Reviewerly/exHarmony huggingface dataset:
Membangun Konsep Teknis Aplikasi dan Pemanfaatan Metode dalam Penulisan Tugas Akhir Fakultas Ilmu Komputer

Community service activities organized by LPPM Esa Unggul University are carried out using webinars. This activity is a means of sharing knowledge for students of the Faculty of Computer Science regarding final assignments. The Final Assignment is a routine activity at the end of the semester carried out by students and their supervisors at the beginning of the preparation stage. Support for the final project concept can provide opportunities for students with their supervisors to hold discussions to find solutions to the problems of the research topics to be taken. In this abdimas scientific forum webinar, it was held to build concepts and technical applications in writing the final project for students of the Faculty of Computer Science. The result of this community service is to provide an overview of computer science faculty students in building concepts both in terms of application, methodology and writing procedures that are useful for preparing the final project. The Webinar presented several techniques and practices in making applications including: Introduction to Mobile Application Concepts and Methods, Data Management with Machine Learning, Compilation of Engineering Requirements and Creating Websites using the Code Igniter (CI) Framework. From the webinar material, it can help students explore topics and final project research concepts that will be carried out by students during the preparation of the Final Project.
Computing Digital Signature by Transforming 2D Image to 3D: A Geometric Perspective

Recently 2D to 3D face reconstruction has attracted a lot of interest in the area of Computer vision. There has been a recent increase in research into various 3D reconstruction techniques using neural nets, with the majority of approaches producing high-quality results and efficiency. This paper presents an approach to convert 2D facial images to 3D and then use the 3D data and features to construct a unique digital signature. The proposed solution eliminates the need for several pictures and reduces the calculation load. The main objective of this research is to understand the progress that has already been made in this domain and we came up with an open question of whether the generation of a face mesh is possible using a Feature Vector. This would reduce the storage space required for the 3D Facial data. If Face mesh reconstruction is possible using the feature vector only, it would drastically reduce time and space for various 2D and 3D image analysis applications. The feature vector would later be compressed to create a unique digital signature. Accessing any information on the database with a key will be efficient and fast.
Character Modelling with Sketches and ODE-Based Shape Creation

Character models have enormous applications in industry.Efficient creation of detailed character models is an important topic.This paper proposes a new and easy-to-use technique to quickly create detailed character models from sketches.The proposed technique consists of two main components: primitive deformer and shape generators.With this technique, 2D silhouette contours of a character model are drawn or extracted from an image or sketch.Then, proper geometric primitives are selected and aligned with the corresponding 2D silhouette contours.After that, a primitive deformer is used to create a base mesh and three shape generators are used to add 3D details to the base mesh.The primitive deformer and three shape generators are developed from ODE-driven deformations.The primitive deformer deforms the aligned geometric primitives to exactly match the 2D silhouette contours in one view plane and obtains a base mesh of a character model consisting of deformed primitives.The shape generators are used to add 3D details to the base mesh by creating local 3D models.The experimental results demonstrate that the new technique can quickly create detailed 3D character models from sketches with few manual operations.The new technique is physics-based and easy to learn and use.
Simultaneous Path and Speed Planning of Driverless Racing Car

Abstract：The autonomous racing car(ARC) is supposed to plan path and speed simultaneously to meet the requirement of the fastest driving strategy.However, the spatiotemporal coupling relationship between path and speed makes the optimization problem difficult to formulate and solve.To deal with this problem, a simultaneous path and speed planning(SP 2 ) approach for autonomous racing car is proposed in this paper to achieve rapid cooperative trajectory planning.First, a trajectory planning problem considering track boundaries and vehicle dynamics constraints is formulated.Second, the steady action space and action-state space including spatiotemporal information of the ARC is built, and the available action-state transition relationship is determined via offline traversal.After that, we transform the time optimal trajectory optimization problem into an optimization problem with the objective of achieving the longest distance per time step and apply multi-step recursive optimization scheme to render a trajectory with the fastest lap time.Last but not least, simulations and experiments in a small scaled platform is conducted.Results show that the proposed SP 2 approach can efficiently provide global path and speed for the ARC, with a faster lap times compared with the traditional approach of following the center line of track. Key words：autonomous racing car(ARC)
Konsistensi Sistem Prakiraan Iklim Musiman Menggunakan Indikator ENSO di Daerah Tipe Iklim D3 dan D4 Kabupaten Lombok Tengah

The purpose of this study was to determine the consistency of seasonal climate forecasts using the ENSO phenomenon. The ENSO indicator categorized with El Nini, Neutral and La Nina were coorelated to rainfall categorized with Below Normal, normal and above normal. The research was carried out in the D3 dan D4 type of climate in ccentral Lombok regency. The method used in this research is descriptive method, which is a method aimed at solving problems by collecting data, compiling, analyzing, interpreting data, and drawing conclusions. This research was conducted in Central Lombok in climate types D3 and D4 covering the areas of Kopang, Mantang, Praya, Puyung, Pringgarata, Penujak, Mujur, Pujut, and Janapria. Using monthly rainfall data for ± 50 years from 1970-2019. Rainfall data were taken at Kopang, Mantang, Praya, Puyung, Pringgarata stations for type D3 and Penujak, Mujur, Pujut, and Janapriafor type D4. SOI data can be accessed at http://www.bom.gov.au/climate/current/soi2.shtml. The results showed that from 1970-2019 (50 years) there was a consistent value in all areas that were predicted, namely above 50% and inconsistency below 50%. Consistency describes how accurate the results of the forecast are. If the percentage of the consistency value is above 50%, it means that it is better to predict using the forecasting method by looking for consistency than using the average. Therefore, if you use a forecasting system, if an El Nino phenomenon occurs there will be a shortage of water, you can adjust the planting time, prepare irrigation water first, reduce the planting area, and delay the planting time first.Keywords: Climate Variations, SOI, Rainfall, Climate D3 and D4
BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic

In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checkers and reachability analysis tools. Given a bounded input set, bound propagation algorithms aim to compute tight bounds on the output of the NN. So far, linear and convex optimizations have been used to perform bound propagation. Since neural networks are highly non-convex, state-of-the-art bound propagation techniques suffer from introducing large errors. To circumvent such drawback, BERN-NN approximates the bounds of each neuron using a class of polynomials called Bernstein polynomials. Bernstein polynomials enjoy several interesting properties that allow BERN-NN to obtain tighter bounds compared to those relying on linear and convex approximations. BERN-NN is efficiently parallelized on graphic processing units (GPUs). Extensive numerical results show that bounds obtained by BERN-NN are orders of magnitude tighter than those obtained by state-of-the-art verifiers such as linear programming and linear interval arithmetic. Moreoveer, BERN-NN is both faster and produces tighter outputs compared to convex programming approaches like alpha-CROWN.
CITISTOR E-COMMERCE WEBSITE

E-commerce is a boom in the modern business.E-commerce means electronic commerce.Ecommerce (Electronic commerce) involves buying and selling of goods and services, or the transmitting of funds or data, over an electronic network, predominantly the Internet.E-commerce (Electronic commerce) is a paradigm shift influencing both marketers and the customers.Rather e-commerce is more than just another way to boost the existing business practices.It is leading a complete change in traditional way of doing business.This significant change in business model is witnessing a tremendous growth around the globe and India is not an exception.A massive internet penetration has added to growth of E-commerce and more particularly start-ups have been increasingly using this option as a differentiating business model.The current research has been undertaken to describe the scenario of E-Commerce, analyze the trends of E-Commerce.The study further examines the key variables imperative for the success of E-commerce business models.
Cross-modal Task Understanding and Execution of Voice-fingertip Reading Instruction by Using Small Family Service Robotic

The correct understanding of human task instructions is an important basic condition for family service robots to carry out their work. In daily family scenarios, single-modal voice commands often have the problem of missing pronoun references, which makes robots unable to identify the target object to be operated. In this paper, a novel cross-modal instruction task understanding and execution framework based on the fusion of speech and visual information is proposed, which is based on the computing architecture of robot terminal and cloud server. By inputting the speech recognition results of the sound into the pre-trained Bert model, the first-level task classification label is obtained. Then, the robotic turns the camera to the location of the sound source. By using the lightweight visual object detection model to obtain the target area pointed by the finger, the robotic completes the confirmation of the visual instruction and entity of the object, and obtains the semantic label of the visual entity. The information of visual entity semantic label and the first level task classification label is fused to obtain the second level subtask classification label. Finally, the experimental results confirm that the framework can be used for robot task understanding and execution of cross-modal instructions, and will be helpful for promoting the application of family service robots.
Demand Response Transaction Framework Based on Blockchain and Secure Multi-party Computation

With the development of system-level demand response transactions, diversified demand side resources are participating in the interaction with the power grid, which intensifies the problem of data mutual trust between power grid companies and users. In this regard, the potential data trust problems in the process of DR market clearing and load reduction calculation are analyzed, including data leakage and data abuse. Then, a DR transaction framework based on blockchain is proposed with secret sharing based secure multi-party computation adopted. For user different quotation modes, additive secret sharing algorithm is introduced for DR market clearing, and polynomial interpolation-based secret sharing algorithm is adopted for the trusted calculation of user load reduction. Under the proposed DR transaction framework based on blockchain and secure multi-party computation, data mutual trust problems between power grid companies and users can be properly solved.
DIGI-NOTICE APPLICATION USING ETHEREUM BLOCKCHAIN

The maintenance of any confidential records pertaining to education, health, or finance has seen a dramatic rise in the use of advanced technologies.However, because of their uncertainties, current sophisticated technologies have some problems.In terms of anonymity, attack-free security, transparency, dependability, and flexibility, these technologies have some shortcomings.Consequently, a blockchain application can be created to handle the events of a department or college.Decentralization improves the usability and upkeep of this application without adding to the costs incurred by hosting a centralised application on a leased third-party server.This programme does not rely on any third-party storage software because it stores the documents effectively using the IPFS file system.This method allows for seamless communication between students and teachers without the interference of outside programmes like WhatsApp or Google Classroom.
UniLoc: Unified Fault Localization of Continuous Integration Failures

Continuous integration (CI) practices encourage developers to frequently integrate code into a shared repository. Each integration is validated by automatic build and testing such that errors are revealed as early as possible. When CI failures or integration errors are reported, existing techniques are insufficient to automatically locate the root causes for two reasons. First, a CI failure may be triggered by faults in source code and/or build scripts, whereas current approaches consider only source code. Second, a tentative integration can fail because of build failures and/or test failures, whereas existing tools focus on test failures only. This article presents UniLoc, the first unified technique to localize faults in both source code and build scripts given a CI failure log, without assuming the failure’s location (source code or build scripts) and nature (a test failure or not). Adopting the information retrieval (IR) strategy, UniLoc locates buggy files by treating source code and build scripts as documents to search and by considering build logs as search queries. However, instead of naïvely applying an off-the-shelf IR technique to these software artifacts, for more accurate fault localization, UniLoc applies various domain-specific heuristics to optimize the search queries, search space, and ranking formulas. To evaluate UniLoc, we gathered 700 CI failure fixes in 72 open source projects that are built with Gradle. UniLoc could effectively locate bugs with the average mean reciprocal rank value as 0.49, mean average precision value as 0.36, and normalized discounted cumulative gain value as 0.54. UniLoc outperformed the state-of-the-art IR-based tool BLUiR and Locus. UniLoc has the potential to help developers diagnose root causes for CI failures more accurately and efficiently.
Resolving Lexical Verb Ambiguity in Yoruba through Semantic Web Annotations

Lexical verb ambiguity refers to the potential for multiple interpretations of units of speech in languages that renders it difficult or impossible to understand a lexical item without additional information. The goal of semantic web research is to allow the vast range of web-accessible information and services to be more effectively exploited by both humans and automated tools. Technological advancement has given rise to scholars developing automated translation systems from English to the Yorùbá language. However, the problem arises that these tools are not coping easily with semantic nuances found especially in Yorùbá verbs. To explore this problem, a number of Yorùbá lexical ambiguous verbs which may pose serious challenges to the existing machine translation tools were generated and analyzed. To facilitate a solution to the problem, the identified verbs are annotated following the Qualia Structure (QS) of the Generative Lexicon. Protégé 4.5 is employed as the editing tool to map the semantic interpretations of the QS to the semantic web. With the aid of the unique formal specification implemented for each of the lexically ambiguous verbs, the existing automated translation system can now utilize the Internationalized Resource Identifier (IRI) of the Unified Resource Locator (URL) found in the model developed to link the loop and thereby alleviate the problem of ambiguity for the automated translation system. This paper, therefore recommends that more efforts be geared towards ontological annotation in order to foster more automated tools that will be built for the Yorùbá language.
Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue

There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. We propose an automated method to process histology images of testicular tissue.Testicular tissue consists of seminiferous tubules. Segmenting the epithelial layer of the seminiferous tubule is a prerequisite for developing automated methods to detect abnormalities in tissue. We suggest an encoder-decoder fully connected convolutional neural network model to segment the epithelial layer of the seminiferous tubules in histological images. The ResNet-34 is used in the feature encoder module, and the squeeze and excitation attention block is integrated into the encoding module improving the segmentation and localization of epithelium.We applied the proposed method for the two-class problem, where the epithelial layer of the tubule is the target class. The F-score and Intersection over Union of the proposed method are 0.85 and 0.92. Although the proposed method is trained on a limited training set, it performs well on an independent dataset and outperforms other state-of-the-art methods.The pretrained ResNet-34 in the encoder and attention block suggested in the decoder result in better segmentation and generalization. The proposed method can be applied to testicular tissue images from any mammalian species and can be used as the first part of a fully automated testicular tissue processing pipeline. The dataset and codes are publicly available on GitHub.
Advanced Optimization Methods and Applications

Optimization methods are finding more applications in all domains, as they play an essential role when dealing with real-life problems [...]
Proyecto Soka. Música y teatro para la toma de conciencia sobre la violencia escolar.

School bullying continues to be one of the main latent scourges in the educational field. On the occasion of the Day of Non-Violence and School Peace (academic year 2021/2022) the subject of Educational Innovation and Research Projects in Music promotes an interdisciplinary and multi-artistic project with social impact that explains its consequences from the point of view of a teacher. Through the participation of the students of the Music itinerary of the Máster Universitario en Profesorado de Educación Secundaria Obligatoria, Bachillerato, Formación Profesional y Enseñanza de Idiomas the representation of a musical dramatization is undertaken within the framework of the Faculty of Education Sciences (Universidade da Coruña), in line with the following objectives: 1) vindicate the figure of the teaching staff before abuse and/or school violence within a faculty that forms the future of the profession; 2) exalt the value of art as an instrument of social change; and 3) promote the benefits of project-based learning among students. The academic results reinforce the validity of an experiential approach to project-based methodologies, highlighting the degree of human cohesion between teachers and students. For its part, the university community highlights the degree of innovation of the proposal and its importance in order to make real problems visible from another perspective.
ASSOCIAÇÃO ENTRE ESCORES DE ANSIEDADE E DEPRESSÃO E VEF1 EM PACIENTES COM FIBROSE CÍSTICA ADULTOS

CASE REPORTSa middle-aged man with acute intestinal obstruction due to an ileo-ileal intussusception of inverted Meckel's diverticulum with a lipoma that was managed by laparoscopy.Worthy of note is the very scarce number of case reports on this exceeding uncommon association.
TELECONSULTAS DE PACIENTES COM FIBROSE CÍSTICA DURANTE A PANDEMIA DE COVID-19

CASE REPORTSa middle-aged man with acute intestinal obstruction due to an ileo-ileal intussusception of inverted Meckel's diverticulum with a lipoma that was managed by laparoscopy.Worthy of note is the very scarce number of case reports on this exceeding uncommon association.
Children’s Magazines in the Media Education System (Content Audit)

The article is devoted to the problem of involving children of preschool and primary school age in the sphere of media education.How does the consumption of media content affect the formation of a reading culture and the related interest of 7-12 year old children in knowledge?This aspect, in our opinion, requires careful study and reflection.In the existing studies of media preferences of the new audience, as a rule, the focus is on the age segment from 12 to 20 years and older.The peculiarities of content consumption by younger children remain practically unexplored, while practice shows that "immersion" in the media environment and contact with gadgets in a child begins much earlier -even at an unconscious age, that is, from one and a half years.Particular attention in this article is paid to such a resource of media education as children's magazines that offer new, entertaining, popular science content and a special digital reading model.This article presents the results of a study whose purpose was to determine the key characteristics of the functioning of popular science magazines for children in the digital environment, taking into account the changed practices of media education and media consumption.
The research of the integrated courses teachers subject level competence in the natural science education

programs of advanced training courses for the training of relevant specialists.We consider it necessary to introduce specialized (physics, chemistry, biology) professional development courses for teachers who teach science integrated courses.This is an urgent issue, becauce
Short-Term Cryptocurrency Price Fluctuation Prediction Framework Using Machine Learning

Abstract: Our project aim is to predict the future price of the bitcoin using machine learning algorithms. In the modern world cryptocurrency is become more trendy and reaches the youngsters to invest in the stock market and to generate some profitable trades. To invest their money in cryptocurrency we are just helping the investors like peoples and also involving the organization to invest in the bitcoin and to make good profitable trades. Initially it utilizes the historical data to predict the future price of the bitcoin. It involves considering factors such as market sentiment, news and events ,technical analysis, and global economic trends. Different machine learning algorithms are applied on the a data and the accuracy is compared to see which algorithm performed better. It includes the performance metrics like precision, recall scores are also taken into consideration for evaluating the model In cryptocurrency market it contains 'n' number of coins .Among those coins we can take any coin to predict the future price which will able to help the investors who are all investing their money in cryptocurrency market.