Reviewerly/exHarmony huggingface dataset:
Embed More Ignore Less (EMIL): Exploiting Enriched Representations for Arabic NLP

Our research focuses on the potential improvements of exploiting language specific characteristics in the form of embeddings by neural networks. More specifically, we investigate the capability of neural techniques and embeddings to represent language specific characteristics in two sequence labeling tasks: named entity recognition (NER) and part of speech (POS) tagging. In both tasks, our preprocessing is designed to use enriched Arabic representation by adding diacritics to undiacritized text. In POS tagging, we test the ability of a neural model to capture syntactic characteristics encoded within these diacritics by incorporating an embedding layer for diacritics alongside embedding layers for words and characters. In NER, our architecture incorporates diacritic and POS embeddings alongside word and character embeddings. Our experiments are conducted on 7 datasets (4 NER and 3 POS). We show that embedding the information that is encoded in automatically acquired Arabic diacritics improves the performance across all datasets on both tasks. Embedding the information in automatically assigned POS tags further improves performance on the NER task.
A novel camera calibration method based on multiple calibration planes

In the large-scale vision measurement of the industrial field, it needs to not only build the high quality image acquisition system, but also calibrate the camera precisely. This paper proposes a camera calibration method based on 3D target, which consists of multiple planes with large viewing angle and range. Based on Zhang's calibration method, multiple intermediate coordinate systems are used to unify the vision sensor to the preset global coordinate system. The singular value decomposition method is adopted to solve the rotation matrix, when the external parameters are fused. Numerical experiments show that this method has high precision and stability.
International standard HL7 FHIR as a working framework for development of common healthcare information space of the Republic of Belarus

The positions of theRepublicofBelarusin the field of ICT, the levels of medical data store and collection, HL7 FHIR standard are considered. International standard HL7 FHIR for the exchange of medical data, designed to improve the interaction of independent medical information systems and reduce the cost of developing interfaces. FHIR introduces the term Resource as the main unit of meaningful information for transferring and storing data, describes a set of information resources and a protocol for interacting with the resource store through a REST service or messaging. A fixed basic set of information resources is designed to cover 80 % of information scenarios. This standard is used in the national system of electronic prescriptions in theRepublicofBelarus.
Application of the Ordinary Kriging to the Laundry Business Survey Result Data: Number of Items in a Day in Banda Aceh City

Laundry business is one of the fast-growing Micro, Small, and Medium Enterprises (MSMEs or UMKM in Bahasa) in Banda Aceh City, Indonesia. This business has been established in almost all the villages in the city. This study aims to map the laundry business in the city of Banda Aceh based on the number of items entered in a day. Laundry location data collected from the results based on a preliminary survey in Banda Aceh from 2019 to 2020. The amount of 225 data was analyzed descriptively and interpolated precisely using the ordinary kriging method. The results showed that the locations of the laundry business spread along the main city road. Almost all-subdistrict in Banda Aceh has a large number of laundry orders. Based on ordinary kriging interpolation using an exponential semivariogram, the Syiah Kuala Sub-district in which many students inhabit obtained the largest number of the laundry order in a day. Additionally, the Kuta Alam Sub-district, the Kuta Raja Sub-district, and the Meuraxa Sub-district also reach a large number of around 61 kg in a day.
Using Hessians as a Regularization Technique

In this paper we present a novel, yet simple, method to regularize the optimization of neural networks using second order derivatives. In the proposed method, we calculate the Hessians of the last n layers of a neural network, then re-initialize the top k percent using the absolute value. This method has shown an increase in our efficiency to reach a better loss function minimum. The results show that this method offers a significant improvement over the baseline and helps the optimizer converge faster.
[Paper] Noise Bias Compensation for Color Images after Tone Mapping

An image after tone mapping (TM) has noise bias, i.e., noise values with a non-zero mean, because of the non-linearity of the TM function. Therefore, noise reduction filters based on the zero-mean assumption do not work well for such images. To overcome this limitation, noise bias compensation (NBC) divides pixels into subsets depending on their values and adaptively adjusts them using a Bayesian approach. However, previous studies on NBC target only gray-scale images and assume that the noise mean before TM is zero. This paper proposes a method for NBC that targets color images processed by TM with a non-zero noise mean. The proposed method adaptively calculates the compensation values based on prior knowledge that represents noise corresponding to each pixel value of RGB channels with a Bayesian approach. Experimental results show this Bayesian approach successfully reduces noise even for color images containing noise with a non-zero mean.
Integration Trend of Digitalization of Teachers' Labor in the Practice of Russian Universities

The article examines the features of the organization of the educational process in the context of digitalization, analyzes the integration tendency of teachers' work in the practice of Russian universities, provides a comparative analysis of the practice of the Open University. This paper analyzes the educational experience that has developed in the process of emergency delivery of educational content to students during a pandemic. The models of mixed, purely distance and duplicate education are analyzed. The article substantiates the idea that there is a need for the formation of criteria for the quality of education online, and, therefore, the criteria for the quality of the organization of the educational process, including the work of teachers, are changing. This paper outlines some steps that are being taken by Russian universities in the field of researching trends related to improving the digital environment as a whole, regardless of the pandemic and other factors.
A Path Tracking Control Algorithm for Smart Wheelchairs

Since the 1980s several works were published proposing solutions for users with severe mobility impairments that prevent them from operating motorized wheelchairs through mechanical joysticks. Such solutions commonly focus on assistive interfaces and wheelchair automation. The ultimate objective is to offer a comfortable and safe conduction no matter the user's mobility impairments. This paper proposes a path tracking control algorithm for smart wheelchairs that minimizes or even eliminates the negative effects of caster wheels' misalignments when the wheelchair changes its trajectory. These effects reflect on path deviations from the planned trajectory that demand a series of user interventions for path correction. Such interventions are inhibited when assistive interfaces are employed due to the low rate of commands these interfaces are able to produce. The proposed algorithm can be employed on wheelchairs with conventional designs and requires no installation of expensive sensors and actuators. As a result, issues related to cost, certification, and insurance are minimally impacted by the proposed path tracking solution.
Morality-based Analysis of Viewer’s Comments on Internet TV News Program

With the rise of Internet TV and other new media, people are now viewing the news through a variety of conduits. In addition, the influence of news media on people is changing. Viewers can post comments in Internet TV, and these comments has the viewers’ opinions of the news contents. Therefore, analysis of viewers comments is important in revealing the effect of the news. In addition, these comments are posted based on the morality of the viewers, and point of view of morality is considered important in the analysis of viewer comments in news. Therefore, this study purpose is to clarify the opinion on Internet TV news programs from a moral-based analysis viewers’ comments. This study analyzed the trend of viewer comments on ABEMA news programs using comment length and the application of two methods. First, the morality of viewer comments was analyzed by calculating the moral/immoral expression rate for each program using the moral foundation dictionary. Second, the distributed expressions of viewer comments (calculated by Doc2Vec) were clustered by k-means++, and program trends were analyzed using the cluster characteristics. The results indicated that there was no difference in comment length between the two program types. Comments on soft-news programs had a high moral/immoral expression rate for politics or current events. In contrast, comments of hard-news programs did not show a characteristic trend. A viewer can easily participate in the discussion, because the soft-news program deals with the same news for a long time as the news content is limited compared to the non-discussion program.
Hybrid Multi-Robot System for Drilling and Blasting Automation

Multi-robot systems possess the potential of becoming the next generation of robots in the mining industry due to their robustness and scalability. However, they present challenges for the system to efficiently allocate tasks to each robot and allow them to navigate toward their targets safely. This paper introduces a hybrid approach method for a multi-robot system, alongside with a case study in drilling and blasting automation. A Centralized Control Unit delegates tasks and information among the robots in the system, each equipped with a decentralized motion planner that supports cooperative inter- robot collision avoidance. The proposed system inherits the advantage of a centralized multi-robot system in providing a time-wise optimal solution; while also possessing the computational benefit and scalability of a decentralized system. Simulations were conducted to validate the proposed method and discuss insights into the efficacy and performance of the proposed method.
Jousting with Verse: The Poetics of Friendship in Duecento<i>Comuni</i>

Despite the divisive factionalism that riled Italian comuni in the late Duecento, the language of friendship dominated the vernacular poetry circulating in late medieval urban centers, particularly in tenzoni. In these disputatious exchange poems, vernacular authors frequently referred to one another as “amico,” no matter what relationship they may have had with their interlocutors. The usage of the language of “amicizia” in these exchanges defies conventional praises of friendship, which would conceive of friendship as an unambiguous moral good. Instead, the ambivalent language of friendship in tenzoni reflects both the anxiety of duplicitous speech that plagued urban life and a hope for the salvation of the comune from partisan conflict. These tenzoni collectively propose a model of friendly antagonism, which, in its engagement even across ideological lines, strives to convert dispute from violent partisanship into a competitive joust in the game of honor.
A computer system for the leukocytes classification in medical images

Abstract The paper is about the development of a computer system for the white blood cells images classification. Solving the leukocytes classification task makes it possible to diagnose not only the blood diseases, but also a wide range of other diseases, as well as to evaluate the overall state of a person’s health. Modern leukocytes classification methods have a fairly large number of shortcomings, which make a problem of finding the most effective method as a tool to solve this task. In our computer system we use the method based on using a trained convolutional neural network as a binary classifier for the leukocytes classification. This study shows the advantages of using this architecture and a deep learning technology to solve the objects classification task in medical digital images. The developed system allows one, in most cases, correctly and with a high speed to determine whether the white blood cell belongs to one of the two classes, which indicates to the possibility of using this system as an auxiliary tool for the blood hematological analysis.
Choosing a Web Service for Creating a Digital Educational Event

The analysis of features and usability of online services of four platforms Simpoll, Survio, Mentometer and Google Forms that can be taken into account by teachers when choosing them for creating an educational resource. The most significant differences between these services and the factors that contribute to the rapid content filling of online surveys for any educational activities, taking into account their features, are identified. The main features of each of the presented services are described. The paper presents the results of designing a digital event using an open survey form on the example of an ecological and mathematical interactive rapid test for students of all forms and types of education. The survey on the quality of the event showed a high 85% level of satisfaction and interest of respondents in this testing format.
Dimensions of Transparency in NLP Applications.

Broader transparency in descriptions of and communication regarding AI systems is widely considered desirable. This is particularly the case in discussions of fairness and accountability in systems exposed to the general public. However, previous work has suggested that a trade-off exists between greater system transparency and user confusion, where `too much information' clouds a reader's understanding of what a system description means. Unfortunately, transparency is a nebulous concept, difficult to both define and quantify. In this work we address these two issues by proposing a framework for quantifying transparency in system descriptions and apply it to analyze the trade-off between transparency and end-user confusion using NLP conference abstracts.
MATLAB Simulation of Path Planning and Obstacle Avoidance Problem in Mobile Robot using SA, PSO and FA

In Robotics, Path Planning and Obstacle Avoidance (PPOA) have turned out to be a significant research domain. PPOA entails planning a smooth obstacle-free path from start to finish in minimal time and cost. As the complexity of the map or problem increases, many approaches become unreliable and inefficient. In this paper, a comparison of Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) are made on MATLAB 2018a for PPOA problem in robotics based on three different environments. The objective of the paper is to practically demonstrate a comparison of the algorithms mentioned above on three maps with varying levels of complexity to obtain a collision-free optimal path through simulation on MATLAB 2018a. Simulation results depict that one algorithm is not suitable for all types of environments.
Probabilistic solar irradiance forecasting using numerical weather prediction ensembles over Australia

Uncertainty information accompanying solar irradiance forecasts is increasingly being regarded important to the solar energy industry. We use solar irradiance forecasts from three leading operational numerical weather prediction models (i.e., IFS, GFS and ACCESS) to quantify such information for 12 locations across Australia. Probabilistic forecasts are formed through two ensemble approaches: one is a multiple-model approach which employs deterministic forecasts from the three models and the other is to use the 51 ensemble forecasts from the IFS ensemble model. It was found that the multiple-model approach exhibits an excellent performance, as evaluated by both deterministic and probabilistic metrics, despite it has fewer ensemble members.
A Data-Driven Weighted LP Method for Formant Estimation

In this paper we propose an algorithm for formant estimation using a data-driven approach. The vocal tract is typically modeled as an all-pole filter, whose spectral peaks are taken as the formant estimates. Estimating the filter parameters by minimizing the 12-norm of the residual signal leads to pitchlocking tendency. This is avoided if the 11-norm criterion is used, but the solution is computationally burdensome. Minimizing the weighted 12-norm provides a good compromise between the pitchlocking tendency and computation, but requires the knowledge of the Glottal Closure Instants (GCIs). In our method knowledge of the GCI is not required; instead, we adjust the weighting sequence parameters in a data-driven manner. Our results on both synthetic as well as natural voiced-speech show that our method is superior to LPC; when compared with SWLP, our method gives estimates with lower variance.
Analysis of embedded medical system and nursing care of pediatric severe infection

Health is one of the global challenges for humanity. In the past ten years, health care has attracted considerable attention. The primary goal is to establish and monitor the pediatric medical staff who has not been admitted, the pediatric monitoring system, and reliable to carry out their normal daily life activities. Recently, the pediatric monitoring system is one of the significant advances of the improved technology. Currently, a modern way has been required. The critical role that experts traditionally play in health care. Need to visit the pediatric ward for infectious diseases and advice necessary. There are two fundamental problems associated with this approach. Second, the child is still admitted to the hospital with bedside biomedical equipment for some time, and first, medical staff must be present at all times on the pediatric website. To solve these two problems, the pediatric department provides knowledge and information on preventing infections and diseases. Second, there is a need for a reliable, easily accessible Pediatric Surveillance System (PMS). To improve the above situation, it can use technology in smarter ways. In recent years, along with the Raspberry Pi, medical sensors have played a significant role. The wearable sensor monitors the contact between the human body and his or her physiological parameters. Today, buy various sensors in the market, such as Electrocardiography (ECG) sensors, temperature sensors, pulse monitors, etc. The cost of the sensor varies in flexibility and accuracy, depending on its size.
Chinese Chemical Named Entity Recognition Based on Morpheme

Named Entity Recognition is one of the most important tasks of natural language processing. The Chinese chemical term has complex structure and flexible combination, which makes recognition more difficult. This paper presents a M-CRF method to improve the effect of NER (Named Entity Recognition) for complex (length>=10) chemical terms by jointly fitting morpheme feature and N-gram feature. Starting with the composition of chemical morphemes, we construct a chemistry morpheme classification. Then the M-CRF is modeled to fitting morpheme category features and n-gram features on the chemistry patent dataset. By evaluating the experimental results of different models, it is found that morpheme category features has greatly improved the effect of excessively long and complex terms in chemical NER, and M-CRF model outperforms and achieves state-of-the-art performance (F-score 92.52%). However, a few terms such as single-char and double-char terms has poor effect because they can be used alone or as a component, which is one of the important improvement directions in future.
Information Theoretic Weighted Fuzzy Clustering Ensemble

number of data objects.Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets.The experimental results demonstrate ef ciency and suitability of the proposed method.
Contextual assistant framework for the Sinhala language

Continuous customer relationship plays an important role in the success of any business milieus in today's world. Nonetheless, it can be harder to achieve consistent engagement with the customers round the clock and therefore many businesses have paved their focus in using a variety of solutions in overcoming this scenario. Contextual assistants that can have both linear and non-linear conversations with humans implicitly plays a prominent role in such situations. In contrast to resource-rich languages, creating a contextual assistant for resource-poor languages like Sinhala has been difficult mainly due to the unavailability of a rich digital footprint and the complexity of the language. Hence, this research was conducted to propose and implement a novel and common architecture of a contextual assistant framework for the Sinhala language. Here we have used a deep learning Intent Mapping (IM) model to map the consumer response to a predefined “Intent” and a Feature Extraction Mechanism (FEM) to extract related information from the input text. A set of data types for this framework were defined and FEM was trained to identify them efficiently. The IM model gave an accuracy output of 89.67 percent. The results depicted that the implemented system performs with higher accuracy in linear conversations.
La investigación educativa como umbrales de sentidos por-venir

In this dossier, we propose to install the question about criticism, about how to remain critical in times of digitization of culture that seems to drag with it precisely any perspective of knowing that is not willing to think beyond the drifts of applicability. In this sense, educational research opens as a journey, as a search for readings, techniques, developments and forms of conceptualization that bring us closer to understanding what schooling is being today. The group of articles that make up this special dossier on educational research opens up new meanings, as thresholds that project us to take the floor in public affairs and delves into the threads, the power relations and the crystallizations of such a social practice. Central as is the one that allows those who come to the world to be part of it.
Performance Tuning of Queries in Distributed System using Secure Materialized View Approach

Data warehouse is technique to collect and process data from heterogenous, distributed repository in distinct manner so that knowledge can extract from it's this information are stored in autonomous system there must be some defined way to access it. To access this information we must consider the time and space complexity as data warehouse include historical data that means data from decades so there must be some efficient way to access it. To enhance query performance different approaches are there like hybrid integration system is one of the alternative where most frequent used data are stored in intermediate table i.e. the space from where distributed system can access data commonly, query optimized techniques, implementation of appropriate data type, minimal time query complexity which indirectly effect to accelerate performance of query. We are focused on materialized view that is the provision where you can store the most recent data which extracted for commonly used query in avg transaction into a specialized space or we say MV table so that later on when query fire it will map with table query and resultant data will be extracted. Ultimately we are trying to reduce access time of query. As rather than going to base table every time it will search in MV table and return to user it reduce the access time of complex operation like merge, join and aggregation.