Reviewerly/exHarmony huggingface dataset:
A Synchronous Magnitude Estimation with P-Wave Phases’ Detection Used in Earthquake Early Warning System

How to estimate an earthquake's magnitude rapidly and accurately is a challenge for any earthquake early warning system. In order to reach a balance between accuracy and timeliness, a synchronous magnitude estimation method with P-wave phases' detection is proposed. In this method, the P-wave phases are detected by the changes of the signal-to-noise ratio (SNR) of the seismic records, where the SNRs are calculated by the short-term power and long-term power ratio (STP/LTP). Meanwhile, the variations of the SNR are applied to estimate the magnitude of the earthquake. By the statistics of some earthquake cases, a synchronous magnitude estimation model of the variation of the P-wave phases' SNR, the earthquake magnitude, and the hypocentral distance was built. Compared with some other magnitude estimation methods, the suggested method inherits the robustness of the STP/LTP method and is more accurate and rapid than the peak displacement (Pd) method.
Design and Build a Web App-Based Conference Registration System Using the Waterfall Model

During the pandemic, many events are held online. conference registration (webinars, seminars, workshops) which basically will cause its complexity for admins if there are excess registrants or over participants from achieving the registration quota target. In addition, the admin must monitor regularly to ensure that there are no excess registrants that result in inefficient. The method used in the development of this application is the waterfall method. The purpose of this research is to build a web-based conference registration system that can make it easier for admins/organizers to manage the registration process. The results of this study the system can display the results of the recap from the admin so that the user can see the quota from the conference registration organized by the committee as well as testing of users from several tests to produce an application system that runs well.
IMPROVING LEARNING MATHEMATICS ACTIVITY WITH THE THINK PAIR SHARE LEARNING MODEL

Students are required to be actively involved during the learning process in accordance with the development of 21st-century learning. Student learning activities can occur through expressing opinions, solving problems, and discussing materials during the learning process. Learning models should always implement the stimulation of these abilities. Thus, this study is to analyze the influence of the Think Pair Share learning model on students' learning activities. This study was an experimental study with a pretest-posttest control group design. The population in this study was all class 8 as many as 192 students in one of the junior high schools in Yogyakarta, Indonesia. Students belonging to class VIIIA (32 students) were the experimental students' group, and students in VIIIC (32 students) were the control group. Data was collected based on 15 items of students’ activeness in learning mathematics. The results demonstrated that the application of the Think Pair Share learning model had an impact on the activeness of student learning. In addition, Think Pair method can improve the activeness of learning mathematics students, higher than in classes that implemented conventional learning models (25.31 &gt; 15.78). This is because the application of the Think Pair share learning model provides opportunities for students to think, respond, and present problems provided by teachers so that students are fully involved during the mathematics learning process. The recommendations for further research can be carried out in several schools not just one school, student learning activity can also be increased by using other cooperative models, and can be further improved whether there are other competences that can boost the model's usage.
TOPIC MODELING BY USING LDA

Topic modelling plays a significant Information retrieval showed good performance on a wide variety of tasks over the years. It has wide research in machine learning and text mining. The combination of the probability mixture model and the term model presents the topic model. They are the language modelling framework of information retrieval. The present topic modelling methods are probabilistic latent semantic analysis and latent dirichlet allocation. Two main faults of topic modelling are First, common or popular words are different topics, often causing ambivalence to understand the topic. Second, single words have to be presented correctly. Actual problems of topic modelling are Efficiency and scaling are discussed and compared in different types of Topic modelling. The review of article is about how topic modelling for information retrieval is done using LDA(Latent Dirichlet Allocation).
The Proto-Langspil: Launching an Icelandic NIME Research Lab with the Help of a Marginalised Instrument

Historically marginalised instruments witness and bear vital stories that can deeply affect identity and galvanise communities when revitalised. We present the proto-langspil as a contemporary interpretation of the langspil, an Icelandic monochord-like folk instrument, and describe its agential and performative contributions to the first Icelandic NIME research lab. This paper describes how the proto-langspil has served as an instrument in establishing the research methodology of our new lab and concretised the research agenda via a series of encounters with music performers and composers, luthiers, anthropologists, musicologists, designers and philosophers. These encounters have informed and challenged our research practices, mapped our surroundings, and embedded us in the local social fabric. We share our proto-langspil for replication, and reflect on encounters as a methodology framing mechanism that eschews the more traditional empirical approaches in HCI. We conclude with a final provocation for NIME researchers to embrace AI research with an open mind.
The Effects of Collaborative Tasks on The Speaking Skills of Malaysian Adolescents in an ESL Classroom

The ability to speak effectively is absolutely essential in the learning and teaching of second or third language.This paper aims to investigate the effects of Collaborative Tasks on the Speaking Skills of Malaysian Adolescents in an ESL Classroom by conducting collaborative speaking tasks.65 students took part in a survey conducted on 16-year-old students from Zone Sentul, Kuala Lumpur.For eight weeks, students were asked to participate in collaborative speaking activities in English language classrooms.Students were asked to complete a five-point Likert Scale Questionnaire utilizing Google Form as a means of gathering data for this research.The data were analyzed by frequency.According to the findings, the majority of students preferred group work speaking activities which trained them to speak confidently during their speaking lessons.The positive replies from the questionnaire revealed that their speaking abilities have been boosted and their teachers' participation had been minimal.with their friends and that the participation of their language teachers would be minimal.This research provides teachers some suggestions for planning group work speaking lessons as one of the most successful strategies for students to improve their speaking abilities.Future research recommendations were also discussed.
Decolonizing the Violin with Active Shoulder Rests (ASRs)

Beginning, amateur, and professional violinists alike make use of a shoulder rest with a typical form factor for ergonomic support.Numerous commercial devices are available.We saturate these inert devices with electronics and actuators to open a new design space for "active shoulder rests" (ASRs), a pathway for violinists to adopt inexpensive and transparent electroacoustic interfaces.We present a dual-mode ASR that features a built-in microphone pickup and parametric control of mixing between sound diffusion and actuation modes for experiments with active acoustics and feedback.We document a modular approach to signal processing allowing quick adaptation and differentiation of control signals, and demonstrate rich sound processing techniques that create lively improvisation environments.By fostering participation and convergence among digital media practices and diverse musical cultures, we envision ASRs broadly rekindling creative practice for the violin, long a tool of improvisation before the triumph of classical works.ASRs decolonize the violin by activating new flows and connectivities, freeing up habitual relations, and refreshing the musical affordances of this otherwise quintessentially western and canonical instrument.
Evaluating polaris~ - An Audiovisual Augmented Reality Experience Built on Open-Source Hardware and Software

Augmented reality (AR) is increasingly being envisaged as a process of perceptual mediation or modulation, not only as a system that combines aligned and interactive virtual objects with a real environment.Within artistic practice, this reconceptualisation has led to a medium that emphasises this multisensory integration of virtual processes, leading to expressive, narrative-driven, and thought-provoking AR experiences.This paper outlines the development and evaluation of the polaris~ experience.polaris~ is built using a set of open-source hardware and software components that can be used to create privacy-respecting and cost-effective audiovisual AR experiences.Its wearable component is comprised of the open-source Project North Star AR headset and a pair of bone conduction headphones, providing simultaneous real and virtual visual and auditory elements.These elements are spatially aligned using Unity and PureData to the real space that they appear in and can be gesturally interacted with in a way that fosters artistic and musical expression.In order to evaluate the polaris~, 10 participants were recruited, who spent approximately 30 minutes each in the AR scene and were interviewed about their experience.Using grounded theory, the author extracted coded remarks from the transcriptions of these studies, that were then sorted into the categories of Sentiment, Learning, Adoption, Expression, and Immersion.In evaluating polaris~ it was found that the experience engaged participants fruitfully, with many noting their ability to express themselves audiovisually in creative ways.The experience and the framework the author used to create it is available in a Github respository.
Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages

Twitter contains an abundance of linguistic data from the real world. We examine Twitter for user-generated content in low-resource languages such as local Indonesian. For NLP to work in Indonesian, it must consider local dialects, geographic context, and regional culture influence Indonesian languages. This paper identifies the problems we faced when constructing a Local Indonesian NLP dataset. Furthermore, we are developing a framework for creating, collecting, and classifying Local Indonesian datasets for NLP. Using twitter's geolocation tool for automatic annotating.
Application of the Neural Network Based on the Multilayer Perceptron Genetic Algorithm in Chinese-English Two-Way Translation

With the increasing application of the perceptron genetic algorithm neural network in Chinese-English two-way translation, there are many translation problems to be solved. In order to solve the translation problem of Chinese-English parallel corpus, the multilayer perceptron method, genetic word alignment model (GA), language model, and neural network method (including the translation model and bilingual pretraining model) are designed, which are combined into the ga-mlp-nn combination model to measure the parallelism of Chinese and English sentences from different emphases. The results show that the ga-mlp-nn model has good performance in filtering high-quality parallel corpus. The final experimental results show that compared with a single system, the improved multisystem fusion method based on weight multiplication has achieved better results in the test set. In the last five groups of evaluation results, the system submitted in this paper ranks second and first in multiple datasets, which has a certain reference value for the research of corpus filtering.
Can pruning improve certified robustness of neural networks?

With the rapid development of deep learning, the sizes of neural networks become larger and larger so that the training and inference often overwhelm the hardware resources. Given the fact that neural networks are often over-parameterized, one effective way to reduce such computational overhead is neural network pruning, by removing redundant parameters from trained neural networks. It has been recently observed that pruning can not only reduce computational overhead but also can improve empirical robustness of deep neural networks (NNs), potentially owing to removing spurious correlations while preserving the predictive accuracies. This paper for the first time demonstrates that pruning can generally improve certified robustness for ReLU-based NNs under the complete verification setting. Using the popular Branch-and-Bound (BaB) framework, we find that pruning can enhance the estimated bound tightness of certified robustness verification, by alleviating linear relaxation and sub-domain split problems. We empirically verify our findings with off-the-shelf pruning methods and further present a new stability-based pruning method tailored for reducing neuron instability, that outperforms existing pruning methods in enhancing certified robustness. Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training, and up to 24.5% under adversarial training on the CIFAR10 dataset. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models across different datasets. Our findings offer a new angle to study the intriguing interaction between sparsity and robustness, i.e. interpreting the interaction of sparsity and certified robustness via neuron stability. Codes are available at: https://github.com/VITA-Group/CertifiedPruning.
On the fast convergence of minibatch heavy ball momentum

Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the gap between theory and practice by showing that stochastic heavy ball momentum retains the fast linear rate of (deterministic) heavy ball momentum on quadratic optimization problems, at least when minibatching with a sufficiently large batch size. The algorithm we study can be interpreted as an accelerated randomized Kaczmarz algorithm with minibatching and heavy ball momentum. The analysis relies on carefully decomposing the momentum transition matrix, and using new spectral norm concentration bounds for products of independent random matrices. We provide numerical illustrations demonstrating that our bounds are reasonably sharp.
Mapper4Live: Using Control Structures to Embed Complex Mapping Tools into Ableton Live

This paper presents Mapper4Live, a software plugin made for the popular digital audio workstation software Ableton Live.Mapper4Live exposes Ableton's synthesis and effect parameters on the distributed libmapper signal mapping network, providing new opportunities for interaction between software and hardware synths, audio effects, and controllers.The plugin's uses and relevance in research, music production and musical performance settings are explored, detailing the development journey and ideas for future work on the project.
PERANCANGAN SISTEM INFORMASI SCM PADA UD. MEBEL JAYA

Technology and information today are growing rapidly and have a major influence on human behavior and habits. Computers are very helpful and facilitate human work, effective and efficient computer work produces accurate information so that many companies use information technology to improve their quality in all aspects, namely to compete in the business world, therefore UD. Mebel Jaya needs to implement a Supply Chain Management Information System (Supply Chain Management) which aims to facilitate inventory management, starting from the process of ordering goods from suppliers, procurement of goods to managing goods to customers. UD.Mebel Jaya is a business engaged in the furniture manufacturing industry which focuses on the production of furniture / furniture such as tables, chairs, cabinets, and frames. The system is made using a web-based system design and uses supporting applications such as XAMPP and the Hypertext Preprocessor (PHP) programming language and DBMS (Database Management System) using MySQL, it is hoped that this application can help companies store all data that is still using a manual system, or not computerized system with the aim of maximizing performance and profit for the company, suppliers, including customers and can provide satisfaction to suppliers and companies so that carrying out business processes will be easy.&#x0D;
A Sea of Words: An In-Depth Analysis of Anchors for Text Data

Anchors (Ribeiro et al., 2018) is a post-hoc, rule-based interpretability method. For text data, it proposes to explain a decision by highlighting a small set of words (an anchor) such that the model to explain has similar outputs when they are present in a document. In this paper, we present the first theoretical analysis of Anchors, considering that the search for the best anchor is exhaustive. After formalizing the algorithm for text classification, we present explicit results on different classes of models when the vectorization step is TF-IDF, and words are replaced by a fixed out-of-dictionary token when removed. Our inquiry covers models such as elementary if-then rules and linear classifiers. We then leverage this analysis to gain insights on the behavior of Anchors for any differentiable classifiers. For neural networks, we empirically show that the words corresponding to the highest partial derivatives of the model with respect to the input, reweighted by the inverse document frequencies, are selected by Anchors.
Designing and worlding: Prototyping equivocal encounters

To sustainably coexist with other ontologies, human beings must overcome the perception of being the center of the world.This article describes how a video log refuted the initial -anthropocentric -hypothesis explaining the breakdown of an experiment with Judy and Gombe, chimpanzees that inhabit the National Zoo of Chile.This shows that it is wrong to ascribe human affects and reactions to other ontologies, and also that there is a long way to go to apprehend, comprehend, and sustainable coexist with other ontologies in more-than-human ecosystems.
VCT: A Video Compression Transformer

We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, including motion prediction and warping operations, resulting in complex models. Instead, we independently map input frames to representations and use a transformer to model their dependencies, letting it predict the distribution of future representations given the past. The resulting video compression transformer outperforms previous methods on standard video compression data sets. Experiments on synthetic data show that our model learns to handle complex motion patterns such as panning, blurring and fading purely from data. Our approach is easy to implement, and we release code to facilitate future research.
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution

Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains. However, given a new detection task, it is unclear how to choose an algorithm to use, nor how to set its hyperparameter(s) (HPs) in unsupervised settings. HP tuning is an ever-growing problem with the arrival of many new detectors based on deep learning, which usually come with a long list of HPs. Surprisingly, the issue of model selection in the outlier mining literature has been "the elephant in the room"; a significant factor in unlocking the utmost potential of deep methods, yet little said or done to systematically tackle the issue. In the first part of this paper, we conduct the first large-scale analysis on the HP sensitivity of deep OD methods, and through more than 35,000 trained models, quantitatively demonstrate that model selection is inevitable. Next, we design a HP-robust and scalable deep hyper-ensemble model called ROBOD that assembles models with varying HP configurations, bypassing the choice paralysis. Importantly, we introduce novel strategies to speed up ensemble training, such as parameter sharing, batch/simultaneous training, and data subsampling, that allow us to train fewer models with fewer parameters. Extensive experiments on both image and tabular datasets show that ROBOD achieves and retains robust, state-of-the-art detection performance as compared to its modern counterparts, while taking only $2$-$10$\% of the time by the naive hyper-ensemble with independent training.
OpenCitations, an open e-infrastructure to foster maximum reuse of citation data

OpenCitations is an independent not-for-profit infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. OpenCitations collaborates with projects that are part of the Open Science ecosystem and complies with the UNESCO founding principles of Open Science, the I4OC recommendations, and the FAIR data principles that data should be Findable, Accessible, Interoperable and Reusable. Since its data satisfies all the Reuse guidelines provided by FAIR in terms of richness, provenance, usage licenses and domain-relevant community standards, OpenCitations provides an example of a successful open e-infrastructure in which the reusability of data is integral to its mission.
The Concentric Sampler: A musical instrument from a repurposed floppy disk drive

This paper focuses on the redundancy and physicality of magnetic recording media as a defining factor in the design of a lo-fi audio device, the Concentric Sampler.A modified floppy disk drive (FDD) and additional circuitry enables the FDD to record to and playback audio from a 3.5" floppy disk.The Concentric Sampler is designed as an instrument for live performance and a tool for sonic manipulation, resulting in primitive looping and time-based granular synthesis.This paper explains the motivation and background of the Concentric Sampler, related applications and approaches, its technical realisation, and its musical possibilities.To conclude, the Concentric Sampler's potential as an instrument and compositional tool is discussed alongside the future possibilities for development.
Exploring Capabilities of Monolingual Audio Transformers using Large  Datasets in Automatic Speech Recognition of Czech

In this paper, we present our progress in pretraining Czech monolingual audio transformers from a large dataset containing more than 80 thousand hours of unlabeled speech, and subsequently fine-tuning the model on automatic speech recognition tasks using a combination of in-domain data and almost 6 thousand hours of out-of-domain transcribed speech. We are presenting a large palette of experiments with various fine-tuning setups evaluated on two public datasets (CommonVoice and VoxPopuli) and one extremely challenging dataset from the MALACH project. Our results show that monolingual Wav2Vec 2.0 models are robust ASR systems, which can take advantage of large labeled and unlabeled datasets and successfully compete with state-of-the-art LVCSR systems. Moreover, Wav2Vec models proved to be good zero-shot learners when no training data are available for the target ASR task.