Reviewerly/exHarmony huggingface dataset:
A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram

X-ray computed tomography and, specifically, time-resolved volumetric tomography data collections (4D datasets) routinely produce terabytes of data, which need to be effectively processed after capture. This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process. For this paper these highly sampled data are used to aid feature detection in the rapidly collected tomograms by assisting with the upsampling of their projections, which is equivalent to upscaling the θ-axis of the sinograms. In this paper, a super-resolution approach is proposed based on deep learning (termed an upscaling Deep Neural Network, or UDNN) that aims to upscale the sinogram space of individual tomograms in a 4D dataset of a sample. This is done using learned behaviour from a dataset containing a high number of projections, taken of the same sample and occurring at the beginning or the end of the data collection. The prior provided by the highly sampled tomogram allows the application of an upscaling process with better accuracy than existing interpolation techniques. This upscaling process subsequently permits an increase in the quality of the tomogram's reconstruction, especially in situations that require capture of only a limited number of projections, as is the case in high-frequency time-series capture. The increase in quality can prove very helpful for researchers, as downstream it enables easier segmentation of the tomograms in areas of interest, for example. The method itself comprises a convolutional neural network which through training learns an end-to-end mapping between sinograms with a low and a high number of projections. Since datasets can differ greatly between experiments, this approach specifically develops a lightweight network that can easily and quickly be retrained for different types of samples. As part of the evaluation of our technique, results with different hyperparameter settings are presented, and the method has been tested on both synthetic and real-world data. In addition, accompanying real-world experimental datasets have been released in the form of two 80 GB tomograms depicting a metallic pin that undergoes corruption from a droplet of salt water. Also a new engineering-based phantom dataset has been produced and released, inspired by the experimental datasets.
Empleo de la gamificación en un curso de Fundamentos de Biología

&nbsp;Currently, at the Instituto Parauniversitario Plerus, there are limitations on the part of students such as studying at the last minute, not using reference tools such as textbooks or articles, and studying for a grade and not for the knowledge itself. Therefore, the objective of this research was to reinforce the course contents, the continuous study, and the self-evaluation of the acquired knowledge through the development of gammification tasks to favor the learning of the students of this course. For the development of the didactic strategy, 29 students from Fundamentals of Biology course, at the Instituto Parauniversitario Plerus, took part: 24 of whom completed the course and five withdrew it. For this group, a portfolio and reinforcement activities (plasticine models, homemade experiments, word search puzzles, crossword puzzles, among others) were established. The realization of these activities provided rewards through the gift of stickers to each student. These rewards were collected and exchanged for didactic prizes, such as a percentage in a short exam. A game called Biology cr was also developed in the Reinos® mobile application. In it, each student developed unique selection questions that were reviewed by the teacher and then incorporated to be answered by other classmates. To determine the results, the grades obtained by the students enrolled in the Fundamentals of Biology course were reviewed, and a survey was carried out, which each student completed once the implementation of this strategy was concluded. To analyse the data, a sociodemographic analysis was carried out for age and sex, and a descriptive analysis with frequency distribution was carried out for the rest of the questions. From a teaching perspective, good results were obtained, with 66.7% of the student body passing. With regard to the student perspective, there were positive aspects (improvement in the understanding of subjects, obligation to constantly take up the subjects studied in class and understand them on their own, including personal research, if necessary) and negative aspects (academic load increase, preference for the use of traditional didactic strategies and lack of delimitation of the most relevant aspects of each of the subjects), which must be considered for future experiences.
Texture Feature Extraction for Batik Images Using GLCM and GLRLM with Neural Network Classification

Batik has a vast variety of motifs and colors. Aside from its popularity as being part of Indonesian culture, it has become the source of Indonesia’s income. Batik was more promising in the past years for the business opportunities. Batik has economic and high export value as the commodity. Batik has become the main part of national culture; however there is a lack of understanding for many people, as they are still unaware about batik motifs and patterns. Therefore, it is needed for building a model to identify batik motifs. This study aims to combine the features of texture and the feature of shapes’ ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using gray level co-occurrence matrices (GLCM) which include Contrast, Correlation, Homogeneity and Energy. And include the Gray level Run length matrices (GLRLM) which includes Gray Level Non-Uniformity (GLN), Long Run Emphasis (LRE), Short Run Emphasis (SRE), Run Percentage (RP). At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, and the combination of GLCM and GLRLM. From the three features used in the classification of batik image with artificial neural networks it includes Probabilistic Neural network, it was obtained that GLCM feature has the lowest accuracy rate of 78% and the combination of GLCM and GLRLM features produces a greater value of accuracy by 84%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the probabilistic neural network with the combination of GLCM and GLRLM features in batik image.
GFSOM: Genetic Feature Selection for Ontology Matching

This paperBelhadi, Hiba studies theAkli-Astouati, Karima ontology matching problem and proposes a genetic feature selection approach forDjenouri, Youcef ontology matching (GFSOM),Lin, Jerry Chun-Wei which exploits the feature selection usingWu, Ming-Tai the genetic approach to select the most appropriate properties for the matching process. Three strategies are further proposed to improve the performance of the designed approach. The genetic algorithm is first performed to select the most relevant properties, and the matching process is then applied to the selected properties instead of exploring all properties of the given ontology. To demonstrate the usefulness and accuracy of the GFSOM framework, several experiments on DBpedia ontology database are conducted. The results show that the ontology matching process benefits from the feature selection and the genetic algorithm, where GFSOM outperforms the state-of-the-art ontology matching approaches in terms of both the execution time and quality of the matching process.
Buffer Occupancy-Based Transport to Reduce Flow Completion Time of Short Flows in Data Center Networks

Today’s data centers host a variety of different applications that impose specific requirements for their flows. Applications that generate short flows are usually latency sensitive; they require their flows to be completed as fast as possible. Short flows suffer to quickly increase their sending rate due to the existing long flows occupying most of the available capacity. This problem is caused due to the slow convergence of the current data center transport protocols. In this paper, we present a buffer occupancy-based transport protocol (BOTCP) to reduce flow completion time of short flows. BOTCP consists of two parts: (i) A novel buffer occupancy-based congestion signal, and (ii) a congestion control scheme that uses the congestion signal to reduce flow completion time of short flows. The proposed buffer occupancy-based congestion signal gives a precise measure of congestion. The congestion control scheme makes a differentiated treatment of short and long flows to reduce flow completion time of short flows. The results show that BOTCP significantly improves flow completion time of short flows over the existing transport protocols in data center networks.
Evaluation of Region of Interest Digital Cytology Compared to Light Microscopy for Veterinary Medicine

Digital microscopy (DM) has been employed for primary diagnosis in human medicine and for research and teaching applications in veterinary medicine, but there are few veterinary DM validation studies. Region of interest (ROI) digital cytology is a subset of DM that uses image-stitching software to create a low-magnification image of a slide, then selected ROI at higher magnification, and stitches the images into a relatively small file of the embedded magnifications. This study evaluated the concordance of ROI-DM compared to traditional light microscopy (LM) between 2 blinded clinical pathologists. Sixty canine and feline cytology samples from a variety of anatomic sites, including 31 cases of malignant neoplasia, 15 cases of hyperplastic or benign neoplastic lesions, and 14 infectious/inflammatory lesions, were evaluated. Two separate nonblinded adjudicating clinical pathologists evaluated the reports and diagnoses and scored each paired case as fully concordant, partially concordant, or discordant. The average overall concordance (full and partial concordance) for both pathologists was 92%. Full concordance was significantly higher for malignant lesions than benign. For the 40 neoplastic lesions, ROI-DM and LM agreed on general category of tumor type in 78 of 80 cases (98%). ROI-DM cytology showed robust concordance with the current gold standard of LM cytology and is potentially a viable alternative to current LM cytology techniques.
Symmetric Algorithms I

This chapter explores basic concepts of symmetric key cryptography. The knowledge of encryption key is implicitly or explicitly equivalent to knowing the decryption key. These algorithms are typically fast and much more efficient than asymmetric algorithms in both hardware and software. Symmetric encryption gives a level of authentication since the information is encoded with one symmetric key; it cannot be decoded with some other key. Secure file transfer protocols make use of symmetric key primitives to encrypt data. Shannon introduced the concept of two primitive operations that can lead to a specific end goal of accomplishing strong encryption. In the design of symmetric encryption systems, Substitution and Permutation network is a typical component to accomplish both confusion and diffusion. A cryptographic mode refers to the way in which plaintext can be encrypted into ciphertext by combining the basic cipher, some sort of feedback, and some elementary operations.
Zernike Moment-Based Facial Expression Recognition Using Two-Staged Hidden Markov Model

Facial expression recognition is an application used for biometric software that can be used to recognize special expressions in a digital image by analysing and comparing different patterns. This software is popularly used for the purpose of security and is commonly used in other applications such as medicines, home security, human–computer interface, credit card verification and surveillance systems. Recognizing faces becomes very difficult when there is a frequent change occurs in facial expressions. In this paper, new improved extension of HMM is used to identify continuous effective facial expressions. Extraction of features is done using Zernike moments. The new improved version of HMM consists of two stages: first stage denotes the expression made by individual features like nose, mouth, etc., and second stage denotes the mixture of individual features like smile, fear, etc. Seven universal facial expressions are recognized, i.e. sadness, surprise, neutral, anger, disgust, fear and joy. Experimental result shows the better performance of the proposed system using JAFFE data sets.
&lt;b&gt;Earthquake Early Warning Using Ocean Bottom Seismic Data for Railways&lt;/b&gt;

This article explains processes for improving the railway earthquake early warning system. These processes were introduced to use data transmitted in real time from recently developed ocean bottom seismic networks. Three mechanisms were designed to be able to exploit this data: 1) an algorithm for servers in OBS system base stations; 2) a procedure to allow transmissions between the servers and railway company receivers; and 3) a system built into the receiver to determine whether running trains need to be stopped or not. Confirmation was obtained that the proposed processes were able to reduce the risks to railways from earthquakes, because they are capable of extending the lead time before the arrival of strong seismic motion.
XOR Encryption Techniques of Video Steganography: A Comparative Analysis

Various steganographic techniques are best combined with different encryption techniques for the enhanced security and hiding purpose. Among all encryption schemes the XOR technique is more often used for less distortion. XOR of secret message with a secret key is one of the commonly used approaches for the cryptographic purpose. This paper presents a different XOR methodology without using a secret key. Also, comparison is done among the two XOR techniques namely with secret key and without secret key. The result analysis among the implemented techniques yield that XORing with the LSB without using secret key shows less distortion and effective encryption as compared with XORing with secret key.
Building Trustful Crowdsensing Service on the Edge

Edge computing enables the data to be processed in the edge of networks in order to decrease the latency of crowdsensing services. However, due to the distributed environment and vulnerability of edges, it is difficult for different edges to reach consistency to provide the same service and protect the data from tampering at the same time. To solve these problems, the Blockchain, a credible and natural decentralized technique, is considered as a suitable tool. In this paper, we proposed a Blockchain-based edge crowdsensing service system in which the edge runs a changeable auction algorithm for every task that the users request to find a winner who can provide corresponding sensing data. Specifically, based on PBFT algorithm, we proposed a consensus algorithm named Leader Stable Practical Byzantine Fault Tolerance (LS-PBFT). This algorithm enables all edges to collaboratively maintain an updated, consistent and credible ledger in Blockchain. Furthermore, the data generated in this process are constructed as a multi-transaction, which can be packaged into a block and stored in the block. Simulation results reveal that the proposed system is not only efficient in generating and storing blocks but also feasible in resisting attacks of malicious users and edges. Our experiments also show that LS-PBFT takes less than 50% of the time cost by PBFT to reach consensus.
Innovation Technologies in Student’s Independent Activity and Creativity Development: The Case of Medical Education

Adaptive learning technologies, applied with supportive symbols and signals allow developing the subject and communicative competencies, as well as shaping the creative abilities of students. This article provides the assessment criteria for preliminary, formative, midpoint and summative assessments. This article substantiates the hypothesis on improving the Emergency Medical Service student’s independent performance at the stage of supportive notes introduction. Methodology is original as it contains the following method: students are the one to write notes as an effective means of independent activity organization. Besides, this method can be useful as an alternative recording technique. Accordingly, innovation-driven education (in our case, independent learning and skills developing for quality outcomes) is one of the main components of the teaching and learning process. The statistics have revealed that students gain better subject knowledge through this learning technology.
Quantitative Evaluation of Potential Tendency Differences between English and Japanese in Detecting Appropriate Respondents at Q&amp;A Sites

In order to eliminate mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for Japanese and English statements have experimentally been obtained. Factor scores are then estimated by using the feature values of statements. So far the possibility of searching Japanese respondents capable of giving appropriate answers to a newly posted question has been established. It has been shown that the distance and the number of appearance may help us select users who can give appropriate answers to a question. In the similar fashion, this paper tries to find the possibility of detecting respondents who can appropriately answer a newly posted question at English Q&A sites. As a result of analysis, while there were several users who iteratively gave answer statements for Japanese, there are only a small number of respondents who only posted at most two answers for English.
A CORRECT POLYNOMIAL TRANSLATION OF S4 INTO INTUITIONISTIC LOGIC

Abstract We show that the polynomial translation of the classical propositional normal modal logic S4 into the intuitionistic propositional logic Int from Fernández is incorrect. We give a modified translation and prove its correctness, and provide implementations of both translations to allow others to test our results.
Parity-Assisted Generation of Nonclassical States of Light in Circuit Quantum Electrodynamics

We propose a method to generate nonclassical states of light in multimode microwave cavities. Our approach considers two-photon processes that take place in a system composed of N extended cavities and an ultrastrongly coupled light–matter system. Under specific resonance conditions, our method generates, in a deterministic manner, product states of uncorrelated photon pairs, Bell states, and W states in different modes on the extended cavities. Furthermore, the numerical simulations show that the generation scheme exhibits a collective effect which decreases the generation time in the same proportion as the number of extended cavity increases. Moreover, the entanglement encoded in the photonic states can be transferred towards ancillary two-level systems to generate genuine multipartite entanglement. Finally, we discuss the feasibility of our proposal in circuit quantum electrodynamics. This proposal could be of interest in the context of quantum random number generator, due to the quadratic scaling of the output state.
Information and Education Means of Social Educational Networks

The publication defines various aspects of the concept of “information and education space”, the key features of modern educational process are revealed. Authors represent social networks as one of the means of formation of such space outlining positive and negative aspects. In order to study the effectiveness of using the social nets in education process the survey has been performed in the frame of the research. The survey studied to what extent the students and lecturers are being equipped, the types of information they used, time costs for the information search, their experience of learning and teaching with the help of thematic internet sites and social nets, the wish to use the possibilities of the Internet in their own practice, the ways of accepting and implementation the information online and the attitude of the students and the lecturers to education through social nets.
Rule-Based Automatic Question Generation Using Semantic Role Labeling

This paper proposes a new rule-based approach to automatic question generation. The proposed approach focuses on analysis of both syntactic and semantic structure of a sentence. Although the primary objective of the designed system is question generation from sentences, automatic evaluation results shows that, it also achieves great performance on reading comprehension datasets, which focus on question generation from paragraphs. Especially, with respect to METEOR metric, the designed system significantly outperforms all other systems in automatic evaluation. As for human evaluation, the designed system exhibits similar performance by generating the most natural (human-like) questions.
Research on knowledge management of operational support system for aerospace manufacturers

Nowadays, knowledge map as an efficient tool of knowledge management is widely used in various fields. In this paper, addressing the operational support of aerospace manufacturers, we explore the construction method of knowledge map and possible applications. We propose a five-step procedure for knowledge map construction, consisting of knowledge definition, knowledge extraction, knowledge description, knowledge links and knowledge map creation. Following this procedure, the knowledge map of operational support system for aerospace manufacturers is constructed in an example way. We also show the potential applications of knowledge map in aerospace manufactures.
The impact of sample volume in random search on the bbob test suite

Uniform Random Search is considered the simplest of all randomized search strategies and thus a natural baseline in benchmarking. Yet, in continuous domain it has its search domain width as a parameter that potentially has a strong effect on its performance. In this paper, we investigate this effect on the well-known 24 functions from the bbob test suite by varying the sample domain of the algorithm ([-α,α]n for α ε {0.5, 1, 2, 3, 4, 5, 6, 10, 20} and n the search space dimension). Though the optima of the bbob testbed are randomly chosen in [-4,4]n (with the exception of the linear function f5), the best strategy depends on the search space dimension and the chosen budget. Small budgets and larger dimensions favor smaller domain widths.
Studying Affective Tutoring Systems for Mathematical Concepts

Students face difficulties in learning mathematical processes. As a result, they have negative emotions toward mathematics. The use of technology is employed to change the student’s attitude toward mathematics. Some methods utilize intelligent tutoring systems to recognize student’s emotional state and adapt the learning process accordingly. These systems, known as affective tutoring systems (ATSs), sense the emotional state of a student and then intelligently attempt to suggest appropriate strategies that can guide the learning process and ultimately shift the negative attitude of students toward mathematical learning. This article presents a survey of ATSs, which teach mathematic content, and tries to find common elements among them. It examines the kind and the number of student’s emotions that can be recognized and the strategies and methods that these ATSs use to recognize student’s emotional state. There are findings that agree with other studies about the recognized emotions and the methods that are used.