of the evaluation was to obtain in-depth feedback from potential users about the website design and content in order to make the toolkit user friendly and useful. We used an online video conferencing software to conduct contextual observations and interviews with geographically dispersed participants. Findings from the evaluation study indicated an overall favorable review of the toolkit's interface and navigation, but relatively less favorable review of content relevance and broad appeal for the participants. Feedback from the participants was used to inform the development of the toolkit to increase the likelihood of its use and value for intended users.
Refactoring Faces Under Bounding Box Using Instance Segmentation Algorithms in Deep Learning for Replacement of Editing Tools

Immense amount of funds and time are being invested in video and photo editing. This has an involvement of resources in a high number. Software as well as hardware. Software is handled by an entire editing team. Hardware is used for the purpose of supporting the editing tools. Resource exhaustion is the main cause and is detrimental for the reach of the organization. The work that we present in this paper describes an approach for the problem. Deep Learning is being used as a replacement for a lot of heavy ended monotonous work. Under this notion, the same can be extended to this specific problem. Automating the problem using Neural Network intended for Image Processing applications. This network will directly do all the necessary amount of work and refactor the entire image and video without using any specific tool or special effect.
A Comparative Study of Amazon Product Reviews Using Sentiment Analysis

Online shopping is an electronic business that allows people from all over the world to buy goods of their interest via web and various applications. Nowadays, these facilities are provided by famous E-commerce platforms such as Amazon, Flipkart, Snapdeal etc. Online shopping is one of the best businesses running over the Internet and hence it becomes the prime responsibility of these platforms to provide the best-rated products at the most feasible price. This paper provides a mechanism that can be used by various online shopping platforms to analyze the reviews given by the buyers, using sentimental analysis in order to maintain good service amongst their users. Sentimental Analysis is one of the most trending research areas in the domain of Natural Language Processing. It is defined as the technique that helps in the analysis of people&#x2019;s emotions, sentiments from written text. In this paper, various Machine Learning classification algorithms have been used for finding the polarity of the reviews. Specifically, comparative analysis of algorithms such as Stochastic Gradient Descent, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine has been done. Performance evaluation of these algorithms has been done on the basis of the accuracy achieved. The observed results show that the Stochastic Gradient Descent with Bag of Words model outperforms other algorithms and shows the highest accuracy of 88.76%.
Open APIs recommendation with an ensemble-based multi-feature model

Recommending the appropriate APIs from a large volume of Open APIs to application developers both accurately and efficiently has become a challenging problem. Established work usually takes only one feature of Open APIs into account, which decreases the accuracy of recommendations. In order to overcome this problem, we propose an ensemble-based approach to Open APIs recommendation with a multiple feature model, which integrates both machine learning and deep learning and synthesizes a set of multiple features to accurately make the recommendation. This approach employs One-hot, Word2vec similarity and Matrix Factorization techniques to obtain the multiple features information respectively, then concatenates the features to obtain a Multivariate Information Feature (MIF) matrix, and leverages an optimized Gradient Boosting Decision Tree (GBDT) and Gated Recurrent Unit (GRU) for feature selection. GBDT is good at processing dense numerical features, while GRU is good at processing sparse categorical features, Finally, the results are synthesized to obtain a recommendation result. We have compared our approach with other four Open APIs recommendation approaches on the Programmable Web, and verified the effectiveness of our approach in precision, recall, F1-measure.
Wireless Training-Free Keystroke Inference Attack and Defense

Existing research work has identified a new class of attacks that can eavesdrop on the keystrokes in a non-invasive way without infecting the target computer to install malware. The common idea is that pressing a key of a keyboard can cause a unique and subtle environmental change, which can be captured and analyzed by the eavesdropper to learn the keystrokes. For these attacks, however, a training phase must be accomplished to establish the relationship between an observed environmental change and the action of pressing a specific key. This significantly limits the impact and practicality of these attacks. In this paper, we discover that it is possible to design keystroke eavesdropping attacks without requiring the training phase. We create this attack based on the channel state information extracted from the wireless signal. To eavesdrop on keystrokes, we establish a mapping between typing each letter and its respective environmental change by exploiting the correlation among observed changes and known structures of dictionary words. To defend against this attack, we propose a reactive jamming mechanism that launches the jamming only during the typing period. Experimental results on software-defined radio platforms validate the impact of the attack and the performance of the defense.
A decentralized open web cryptographic standard

Security in web services is not well defined and is largely based on measures employed by the organization providing the service, the effectiveness of which vary greatly depending on the expertise, implementation, and business motivation. To address the mentioned issue, this paper proposes an open standard called Decentralized Open Web Cryptographic Standard (DOWCS) and reference implementation for decentralized protection of sensitive data. Services may adhere to the standards, to assure security to the end-user. Taking OAuth and PGP as reference models, the standard incorporates multiple layers of security to ensure secrecy of the said data while also decentralizing the key information required to derive the confidential data from the encrypted format.
Selection of diagnosis with oncologic relevance information from histopathology free text reports: A machine learning approach

Histopathology reports are a primary data source for the case definition phase of a Cancer Registry. By reading the histopathology report, the operator that evaluates an oncology case can define the morphology and topography of cancer, and validate the case with the highest diagnosis base. The key problem of the Catania-Messina-Enna Integrated Cancer Registry (RTI) is that these reports are written in natural language and relevant information for cancer evaluation is only a little part of the total annual histopathological reports. In this population-based retrospective cohort study, we try to optimize the working time spent by the RTI operators in seeking and selecting the right information among the histopathology reports in the east Sicily population, by developing a binary classifier on a training set of labeled historical data and validating its outcome by a test set of labeled data created by the operators during the years. Using a machine learning algorithm we built a classification model that evaluates each free text report and returns a score that indicates the probability that it contains oncologic relevant information. The best performing algorithm, among the eight analyzed in this study, was the LightGBM that reached an F1-Score of 98.9%. Using the chosen classifier we shortened the time for case evaluation, improving the timeliness of cancer statistics.
Analysis on Credit Card Fraud Detection and Prevention using Data Mining and Machine Learning Techniques

In the recent era, everybody are dealing with the digital data. In such scenario individual one heavily depend on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the huge challenge for banks and IT professional to identify and resolve such a critical problems. This critical issue can be tackle with the help of machine learning. This articles mainly emphasis on various data mining algorithms such as like C4.5, CART algorithms, J48, Na&#x00EF;ve Bayes algorithm, EM algorithm, Apriori algorithm, SVM and so on and also inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identify the anomalies in transaction and provide appropriate results.
Uncertainty Quantification for Trusted Machine Learning in Space System Cyber Security

In recent years, the Aerospace Corporation has been developing machine learning systems to detect cyber anomalies in space system command and telemetry streams. However, to enable the use of deep learning in such high consequence environments, the models must be trustworthy. One aspect of trust is a model’s ability to accurately quantify the uncertainty of its predictions. Although many deep learning models output what seem to be confidence scores, current academic research has repeatedly shown that models often return high confidence even when very wrong and are unable to diagnose and respond appropriately to out-of-distribution inputs. This can result in catastrophic overconfidence when models are faced with adversarial inputs or concept drift. Even on routine inputs, without reliable uncertainty quantification, it is hard for human-machine teaming to take place as humans cannot trust the model’s reported confidence score. In short, all models are wrong sometimes, but models which know when they are wrong are considerably more useful. To this end, The Aerospace Corporation conducted a literature review and implemented current state of the art methods, including deep ensembles and temperature scaling for confidence calibration, to accurately quantify the uncertainty of deep learning model predictions. We further incorporated and tested these techniques within the existing cyber defense model framework for more trustworthy cyber anomaly detection models. We show that not only are these techniques successful, they are also easy to implement, extensible to many applications and machine learning model variants, and provide interpretable results for a wide audience. From this, Aerospace recommends further adoption of such techniques in high consequence environments.
NightDNet: A Semi-Supervised Nighttime Haze Removal Frame Work for Single Image

Because the radiation of the night scene is relatively complex, compared to the situation where the atmospheric light is globally consistent during the day, the night image dehazing is more challenging. Due to the complex atmospheric imaging conditions, the night haze image synthesis method is not mature enough, and the synthesized night haze images are easy to distinguish. Existing night dehazing methods use hand-designed prior knowledge, but prior knowledge is not very stable for real scenes and is prone to serious failures. In this work, we propose a weakly supervised image translation method. This method is a generative confrontation network combining haze atmosphere imaging model to translate the image from hazy to clear domain. The train and test data we use is all public and real night outdoor images, so the trained model can generate realistic nighttime images without haze.
An Efficient Blockchain Based Electronic Voting System Using Proxy Multi-signature

Voting has always played an important role in group decision-making, and ensuring the authenticity and reliability of voting results is also the most indispensable to the voting system. Recently, due to the immutability of blockchain, with the development of blockchain technology, voting systems based on blockchain have also been widely used. However, most of the blockchain-based voting systems face the problem of occupying too much storage space and difficult to maintain. Therefore, it is necessary to try to save system storage space and save maintenance costs. Therefore, this paper proposes an efficient blockchain-based voting system using a novel proxy multi-signature, which can save storage space and improve democratic participation and decision-making.
Model Proposal for a Yolo Objection Detection Algorithm based Social Distancing Detection System

Social Distancing is a procedure that is very effective in controlling the transmission of infectious diseases. Social distancing as the name says is the practice of keeping in distance from others physically, to reduce the spreading of diseases. This Social Distance Detection System brings an emphasis on monitoring the distance between people using technologies namely Open-CV and Deep Learning. This publication focuses on detecting people by a method called object detection and calculating the distance between them. When the distance between people becomes less than the standard value, certain indications and alerts will be displayed. This also indicates the number of Social Distancing violations.
JAC-Net: Joint learning with adaptive exploration and concise attention for unsupervised domain adaptive person re-identification

Existing unsupervised domain adaptive (UDA) methods of person re-identification (re-ID) often use clustering to generate and optimize pseudo-labels. However, the pseudo-labels generated in this way contain noise, which is gradually amplified during the iterative process, leading to a lower recognition accuracy than for supervised methods. This paper proposes Joint Learning with Adaptive Exploration and Concise Attention Network (JAC-Net), which uses two identical networks to optimize person re-ID in an unlabeled target domain. Based on the pseudo-labels generated by clustering, JAC-Net optimizes the training network by combining a joint learning network (JLN) with a concise attention module (CAM). Inspired by the teacher-student network, JLN uses two identical networks to share knowledge for network learning, and also applies adaptive exploration learning strategies to automatically assign weights to the two identical networks and to balance the impact of the knowledge from the two networks. As a parameter-free attention module, a CAM is added to the feature map extracted in specific layers of ResNet50 without changing the high-order semantic features. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets show that JAC-Net achieves well performance and reaches a similar level of supervised learning.
A Systematic Literature Review on Face Morphing Attack Detection (MAD)

Morphing attacks involve generating a single artificial facial photograph that represents two distinct qualities and utilizing it as a reference photograph on a document. The high quality of the morph raises the question of how vulnerable facial recognition systems are to morph attacks. Morphing Attack Detection (MAD) systems have aroused a lot of interest in recent years, owing to the freely available digital alteration tools that criminals can employ to perform face morphing attacks. There is, however, little research that critically reviews the methodology and performance metrics used to evaluate MAD systems. The goal of this study is to find MAD methodologies, feature extraction techniques, and performance assessment metrics that can help MAD systems become more robust. To fulfill this study's goal, a Systematic Literature Review was done. A manual search of 9 well-known databases yielded 2089 papers. Based on the study topic, 33 primary studies were eventually considered. A novel taxonomy of the strategies utilized in MAD for feature extraction is one of the research's contributions. The study also discovered that (1) single and differential image-based approaches are the commonly used approaches for MAD; (2) texture and keypoint feature extraction methods are more widely used than other feature extraction techniques; and (3) Bona-fide Presentation Classification Error Rate and Attack Presentation Classification Error Rate are the commonly used performance metrics for evaluating MAD systems. This paper addresses open issues and includes additional pertinent information on MAD, making it a valuable resource for researchers developing and evaluating MAD systems.
Development and evaluation of a teleoncology system for breast cancer during the COVID-19 pandemic

Background: The present study describes the steps of developing a hybrid teleoncology system to provide treatment plans for breast cancer patients. Materials & methods: This research was conducted in four stages, including developing a proposal for experts, identifying and analyzing system requirements, designing a prototype and implementing and evaluating the final version of the hybrid teleoncology system. Results: The results of the usability evaluation showed that the users evaluated the system at a good level and, in practice, the implemented system was perceived to be useful by specialists in providing treatment plans for cancer patients. Conclusion: The hybrid teleoncology system is a practical alternative to traditional methods for providing treatment plans to breast cancer patients.
Optical color ghost cryptography and steganography based on multi-discriminator generative adversarial network

An optical color ghost cryptography and steganography based on multi-discriminator generative adversarial network (MDGAN) is proposed. The system comprises a digital light projector configured to project color patterns onto a secret image and a bucket detector for collecting the light intensity in the output plane, rather than using three basic independent color channels. By applying a non-obvious data integration process, the detected bucket signals are hidden into those of a non-secret image. It can be shown that the decrypted images with conventional decryption methods always have serious noise and distortion. In this method, the periphery and detail of the secret images are reconstructed by using a pretrained MDGAN, where a multi-discriminator is employed to achieve a better image understanding. By continuously optimizing the parameters of the generator network, a high quality of image reconstruction can be achieved even if the sampling rate is extremely low. The feasibility of the proposed method is demonstrated by numerical simulations.
Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositions

Due to the convenience of the statistical interpretation and parameter estimation, a normal distribution is typically assumed for multivariate longitudinal data analysis. However, this assumption may be questionable in practice, because it is possible that outliers exist or that the underlying data will show heavy tails. In addition, since the covariance matrix should explain complex correlation structures, it must be positive-definite, and as it is also high-dimensional, the modeling of the covariance matrix is not easy. To solve these problems, we propose the robust modeling of multivariate longitudinal data by considering multivariate t distribution, and we exploit modified Cholesky and hypersphere decompositions to model the covariance matrix. The estimation of the models is shown to be robust when the data include outliers and exhibit heavy tails. The performance of our proposed model and algorithm is illustrated using a nonalcoholic fatty liver disease data set and several simulation studies.