Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Methods Used in Computer Vision

Artificial intelligence is a branch of research that allows machines to solve intuitive problems by imitating the way the human brain works. Meaningful information can be extracted from image and video analysis using computer vision methods. Computer vision studies in areas such as digital image processing, object recognition, object tracking have been strengthened with artificial intelligence-based components developed in recent years. Thus, faster and more precise results can be obtained. The aim of this study is to reveal the artificial intelligence methods used in the field of computer vision, the principles and constraints of the methods in the light of the related literature.
SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification

We propose SpeakerNet - a new neural architecture for speaker recognition and speaker verification tasks. It is composed of residual blocks with 1D depth-wise separable convolutions, batch-normalization, and ReLU layers. This architecture uses x-vector based statistics pooling layer to map variable-length utterances to a fixed-length embedding (q-vector). SpeakerNet-M is a simple lightweight model with just 5M parameters. It doesn't use voice activity detection (VAD) and achieves close to state-of-the-art performance scoring an Equal Error Rate (EER) of 2.10% on the VoxCeleb1 cleaned and 2.29% on the VoxCeleb1 trial files.
Accuracy improvement of Best Scanline Search Algorithms for Object to Image Transformation of Linear Pushbroom Imagery

Accuracy improvement of Best Scanline Search Algorithms for Object to Image Transformation of Linear Pushbroom Imagery
Artificial Intelligence as an Art Director

My research focuses on the design of an artificial intelligence-based system that can perform the tasks of a videogame art director. I began with the analysis of game developers’ design processes, workflows, and methodologies. I observed they involve fast-paced proposal-evaluation-correction cycles. This, along with my interest in generative methods, led me to consider using Variational Autoencoders (VAEs), which also include similar cycles, and do not require a high number of samples to produce satisfactory results. Currently, I am using VAEs on experiments over Pokémon images, with positive results.
Robot player adaptation to human opponents in physical, competitive robogames

A key issue for a device involved in a competitive game is to be able to match the ability of the opponent, thus making the game interesting. When the game is played by a human player against a machine, this should be able to adapt online to the opponent's ability, which may change during the game. When the playing machine is a robot, and the game involves physical activity, adaptation should consider the player's ability, but also other aspects directly related to the physical nature of the game. Adaptation could be obtained either intrinsically, by designing the interaction, or explicitly, by modeling aspects of the opponent that can enable to estimate performance, behaviors, and strategies. We present in this paper different approaches to design the two types of adaptation, together with two competitive, physically interactive robogames exploiting them.
Design and Manufacture of Solar-Powered Wheelchair

This paper presents a study of a low-cost solar-powered wheelchair for disabled people of rural areas in South Africa.There are manual wheelchairs on the market which always require an able-bodied person to assist in steering the disabled person around.Other wheelchairs use a battery that is charged by electricity when it is flat.Electricity is expensive and scarce in rural areas.The main components of the solar-powered wheelchair are the wheelchair structure, solar panel, DC motor, charging controller, switch and wheels.The proposed design is very useful for physically disabled people of rural areas and affordable for the low-income earners of countries like South Africa.The wheelchair is cost effective, user friendly, selfdriven and independent.The solar-powered wheelchair will help physically disabled people with their daily movements and will be very effective in both rural and urban areas.The maximum speed and load that the wheelchair can handle are 11.25 m/min and 155 kg, respectively.
Autoregressive Score Matching

Autoregressive models use chain rule to define a joint probability distribution as a product of conditionals. These conditionals need to be normalized, imposing constraints on the functional families that can be used. To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariate log-conditionals (scores), which need not be normalized. To train AR-CSM, we introduce a new divergence between distributions named Composite Score Matching (CSM). For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. Compared to previous score matching algorithms, our method is more scalable to high dimensional data and more stable to optimize. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
The UPC Speaker Verification System Submitted to VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20)

This report describes the submission from Technical University of Catalonia (UPC) to the VoxCeleb Speaker Recognition Challenge (VoxSRC-20) at Interspeech 2020. The final submission is a combination of three systems. System-1 is an autoencoder based approach which tries to reconstruct similar i-vectors, whereas System-2 and -3 are Convolutional Neural Network (CNN) based siamese architectures. The siamese networks have two and three branches, respectively, where each branch is a CNN encoder. The double-branch siamese performs binary classification using cross entropy loss during training. Whereas, our triple-branch siamese is trained to learn speaker embeddings using triplet loss. We provide results of our systems on VoxCeleb-1 test, VoxSRC-20 validation and test sets.
A Machine Learning Approach for Human Activity Recognition.

Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals. In this work, we developed a system using the Internet of Things (IoT) and machine learning technologies in order to monitor and assist individuals in their daily life. We compared the data collected using a mobile application and a wearable device with built-in sensors (accelerometer and gyroscope) with the data of a publicly available dataset. By this way, we were able to validate our results and also investigate the functionality and applicability of the wearable device that we choose for the Human Activity Recognition problem. The classification results for the different types of activities presented using our dataset (99%) outperforms the results from the publicly database (97%).
Entanglement of assistance in three-qubit systems

The entanglement of assistance quantifies the amount of entanglement that can be concentrated among a group of spatially separated parties using the assistance of some auxiliary system. In this paper we study the entanglement of assistance in the simplest scenario of three qubits. We consider how much entanglement is lost when the helper party becomes uncoupled by local measurement and classical communication. For three-qubit pure states, it is found that lossless decoupling is always possible when entanglement is quantified by the maximum probability of locally generating a Bell state. However, with respect to most other entanglement measures, such as concurrence and entanglement entropy, a lossless decoupling of the helper is possible only for a special family of states that we fully characterize.
Universal non-Markovianity detection in hybrid open quantum systems

Abstract A universal characterization of non-Markovianity for any open hybrid quantum systems is presented. This formulation is based on the negativity volume of the generalized Wigner function, which serves as an indicator of the quantum correlations in any composite quantum systems. It is shown, that the proposed measure can be utilized for any single or multi-partite quantum system, containing any discrete or continuous variables. To demonstrate its power in revealing non-Markovianity in such quantum systems, we additionally consider a few illustrative examples.
PENINGKATAN KETERAMPILAN MENULIS PUISI DENGAN MEDIA GAMBAR

Writing learning activities, students still experience many difficulties because teachers use conventional learning methods. So that causes the interest and enthusiasm of students in learning to be less, and the results achieved are not optimal. With media in the form of images will make it easier for students to express their ideas. Therefore, this research aims to improve students' poetry writing skills by using picture media. Because media images can stimulate students to give imagination and make students creative in writing poetry. The research method used is qualitative. The subject of this research is grade V. The results of the study indicate that the ability to write poetry based on image media can improve students' poetry writing skills. Students can make poetry titles from predetermined themes, can choose new and creative words (diction), and can use speech. The creativity of students writing poetry uses two types of majors, and they are contrasting forms and comparison forms
PRAN: Progressive Residual Attention Network for Super Resolution

Single image super resolution (SISR) based on deep learning has made great progress in recent years. As the method continues to improve, different network structures have been proposed to better perform SR feature extraction for reconstruction. A deep structure has a good ability to generate high-quality SR features, but the complex structure may also cause problems such as hard training and overfitting. Many efforts have also been made to solve these problems, such as feedback structure and attention mechanism. However, naively applying these methods to SR networks may be useless. Hence, in this research, we took a further step by introducing progressive residual attention to generate high-quality SR images. In experiments, we compared the reconstruction results and training progress with other SR methods based on normal structures. The proposed network achieves fast convergence speed and better SR results.
Using Kotobee E-Book Design and Software Use in Teaching Chemistry in High Schools

The strong development of information and communication technology (ICT) in recent years has had a great impact on education in countries in the world. ICT has opened great prospects of innovating methods and forms of teaching. An advantage of Kotobee Author is that users can add many simulation experiment videos, images, and practical knowledge. Kotobee Author allows the export of e-books to various file formats appropriate for reading devices, depending on users’ needs. Therefore, readers can interact with and use e-books whenever and wherever on computers and handheld electronic devices like smartphones. In the context of the 4th Industrial Revolution, learners can easily use Kotobee Reader on compact mobile devices such as tablets and smart-phones. Kotobee Reader is a specialized application for reading e-books that are designed based on Kotobee Author. Kotobee Reader can work on three common operating systems: IOS, Windows, and Android.
The impact of using online social media networks on employees’ productivity in higher educational institutions

The purpose of this study was to examine the impact of the online social media networks (OSMNs) on productivity at workplace among 88 administrative staff at Prince Sattam bin Abdulaiziz University for the academic year 2020-2021. This study finds that using online social media networks by PSAU’s employees at the workplace enhances their productivity. The majority of the employees (59%) perceive that using the OSMNs at workplace have a positive impact on their productivity. In addition, the majority of the employees (33%) regu-larly use WhatsApp as a useful online social media network at the workplace. The results also indicate that the majority of the employees (66%) use the OSMNs at workplace more than once a day. Further, the majority of the PSAU’s employees (39%) use the OSMNs at work-place less than half an hour per a day. Furthermore, 39% of the PSAU’s employees use the OSMNs at workplace to keep in touch with their families and friends, and 34% of the employees use the OSMNs to search for work-related information. The results of this study should be useful to policy makers in Saudi Arabia at the country, ministry of education, PSAU, and elsewhere in gaining a deeper understanding on how using the OSMNs at work-place can enhances the employees’ productivity.
Ontology-based Sentiment Analysis for Brand Crisis Detection on Online Social Media

This paper discusses detection of brand crisis on online social media, i.e. when a brand is being suffered from unexpectedly high frequency of negative comments on online channels such as social networks, electronic news, blog and forum. In order to do so, we combined the usage of probabilistic model for burst detection with ontology-based aspect-level sentiment analysis technique to detect negative mention. The burst on online environment is a trendy topic that is rapidly growing recently. Thus, a burst with high frequencies of negative mentions to a brand implies a potential online crisis occurring with that brand. Our experimental results show that the aspect-level sentiment analysis technique is extremely useful for detecting of negative mentions that related with the products and brands.
Robust &amp; Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision

Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultaneously guarantees the validity and asymptotic optimality for known environments. \textit{Validity:} Given a feasible initial guess, our algorithm guarantees the satisfaction of all constraints throughout the process of optimization. \textit{Asymptotic Optimality:} We use an asymptotic exact piecewise approximation of the trajectory with an automatically adjustable resolution of its discretization. The trajectory converges under refinement to the first-order stationary point of the exact non-convex programming problem. Our method has additional practical advantages including joint optimality in terms of trajectory and time-allocation, and robustness to challenging environments as demonstrated in our experiments.
A Blockchain based and GDPR-compliant design of a system for digital education certificates

Blockchain technology supports building transparent and decentralized systems in which the executed transactions can be easily traceable. Suppose one such system is intended to manage and process personal data. In that case, complementary mechanisms are required that make it possible for the system to comply, for instance, with data protection regulations. This work studies the integration of off-chain capabilities in blockchain-based solutions. In particular, we have focused on mechanisms that support safely moving data or computational operations outside the core blockchain network. We have carried out a thorough analysis of the European data protection regulation and discussed the weaknesses and strengths regarding the security and privacy requirements established by that regulation of solutions built using traditional blockchain technology.As a direct consequence of this study, we have conceived, and present in this paper, a system architecture for the design of privacy-aware solutions that use that kind of technology and put forward a systematic approach for performing a security and privacy threat analysis of one such solution. We illustrate the use of the proposed methodological tools, presenting and discussing the high-level design and security and privacy assessment of a system that provides services to handle, store, and validate digital academic certificates.
PENGARUH PENGGUNAAN GOOGLE CLASSROOM TERHADAP PEMAHAMAN KONSEP PADA MATAKULIAH KALKULUS

Information technology has developed rapidly. This greatly affects the world of education, the only thing is that learning can be done anywhere and anytime without limits. One of the ways to learn online (online) is by using google classroom. This study aims to determine the effect of using google classroom on students' understanding of concepts in calculus course. This study used a quasi-experimental research design. The research was conducted at the informatics engineering study program, PGRI Madiun University in the even semester of the 2019/2020 academic year. The research sample was the second semester students who took calculus in class 2A and 2B. In this study, data on concept understanding was obtained in the form of quantitative data. The conceptual understanding data were analyzed using the normality and homogeneity test. Furthermore, the hypothesis test was carried out using the independent simple t-test. The results showed that there was an influence on conceptual understanding between students using google classroom and students using conventional learning.
Lightweight Convolutional Neural Networks for Vehicle Target Recognition

In order to improve the network learning effect, based on the existing target learning framework MobileNet-V2, an ELU function is inserted as an activation function. Firstly, use the expansion convolution to increase the number of channels to get more features to activate and output through the ELU function, which can alleviate the disappearance of the gradient of the linear part, the nonlinear part is more robust to the noise of the input change. Then, the way of the residual connection combine high-level features with low-level features and then output. Finally, output to softmax using global pooling. The experimental data shows that compared with the current mainstream lightweight deep learning target recognition algorithm,E-MobileNet has improved the accuracy of recognition and the frame rate per second in the same test environment of the same test set. The experimental data fully demonstrates that the use of the ELU activation function and the global pooling layer reduces the number of parameters, enhances the generalization ability of the model, and improves the robustness of the algorithm. On the basis of ensuring the light weight of the neural network model, the recognition accuracy of the target is effectively improved.
AutoML and Interpretability: Powering the Machine Learning Revolution in Healthcare

An AutoML and interpretability are both fundamental to the successful uptake of machine learning by non-expert end users. The former will lower barriers to entry and unlock potent new capabilities that are out of reach when working with ad-hoc models, while the latter will ensure that outputs are transparent, trustworthy, and meaningful. In healthcare, AutoML and interpretability are already beginning to empower the clinical community by enabling the crafting of actionable analytics that can inform and improve decision-making by clinicians, administrators, researchers, policymakers, and beyond.
Newsminer: Enriched Multidimensional Corpus for Text-Based Applications

News websites are rich sources of terms that can compose a linguistic corpus. By introducing a corpus into a Data Warehousing environment, applications can take advantage of the flexibility that a multidimensional model and OLAP operations provide. This paper presents Newsminer, an exploratory OLAP framework, which offers a consistent and clean set of texts as a multidimensional corpus for consumption by external applications. The proposal integrates real-time gathering of news and semantic enrichment, which adds automatic annotations to the corpus. The multidimensional facet allows users and applications to obtain different corpora by selecting news categories, time slice, and term selection. We performed two experiments to evaluate the semantics enrichment and the feasibility of real-time during Newsminer’s ETL.
Spectral thinking as spiritual search...und electronics ist auch dabei: Jonathan Harvey's Tombeau de Messiaen, String Quartet No. 4 and Speakings

This article aims at examining the relationship between spirituality and spectral thinkingin Jonathan Harvey’s music; for this purpose I will consider three works by Harvey datingfrom 1994 to 2008: Tombeau de Messiaen (1994) for piano and tape, String Quartet No. 4(2003) with live electronics and Speakings (2007-2008) for orchestra with live electronics.The analysis of these works enables us not only to highlight Harvey’s peculiar approachto spectralism, but also to see how new technologies were essential for him in order tocreate a spiritual music. There is a deep connection between Harvey’s idea of music as apath to emptiness and to a “pure land” (as Buddhist concepts), and the idea of ambiguityin music. The latter is achieved through the melting of spectral thinking, intervallicism andnew technologies. An ever changing and evolving sound, the difficulty for the listener torecognize sound origin, the halo that results from the creation of a spectral environmentfor a monodic line, the construction of timbral hybrids, all are aspects of ambiguity inmusic as a spiritual goal1.
Meta Analisis Model Pembelajaran Inquiry untuk Meningkatkan Hasil Belajar Siswa Sekolah Dasar

The purpose of this study is to reanalyze the use of the Inquiry learning model to improve primary school student learning outcomes. This type of research was a meta-analysis. The research began with formulating a research problem, then testing the relevant research results for analysis. The data collection technique used non-test in the form of searching electronic journals through Google Scholar as well as documentation studies in libraries. The search results obtained 29 articles from journals and 3 theses articles from the repository. Based on the results of the analysis, it revealed that the Inquiry learning model was able to improve the student learning outcomes ranging from the lowest 1.4% to the highest 58.33% with an average of 20.88%. From the calculation of the effect size on the 29 articles and 3 theses analyzed, it was resulted that the effect size was 4.94 on average.
Is Chaotic Randomization Advantageous for Higher Dimensional Optimization Problems?

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports analysis on the performance of popular and CEC 2019 competition winning strategy of Differential Evolution (DE), which is jDE, for optimization problems of higher dimensions. Experiments utilize ten chaos-driven quasi-random schemes for the indices selection and chaotic-driven crossover operations in the DE. All important performance characteristics are recorded and analyzed with simple descriptive statistics, Friedman rank tests and target-based comparisons analyzing distribution of hitting p% best minimum values over all versions and runs of jDE. The test suite was CEC 2015 in 50D.