Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF ALGORITHMIC SUPPORT AND A DEDICATED DEVICE FOR IMAGING THE INTERNAL ORGANS OF THE PATIENT ACCORDING TO MRI AND CT SCANNERS

In work the algorithm of restoration of the images damaged as a result of influence of noise of various nature is considered. The advantages and disadvantages of the existing approaches, as well as the prospects of using artificial neural networks, are noted. A double-layer neural network is used as an image restoration tool, and it is assumed that the location of the damaged pixels is known. A neuron is represented as a 3x3 array, where each element of the array has a pixel color value that corresponds to the value of that color in the palette. The neural network is trained on intact images, while the color difference of pixels acts as a learning criterion. For a more accurate restoration, it is recommended at the training stage to select images similar in color to damaged ones. At the recovery stage, neurons (3x3) are formed around the damaged pixels, so that the damaged pixel is located in the middle of the neuron data array. The damaged pixel is assigned a neuron value depending on the average value of the weights matrix. An algorithm for the restoration of pixels, as well as its software implementation. The simulation was carried out in the RGB palette separately for each channel. To assess the quality of the recovery were selected groups of images with varying degrees of damage. Unlike existing solutions, the algorithm has the simplicity of implementation. The research results show that regardless of the degree of damage (within 50%), about 70% of damaged pixels are restored. Further studies suggest a modification of the algorithm to restore images with enlarged areas of damage, as well as adapting it to restore three-dimensional images.
Comparison of inversion methods of complex refractive index for rough surface objects by polarization measurement

Determining index of refraction of rough surface object is important for target identification and classification by polarization measurement, but it is difficult to directly acquire the imaginary part of complex refractive index. Many numerical inverse methods are proposed, which are Vimal-Milo method and improved Vimal-Milo inversion method. The first method is evaluated by P-G model, which is based on scalar bidirectional reflectance distribution function with a mode of “polarization ratio – angle correlation”. But this method is only applied in almost specular reflection and its defects are explicit in application. The second method named improved Vimal-Milo method is proposed based on the mode of ‘relative polarization component – angle correlation’. In this method, the inversion formula for the complex refraction index was semi-empirical deduced and global search algorithms such as genetic algorithm or Hooke – Jeeves search algorithm were used. Although two inversion methods are same based on P-G model, polarization measurement are different of which one is polarization ratio and the other is S1 component. The simulation results showed that (1) roughness parameters (correlation length and height standard variance of microfacet) have little effect to inversion of complex refraction index, and they must be calibrated accurately. (2) stokes values and reflection angle have smaller effect and they can be measured by polarimeter and angulometer. (3) incident angle is strongly correlated with complex refraction index and must be measured largely and precisely. (4) we must try to avoid poor specular reflection condition and improve degree of polarization.
The First Library Session of the People’s Commissariat of Education in Moscow: Continuity and New Challenges

Based on the archival documents of the Russian State Library (RGB), the article studies the questions on the situation with librarianship in Russia before the October Revolution of 1917 and in the early post-revolutionary years. Introduction of the new sources and the new interpretation of the continuity and interaction of specialists after the Revolution determine the scientific novelty of the study. The source base of the research are the materials of the First All-Russian Congress on Librarianship of 1911 and the First Library Session of the People’s Commissariat of Education in 1919. For the first time the article introduces into scientific circulation the bulk of the materials of the First Library Session, not published in full and stored in the Archive of the Russian State Library. Despite the fundamental changes in life associated with social processes after the October Revolution, practical experience accumulated by libraries in pre-revolutionary times within the Russian Empire, as well as its theoretical understanding became largely the basis for the new organization of librarianship. Evaluation of the development of librarianship in Russia and the need for radical changes in librarianship were formulated at the Congress of 1911. Thanks to the active participation of librarians and book experts in the discussion of the structure of the librarianship, many historical shortcomings of the old system have received professional and competent solution. The dialogue of the new government with the old personnel facilitated this process. The Bolshevik leadership duly appreciated professionalism of the library staff and largely used their suggestions made at the First Library Session, which became the basis for the construction of library network in the country and organization of professional development training. The leadership of the People’s Commissariat of Education took over the establishment of ideological control over the activities of libraries, leaving the practical work under the supervision of employees of these institutions. The author concludes that the subsequent success of the development of librarianship in the USSR demonstrated the correctness of the developed plan, built on the opinion of professionals and created by them in the main features.
A Study on Data Hiding Technique Using Prime Numbers

In a globe full of communications, it is necessary to guard the data in arrange to make certain the isolation of the users. There is a lot of responsive data present in the mesh and it requirements. A novel data hitting technique is future, as an development over the Fibonacci LSB data-hiding technique. The traditional LSB technique is the simplest, but using this method it is promising to implant only in few bit-planes, since picture excellence becomes radically misshapen when embed in higher bit-planes. Battista et al. proposed an development over this by using Fibonacci putrefaction modus operandi and generate a deferent set of practical bit-planes all mutually, thereby growing the numeral of bit-planes. we propose two new hashing functions, prime modulo and odd-multiplier displacement, that are resistant to pathological behaviour and yet are able to eliminate the worst-case disagreement performance in the L2 cache. We show that these two schemes can be implemented in fast hardware using a set of thin totalling operation, with insignificant disintegration in the L2 hoard. We assess the scheme on 23 reminiscence intensive applications. It produces different secret message text for the same plain text which confuses the hacker to attain the unique text and makes it near impossible to break it.
A Particle Filter-based Estimation Method Considering Communication Delay

This paper proposes the state and parameter estimation method in order to compensate for random communication delay. By using time-stamp information, particle filter can be available even in the presence of the delay. The numerical simulation verifies that estimation error can be reduced by using the proposed method.
Continuous and Noncontinuous Syntactic n-grams

In the previous chapters, we introduced the new concept of syntactic n-grams, i.e., n-grams obtained following paths in syntax trees. The discussion that follows in this chapter addresses the comparison of continuous syntactic n-grams with non-continuous syntactic n-grams (i.e., n-grams with bifurcations (ramifications) and n-grams without them).
Design and Implementation of Internet of Things and Cloud Based Platform for Remote Health Monitoring and Fall Detection

With the proliferation of the Internet of Things (IoT) and cloud computing technologies in various fields, remote health monitoring and fall detection are two vital applications that are expected to adopt these technologies. This is due to the fact that it not only provides efficient way for logging the patients’ health information, thus providing an electronic record for all the vital health signs that the patient is monitoring utilizing various medical IoT devices, but also it can be used to send an alert message to the healthcare personnel in case of detecting any abnormal behavior hence providing an immediate assistance. Fall detection is another important application of these technologies, where wearable sensors can be used to send an alert message to the healthcare personnel in case of detecting unpredicted fall. In this book chapter, the design and implementation of a simple and cost effective healthcare monitoring and fall detection system that utilizes of-the-shelf electronic components is provided. The system consists of a microcontroller, medical sensors and communication module that are used to collect the patients’ information and send it to the cloud for further processing and analysis. Furthermore, a fall detection system that utilizes wearable sensors is proposed where it can detect unpredicted fall. One unique feature in this system that it utilizes voice recognition technology to interact with the patient after detecting a fall, thus verifying if the patient needs an assistant or not, which in turn reduces the false alarms and improves the system accuracy.
Bringing Food and Nutrition Information to Filipinos in Just a Click of the Finger Through iFNRI: The Potentials of Information Technology to Inform, Educate and Serve

Nutrition literacy has been defined to identify the degree to which individuals can obtain, process, and understand the basic nutrition information and services needed to make appropriate nutrition decisions.
A Multi-metrics Integrated FR IQA Method Based on Machine Learning

In this paper, a FR (full reference) image quality prediction model learned by AdaBoost BP neural network is proposed by using a bag of features. Firstly some FR image quality assessment methods to improve metrics by considering human visual system are summarized. Further, a group of FR IQA (image quality assessment) metrics are extracted as the candidates of the representation of images. AdaBoost algorithm is finally used to enhance BP network built on a bag of features (9 chosen metrics) for image quality prediction. Then the learned prediction model can map a bag of features into an image quality score as MOS or DMOS. Our experiments are executed on TID2008, TID2013 and LIVE Dataset. Experimental results show that our proposed multi-metrics integrated prediction model works well and get better results than any single metric under complex situations.
Design and Implementation of Salary Management System for Printing Enterprises

In order to make staff salary management more standardized, this paper designs a salary management platform for employees of printing company, which is based on the Asp.Net framework and uses C# language and SQL Server database to store data, with manage data information and staff attendance, assessment, performance, welfare, rewards and punishments and other functional modules, to achieve full management of the remuneration of printing enterprises employees and promote the fairness of the printing enterprises.
Optimal Coherent Filtering for Single Photons

We propose a quantum buffer which optimally filters the temporal-spectral mode of single photon source. We theoretically show that using a Cs vapour, an increase of indistinguishably with minimal loss of brightness can be achieved.
Curriculum Learning for Tightly Coupled Multiagent Systems

In this paper, we leverage curriculum learning (CL) to improve the performance of multiagent systems (MAS) that are trained with the cooperative coevolution of artificial neural networks. We design curricula to progressively change two dimensions: scale (i.e. domain size) and coupling (i.e. the number of agents required to complete a subtask). We demonstrate that CL can successfully mitigate the challenge of learning on a sparse reward signal resulting from a high degree of coupling in complex MAS. We also show that, in most cases, the combination of difference reward shaping with CL can improve performance by up to 56%. We evaluate our CL methods on the tightly coupled multi-rover domain. CL increased converged system performance on all tasks presented. Furthermore, agents were only able to learn when trained with CL for most tasks.
Structured illumination fiber probe for high-resolution surface feature imaging of 3D printed and composite samples

The design flexibility of aerospace components has been revolutionized with the recent advancements in the manufacturing of composite structures and 3D printed components. Since the manufacturing process is dependent on the control environment, variations from the nominal conditions can result in the degraded surface quality of the manufactured part. It is critical to image these surface features at high-resolutions to enable process feedback, therefore, improving the overall efficiency and process control. In this context, we develop and demonstrate a structured illumination optical fiber probe with embedded speckles for high-resolution surface feature imaging of 3D printed and composite samples. The improved sectioning capabilities by modifying the structured illumination patterns is validated using a plane mirror sample. This method is envisaged for high-resolution surface feature imaging to improve the overall manufacturing process efficiency of critical components.
2019 IEEE Fellows Elevation and Recognition

Lists MMTS members who were elevated to the status of IEEE Fellow.
Towards Bidirectional Synchronization Between Communicating Processes and Session Types

Session types are a type discipline for eliminating communication errors in concurrent computing. These types can be thought of as a representation of communication protocols implemented by communicating processes. One application scenario that can be naturally supported by session types is semantics-preserving transformation of processes in response to protocol changes due to optimization, evolution, refactoring, etc. Such transformation can be seen as a particular kind of synchronization problem that has long been studied by the bidirectional transformations (BX) community. This short paper offers a preliminary analysis of the process-type synchronization problem in terms of BX, describing the prospects and challenges.
Status of Intelligent Building Development of China—Questionnaire Analysis

A questionnaire survey on the status of intelligent building development was carried out. Based on actual survey questionnaire data, the development status of technology and applications was described in this paper, mainly discussing the existing problems and the proper future development. It is aimed at providing valuable findings on intelligent building development, exploring an innovative way to build a practical, reliable, and easy-used intelligent system to meet the requirements of users. Among all the designing aspects, the configuration of both hardware and software is the most tedious and error-prone part when building an intelligent system, as a huge workload is inevitable when solving the difficulties on system transformation. Unsuitable operation logic of the system and equipment is another problem, as the distance between the original expectation of the intelligent system and the real operation effect is still large. Though some building systems realize integration between the subsystems, they work independently, thus enabling different subsystems to work as a whole is another problem which needs to be emphasized. A much simpler configuration process or structure of the software and hardware system is significantly required in this field. If the controller could adapt its own operation logic automatically according to preset equipment parameters, this problem might be solved fundamentally.
Dynamic-Keyword Extraction from Social Media

Traditional keyword extraction methods make the assumption that corpora is static. However, in social media, information is highly dynamic, with individual words showing a dynamic behaviour. In this paper we propose an unsupervised approach that jointly models words' temporal behaviour and keyword's semantic affinity, to address the task of dynamic-keyword extraction. Experiments show the method effectiveness and confirm the importance of exploiting keyword dynamics.
Improvement of MRI images through heterogeneous interpolation techniques

This paper presents the heterogeneous implementation and evaluation of five algorithms to improve the resolution in magnetic resonance images, these are Nearest-Neighbor, Bilinear, Bicubic, Catmull-Rom and Lanczos. The experiments were carried out using the database BraTS 2017 considering T 1, T 2, T 1ce and F lair acquisition modes. To measure the efficiency and quality of the results, the metrics PSNR, MAE, SSIM, as well as the Runtime were used. The results show that for all modalities the Bicubic algorithm has a PSNR > 35, MAE <; 1.089, SSIM > 0.958 and RT <; 5.205 seconds per whole database. By other hand, Catmull-Rom achieves favorable results compared with Bicubic algorithm, maintaining the balance between quality and a less processing time than mentioned method.
Multi-label Active Learning with Error Correcting Output Codes

Due to the demand of practical problems, multi-label learning has become an important research where each instance belongs to multiple classes. Compared with single-label problem, the labeling cost for multi-label one is rather expensive because of the diversity and non-uniqueness of the labels. Therefore, the active learning which reduces the cost by selecting the most valuable data to query the labels attracts a lot of interests. Although several multi-label active learning (MLAL) methods were proposed, they often identify the label merely through a classifier via one-versus-all (OVA) strategy for each class, which makes the classification model very fragile, thus having a serious impact on the later selection criteria. In this paper, we utilize a new multi-label Error Correcting Output Codes (ECOC) method which determines the label of an instance on each class by combining multiple classifiers. This makes our classification model has a good ability of error-correcting and thus ensures the effectiveness of evaluation information in the selection process. Then we combine two effective selection strategies, the margin prediction uncertainty and label cardinality inconsistency, to complement each other and select the most informative instance. Based on this combination, we propose a novel MLAL framework, termed Multi-label Active Learning with Error Correcting Output Codes (MAOC). Experiments on multiple benchmark multi-label datasets demonstrate the efficacy of the combination in proposed approach.
Result of the Methodology for Learning English Distance with the Use of TICs. Case Study: Central University of Ecuador

The study presents the results of the implementation and evaluating of the methodological and technological proposal for the learning of English as a foreign language at the Central University of Ecuador (UCE) Distance, making use of information technologies and based on a previous analysis from the pedagogical and technological. For the result was based on the analysis of the marks for English skills of the students that make up the sample, which are 784 students and 302, for level 6, an A2 + that represents the Common European Framework [1], Assumed for the investigation and attending to criteria of viability and resources with which it counts for the development of the intervention.
Construction of the Computer Platform Framework Based on the University Financial Management System

At present, most universities in China are still in the second stage of the financial information construction. Generally speaking, the degree of the information is relatively low, and the accounting is still the main function of the financial management. The daily reimbursement of the financial personnel and other data entry work is heavy, and business processes are not smooth. The timeliness of the data generation is poor, and the mode of the information transmission is backward. It is unable to meet the requirements of institutionalization, process and informatization of the financial management in colleges and universities.
Semiconductor-Superconductor Quantum Optoelectronics

We demonstrated experimentally Cooper-pair injection and enhanced light emission in super-semiconductor structures, proposed by us for enhanced two-photon gain, electrically-driven entangled-photon generation and Bell state analyzers. We also demonstrated high-Tc superconductor-semiconductor devices
Pengembangan User Interface Sistem Informasi Planned Maintenance System pada PT. Pertamina Trans Kontinental dengan Menggunakan Metode Design Sprint

PT. Pertamina Trans Kontinental (PTK) is company that active in a maritime logistics, therefore its vessels idle time during maintenance become a crucial aspects for PTK because it affect their core business activity directly. To support every maintenance agenda, PTK develop a computerized maintenance management system (Planned Maintenance System) that help maintaining every detail of the vessels. In its implementation, User Interface Design is an important aspect since UI bridge the interaction between human and the system built. A system implementation without considering interaction between user and the system will result in a difficult interaction between user and the system. By evaluating user interface’s usability, It’s expected that the UI recommendations will be developed in accordance with the problems faced by the users. The evaluation is held before and after the design process which use Design Sprint Method. The proposed design final evaluation conclude that there was a 24.13% increase in score, leveraging the average score from 2.50 to 3.29.
Discussion for Uncertainty Calculation of Containment Leakage Rate

This paper discussed the uncertainty calculation process of containment leakage rate. Containment leakage rate is linear regressed based on the change of containment air standard volume. The uncertainty of leakage rate is the combination of the uncertainty of Type A evaluation and Type B evaluation. The uncertainty of Type A evaluation reflects the effect of random fluctuation of containment air volume on the regression line slope and is calculated using statistical theory directly. The uncertainty calculation of Type B evaluation can be conducted based on empirical data and statistical theory. The uncertainty of sensor data is analyzed based on empirical data. With the knowledge of sensor data uncertainty, the calculation process of leakage rate is further decomposed into several steps and the calculation of each step is established according to statistical theory. All of these jobs determine the uncertainty calculation method of Type B evaluation.