Reviewerly/exHarmony huggingface dataset:
Event Causality Extraction Based On Fusion Attention

Relationship extraction is an important task in the field of information extraction in natural language processing. It refers to judging the logical relationship between events, including co-referential, subordinate, temporal and causal relationships. Causality can be divided into explicit causality and implicit causality. At present, it is difficult to extract the implicit causality between events in natural language processing models. In addition, it is often difficult to extract the long-distance causality in causality extraction. To solve these problems, event causality extraction based on fusion attention (ECEFA) is proposed to transform event causality extraction into causal argument extraction, taking into account sentence level context information and document level context information. ECEFA uses Albert to generate word embedding, which can alleviate the common problem of polysemy in Chinese and improve the semantic understanding ability of the model. Dependency syntax is introduced to solve the problem of long-distance dependence in causal argument extraction and avoid the omission of implicit causal events. The experimental results on the CEC2.0 Chinese corpus show that the proposed model improves 2.54%, 2.83% and 2.69% in precision, recall and F1-score, respectively, compared to the best baseline model CSNN. On the dam operation log corpus, the improvement over the best baseline JMCEE was 3.25%, 3.12% and 3.18% in precision, recall and F1-score respectively.
Java applications for English vocabulary learning

We present in this paper a suite of four software applications that will help a user to learn the basic vocabulary (about 6000 words) of the English language. The software program asks the user to choose a word from a list of other words. The originality of these applications consists mainly in the modality in which the choices for a tested word are selected. The software applications are graphical applications written in Java programming language. Applications can be modified so that the user can learn other foreign languages. They can also be integrated on e-learning platforms. In the context of the COVID pandemic, online learning has grown.
Data Mining Pengelompokan Akta Nikah Berdasarkan Usia Nikah Atau Domisili Menggunakan Metode Clustering:

One way to find out the marriage certificate data is to group the prospective bride and groom data that has been recorded at the Langkat Ministry of Religion. The analysis was carried out using the Clustering method using the K-Means method which was translated into a software. This software is used for grouping data. The output is to find out the grouping of marriage certificate data with the closest relationship between the age group of the groom, occupation and address. The results showed that the age of the groom-to-be between 19-28 years old had a civil servant job at the address/district of Kutambaru.&#x0D; Keywords: age, occupation, address/location and clustering
Healthcare Chatbot using Artificial Intelligence

Abstract: Healthcare as one of the vital spheres of human life is badly falling behind technological progress. While e-commerce, hospitality, and the food industry are advanced and augmented with various technological solutions, healthcare still sticks to the traditional way of doing things. Why can you order a pizza in ten seconds but must wait for an appointment with a doctor for days and weeks? How can you make sure that urgent medical help is indeed urgent? Why finding a credible answer to an important question about your health is so annoyingly difficult? I am also deeply concerned about the lack of attention that the healthcare industry receives. And the first and easiest solution I can offer to change it is to implement chatbot technology in healthcare. Healthcare chatbots will never substitute doctors. But they provide plenty of opportunities to facilitate their job or to improve their performance. The medical chat-bots functioning depends on Natural Language Processing which helps users to submit their problems about their health. The user can ask any personal query related to health care through the chatbot without being out physically available to the hospital. By using Google API for voice-text and text-voice conversion query is sent to the chatbot and gets a related answer and displays the answer on the android app. The system’s major concern behind developing this web-based platform is analyzing customers’ sentiments.
Location Based Local Workers Finding Application for Android

Abstract: This project aims to select unorganized workers like electricians, mechanic, plumbers, painters, carpenters, etc near us through the android app. It is difficult for customers to find any service anytime anywhere in an emergency so we have developed an android app that helps customers to find the nearest plumbing, electrical, painting, etc jobs easily without any problem through this android app we can provide all services anytime anywhere. It also saves time by finding employees immediately and reducing unemployment.
Reversible data hiding with dual pixel-value-ordering and minimum prediction error expansion

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual PVO (dPVO) for Prediction Error Expansion (PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward . In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1 × 3. In the backward phase, minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains the lowest predicted pixels and the maximum set contains the largest predicted pixels of each image block. Proposed dPVO with PEE is then applied to both sets, so that the pixel values of minimum set are increased and that of the maximum set are decreased by a unit value. Thereby, the pixels predicted in the forward embedding can partially be restored to their original values resulting in both a better embedded image quality and a higher embedding rate. Experimental results have recorded a promising rate-distortion performance of our scheme with a significant improvement of embedded image quality at higher embedding rates compared to the popular and state-of-the-art PVO-based RDH schemes.
Real-Time Mexican Sign Language Interpretation Using CNN and HMM

Mexican Sign Language (MSL) is the primary form of communication for the deaf community in Mexico. MSL has a different grammatical structure than Spanish; furthermore, facial expression plays a determining role in complementing context-based meaning. This turns it difficult for a hearing person without prior knowledge of the language to understand what is to be transmitted, representing an important communication barrier for deaf people. In order to face this, we present the first architecture to consider facial features as indicators of grammatical tense to develop a real-time interpreter from MSL to written Spanish. Our model uses the open source MediaPipe library to extract marks from the face, body position and hands. Three 2D convolutional neural networks are used to encode individually and extract patterns, the networks converge to a multilayer perceptron for classification. Finally, a Hidden Markov Model is used to morphosyntactically predict the most probable sequence of words based on a preloaded knowledge base. From the experiments were carried out, a precision of 94.9% was obtained with σ=0.07 for the recognition of 75 isolated words and 94.1% with σ=0.09 for the interpretation of 20 sentences in MSL in a medical context. Being an approach based on camera inputs and observing that even with a few samples an adequate generalization can be achieved, it would be feasible to scale our architecture to other sign languages and offer possibilities of efficient communication to millions of people with hearing disability.
Implementasi Metode Apriori Pada Sistem Persediaan Bahan Kimia Di Laboraturium Forensik Medan

Data mining is a term used to describe the discovery of knowledge in databases. Data mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from large databases. With data mining, a gem in the form of knowledge will be obtained in a large number of data sets. The Apriori (RF) method is a method that can improve accuracy results, because generating child nodes for each node is done randomly. This method is used to build a decision tree consisting of root nodes, internal nodes, and leaf nodes by taking attributes and data randomly according to the applicable provisions. The root node is the node located at the top, or commonly referred to as the root of the decision tree. The solution for determining chemical stock inventories at the Medan Branch Forensic Laboratory, by applying the Apriori method to determine the correlation coefficient level of frequently used products so that more frequently needed products can be provided to avoid chemical vacancies at the Medan Branch Forensic Laboratory
Kombinasi Algoritma Cipher Block Chaining dan Triangle Chain Cipher dalam Penyandian File Text

Currently the use of information technology as a medium of information exchange is growing very widely. Various types of information such as text, images, sound and video can be converted into digital media that allows it to be reproduced or transmitted through various media. One of them is the internet, through the internet we can easily exchange information with other people in various places. However, there are times when information/messages are confidential, where no other party may know the contents of this information other than the sender and recipient. So we need a security against the message. Cryptography is a technique and art to encode messages or information in a medium, such as text, images, audio or video which aims to avoid suspicion from unauthorized people. For that we need a software that can encode confidential information in a text file. However, in hiding messages in text files using cryptography alone is not necessarily safe, so a useful algorithm is needed to improve security, therefore Cipher Block Chaining and Triangle Chain Cipher algorithms are needed. The research objective is to apply the Cipher Block Chaining and Triangle Chain Cipher algorithms for text file encoding applications. This is necessary because it often happens that the text message sent is a secret message that should not be known to just anyone. Block Chaining Ciphers and Triangle Chain Ciphers are useful algorithms to provide an advantage in terms of increased security in text files.
ANALYSIS OF THE PROBLEM OF FORMATION OF INCLUSIVE COMPETENCES IN FUTURE TEACHERS OF PRE-SCHOOL EDUCATION INSTITUTIONS

The article is devoted to the topical problem of the peculiarities of the development of inclusive competencies in future educators to work with children with special educational needs in the context of inclusive preschool education. In particular, it emphasizes the importance of professional training of educators in working with children with special educational needs; the relationship between the success of the implementation of inclusive education and the quality of training of future teachers to work in inclusive groups; the analysis of the literature sources and normativelegal documents concerning the posed problem is carried out; the important role of educators in the team of psychological and pedagogical support of a child with special educational needs is determined. The essence of training of higher education student is described, specifically: familiarization with psychophysical features of children with special educational needs and taking them into account in the process of education; understanding the impact of psychophysical disorders and clinical diagnosis on behavior, communication; awareness of special methods, techniques, available technical and didactic teaching aids; proficiency at the peculiarities of evaluation criteria; expertise in the content and methods of implementing an individual approach to children with special educational needs; ability to implement corrective goals in the educational process; competence in working with members of the multidisciplinary team of the institution, as well as with families of children with both typical development and special educational needs. The main types of professional training of future teachers of preschool education in the conditions of inclusion are considered; the importance of psychological training in applicants for higher education Specialty 012 Preschool education to work with children with special educational needs is emphasized; the structure of readiness for work in the conditions of inclusive education is indicated and analyzed, and the stages of development of inclusive competence in future specialists are determined; the requirements for the development of such professional competencies in the educator are considered. According to the outcomes of the work, the results are conducted, conclusions are drawn, perspective directions of research are determined.
A Neural System for Acute Disease Detection from Facial Images

The COVID-19 pandemic significantly changed the way people work and interact. The periods of lockdown prevented in many cases physical interaction and affected all aspects of human life, including healthcare. Non-acute treatment and preventive health appointments were often postponed, which has negative effects on the health of the general population. On the other hand, the use of various online videoconferencing services has grown rapidly. They deal with large amounts of video data, often capturing the faces of individuals. This is an opportunity for decision support systems that can determine the health conditions of individuals on the basis of facial images. In this work, we design and evaluate an artificial neural network-based system for acute disease detection from facial images.
Parallel computing learning in electronic engineering based in the treatment of images

This work presents the main activities and results of a parallel programming learning process for Graphics Processing Units (GPU) language CUDA based on algorithms for processing and generating 2D and 3D images. The proposed learning activities focus on the key points of parallel programming, such as the optimal use of the different types of memory. The learning process has been proposed as a set of master classes on image theory and parallel programming, along with practical CUDA programming sessions for 2D and 3D image generation and processing. Results show the student satisfaction for the proposed learning process and similar marks than before its application.
Innovation, Research and Development in Engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, Research and Development (I+R+D) of the education in engineering, mainly Electric and Computer Engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.
Design of a space based telescope with variable beam direrction for a quantum communication link

The optical system of a satellite based telescope was designed. The resulting module has a variable output parallel beam angle and a near diffraction limited performance in the range of motion.
A systematic review on the use of AR/VR techniques in Remote Laboratories

This paper presents a systematic review on AR/VR techniques integrated into remote laboratories in the last 5 years. Of the publications found, 5 were about cases of AR/VR remote labs and 6 were literature reviews that picture AR/VR remote labs as trends for the future of education. AR/VR remote labs provide learning opportunities and their integration is well appreciated by students and teachers.
HOW TO INCREASE SOCIOLOGY STUDENTS' SATISFACTION WITH THE QUALITY OF MATHEMATICAL EDUCATION?

The article analyzes the increase in the level of sociology students' satisfaction with the quality of higher education with the help of modern approaches to teaching mathematical disciplines to sociologists.
Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic

The world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population’s mental health and health-related programs. It is complex for public organizations to measure the population’s mental health to incorporate its feedback into their decision-making process. A significant portion of the population has turned to social media to express the details of their daily life, making these public data a rich field for understanding emotional and mental well-being. To this end, by using open sentiment analysis tools, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies. Several modern language models were evaluated and compared using intrinsic and extrinsic tasks, that is, the sentiment analysis evaluation of public domain tweets related to the COVID-19 pandemic in Mexico. This study provides a fair evaluation of state-of-the-art language models, such as BERT and VADER, showcasing their metrics and comparing their performance against a real-world task. Results show the importance of selecting the correct language model for large projects such as this one, for there is a need to balance costs with the model’s performance.
Fall Detection System Based on Far Infrared Images

The world is in an aging process that will significantly lift the average age of its population over the course of the next decades. This phenomenon, more acute in the developed countries but present everywhere, will make the elderly care sector a very relevant one from all possible points of view, the economical one included. The elderly care sector has, so far, automated a low number of tasks. However, a higher number of them will need to be automated if universal elderly care at reasonable costs is desired. One of the tasks candidate to this automation will be elderly surveillance and, within that field, fall detection. Although developed over different technologies, all automatic detection systems can be associated to one of the following groups, wearables, ambient and vision based. Vision-based fall detection systems have experienced fast development over the last years, mainly propelled by the incorporation of artificial neural networks able to characterize human activities in general and falls in particular. All fall detections systems based on artificial vision developed so far extract static or kinematic descriptors from visual or near infrared images to assess fall probability. Within the framework of this PhD, for the very first time, a proof of concept of a fall detection system able to infer dynamic descriptors from far infrared (FIR) images is developed. This way, the problems associated to insufficient amount of human fall real world data, privacy protection and suboptimal system performances under poor illumination conditions, main identified deficiencies in today's systems, can be overcome.
Bayesian Optical Heterodyne Tomography

We introduce a complete workflow for Bayesian quantum state tomography of generic continuous-variable states. We also summarize experimental results applying this workflow to the tomographic reconstruction of thermal and coherent states of light.
Implementación estrategias didácticas apoyadas en el uso de las TIC para la modificación de los patrones de aprendizaje de los estudiantes de la asignatura fundamentos de mercadeo

The present classroom project aimed to determine didactic strategies for modifying the learning patterns of the students of the marketing fundamentals subject; proposing the application of project-based learning as a didactic and evaluative strategy; which is an innovative methodology that allows satisfying the needs of the different actors in the process; students, teaching-learning purposes, teachers and educational institutions. It is proposed to change the educational approach towards the teaching-learning process, based on the student and the way he learns, starting from the knowledge of his learning patterns and incorporating current elements such as information and communication technologies as elements fundamental for the achievement of the training objectives.
Pengembangan Media Visual Maket Materi Mengidentifikasi Lambang/Simbol untuk Siswa Kelas III SDN Mojoroto 4 Kota Kediri

This research was motivated by the result of observations at SDN Mojoroto 4 Kediri City in grade 3 student. It was found that students understanding of the material symbols/ symbols (traffic signs, scouts, and state symbols) was not maximized. This happens because teachers have not found learning media that can help students learning process in understanding the material symbols/ symbols (traffic signs, scouts, and state symbols). This study aims to determine the validity of the media, the teachers response, and the effectiveness of the maket visual media. in this study using the Borg and Gall development model. Furthermore, the techniques used in collecting data in the form of a questionnaire include a media construction validation questionnaire, a material validation questionnaire, a teacher response questionnaire to maket visual media, and a written test. The result of the research on the development of the maket visual media are as follows: (1) the maket visual media is declared valid and can be used without revision with a percentage of 95%, (2) material symbols/ symbols (traffic signs, scouts, and state symbols) declared valid and can be usedin the learning process with a percentage 92,5%, (3) the teacher response to the media is declared good and can be used in the learning process by the percentage is 100%, and (4) maket visual media is declared effective and can be used in the learning process with a classical completeness percentage of 75%. Based on the result of the research above, it can be concluded tha the maket visual media is valid and suitable for use in learning Indonesian language subjects with symbols/ symbols (traffic signs, scouts, and state symbols).