Reviewerly/exHarmony huggingface dataset:
Tools for teaching electronics workshop in a virtual academic setting

This paper describes the teaching strategy carried out in the Electronics Workshop class, corresponding to the Electromedicine University Technician degree curricula, belonging to the College of Engineering (CE) - National University of the Center of the Buenos Aires Province (UNCPBA), during the years 2020 and 2021, in the context of academic virtual setting due to pandemic situation. The Teaching methodology was modified, replacing face-to-face experimental activities, using the CE- Virtual platform (Moodle) supported by a pedagogical proposal and the use of different tools such as simulators at different levels, software for simple printed circuit boards design, and video supporting. Central proposal aspects are presented, and how was implemented to achieve the class planning objectives.
In-Vehicle Safe Driving Aid and Inter-Vehicle Interaction Technology

Management of road safety and traffic efficiency is the responsibility of all road users. However, safe driving is no longer possible for many, especially when travelling on either long-distance trips or under scotopic conditions when the ambient illumination is limited. In this project we developed a safe driving intelligent system with near real-time inter-vehicle communication capabilities. This system allowed the acquisition and sharing of images of road conditions and their potential risk via Google Cloud service with other road users. The quantitative analysis of images acquired at different times of the day revealed a high correlation between the image Signal to Noise Ratio (SNR) and illumination on the detector. Our results recorded the highest mean SNR value of 25.1 ± 0.5 dB at noon time before the value gradually declined as the day approached evening. Meanwhile, an investigation into the quality of images taken from a vehicle travelling at a range of speeds revealed a relatively high inconsistency in the mean SNR value of 9.1 dB. We attribute this problem to the low frame rate of the employed imaging system. The fast system response time of 5.6 ± 1.65 s that allows rapid image acquisition and immediate distribution makes it a feasible and useful driving aid for a safer driving experience, and to increase the safety of the community.
Voice Assistants in Healthcare: The Patient's Perception

The significant rise of voice assistants in the last years is revolutionizing our daily lives. The &#x201C;voice input&#x201D; enables users to operate and control the device in many new ways. For this reason, a study was conducted in Swiss hospitals to explore the potential utilization of a voice assistant in the healthcare sector. This study examines the usage of voice assistants at hospitals and summarizes the findings of the theory. Furthermore, it also analyses risks and challenges. With this condensed information, a survey was carried out. The perception of patients regarding voice assistants has been taken into consideration as well as their concerns towards privacy, data protection, and device security were clarified. Legal requirements using such devices and the acceptance by the patients were also examined. Lastly, recommendations of possible utilization areas of the voice assistants are stated.
Fiber-optic elements spectroscopy against Trojan-horse attack in 1500 - 2000 nm range

In this work, we measured transmission spectra of fiber-optic elements, which are used as countermeasures against quantum hacking in the 1500 - 2000 nm range. We found that investigated fiber-optic elements transmit light in 1800 - 2000 nm which opens possibilities for realization of trojan-horse attack on quantum key distribution systems at this range. To solve that problem, we suggest a simple way to decrease transmission in long-wavelength range.
A Coupled Variational System for Image Decomposition along with Edges Detection

In order to better decompose the images and protect their edges, in this paper, we proposed a coupled variational system consisting of two steps. The first step, an improved weighted variational model is introduced to obtain the cartoon and texture. Using the obtained cartoon image, in the second step, a new vector function is obtained for describing the pseudo edge of the considered image by one Tikhonov regularization variational model. Because Tikhonov regularization model is equivalent to carrying out a Gaussian linear filtering, the obtained vector function is smoother. To solve the coupled system, we give the alternating direction method, primal-dual method and Gauss-Seidel iteration. Using the coupled system, we can not only separate out the cartoon and texture parts, but also extract the edge. Extensive numerical experiments are given to show the effectiveness of the proposed method compared with other variational methods.
High Resolution Image Generation Using Learning Super-Resolution for Low Resolution Images

Cameras have been installed in a variety of locations. This is to record evidence in case of trouble. However, many of these images have low resolution, which reduces the effectiveness of the evidence. In particular, license plate images can identify the parties involved, so it is important to read the characters. In this paper, we aim to improve the resolution of license plate images by using super-resolution with adversarial learning method.
Design and Practical Application of Sports Visualization Platform Based on Tracking Algorithm

Machine learning methods use computers to imitate human learning activities to discover new knowledge and enhance learning effects through continuous improvement. The main process is to further classify or predict unknown data by learning from existing experience and creating a learning machine. In order to improve the real-time performance and accuracy of the distributed EM algorithm for machine online learning, a clustering analysis algorithm based on distance measurement is proposed in combination with related theories. Among them, the greedy EM algorithm is a practical and important algorithm. However, the existing methods cannot simultaneously load a large amount of social information into the memory at a time. Therefore, we created a Hadoop cluster to cluster the Gaussian mixture model and check the accuracy of the algorithm, then compare the running time of the distributed EM algorithm and the greedy algorithm to verify the efficiency of the algorithm, and finally check the scalability of the algorithm by increasing the number of nodes. Based on this fact, this article has conducted research and discussion on the visualization of sports movements, and the teaching of visualization of sports movements can stimulate students' interest in physical education. The traditional physical education curriculum is completely based on the teacher's oral explanation and personal demonstration, and the emergence of visualized teaching of motor movements broke the teacher-centered teaching model and made teaching methods more interesting. This stimulated students' interest in sports and improved classroom efficiency.
Few-Shot Learning for Handling Highly Censored Survival Data

Recently, survival models have found vast applications in biostatistics, bioinformatics, reliability engineering, finance and related fields. But survival data often face the small sample size and highly censored problem due to long experimental periods and high data collection costs. The lack of sufficient samples severely hinders the predictive power of survival models, especially when data-driven machine learning methods are increasingly used in survival analysis. In this research, we want to extend the Few-shot Learning (FSL) strategy to survival analysis by proposing two FSL algorithms, namely Parametric algorithm for Survival Data Augmentation via a Two-stage process (PSDATA) and non-Parametric algorithm for Survival Data Augmentation via a Two-stage process(nPSDATA), which can effectively expand the small sample survival data set and validate the effectiveness of the algorithms on both simulated and real data sets based on RSF and Cox models. Extensive experiments have shown that both strategies can improve the predictive performance substantially. Further experiments have revealed that the cost of data collection can be reduced by a large extent with only a slight decrease in predictability.
Individualized Recommendation Method of Multimedia Network Teaching Resources Based on Classification Algorithm in a Smart University

Abstract Current recommendation methods of multimedia network teaching resources cannot classify the resources with high precision, which leads to large deviation of recommendation results and low recall. Therefore, a personalized recommendation method of multimedia network teaching resources based on classification algorithm is proposed. Adaptive sliding window mutual information method is used to process historical and incremental data of teaching resources and extract the characteristics of multimedia network teaching resources. Two-step clustering algorithm combined with business identifier code criteria are used to complete resource classification. Based on structured query language (SQL) multimedia network teaching resources data recommendation database, document query and processing, combined with SQL structure, a personalized recommendation of multimedia network teaching resources is completed. The experimental results show that the recall of the classification results is higher than 99.58 %, and the highest F1 value is 97.28 %. The precision of personalized recommendation of multimedia network teaching resources is always higher than 90 %, and the recall rate is also higher.
Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day.This large stream of data is called big data, and the amount is increasing each day.This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models.Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results.Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling.The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard.The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset.There were four combinations of model and sampling technique.The F-measure for the AlexNet model was 0.807 while that for the Den-seNet model was 0.808.Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852.Combination 3 was the AlexNet model and reservoir sampling.It had an average F-measure of 0.8545.Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017.Finally, combination 4 was the DenseNet model and reservoir sampling.It had an average F-measure of 0.8111.Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.
Realization of the iSWAP-like gate among the superconducting qutrits

High-dimensional quantum systems, such as qutrits (quantum three-level systems), have multiple accessible energy levels beyond the two-level qubits. Therefore, qutrits can offer a larger state space to improve the efficiency of quantum computation. Here, we demonstrate a high-fidelity iSWAP-like gate operation on a frequency-tunable superconducting qutrits system. The superconducting quantum system consists of two qutrits that are coupled via a resonator with fixed qutrit-resonator coupling strengths. Through designing the frequency pulse profile and optimizing the parameter values, the gate error can be suppressed below 1.5 × 10 −3 . To bear out the feasibility of the proposal, we have conducted our study with experimentally accessible parameters. As the resonator can mediate the interaction between the irrelevant qutrits, the presented approach can also be used to couple multiple qutrits together, providing a good platform for quantum information processing.
The Application of Big Data Technology in the Construction of Financial Shared Service Center of Agricultural Enterprise Group

The prerequisite for the reasonable construction and successful implementation of the financial sharing center of the agricultural enterprise group is that the agricultural enterprise group must fully understand the financial sharing center, understand the steps and key points of its construction, and comprehensively design and re-engineer the business process. This paper combines the big data technology to construct the financial sharing center system of agricultural enterprise groups, combines the machine learning algorithm to improve the big data processing algorithm, and builds the intelligent financial sharing platform with the support of the improved algorithm, and builds the overall system architecture and operation process. After constructing the system, the performance verification and practical verification of this system are carried out through experimental research. From the research results, it can be seen that the system constructed in this paper has good data processing effects and data sharing effects, and has good security, which meets the financial sharing needs of agricultural enterprise groups.
Augmented security for healthcare data using obfuscation and elliptic curve cryptography algorithm in health cloud environment

Summary In the past few years, the growing demands of cloud computing became high in the healthcare sector. The electronic health record (EHR) has turned out to be the chosen system to uphold patient's healthcare data. Due to the advancement of cloud computing, the users in the cloud are empowered to access their data with elasticity and offering huge storage capability at minimum costs. This motivates EHR maintainers to transfer their personal storage to the remote cloud servers as it significantly reduces the burden of storage and computation. Nevertheless, securing healthcare data file poses a gigantic challenge as data outsourced in the cloud are too sensitive. Several security features such as data confidentiality, data privacy, access control, data integrity, and data security necessities to be satisfied. This article proposed a secure sharing of healthcare data file by incorporating obfuscation technique and elliptic curve cryptography (ECC) algorithm to enforce better data confidentiality and integrity. Data obfuscation is a beneficial technique in preserving the data confidentiality on the cloud by minimizing the risk of data theft and data leakage. Moreover, ECC methodology aims to provide a high level of security, low communication and computation cost with smaller key size. Experimental analysis reveals better performance than other existing schemes in terms of computation time, communication time, key generation time, throughput rate, turnaround time, and uploading speed. Besides, it exposes that the proposed framework is highly secure and efficient in the field of healthcare system.
Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading.Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline.Abstractive summarization task is framed as seq2seq modeling.Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper.The novelty is to parallelize the sequence computation training by incorporating feed-forward, the self-normalized neural network in the Extractive phase using Intra Cosine Attention Similarity (Ext-ICAS) with sentence dependency position.Also, it does not require any normalization technique explicitly.Our proposed abstractive Bidirectional Long Short Term Memory (Bi-LSTM) encoder sequence model performs better than the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder with minimum training loss and with fast convergence.The proposed model was evaluated on the Cable News Network (CNN)/Daily Mail dataset and an average rouge score of 0.435 was achieved also computational training in the extractive phase was reduced by 59% with an average number of similarity computations.
The Development of the Postcard Automatic Generating/Printing System (KadaVenir) that Records Travel Memories

Tourist information is classified into “Pre-information” that motivates tourism and promotes efficiency of behaviors, “Spot-information” that is obtained in tourist spots during tourism, “Post-information” that summarizes tourist’s behavior after tourism. Postcards are purchased as memories of a tourist’s journey, that is “Post-information.” Besides, tourists send postcards with a message to friends. This means that postcards are used not only as “Post-information” but also as “Pre-information” for others. We developed the postcard automatic generating/printing system(KadaVenir) that records travel memories. When a tourist taken photograph that same location and composition as a postcard, KadaVenir generates the postcard from the photograph taken by tourist and prints it on a printer. This paper describes outline of KadaVenir.
Scheduling of Cloud Computing Tasks via Intelligent Optimization Methods

Distributed green cloud datacenters (DGCDs) are increasingly deployed around the world. DGCDs integrate many renewable sources to provide clean power and decrease their operating cost. They are spread over multiple locations, where renewable energy availability, bandwidth prices and grid electricity costs have high geographical diversity. This paper focuses on delay-bounded applications in DGCDs and performs cost and energy-effective scheduling of multiple heterogeneous applications subject to delay-bound constraints. The minimization problem of operational cost of DGCDs is formulated and successfully solved by using Firefly, bat, and simulated annealing-bat algorithms. Data-driven experiments are conducted to assess and compare their effectiveness to solve it. The Firefly algorithm is shown to well outperform its peers.
Universal Networking Language Approach to Machine Translation Between Bulgarian and Slovak Language

The paper deals with the formal interpretation of grammar features of both Bulgarian and Slovak languages in order to be used for machine translation. It employs Universal Networking Language (UNL) as a common formal framework to encode the specific grammar features for both languages by employing the semantic networks. The implementation presents a developed lexical database in UNL framework by using a specific combination of grammar knowledge (mostly inflectional morphological rules) as well as semantic knowledge (lexical relations and definitions) in order to maintain the translation equivalency in processing both languages for machine translation purposes.
Circle Detection by an Improved Edge Drawing Algorithm

Circle detection is a fundamental problem in pattern recognition and computer vision. We propose a novel edge drawing method to improve the efficiency and accuracy of circle detection by improving the quality of the contours. Based on the characteristics of the circle, we refine the search direction and reduce the scope of the search, thus improving the accuracy and efficiency of the circular contour search. After obtaining the contours, we first perform linear fitting to obtain preliminary arcs, then group the arcs, obtain and optimize the candidate circle parameters using the Theil-Sen estimator and linear error refinement, and verify the candidate circle parameters. Experimentally, our algorithm has proven to be more accurate and efficient than other circle detection algorithms.
Consumer-Centric E-Commerce Systems

This chapter focuses on meeting the challenges such as security, reliability, and performance of e-commerce and user interface design advancement using new business strategies and state-of-the-art technical solutions. Security for user data and payment data are addressed with secured data communication and block chain technology, respectively. The shortcomings of popular e-commerce platforms are discussed by taking Amazon as an example. Online scenario now and in the future is analyzed with an envisioned case study. Bright opportunities for transformation in business models, aggregator, user interface, and vendor/customer recommendations are investigated utilizing deep learning technologies. The solutions provided and strategies explained in this chapter will help e-commerce at least for the next 10 years to have flawless and swift progress.
Mining high utility itemsets with time‐aware scheduling using Apache Spark

Summary Since the last decade, Market Basket Analysis has been propelled by augmentation of revenue information. Termed as high utility itemset mining (HUIM), this task considers the factors of purchase quantity and unit profit of the items in the transaction database. Although several sequential algorithms to mine HUIs exist, their performance degrades as the database becomes voluminous. Distributed computing solutions such as Apache Hadoop and Apache Spark have proven effective in alleviating this bottleneck. In this regard, the current study develops a parallel workflow to adapt a single‐phase tree‐based algorithm called the single phase utility computation (SPUC) algorithm on a Spark cluster. Based on the time taken to mine individual conditional pattern bases in SPUC, an assignment strategy that partitions the search space across the cluster is proposed in parallel SPUC (PSPUC) algorithm. Experimental evaluation conducted using real and synthetic datasets demonstrate that PSPUC outperforms PHUI‐Growth algorithm. Apart from this, PSPUC in conjunction with the time‐aware assignment strategy converges mining faster than a random assignment of items. A linear speedup of PSPUC is also demonstrated.
A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

The use of Amazon Web Services is growing rapidly as more users are adopting the technology.It has various functionalities that can be used by large corporates and individuals as well.Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions.In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data.The data is managed to the EC2 by using elastic load balancing.The collected data is subjected to preprocessing approaches to clean the data, and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments.High accuracy of 94.17% is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.