Reviewerly/exHarmony huggingface dataset:
A Novel and Secure Model for Sharing Protected Health Record (PHR) Based on Blockchain and Attribute Based Encryption

With the development of electronic information technology, electronic medical records (EMRs) are considered a common way to store the patients’ data in hospitals. This information is recorded and stored in the databases of various hospitals and medical institutions, so patients do not have any control over their medical information. Due to the fact that medical information is one of the important assets of the people and the health system, there are serious concerns about the security and privacy of medical data and how to access this information. One of the most important challenges in e-Health is the storage and access control of medical data. In this paper, we propose a novel, secure and efficient model named SBA-PHR which is based on blocking technology and attribute-based encryption to share and store medical data in such a way as to maintain the user's privacy and fine grain access control. In SBA-PHR we have improved the revocation phase in ABE by using private blockchain. We prove the security of our proposed scheme in the formal model and its proper functioning based on BAN logic and establish its user data confidentiality, patient anonymity and privacy. Moreover, the computational and storage complexity of our proposed scheme demonstrates its efficiency and scalability.
RETRACTED: Design and Development of Temperature Measurement and Control System Based on Mcu under the Background of Internet of Things

Abstract Internet of things technology is a new technology recently developed, it also belongs to an area of information technology, and its appearance makes it means more perfect and mature. At present, industrial automation is the trend of the development of the present era, and the single chip microcomputer is the core of industrial automation. It is the core system of process control, process monitoring and Mechatronics integrated control of industrial automation. With the rapid development of single chip microcomputer, especially the hardware of MCU. These changes have brought great changes to the development language of SCM software. The development of SCM control system to intelligent control gradually improves the development efficiency of the system, and also ensures the quality of products. In order to explore the application effect of Internet of things technology in the TMACS(temperature measurement and control system) design and development of single chip microcomputer, we choose h and G factories as experimental research objects, among which h factory applies Internet of things technology in its single chip microcomputer working system, and factory g still adopts conventional methods. From the experimental results, we can see that the accuracy of H plant is up to 95%, while the highest accuracy rate of plant G is only 75%, which is 20% lower than that of H plant.
Custom Built of Smart Computing Platform for Supporting Optimization Methods and Artificial Intelligence Research

This paper describes a prototype of a computing platform dedicated to artificial intelligence explorations. The platform, dubbed as PakCarik, is essentially a high throughput computing platform with GPU (graphics processing units) acceleration. PakCarik is an Indonesian acronym for Platform Komputasi Cerdas Ramah Industri Kreatif, which can be translated as “Creative Industry friendly Intelligence Computing Platform”. This platform aims to provide complete development and production environment for AI-based projects, especially to those that rely on machine learning and multiobjective optimization paradigms. The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. The authors conclude that PakCarik is ready to be deployed in real-world applications and it can be made even more powerful by adding more GPU cards in it.
A Mathematical Model to Evaluate Visual Sensing Coverage of Emergency Signs on Moving Vehicles

Emergency vehicles have been employed in rescue operations and supportive services, attending victims and managing critical situations in smart cities. Such vehicles, notably ambulances, fire trucks, police cars and transit agents vehicles, may be tracked and monitored in some applications for different functions. When such emergency vehicles are not equipped with GPS receivers, cameras can be used to view emergency signs printed on them, allowing indirect identification of emergency vehicles, although many complexities have to be considered when performing visual sensors-based tracking and monitoring. In this context, this paper proposes a mathematical model focused on the evaluation of the coverage efficiency of a group of visual sensors over moving vehicles, aimed at visual coverage of emergency signs. For that, vehicles, emergency signs and visual sensors are mathematically modelled in this paper, with coverage interactions among these elements being computed based on proposed geometry equations and algorithms. Doing so, the effectiveness of the positioning and configurations of visual sensors can be evaluated without requiring actual deployment, potentially reducing costs when assessing visual monitoring systems in this scenario.
A hybrid threat model for system-centric and attack-centric for effective security design in SDLC

Threat modeling is an essential activity in the security development lifecycle. To provide security at the design phase of software development, Microsoft introduced threat modeling stride to identify the vulnerabilities and attacks of application. An efficient solution is necessary to deal with these issues in the software development life cycle. In this context, the paper focused on the analysis of threats and attack tree techniques that are traditionally available and frequently used. Automated Threat modeling enables to simulate attacks and visualized the existing vulnerabilities and misconfiguration. A hybrid model is proposed based on system-centric and attacker-centric to identify the threats in the software application during the software design phase. This model is built by STRIDE by defining security architecture and then analyzed the risks regarding its security characteristics and applied to its real application system. Our model is applied in a case study of the health center management system and shows a better result is identifying the threats and severity in the design phase. And also attack tree defines the stages of threats to understand the severity.
Advanced Driver-Assistance System with Traffic Sign Recognition for Safe and Efficient Driving

Advanced Driver-Assistance Systems (ADAS) coupled with traffic sign recognition could lead to safer driving environments. This study presents a sophisticated, yet robust and accurate traffic sign detection system using computer vision and ML, for ADAS. Unavailability of large local traffic sign datasets and the unbalances of traffic sign distribution are the key bottlenecks of this research. Hence, we choose to work with support vector machines (SVM) with a custom-built unbalance dataset, to build a lightweight model with excellent classification accuracy. The SVM model delivered optimum performance with the radial basis kernel, C=10, and gamma=0.0001. In the proposed method, same priority was given to processing time (testing time) and accuracy, as traffic sign identification is time critical. The final accuracy obtained was 87% (with confidence interval 84%-90%) with a processing time of 0.64s (with confidence interval of 0.57s-0.67s) for correct detection at testing, which emphasizes the effectiveness of the proposed method.
Identification of Unclassified Ships Implementing AIS Information and SAR Image-Based Ship Detection Results

Monitoring and detecting ships via machine learning based algorithm were regarded efficient in martial and economic manners. As an algorithm regarding automated training data retrieval from SAR image was proposed, the identification of unclassified ships without AIS information could be raised as another challenging issue of ship surveillance. This study presented the effective identification algorithm of discerning unclassified ships from AIS information and the results of conventional ship detection based on machine learning. The accurately detected ships were selected from the conventional ship detection results, followed by the preprocessing of AIS information corresponding to the SAR images containing the detection results. Superposition of AIS information on accurate detection results was conducted and concluded the ships without AIS information as unclassified ships. From 3 Sentinel-1 SAR images, it obtained the average rate of identification as 85.67%. Additional research implementing the identification algorithm accompanied by rapidly acquired satellite or airborne SAR images could be effective in rendering a ship surveillance system with rapid response.
Policies, Procedures, and <scp>Decision‐Making</scp>: Data Managers and the Research Lifecycle

Abstract Research data is an asset. Researchers may be required to provide access to their data by scientific funders or academic journals and deposit their data in archives. Managers of archives are guided by principles, policies, and the law when curating and providing access to data. Practices around data storage and access, however, are not always cut and dry; research data managers sometimes need to interpret policies. This paper presents findings from qualitative interviews with 15 data managers from 8 repositories in the U.S. These repositories were all affiliated with universities but served varied constituents and provided a range of services. Differences revealed opportunities and challenges in managing data repositories regarding, for instance, who can access data and the level of protection data requires. We also found that data‐related policy challenges may stem from any stage of the research lifecycle.
Deep Semantic Parsing with Upper Ontologies

This paper presents a new method for semantic parsing with upper ontologies using FrameNet annotations and BERT-based sentence context distributed representations. The proposed method leverages WordNet upper ontology mapping and PropBank-style semantic role labeling and it is designed for long text parsing. Given a PropBank, FrameNet and WordNet-labeled corpus, a model is proposed that annotates the set of semantic roles with upper ontology concept names. These annotations are used for the identification of predicates and arguments that are relevant for virtual reality simulators in a 3D world with a built-in physics engine. It is shown that state-of-the-art results can be achieved in relation to semantic role labeling with upper ontology concepts. Additionally, a manually annotated corpus was created using this new method and is presented in this study. It is suggested as a benchmark for future studies relevant to semantic parsing.
A Temporal Approach to Unsupervised Anomaly Detection

the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.
BCT Boost Segmentation with U-net in TensorFlow

In this paper we present a new segmentation method meant for boost area that remains after removing the tumour using BCT (breast conserving therapy). The selected area is a region on which radiation treatment will later be made. Consequently, an inaccurate designation of this region can result in a treatment missing its target or focusing on healthy breast tissue that otherwise could be spared. Needless to say that exact indication of boost area is an extremely important aspect of the entire medical procedure, where a better definition can lead to optimizing of the coverage of the target volume and, in result, can save normal breast tissue. Precise definition of this area has a potential to both improve the local control of the disease and to ensure better cosmetic outcome for the patient. In our approach we use U-net along with Keras and TensorFlow systems to tailor a precise solution for the indication of the boost area. During the training process we utilize a set of CT images, where each of them came with a contour assigned by an expert. We wanted to achieve a segmentation result as close to given contour as possible. With a rather small initial data set we used data augmentation techniques to increase the number of training examples, while the final outcomes were evaluated according to their similarity to the ones produced by experts, by calculating the mean square error and the structural similarity index (SSIM).
Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.
BUDS+: Better Privacy with Converger and Noisy Shuffling

Advancements in machine learning and data science deal with the collection of a tremendous amount of data for research and analysis, following which there is a growing awareness among a large number of users about their sensitive data, and hence privacy protection has seen significant growth. Differential privacy is one of the most popular techniques to ensure data protection. However, it has two major issues: first, utility-privacy tradeoff, where users are asked to choose between them; second, the real-time implementation of such a system on high-dimensional data is missing. In this work, we propose BUDS+, a novel differential privacy framework that achieves an impressive privacy budget of 0.03. It introduces iterative shuffling, embedding for data encoding, converger function into a novel comparison system to converge the privacy threshold among the aggregated differentially private and noisy reports to further minimize the attack model’s time.
Multi-Perspective Framework of Digital Infrastructure Utilizing Eo Data

The proposed framework was investigated on a feasible process of multi-dimensional digital-infrastructure management operated at national level following the SDGs. To build a sustainable and resilient infrastructure against aging and mega-disasters, the framework of multi-perspective digital-infrastructure management are essential to share infrastructure information and to design infrastructure investment. The experimental case study shows “i-Recovery” process of digital-infrastructure management focusing on disaster emergency response utilizing three-dimensional (3-D) and EO data under the proposed framework.
What we eval in the shadows: a large-scale study of eval in R programs

Most dynamic languages allow users to turn text into code using various functions, often named &lt;tt&gt;eval&lt;/tt&gt;, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from performing optimizations. This paper aims to provide a better sense of why programmers use &lt;tt&gt;eval&lt;/tt&gt;. Understanding why &lt;tt&gt;eval&lt;/tt&gt; is used in practice is key to finding ways to mitigate its negative impact. We have reasons to believe that reflective feature usage is language and application domain-specific; we focus on data science code written in R and compare our results to previous work that analyzed web programming in JavaScript. We analyze 49,296,059 calls to &lt;tt&gt;eval&lt;/tt&gt; from 240,327 scripts extracted from 15,401 R packages. We find that &lt;tt&gt;eval&lt;/tt&gt; is indeed in widespread use; R’s &lt;tt&gt;eval&lt;/tt&gt; is more pervasive and arguably dangerous than what was previously reported for JavaScript.
Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data.&#x0D; Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency.&#x0D; The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems.&#x0D; The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes.&#x0D; Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data.&#x0D; Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.
On the motivation for using the project-based learning method

The article deals with the problems of implementing the project-based learning method in professional education. The aim of the study is to identify the key factors hindering the full use of the method in educational practice. The research methodology is based on a systematic approach and includes methods of description, comparison, comparative analysis, and generalization. The project-based learning method fully meets the objectives of the competence-based approach in professional education. For a number of areas of training, the project-based training method is also significant in that it allows you to demonstrate the formation of graduate competencies to a potential employer. This is mainly reflected in the results of project activities. The positions of subjects interested in the results of the educational process are formulated. The adoption of the project-based learning method by participants in the educational process – students, teachers, and University administration – is hindered by negative motivation, which clearly prevails in the short term. It is concluded that for the actual, real, and not formal transition to the use of the project-based learning method, measures are needed not to be imposed by the state, but to stimulate participants of the educational process.
Correcting Hierarchical Plans by Action Deletion

Hierarchical task network (HTN) planning is a model-based approach to planning. The HTN domain model consists of tasks and methods to decompose them into subtasks until obtaining primitive tasks (actions). There are recent methods for verifying if a given action sequence is a valid HTN plan. However, if the plan is invalid, all existing verification methods only say so without explaining why the plan is invalid. In the paper, we propose a method that corrects a given action sequence to form a valid HTN plan by deleting the minimal number of actions. This plan correction explains what is wrong with a given action sequence concerning the HTN domain model.
Bayesian semiparametric joint modeling of longitudinal explanatory variables of mixed types and a binary outcome

Many prospective biomedical studies collect longitudinal clinical and lifestyle data that are both continuous and discrete. In some studies, there is interest in the association between a binary outcome and the values of these longitudinal measurements at a specific time point. A common problem in these studies is inconsistency in timing of measurements and missing follow‐ups which can lead to few measurements at the time of interest. Some methods have been developed to address this problem, but are only applicable to continuous measurements. To address this limitation, we propose a new class of joint models for a binary outcome and longitudinal explanatory variables of mixed types. The longitudinal model uses a latent normal random variable construction with regression splines to model time‐dependent trends in mean with a Dirichlet Process prior assigned to random effects to relax distribution assumptions. We also standardize timing of the explanatory variables by relating the binary outcome to imputed longitudinal values at a set time point. The proposed model is evaluated through simulation studies and applied to data from a cancer survivor study of participants in the Women's Health Initiative.