Reviewerly/exHarmony huggingface dataset:
USER INTERFACE AND USER EXPERIENCE OF INDOSPORT MOBILE APPLICATIONS USING A USER CENTERED DESIGN APPROACH

An alternative solution for Indosport, considering the increasingly advanced smartphone development and also followed by the rapid growth of its users in Indonesia. The purpose of this study project is to produce a product in the form of a user interface (UI) and user experience (UX) design in helping to realize targeted mobile applications and not forgetting to apply the principles of visual communication design namely unity and variety, hierarchy, contrast, proportion and balance. The UI and UX design methods go through several stages of work in sequence: pre-production process, production process, and post-production. This design results in UI and UX designs that are equipped with user flow, interface designs, illustrations and prototypes. The main page of the Indosport mobile application is News, Activity, Calendar, and Profile which later on these pages there are several features that are different from the previous page. The making of this UI uses several visual languages such as medium close up, full shot, long shot, close up and medium long shot in the image section contained in the article as well as the depiction of field styles used to almost all visual UI elements. Using several principles in design, namely time to learn, speed of performance, rate of errors by users, retention over time, and subjective satisfaction. The resulting design forms will be distributed directly to the Research and Development division and the IT division from Indosport to be made a native.
Investigation of reading error types, reading levels and reading speeds of students with special learning difficulties

The main aim of this study is to investigate reading error types, reading levels and reading speeds of students with special learning difficulties (SLD). A case study model was used in the research. The research was carried out with 34 students enrolled in Grades 3â€“8, who were diagnosed with SLD in Konya in the 2019â€“2020 academic year. In the data analysis, while â€˜error analysisâ€™ was used for reading error types, the formula Number of Words Correctly Read Ã· Total Word Number of Text Ã— 100 was utilised to determine reading levels. As for the identification of reading rates, the number of words read correctly in one minute was determined. According to the results of the research, the error types displayed by the students with SLD in the texts that are suitable for the classes that they attend and in the texts that belong to lower grade levels than their actual grade levels were found to be misreading, hesitating while reading, repeating, omitting a sound/syllable/word or skipping a line, substituting a sound/syllable or a word, pausing while reading, correction and reversing sounds.&#x0D; &#x0D; Keywords: Special learning difficulties, reading difficulty, reading error types, reading levels, case study.
Penggunaan Media Film Animasi untuk Meningkatkan Keterampilan Menulis Karangan Narasi Siswa Kelas V SDN 110 Pekanbaru

This research is a classroom action research. The purpose of the study was to improve student writing skills of narrative text in elementary school V 110 grade Pekanbaru using animated movies. Data collection techniques are observations and writing tests. Data analysis is quantitative and qualitative. On the initial data the average value is 63.42, where there are no student’s who get grades in the highly skilled category, and 3 students get grades in the skilled category. In cycle I the average value increased to 75.79, where 1 student get grades in the highly skilled category and 27 students get grades in the skilled category and the percentage of research success was 73.68%. In cycle II The average value also increased to 82.23, where 6 students get grades in the highly skilled category and 31 students get grades in the skilled category with a percentage of research success was 97.37%. So it can be concluded that the use of animation movies as media can improve the writing narrative text skill students of grade V SDN 110 Pekanbaru.
Learning-Aided User Intent Estimation for Smart Rollators

With the aging population and rising rates of mobility disability, the demand for advanced smart rollators is increasing. To design control systems which improve safety and reliability, accurate prediction of human intent is required. In this paper, we present a classification method to predict intent of the rollator user using indirect inputs. The proposed classification algorithm uses data collected from an inertial measurement unit and an encoder implemented into a rollator. The developed intent estimation method is experimentally verified on our modified robotic platform. For our experiment with 7 healthy young adults, KNN classification algorithm was able to predict 3 intents (turn left, turn right and walk straight) with 92.9 % accuracy.
Programming at Compile Time

C++ offers many opportunities to write code that runs at compile time instead of at runtime. Templates, for example, provide a unique, functional programming environment, albeit one with a tortuous syntax. In C++ 20, some new keywords offer more precise ways for you to control what takes place at compile time instead of runtime. Some compile-time programming techniques are called metaprogramming, but this term is loosely defined and some may use it exclusively for programming with types instead of values. However you name it, compile-time programming is a valuable aspect of overall C++ programming.
Transformation Of Values In The Social Ideal System Of A Person

This article examines about the influence of the value system on the emergence of a social ideal as a result of the synthesis of a value goal with social reality in the process of socialization of the individual.The article also examines the impact of social and political values on the formation of a social ideal within the social mentality.
Ideation

DesignAgility is an adaptation of the design thinking method and was developed to meet the specific needs of the publishing industry and the media sector in general. The authors of the eponymous book embedded DesignAgility into an agile framework that allows its users to effectively develop and implement media innovations with a small team. This sample chapter of the book focuses on Ideation and describes the qualified brainstorming and idea-generating in the innovation process. The needs of the users aka the ‘personas’ are reflected in so-called ‘user stories’. Through refinement the ideas are developed into possible product or service features. The chapter describes detailed steps from preparation to procedure and includes a checklist of how to easily follow the instructions for ideation. The visualization of the DesignAgility process shows where this step has to be taken within the whole innovation process.
Multidimensional assessment of the eTranslation output for English–Slovene

The Slovene language department of the European Commission Directorate-General for Translation has always been an early adopter of new developments in the area of machine translation. In 2018, the department started using neural machine translation produced by the eTranslation in-house engines. In 2019, a multidimensional assessment of the eTranslation output for the language combination English–Slovene was carried out. It was based on two user satisfaction surveys, an analysis of detected and reported errors and an ex post analysis of a sample. As part of the assessment effort, a categorisation of errors was devised in order to raise awareness among translators of the potential pitfalls of neural machine translation.
Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputs.

The present study aims to compare three systems: a generic statistical machine translation (SMT), a generic neural machine translation (NMT) and a tailored-NMT system focusing on the English to Greek language pair. The comparison is carried out following a mixed-methods approach, i.e. automatic metrics, as well as side-by-side ranking, adequacy and fluency rating, measurement of actual post editing (PE) effort and human error analysis performed by 16 postgraduate Translation students. The findings reveal a higher score for both the generic NMT and the tailored-NMT outputs as regards automatic metrics and human evaluation metrics, with the tailored-NMT output faring even better than the generic NMT output.
Unsupervised Predominant Sense Detection and Its Application to Text Classification

This paper focuses on the domain-specific senses of words and proposes a method for detecting predominant sense depending on each domain. Our Domain-Specific Senses (DSS) model is an unsupervised manner and detects predominant senses in each domain. We apply a simple Markov Random Walk (MRW) model to ranking senses for each domain. It decides the importance of a sense within a graph by using the similarity of senses. The similarity of senses is obtained by using distributional representations of words from gloss texts in the thesaurus. It can capture large semantic context and thus does not require manual annotation of sense-tagged data. We used the Reuters corpus and the WordNet in the experiments. We applied the results of domain-specific senses to text classification and examined how DSS affects the overall performance of the text classification task. We compared our DSS model with one of the word sense disambiguation techniques (WSD), Context2vec, and the results demonstrate our domain-specific sense approach gains 0.053 F1 improvement on average over the WSD approach.
Monte Carlo Tree Search Guided by Symbolic Advice for MDPs.

In this paper, we consider the online computation of a strategy that aims at optimizing the expected average reward in a Markov decision process. The strategy is computed with a receding horizon and using Monte Carlo tree search (MCTS). We augment the MCTS algorithm with the notion of symbolic advice, and show that its classical theoretical guarantees are maintained. Symbolic advice are used to bias the selection and simulation strategies of MCTS. We describe how to use QBF and SAT solvers to implement symbolic advice in an efficient way. We illustrate our new algorithm using the popular game Pac-Man and show that the performances of our algorithm exceed those of plain MCTS as well as the performances of human players.
INTELLIGENT SYSTEM FOR DETECTION OF CYBERCRIME VOCABULARY ON WEBSITES

While the benefits that new technologies have brought to our lives are undeniable, they have also caused new problems that were previously unknown, one of which is the so-called Cybercrime.
Simulation of defocusing effect based on two-step ABCD algorithm while a modal decomposition

Abstract Depicting the multimode laser beam by modal decomposition can potentially assess light field variations in the fiber, during propagation. The practical engineering conditions in the lab however could not realize ideal levels, hence further research on factors influencing this method, such as defocus, is especially necessitated. The grid spacing in observation plane by Fast Fourier Transform is fixed and unchangeable within diffraction imaging, hence possibly yielding erroneous data during obtaining light field intensities. Our research resolves these issues via a Two-step ABCD algorithm, applied in the modal decomposition to characterize various guided modes at the output of multimode fibers. A direct benefit is that the image plane size can be altered, further refining laser facula clarity. Furthermore, the quantitative expressions that analyze defocus factors impacting modal decomposition are acquired. The conclusions thereby prove the modal decomposition algorithm can keep effectiveness in the range of −0.25% to 0.25% of relative defocus for low order eigenmodes, having no suitable limited band for high order eigenmodes, with reference value in engineering applications.
On branching models with alarm triggerings

Abstract We discuss a continuous-time Markov branching model in which each individual can trigger an alarm according to a Poisson process. The model is stopped when a given number of alarms is triggered or when there are no more individuals present. Our goal is to determine the distribution of the state of the population at this stopping time. In addition, the state distribution at any fixed time is also obtained. The model is then modified to take into account the possible influence of death cases. All distributions are derived using probability-generating functions, and the approach followed is based on the construction of families of martingales.
A Local Modified U-net Architecture for Image Denoising

Image denoising is long well-studied issues in image processing, reconstruction and computer vision field, for a variety of image modelling problems. In this proposed work,an extension to traditional deep CNNs, Global skip connection, Normalization, Local pixels statistics, are included to obtain faster training and testing convergence of the model called Local Modified U-net (LM U-net). High frequency information of the pixels lost during decoding process is restored at the encoder end by processing local neighbor pixels. Results shown over60,000 training images, global skip connections providedgood improvements in feature learning for image restoration and denoising. The proposed LM U-net can also be effectively utilized for many other image restoration tasks similar to image super resolution, image deblurring
How Does Duolingo Integrates Emotional Design

This essay aims at examining the patterns of expression of emotional design through analysing a second language learning app called Duolingo, which potentially offers suggestions on emotional design when designing mobile applications for second language acquisition.
Demystifying blockchain by teaching it in computer science

This paper demystifies the advanced computer science topic of block-chain by placing it in the context of course and content development. In presenting suggestions for using blockchain as a tool to...
Toward the pre-cocktail party problem with TasTas+.

Deep neural network with dual-path bi-directional long short-term memory (BiLSTM) block has been proved to be very effective in sequence modeling, especially in speech separation, e.g. DPRNN-TasNet \cite{luo2019dual}, TasTas \cite{shi2020speech}. In this paper, we propose two improvements of TasTas \cite{shi2020speech} for end-to-end approach to monaural speech separation in pre-cocktail party problems, which consists of 1) generate new training data through the original training batch in real time, and 2) train each module in TasTas separately. The new approach is called TasTas$+$, which takes the mixed utterance of five speakers and map it to five separated utterances, where each utterance contains only one speaker's voice. For the objective, we train the network by directly optimizing the utterance level scale-invariant signal-to-distortion ratio (SI-SDR) in a permutation invariant training (PIT) style. Our experiments on the public WSJ0-5mix data corpus results in 11.14dB SDR improvement, which shows our proposed networks can lead to performance improvement on the speaker separation task. We have open-sourced our re-implementation of the DPRNN-TasNet in this https URL, and our TasTas$+$ is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be reproduced with ease.
Model-Based Filtering via Finite Skew Normal Mixture for Stock Data

This paper proposes a flexible finite mixture model framework using multivariate skew normal distribution for banking and credit institutions' stock data in Iran.This method clusters time series stocks data of Iranian banks and credit institutions to filter those data into four groups.The proposed model estimates matrices of time-varying parameter for skew normal distribution mixture using EM algorithm, updating the estimated parameters via generalized autoregressive score (GAS) model.Empirical studies are conducted to examine the effect of the proposed model in clustering, estimating, and updating parameters for real data from 12 sets of stocks.Our stock data were filtered in four trade clusters with best performance.
Learning spatial-channel regularization jointly with correlation filter for visual tracking

The boundary effect of correlation filters is one key issue to limit the performance of visual tracking. Most existing methods focus on using regularization to constrain filters in the spatial domain, but less attention is paid to the channel information that is also important to enhance the discriminative ability of the filter. In this paper, we propose to learn the spatial-channel regularization jointly with the filter for visual tracking. Specifically, the channel regularization is integrated into the spatial regularization, which is exploited to make the filter more compact in spatial and channel domains. It benefits to improve the discriminative ability to identify the target from the background, even distractors. Additionally, the temporal coherence of the target is developed to enable the filter to be robust. The temporal regularization can suppress the sudden change of the spatial-channel regularization on the time axis, which can indirectly control the filter to pay attention to the target’s robust features. The regularization and filter can be jointly optimized by the alternating direction method of multipliers (ADMM) algorithm. We evaluate our method on a standard database. The results show that compared with traditional methods, our tracker has improved significantly in performance and can achieve real-time tracking.
KNOWLEDGE ON MOLE CONCEPT: THE STRUCTURE OF MEMORY NETWORK

Over the past few decades, researchers have focused on the lack of conceptual understanding of mole concept because it is considered to be difficult for students globally. Due to its abstract and mathematical nature, chemistry teachers tend to use algorithmic teaching strategies to provide learning experiences for students, resulting in superficial understanding of the concept. Past researchers have focused more on teaching strategies and learning experiences in the classroom for the topic but relatively less on students’ mental processes. The knowledge embedded in the mind of learners is the result of the learning experiences they are subjected to by their teachers. The objective of this study is to investigate how knowledge on mole concept is organised in the brain of learners using the Connectivity Model of Semantic Processing (CMSP). This theory explains that knowledge is structured in the form of memory networks in the brain, either in the form of hierarchical memory network (HMN) or interconnected memory network (IMN). The mind maps generated by research participants on this concept were analysed based on CMSP as the representation of their memory networks. Then it was compared with their learning experiences in the classroom. The findings show that those who had IMN were able to inter-relate concepts as they were given more emphasis on thinking process during the lesson and the concepts were taught simultaneously. On the contrary, those who learnt one concept after another, separately, had HMN and were not able to relate concepts.
Mixed geometric loss for bounding box regression in object detection

Predicting bounding box with higher intersection over union (IoU) is one of the most important issues in many computer vision tasks. The ℓn-norm loss and IoU-based loss are two conventional approaches to guide a training process in recent methods. However, the optimization direction of ℓn-norm loss is not exactly the same as maximizing the metric. In addition, IoU-based loss suffers from some inevitable disadvantages due to the direct addition of IoU. According to the shape, size, and position properties, we design a mixed geometric (MG) regression loss to increase the similarity and the overlapping area of two bounding boxes. The shape is described by the cosine similarity of diagonal vectors, the size is measured by the length or width of the boxes, and the location is calculated by the center positions of the boxes. Simulation experiments verify that the proposed MG loss can achieve competitive convergence speed and regression accuracy. By introducing the state-of-the-art models in object detection, experiments are carried out on a well-known benchmark dataset, and the results demonstrate the effectiveness of our method in object detection.
Building a Framework for Automated Security Testbeds in Cloud Infrastructures

When exposed to the network, applications and devices are exposed to constant security risks. This puts pressure on hardware and software vendors to test even more than before how secure applicatio ...
Implementation of Dakon-themed game media: Encourage curiosity 21st century in primary school

Thematic learning, especially thematic integrative, basically requires the optimization of the use of varied learning media so that it will help students in understanding the concepts of material which are abstract. Therefore, the use of learning media is important in learning. This study aims to reveal: 1) the eligibility of Dakon-themed game media on the topic “Where I Live”, and 2) the effectiveness of Dakon-themed game media in improving the curiosity of grade IV students of elementary school. This development research was conducted in Kulonprogo Regency, Special Region of Yogyakarta, Indonesia and developed the media according to the 10 steps developed by Borg Gall. The testing subjects consisted of 9 students in the initial field trial phase, 34 students in the main field trial and 74 students in the operational field trial which were divided into experimental and control groups. The data collection was trough tests, expert rating scale and response questionnaire. The effectiveness of the media of media on the theme of topic Where I live in improving the curiosity of analytical students with N-gain, and t-test with an alpha level of 0.05.The results reveal that: (1) the developed Dakon-theme on the topic Where I Live meets the eligibility criteria based on the results of the validation with very feasible criteria; and (2) the developed game is effective in improving curiosity of the fourth grade students of elementary schools.
Pengembangan Sistem Informasi Industri Jasa Menjahit Online Berbasis Web Menggunakan Metode Waterfall

Information systems are currently increasing very rapidly, but it is unfortunate if the utilization is not yet optimal. Reviewing the data from literature studies and observations that have been made, many people need sewing services. The drastic increase in demand makes competition in the convection industry. Most of the work systems in the convection industry are done manually and are not economical. Product development also lacks creativity. Therefore, this researcher will use IT as marketing and design work, this can make processing time shorter and more optimal. Thus, customers will be facilitated in ordering ready-made clothes or clothes that match the customer's wishes by using a web application that only sends data on the size of the clothes or clothing model that the customer wants. This web-based application system can make it easier for customers to transact with the owner and transactions do not have to meet face to face. In addition, clothing sales and large-scale orders can be neatly organized and financial reports can be well structured and organized clearly. The results of this research will be in the form of research reports and web-based online sewing service information systems using the waterfall method.