Reviewerly/exHarmony huggingface dataset:
Designing Digital Musical Instruments

Important considerations for the design of a Digital Musical Instrument (DMI) arise at the intersection of the disciplines of New Musical Interface design (NMI), Human-computer Interaction (HCI), and Interaction Design (IxD). A review of existing research indicates that the ‘success’ of a DMI requires a consideration and support of the experience expectations of three different stakeholders: the musician, the ensemble and the audience within the contexts of composition and performance. Additionally, there is recognition that the instrument also participates in the relationships that arise in composition and performance. DMIs, therefore, stretch NMI, HCI, and IxD into the domains of creative practice and performance. Whilst it is commonly understood by the disciplines of NMI, HCI and IxD that experience has a strong relationship with the more commonly recognized aspects of user experience (UX) such as usability, an understanding of the other humanistic aspects of experience needs further exploration. Many challenges and questions have arisen around the design of DMIs and the considerations that are relevant. This research explores the specific needs of the digital musician and relationships that arise between the composer, digital musician, the DMI, and the audience. The starting point for this research is the DMI Soundscape – a large-scale touch-based interface made of fabric and sensors that provides the opportunity for digital musicians to play and compose music while also providing additional mechanisms to support expression during performance. The research approach of this project is informed by the philosophies of participatory design and research through design. It also takes an ecological view of the relationships that arise between composer, digital musician, DMI and audience that has strong alignments with third-wave HCI considerations as elements of experience. The involvement of musicians as integral to the design process of a new DMI with the stated goal of public performance is aimed at bringing into greater relief the nature of the ecological relationships and the musician's needs within that ecology.
Improving Students’ Grammar Mastery in Writing Recount Text by Using Dyadic Essay

The objectives of this research are (1) to describe how Dyadic Essay could improve grammar mastery in writing recount text by applying the appropriate type of Dyadic Essay, kind of recount text, and kind of exercises are needed by the students; (2) to describe the class situation when the Dyadic Essay is implemented in teaching writing recount text. The method of this research is Classroom Action Research. The subject of the research is eighth grade students of one state junior high school in Surakarta. The research was conducted from December 2019 to March 2020 and the treatment was conducted from January 2020 to February 2020 and it was carried out in two cycles with 8 meetings. The procedures of the research consist of planning, acting, observing, and reflecting. The research data were analyzed by quantitative and qualitative analysis. The results of the research were obtained from comparing score among pre-test, test in cycle 1, and test in cycle 2. It showed that Dyadic Essay technique could improve students’ grammar mastery in writing recount text especially writing personal recount. The exercises needed to the students are reading the text, writing practices, and did homework. From the classroom situation, I found that (a) the students had stronger intrinsic motivation and greater interest in the topic; (b) the students’ active participation in writing class increased; (c) Dyadic Essay technique is helpful for working in pairs and independent activities. Therefore, the researcher recommended teachers and students to use Dyadic Essay technique as an alternative technique in teaching-learning of writing.
A missing link: a distributed cognitive perspective on fit

Purpose This article presents a cognitive framework to study dynamic/adaptive aspects of a collection of popular fit measures used in organisation research, in an attempt to highlight what there is to be gained. Design/methodology/approach This paper uses a distributed e-cognition (DEC) framework to examine the current organisational literature of fit measures. Findings This paper highlights that most measures have a rather narrow focus and do not address dynamic/adaptive aspects in complex social systems (e.g. organisations). To both provide a way to integrate fit measures and cover the cognition gap in this literature, this article highlights the need for a more sophisticated measure. Originality/value This paper provides a novel approach to examining organisational fit literature through a distributed (e)-cognitive framework.
Input Dependent Sparse Gaussian Processes

Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature. Nevertheless, GPs suffer from poor scalability as the number of training instances N increases. More precisely, they have a cubic cost with respect to $N$. To overcome this problem, sparse GP approximations are often used, where a set of $M \ll N$ inducing points is introduced during training. The location of the inducing points is learned by considering them as parameters of an approximate posterior distribution $q$. Sparse GPs, combined with variational inference for inferring $q$, reduce the training cost of GPs to $\mathcal{O}(M^3)$. Critically, the inducing points determine the flexibility of the model and they are often located in regions of the input space where the latent function changes. A limitation is, however, that for some learning tasks a large number of inducing points may be required to obtain a good prediction performance. To address this limitation, we propose here to amortize the computation of the inducing points locations, as well as the parameters of the variational posterior approximation q. For this, we use a neural network that receives the observed data as an input and outputs the inducing points locations and the parameters of $q$. We evaluate our method in several experiments, showing that it performs similar or better than other state-of-the-art sparse variational GP approaches. However, with our method the number of inducing points is reduced drastically due to their dependency on the input data. This makes our method scale to larger datasets and have faster training and prediction times.
웹 코퍼스와 불한 번역 등가어 - 코로나바이러스 관련 연어 관계를 중심으로

Collocations are lexical or grammatical combinations of preferred words in certain uses. In corpus linguistics, collocations can be explored using several methods such as co-occurrences and the n-gram, which allow the lexical distributions around a keyword in a big corpus to be closely observed. Corpus-based translation studies also use these methods to find out the translation correspondences of collocations between two different languages. This article aims to observe co-occurrences between French and Korean and to find their translation equivalences in a Web as Corpus concerning the coronavirus. After building a coronavirus-centered French-Korean corpus from the web, we examined co-occurrences of some keywords like coronavirus/‘코로나 바이러스’, cas/‘확진자’, and vaccin/‘백신’. This method allowed us to carefully examine co-occurring words between French and Korean and to investigate their translation characteristics. In addition, we verified the usefulness of the Web as Corpus in this type of study with regard to specific subjects.
Sistem Informasi Manajemen Masjid (Studi Kasus: Masjid Al-Falah Darul Muttaqin)

Plenary mosque is a term of mosque popularized by the government of Pekanbaru City, Riau. Each plenary mosque follows the standardization of functions and gets direct assistance from the government. One of the plenary mosques in Pekanbaru city is Al-Falah Darul Muttaqin Mosque. The many activities and information that exist in Al-Falah Darul Muttaqin mosque, make the mosque administrator need more time in managing the information in the mosque. In managing the information, administrator Al-Falah Darul Muttaqin mosque still manages manually who has a higher risk of human error and takes longer to fix. Therefore, a website-based mosque management information system is built that can manage the information in Al-Falah Darul Muttaqin mosque using incremental model methods and can provide precise, and accurate information that can be accessed by the public. This model has stages for the design of its software, namely analysis, design, code, test. Based on the results of tests conducted against systems built using blackboxes, it is obtained that the system has been successfully built as expected. In the test user acceptance testing system built obtained that the system is in accordance with the needs or demands of the user and the function is running properly and correctly.
А Content and forms of tests in mathematics. Requirements for the preparation of pedagogical tests

The article highlights important aspects of testing the educational activities of students. In the approach to the test methodology, diametrically opposed views are presented, and the author also expressed his opinion on this matter. The word "test" has been shown to have a broad meaning. However, the article notes that the main object of the research is the term "pedagogical test". The advantages of the test method are listed in order. The difference between the advantages and disadvantages of this method is noted. Among the advantages are the short duration of the test and its coverage of a wide student audience, objectivity, the ability to take into account the individual qualities of students. The article discusses the general requirements for the construction of tests, in connection with which the opinions of methodologists are given and analyzed. Among these examples, the main requirement for test organization was noted - validity. The concept of "validity" is explained. Not all test requirements are equally important - attention was also paid to this issue. The question of the general classification of tests was briefly touched upon. After that, test samples in mathematics were presented. During the presentation of the tests, the main focus was on non-standard tests. In our opinion, it is better to develop students' creative thinking with the help of non-standard tests. Finally, at the end, a conclusion was made, where a high assessment of educational activity was given using the test method.
Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework

We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior components, Skellam-distributed hidden variables and observed data. Two inference algorithms are derived: Expectation-Maximization (EM) algorithm for maximum \emph{a posteriori} estimation and Variational Bayes EM (VBEM) for full Bayesian inference, including the estimation of parameters prior distribution. From this Skellam-based model, we also introduce a new divergence $\mathcal{D}$ between a real-valued target data $x$ and two nonnegative parameters $λ_{0}$ and $λ_{1}$ such that $\mathcal{D}\left(x\midλ_{0},λ_{1}\right)=0\Leftrightarrow x=λ_{0}-λ_{1}$, which is a generalization of the Kullback-Leibler (KL) divergence. Finally, we conduct experimental studies on those new algorithms in order to understand their behavior and prove that they can outperform the classic SNMF approach on real data in a task of automatic clustering.
Discriminative Mutual Information Estimators for Channel Capacity Learning

Channel capacity plays a crucial role in the development of modern communication systems as it represents the maximum rate at which information can be reliably transmitted over a communication channel. Nevertheless, for the majority of channels, finding a closed-form capacity expression remains an open challenge. This is because it requires to carry out two formidable tasks a) the computation of the mutual information between the channel input and output, and b) its maximization with respect to the signal distribution at the channel input. In this paper, we address both tasks. Inspired by implicit generative models, we propose a novel cooperative framework to automatically learn the channel capacity, for any type of memory-less channel. In particular, we firstly develop a new methodology to estimate the mutual information directly from a discriminator typically deployed to train adversarial networks, referred to as discriminative mutual information estimator (DIME). Secondly, we include the discriminator in a cooperative channel capacity learning framework, referred to as CORTICAL, where a discriminator learns to distinguish between dependent and independent channel input-output samples while a generator learns to produce the optimal channel input distribution for which the discriminator exhibits the best performance. Lastly, we prove that a particular choice of the cooperative value function solves the channel capacity estimation problem. Simulation results demonstrate that the proposed method offers high accuracy.
Multimodal Representation for Neural Code Search

Semantic code search is about finding semantically relevant code snippets for a given natural language query. In the state-of-the-art approaches, the semantic similarity between code and query is quantified as the distance of their representation in the shared vector space. In this paper, to improve the vector space, we introduce tree-serialization methods on a simplified form of AST and build the multimodal representation for the code data. We conduct extensive experiments using a single corpus that is large-scale and multi-language: CodeSearchNet. Our results show that both our tree-serialized representations and multimodal learning model improve the performance of code search. Last, we define intuitive quantification metrics oriented to the completeness of semantic and syntactic information of the code data, to help understand the experimental findings.
A Survey on Personal Image Retrieval Systems

The number of photographs taken worldwide is growing rapidly and steadily. While a small subset of these images is annotated and shared by users through social media platforms, due to the sheer number of images in personal photo repositories (shared or not shared), finding specific images remains challenging. This survey explores existing image retrieval techniques as well as photo-organizer applications to highlight their relative strengths in addressing this challenge.
The Neighbor in My Left Hand: Development and Evaluation of an Integrative Agent System With Two Different Devices

We propose a new wearable agent, ''FinU (Friend in You),'' based on the hypothesis that inducing social facilitation effect, i.e., the effect of having other agents around to increase the task performance, is possible, even for wearable agents with a single sensory modality, by establishing beliefs about the agent in advance.Our agent system, FinU, consists of two devices: FinU-Box, a box-shaped device that synchronizes visual, auditory, and tactile stimuli to represent an agent living on the user's hand, and the FinU-Band, a wearable device worn on the hand with a sensory modality limited to tactile stimuli.In this study, in addition to the development of the system, we also conducted preliminarily evaluation by dividing the participants into two experimental conditions: one in which subjects interacted with the agent using the FinU-Box beforehand, and one in which participants did not use the FinU-Band.By having the user learn beliefs about the agent from the FinU-Box, it was expected that the user would attribute higher presence to the agent than if they had not learned beliefs.Participants were then tested on a continuous performance task to measure the effect of social facilitation, which was expected to improve performance, and their performance was compared across conditions.We assumed that higher performance indicated a stronger sense of agent presence.The results showed that the social facilitation effect was stronger for participants who had experienced the FinU-Box beforehand, as they responded more accurately to the task stimuli.This suggests that our system, which combining a device that presents a rich sensory experience with a wearable device that presents a single sensory experience would allow the agent's presence to be more strongly felt by the user.
Centralized Model and Exploration Policy for Multi-Agent RL

Reinforcement learning (RL) in partially observable, fully cooperative multi-agent settings (Dec-POMDPs) can in principle be used to address many real-world challenges such as controlling a swarm of rescue robots or a team of quadcopters. However, Dec-POMDPs are significantly harder to solve than single-agent problems, with the former being NEXP-complete and the latter, MDPs, being just P-complete. Hence, current RL algorithms for Dec-POMDPs suffer from poor sample complexity, which greatly reduces their applicability to practical problems where environment interaction is costly. Our key insight is that using just a polynomial number of samples, one can learn a centralized model that generalizes across different policies. We can then optimize the policy within the learned model instead of the true system, without requiring additional environment interactions. We also learn a centralized exploration policy within our model that learns to collect additional data in state-action regions with high model uncertainty. We empirically evaluate the proposed model-based algorithm, MARCO, in three cooperative communication tasks, where it improves sample efficiency by up to 20x. Finally, to investigate the theoretical sample complexity, we adapt an existing model-based method for tabular MDPs to Dec-POMDPs, and prove that it achieves polynomial sample complexity.
Kesiagaan Dalam Perencanaan Anggaran Penanggulanan Bencana Banjir Kabupaten Bandung

The main problem of this research is that there is no special budget for flood disaster management in Cibiru Hilir Village. This study aims to analyze the design of village funds in flood disaster management and analyze village efforts to raise additional funds for flood disaster management in Cibiru Hilir village. The research method used is descriptive qualitative. Collecting data using interviews, observation, and documentation study. Data analysis was carried out, namely data reduction, data presentation, and concluding. The results of this study indicate that the embodiment of the principle of transparency is quite well implemented with the ease of expressing opinions and openness, the accountability process for budget management issued, accountability to the public, and follow-up on irregularities by the rules, and the application of value for money in 2020 and 2021. still not running optimally in the absence of a special budget for flood disaster management and 2018 and 2019 it has been running economically and flood disaster management services have been running well.
Learning Algebraic Recombination for Compositional Generalization

Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynamically recombining structured expressions in a recursive manner. However, most previous studies mainly concentrate on recombining lexical units, which is an important but not sufficient part of algebraic recombination. In this paper, we propose LeAR, an end-to-end neural model to learn algebraic recombination for compositional generalization. The key insight is to model the semantic parsing task as a homomorphism between a latent syntactic algebra and a semantic algebra, thus encouraging algebraic recombination. Specifically, we learn two modules jointly: a Composer for producing latent syntax, and an Interpreter for assigning semantic operations. Experiments on two realistic and comprehensive compositional generalization benchmarks demonstrate the effectiveness of our model. The source code is publicly available at https://github.com/microsoft/ContextualSP.
Quick and Efficient Algorithm to Compare Trees

The tree is an important data structure in computer science and it finds its applications in various fields right from representing hierarchical data to implementing faster search algorithms or even in compiler design. This paper proposes a simple method to visualize the differences and similarities between any two trees. The result of the comparison is known as the Difference Tree which provides a graphical notation to intuitively understand the differences.
Grieg's Sonic Afterimages

As Neo-Riemannian transformations find their firm place in the analytical tool-kit, their function changes from a heuristic to describe harmonic progressions to a full-fledged analytical methodology. In this phase, the question of meaning looms large. Richard Cohn's (2004) associations between hexatonic poles and the uncanny have done much to pave the way toward a hermeneutics of Neo-Riemannian theory. This analytical vignette focuses on an intriguing SLIDE progression in Grieg's final Lyric Piece , "Efterklang," and offers a contextual analysis in which this harmonic transformation becomes meaningful. While the interpretation offered here remains specific to the piece at hand, the hermeneutic analysis may be used as a model for further such endeavours.
SinSpell: A Comprehensive Spelling Checker for Sinhala

We have built SinSpell, a comprehensive spelling checker for the Sinhala language which is spoken by over 16 million people, mainly in Sri Lanka. However, until recently, Sinhala had no spelling checker with acceptable coverage. Sinspell is still the only open source Sinhala spelling checker. SinSpell identifies possible spelling errors and suggests corrections. It also contains a module which auto-corrects evident errors. To maintain accuracy, SinSpell was designed as a rule-based system based on Hunspell. A set of words was compiled from several sources and verified. These were divided into morphological classes, and the valid roots, suffixes and prefixes for each class were identified, together with lists of irregular words and exceptions. The errors in a corpus of Sinhala documents were analysed and commonly misspelled words and types of common errors were identified. We found that the most common errors were in vowel length and similar sounding letters. Errors due to incorrect typing and encoding were also found. This analysis was used to develop the suggestion generator and auto-corrector.
eMMI (e-MODULE MULTIPLE INTELLIGENCE) : MODUL FISIKA DASAR MAHASISWA DENGAN 3D PAGEFLIP PRO

This study aims to produced eMMI based on multiple intelligence (eMMI) with 3D pageflip pro for biology student’s in IKIP Budi Utomo Malang, in the circulatory system material that is suitable for used in the learning process. This research was developed with a 4-D model consisting of definitions, planning, development and dissemination. Data collection using a questionnaire. This eMMI have been validated by material experts and media experts.The evaluation results from material validation, is known that above 90%, while the results of media validation are above 96%. Small scale test results indicate the validity results above 90%. From the results of the validation of media experts and material experts, it is stated that the e-eMMIe is suitable for use and based on the questionnaire, it can be said that students and teachers are expected to really take advantage of teaching materials in order to create interesting and fun learning in physics learning.