Reviewerly/exHarmony huggingface dataset:
Kahoot! and Anxiety in High School: An Interview Study

Learning English in high school somehow still not becoming the favorite subject. Some students still feel anxiety while learning the language. Therefore English teachers must be able to make English class more fun and interesting. One way to do so is Kahoot!, which can be used as a means to teach several topics in English. The main objective of this study is to better understand the use of Kahoot! and its effect on reducing anxiety among students. This research is an interview study to investigate the needs of seven students as one of the inputs for teachers to design their classes to become more fun and interesting. The study's result shows the students' enthusiasm while learning English using Kahoot! and how it can reduce the anxiety of learning the language. Future studies can be conducted to show the teachers' steps in designing their lessons to be effective and fun simultaneously.
A Proveable Contextual Retrieval Strategy in the Public Cloud Using Optimal Matching over Secured Data

Abstract: Semantic searching over encrypted data is a crucial task for secure information retrieval in public cloud. It aims to provide retrieval service to arbitrary words so that queries and search results are flexible. In existing semantic searching schemes, the verifiable searching does not be supported since it is dependent on the forecasted results from predefined keywords to verify the search results from cloud, and the queries are expanded on plaintext and the exact matching is performed by the extended semantically words with predefined keywords, which limits their accuracy. In this paper, we propose a secure verifiable semantic searching scheme. For semantic optimal matching on ciphertext, we formulate word transportation (WT) problem to calculate the minimum word transportation cost (MWTC) as the similarity between queries and documents, and propose a secure transformation to transform WT problems into random linear programming (LP) problems to obtain the encrypted MWTC. For verifiability, we explore the duality theorem of LP to design a verification mechanism using the intermediate data produced in matching process to verify the correctness of search results. Security analysis demonstrates that our scheme can guarantee verifiability and confidentiality. Experimental results on two datasets show our scheme has higher accuracy than other schemes.
Gesture Detection and Its Applications Using Python and OPENCV

Abstract: In our world, Time and Effort are worth more than money. With everything around constantly becoming easier and easier to do, we still dream of having work done by actually doing next to nothing. Making things happen with the snap of a finger (quite literally with our paper) is so close yet so far from our grasp. In our paper we aim to use these kind of hand gestures to make complex actions happen. Our aim is to create a System that can Intake and Understand Commands and then use said commands in various Static and Dynamic Environments.
Artificial Intelligence in Procurement: An Overview and Case Study of Qatar Foundation

Although artificial intelligence (AI) has been reluctant to gain attraction in the procurement segment of a company, many organizations have started to see its potential in the supply chain activities since it enables numerous sectors in the evolution of their business models.AI refers to intelligent algorithms or apparatuses that are capable of autonomously and systematically performing activities or cognitions without human intervention or mental decisions.In procurement and supply chain applications, AI can enhance design and operational frameworks, data visibility and analytics, opportunity assessment, accurate report generation and contract management, etc.This paper presents opportunities and applications of AI in procurement with particular emphasis on the procurement process carried out by Qatar Foundation (QF).QF is a non-profit organization which has a unique system as it involves over 50 entities with different activities related to education, research, and community development.This one-of-a-kind ecosystem comprises a unique and complex supply chain model.As a result, procuring different materials and services to cope with all entities' requirement is a challenge due to the tremendous amount of data and purchases.The use of AI methodologies is anticipated to provide multiple benefits towards achieving improvements over diverse areas.
An Evaluation of Different Input Transformation for the Classification of Skateboarding Tricks by Means of Transfer Learning

This study aims to investigate the effect of different input images, namely raw data (RAW) and Continuous Wavelet Transform (CWT) towards the discriminating of street skateboarding tricks, i.e., Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through a variety of transfer learning with optimised k-Nearest Neighbors (kNN) pipelines. Six amateur skateboarders participated in the study, executed the aforesaid tricks five times per trick on an instrumented skateboard where six time-domain signals were extracted prior it was transformed to RAW and CWT. It was shown from the study that the CWT-InceptionV3-optimised kNN pipeline could attain an average test and validation accuracy of 90%.
Mobile Application Prototype for the Development of Phonological Awareness in Children’s School

This document exposes a technological tool to support the learning of Phonological Awareness skills, built under the guidelines proposed by Jiménez and Ortíz and aims to train high school children in the acquisition of this metalinguistic skill through a variety of activities (syllabic, intrasyllabic, and phonemic awareness) and immediate feedback, to acquire an excellent reading-writing process.The tool obtained is an application for the Android operating system that is easy to install, learn and configure by the therapist.For the construction of the software prototype, we have chosen to follow a methodology based on: i) the ADDIE instructional model (Analysis-Design-Develop-Implement-Evaluate), ii) the agile development methodology SCRUM, iii) the Methodological Guide for the creation of developments accessible virtual curricula; and iv) the Technology Acceptance Model (TAM); with the contribution of a multidisciplinary team made up of experts in information and communication technologies, linguistic rehabilitation, education, and graphic design.During the five phases of the methodology, the users' requirements were considered.
Quantifying Uncertainty in Ensemble Deep Learning

Neural networks are an emerging topic in the data science industry due to their high versatility and efficiency with large data sets.Past research has utilized machine learning on experimental data in the material sciences and chemistry field to predict properties of metal oxides.Neural networks can determine underlying optical properties in complex images of metal oxides and capture essential features which are unrecognizable by observation.However, neural networks are often referred to as a "black box algorithm" due to the underlying process during the training of the model.This poses a concern on how robust and reliable the prediction model actually is.To solve this ensemble neural networks were created.By utilizing multiple networks instead of one the robustness of the model was increased and points of uncertainty were identified.Overall, ensemble neural networks outperform singular networks and demonstrate areas of uncertainty and robustness in the model.
Robust collaborative 3D target localization using UAVs with state uncertainties

This paper addresses the problem of target localization using measurements collected by a fleet of cooperative Unmanned Aerial Vehicles (UAVs). The positions and orientations of UAVs are partly unknown. The measurement noise and other uncertainties of the system are assumed to be unknown but bounded. Moreover, some of the UAVs of the feet may produce erroneous data (outliers). Several UAVs are assumed to observe a single target, to exchange the collected information. Then they have to characterize the set of all target locations consistent with the measurements, the bounded measurement noise, and the bounded uncertainty of the position and orientation of the UAVs while being robust to possible outliers. A set-membership approach based on set inversion via interval analysis is used. Thick intervals are required to account for the uncertainty in the location and orientation of the UAVs. The robustness to outliers is obtained using q-relaxed intersection.
Controllable Image Captioning with Feature Refinement and Multilayer Fusion

Image captioning is the task of automatically generating a description of an image. Traditional image captioning models tend to generate a sentence describing the most conspicuous objects, but fail to describe a desired region or object as human. In order to generate sentences based on a given target, understanding the relationships between particular objects and describing them accurately is central to this task. In detail, information-augmented embedding is used to add prior information to each object, and a new Multi-Relational Weighted Graph Convolutional Network (MR-WGCN) is designed for fusing the information of adjacent objects. Then, a dynamic attention decoder module selectively focuses on particular objects or semantic contents. Finally, the model is optimized by similarity loss. The experiment on MSCOCO Entities demonstrates that IANR obtains, to date, the best published CIDEr performance of 124.52% on the Karpathy test split. Extensive experiments and ablations on both the MSCOCO Entities and the Flickr30k Entities demonstrate the effectiveness of each module. Meanwhile, IANR achieves better accuracy and controllability than the state-of-the-art models under the widely used evaluation metric.
Library Fund Science in the Perspective of Scientific Approaches

Review of the monographic study: Stolyarov Yu. N. Library Fund Studies: selected. Moscow: Pashkov house, 2022. 507 p.: ill. ISBN 978-5-7510-0840-6.
Transformation of images in the context of constructing photographic reality

The article is devoted to the analysis of image morphology in photography. The figurative structure is considered as three-part: an optical image (image), an aesthetic image (impression) and an artistic image (understanding). The author argues the need for an artistic image in order for photography to become art. Excessive desire for spectacular visual forms, which are achieved through the technical tools of the photographer and thanks to computer processing, is criticized. Affecting expressive means of photography are not an unconditional value, they can create an aesthetically attractive product, but leave the photographic reality without meaning and meaningful depth. The study touches upon the problem of a new visual aesthetics that has come into our world thanks to computer technology and the almost complete automation of the process of obtaining a photographic image. In modern conditions of information-machine civilization, a person only feeds on the illusion of creativity, while, however, entrusting digital technologies with all the main stages of image creation. A mechanistic, albeit often perfect, optical image in the minds of mass culture bearers is perceived as self-sufficient and does not require artistic and philosophical understanding. The creative search of the photographer is hampered by patterns, standards and clichés, which are naturally inherent in photography as a kind of technical and cultural activity.
How COVID-19 Pandemic affects Software Developers’ Wellbeing, and the New Trends in Soft Skills in Working from Home

The coronavirus COVID-19 swept the world in early 2020, working from home was a necessity. In the software industry, thousands of software developers began working from home, many did so on short notice, under difficult and stressful conditions. The emotions of developers can be affected by this situation. On the other hand, some well-known soft skills have been emphasized as required for working remotely. Software engineering research lacks theory and methodologies for addressing human aspects in software development. In this paper, we present an exploratory study focused on the developers’ wellbeing during pandemic, expressed as emotions, and the perceptions of the level in which soft skills are practiced/required in the working from home mode. The results show that high percent expressed to experience positive emotions, however, a portion of respondents expressed to feel negative emotions. In the case of soft skills, some of them are revealed as practiced in high level in working from home, but still there is not consensus.
BEST PRACTICE OF SHORT STORY WRITING WITH HYPNOTIC ENERGY AS POST-PANDEMIC INNOVATION (MENULIS CERPEN DENGAN HYPNOTIK ENERGI SEBAGAI KEGIATAN REFRESHING PASCA PANDEMI BAGI MAHASISWA)

The advancement of a nation can be seen in literary products. Writing is the key to the success and development of a person as they can produce short stories as literary works. Short stories are an example of literary works that contain fragments of indirect or direct life experiences. Messages and morals decided within a short story may reflect the author's values of life and characteristics. High motivation to start is required to develop the ability to present imagination and inspiration. Motivation is rooted in the power of one's mind thinking can change one's life. Motivation has a central role in writing, to have high motivation a writer must be able to penetrate the ego in the subconscious brain. The process of inputting suggestions and affirmations into the subconscious mind is crucial as it affects a person's behavior to write. If the message is already stored in the subconscious mind, a person will change his behavior into new values according to the implemented suggestions and affirmations. The power of the subconscious mind to influence behavior change by 80% is an opportunity and solution. Thus, hypnotic energy is suitable for a writing trick because it will change behaviors into new values. The method used in this research is descriptive qualitative. The researcher describes the surefire trick of writing with hypnotic energy based on conceptual studies from various books and journals. The results of this study produce a formula for writing tricks that make it easier for students to write.
Software project estimation using smooth curve methods and variable selection and regularization methods using a wedge-shape form database

Context: The impact of an excellent estimation in planning, budgeting, and control, makes the estimation activities an essential element for the software project success. Several estimation techniques have been developed during the last seven decades. Traditional regression-based is the most often estimation method used in the literature. The generation of models needs a reference database, which is usually a wedge-shaped dataset when real projects are considered. The use of regression-based estimation techniques provides low accuracy with this type of database. Objective: Evaluate and provide an alternative to the general practice of using regression-based models, looking if smooth curve methods and variable selection and regularization methods provide better reliability of the estimations based on the wedge-shaped form databases. Method: A previous study used a reference database with a wedge-shaped form to build a regression-based estimating model. This paper utilizes smooth curve methods and variable selection and regularization methods to build estimation models, providing an alternative to linear regression models. Results: The results show the improvement in the estimation results when smooth curve methods and variable selection and regularization methods are used against regression-based models when wedge-shaped form databases are considered. For example, GAM with all the variables show that the R-squared is for Effort: 0.6864 and for Cost: 0.7581; the MMRE is for Effort: 0.1095 and for Cost: 0.0578. The results for the GAM with LASSO show that the R-squared is for Effort: 0.6836 and for Cost: 0.7519; the MMRE is for Effort: 0.1105 and for Cost: 0.0585. In comparison to the R-squared is for Effort: 0.6790 and for Cost: 0.7540; the MMRE is for Effort: 0.1107 and for Cost: 0.0582 while using MLR.
INFORMATION &amp; TECHNOLOGY AUDIT OF E-GOVERNMENT USING COBIT A LITERATURE REVIEW

The Indonesian government continues to strive to improve public services to meet global demands, one of which is the development of technology such as a broad and integrated internet known as e-government.In implementing the e-government concept, it is necessary to have an IT audit to align the IT management process with the plans, objectives, and business strategies of government institutions.One framework that can be used as a standard is COBIT.This study uses the Systematic Literature Review research method to answer Research Questions (RQ): RQ1 regarding how the COBIT framework is used in IT audit case studies, especially in the e-government field, RQ2 regarding the COBIT domain used in research.The results of the study obtained 32 journals that were selected through a literature search process, literature selection according to criteria, and quality assessment.The results of the study, especially in the context of the main research question, namely the journals reviewed using the COBIT framework with various versions in evaluating e-government implementation.In COBIT there is a workflow that starts from identifying problems in the organization to analyzing capability level.In this study, it is known that the COBIT 2019 version is more adaptable to organizational conditions and technological developments because this version has more domains and design factors have been added (answer RQ1).The COBIT framework has 5 domains, namely the EDM, APO, BAI, DSS, and MEA domains.The most dominant domains used in assessing e-government implementation in journals are the APO and DSS (answer RQ2).
Analisis Peningkatan Prestasi Belajar dan Kemampuan Berfikir Kreatif Mahasiswa Universitas Teknologi Mataram melalui Penerapan Focus Group Discussion

The purpose of this study was to find out how to increase student achievement and creative thinking skills through the application of focus group discussions and to find out the supporting and inhibiting factors. In this study the data obtained in the field used observation, documentation and interview techniques. The research method used in this research is qualitative research with a case study approach with two data collection methods, namely primary data and secondary data, primary data by interviews and secondary data by collecting exam questions and quizzes during the lesson for one semester. books and archives of student grades related to research objectives. The results of this study indicate that there is still unsatisfactory relationship between the supporting lecturers and the students involved in learning, especially in class. Shows that students are still overwhelmed, confused in interacting with others.
Development of an Intelligent Vital Sign Monitoring Robot System

We want to solve the problem that the elderly or heart patients will not miss the golden time for rescue incase of sudden death or emergency event at home. Therefore, this work proposes an intelligent vital signmonitoring robot system. Considering data protection and privacy issues, the heart rate of the elderly or heart patient can be measured and monitored through the millimetre wave radar on an autonomous mobilerobot (AMR). Additionally, by object detection technology, the infrared thermal imager is integrated todetect the temperature of a person’s head. The measured head temperature, thermal image and heart ratedata can be uploaded to cloud database with blockchain technology to prevent data tampering. We develop an interrupt method for multi-target navigationof AMR to interrupt the patrol when a custom triggersituation happened and to run the interrupt process for vital signs measurement. While the interrupt process is completed, the robot will patrol again. When the system detects an abnormal situation, familymembers or caregivers can be notified through IFTTT service and Line notification messages. The systemcan help us to monitor the situations of elderly and provide response in time when a sudden emergencyevent or health crisis happened.
Obstacle Detection for Unmanned Aerial Vehicle (UAV)

This study aims to develop an obstacle detection system for unmanned aerial vehicles utilisingthe ORB feature extraction. In the past, small unmanned aerial vehicles(UAV)were typically equipped with vision-based or range-based sensors. Each sensor in the sensor-based technique possesses different advantages and disadvantages. As a result, the small unmanned aerial vehicle is unable to determine the obstacle's distance or bearing precisely. Due to physical size restrictions and payload capacity, the lightweight Pi Camera and TF Luna LiDAR sensor were selected as the most suitable sensors for integration. In algorithmdevelopmentandfiltration isused to improve the accuracy of the feature matching process, which is required for classifying the obstacle region and free region of any texture obstacle. The experimentwas under the environment of OpenCV and Spyder. In real-time experiment, the success rate for good texture (40%), poor texture (55%) and texture-less (45%) The findings indicate that the recommended method works well for detectingtextures-less obstacle even though the success rate is only 40% becauseout of 10 test only one test is fail on detecting free region. The sensor calibration and constructing convex hullfor obstacle detectionis recommended in future works to improve the efficiency of the obstacle detection system and classified the free region and obstacle region to create safe avoidance path.
IMPLEMENTATION OF INFORMATION GAIN AND PARTICLE SWARM OPTIMIZATION UPON COVID-19 HANDLING SENTIMENT ANALYSIS BY USING K-NEAREST NEIGHBOR

In early 2020, a new virus from Wuhan, China, identified as the coronavirus or COVID-19, shocked the entire world. (Coronavirus Disease 2019). The government has made various attempts to combat this outbreak, despite the fact that the government's involvement in combating Covid-19 has many benefits and disadvantages. One of the most commonly debated subjects on Twitter is the Indonesian government's response to the Covid-19 virus. This research compares the k-nearest neighbor classification technique, Information Gain feature selection with the K-Nearest Neighbor classification algorithm, and Information Gain feature selection and Particle Swarm Optimization optimization with the K-Nearest Neighbor classification algorithm. Comparisons are performed to determine which method is more accurate. Because it is frequently used for text and data categorization, the K-Nearest Neighbor algorithm was selected. The K-Nearest Neighbor algorithm has flaws, including the ability to be fooled by irrelevant characteristics and being less than ideal in finding the value of k. The selection of the Information Gain feature could indeed solve this issue by decreasing Terms that are less important and to optimize the K-Nearest Neighbor categorization, an optimization method with the Particle Swarm Optimization algorithm is employed to maximize the K-Nearest Neighbor classification. According to the results of this research, the K-Nearest Neighbor categorization with Information Gain feature selection and Particle Swarm Optimization optimization is better than the K-Nearest Neighbor model without selecting features and without optimization and is better than the K-Nearest Neighbor model with Information Gain selecting features, notably 87,33% with a value of K 5.