Reviewerly/exHarmony huggingface dataset:
Dialect-Based Noisy Speech Dataset, Pre-Processing Tools, and Recognition Models for Amharic

Nowadays, to facilitate deep learning for voice recognition, a large number of dialect-based speech dataset for different languages have been produced. However, not such more attention is given for low-resourced languages as Amharic. Amharic is the second most widely spoken Semitic language in the world next to Arabic. The main challenges in solving the Amharic speech recognition problem are the limited availability of parallel speech-text corpora and the complex morphological nature of the language. One way to improve the speech recognition model is to incorporate different dialects into the speech data. Available speech recognition dataset are prepared in a quiet room (studio) to reduce the background noise of the speech, however, in the real environment the speech is noisy due to several interference. In this paper, we present Amharic speech dataset which covers three Ethiopian dialects, namely, Gojjam, Wollo, and Shewa; and the dialects are recorded in a noisy environment. During the feature extraction process, phoneme is extracted from the speech data using Mel Frequency Cepstral Coefficient (MFCC) which is then fed into the training model of Recurrent Neural Network (RNN) and Connectionist Temporal Classification (CTC). The three individual dialects Shewa, Gojjam, and Wollo achieved WER of 31.6, 27.71, and 31.2, respectively. The merged of the three dialect dataset achieved a WER of 19.3. This is the first noisy and dialect-based work and our Amharic speech recognition model also came up with a comparable outcome.
Digital curation and complex decision making: A school district’s literacy initiative

Schools are in the process of incorporating professional development activities to prepare technology-proficient educators to utilize information and communication tools to meet the pedagogical needs of 21st-century students and learners.This research addresses the challenges of and preservation possibilities for the digital work of learning communities across instructional domains and levels.The digital curation efforts that support a school district's literacy and curriculum initiatives are examined.We conclude with suggestions for future research, professional development, and practice.
PELAKSANAAN PENGELOLAAN KEARSIPAN PADA PT WILO PUMPS INDONESIA JAKARTA UTARA

PT Wilo Pumps Indonesia is a supplier company in the field of building services, water management, and industry. Has a wide range of products, especially pumps for building service markets, water management markets, and industrial markets. Wilo is synonymous with high efficiency, high quality, and reliable products. Product innovation regularly sets new standards in terms of energy efficiency and sustainability in the handling of natural resources. Public and private sector organizations, in administrative activities will always be associated with archives. The organization's archive procedures can provide information that is important and needed for the running of the organization. With the increasing activity and dynamics of the organization, it will bring a tendency to increase the need for information to support the decision-making process to achieve goals. The data collection method in the preparation of this article is the method of observation, interviews, and literature study with the analytical method in the form of qualitative analysis, namely the method of data analysis without using static analysis. The results of the implementation of archive management at PT Wilo Pumps Indonesia through the receptionist, secretary, and archivist. Implementation of archive management in the form of sorting, naming documents, indexing, inputting, and storing.
APLIKASI PENGETAHUAN BANGUN RUANG BERBASIS ANDROID UNTUK KELAS V SD ISLAM AT-TAQWA

This android-based spatial knowledge application was created as a media to support the learning of Class V students of SD Islam At-Taqwa about building space that can be done anywhere and anytime, helping students to recognize the theory and concepts of building space, in addition so that students - students have alternative learning media besides books and foster student interest in learning mathematics. Based on the application that has been made by the author, it can be concluded that, Smartphones with the Android operating system are the ones that are widely used in the community, Android is an operating system that supports script writing using the Java language, The spatial application created by the author helps in learning geometry for students. Grade V elementary school students and the general public who want to use the application to help calculate the results of calculating the area and volume of each shape and the manual calculation formula.
Testing of The Method of Gama I Synthetic Unit Hydrograph in The Analysis of The Tojo Watershed Design Flood

Abstract Floods occur due to several factors including topographic conditions, high rainfall intensity, blockage of water flow, lack of water infiltration areas, deforestation, and so on. The Tojo watershed is included in the Parigi-Poso river area, with an area of 211,830 km2 and a river length of 25.52 km. In the Tojo watershed, it is necessary to study flooding, because the watershed always flows every year. This research aims to find out how much flood discharge was obtained from the analysis of the Tojo watershed design flood with the Gama I synthetic unit hydrograph method and to find out how much the Gama I synthetic unit hydrograph analysis compared to the design flood discharge based on river discharge data (AWLR). From the calculation results obtained a comparison between the design flood discharge value processed by rainfall data using the Gama I Synthetic Unit Hydrograph method to the design flood discharge value processed by the Tojo river discharge data using the log person III distribution method for a 2 year return period is 1957,45 %, for the 5-year return period is 1347,64 %, for the 10 year return period is 1224,65 %, for the 25 year return period is 1169,96 %, for the 50 year return period is 1165,67 % and for the 100 years is 1178,75 %.
Plug-and-Play Recipe Generation with Content Planning

Recent pre-trained language models have shown promising capabilities in generating fluent and realistic natural language text. However, generating multi-sentence text with global content planning has been a long-existing research question. Current approaches for controlled text generation can hardly address this issue, as they usually condition on single known control attributes. In this study, we propose a low-cost yet effective framework which explicitly models the global content plan of the generated text. Specifically, it optimizes the joint distribution of the natural language sequence and the global content plan in a plug-and-play manner. We conduct extensive experiments on the well-established Recipe1M+ benchmark. Both automatic and human evaluations verify that our model achieves the state-of-the-art performance on the task of recipe generation
A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates

Most quantum circuits require SWAP gate insertion to run on quantum hardware with limited qubit connectivity. A promising SWAP gate insertion method for blocks of commuting two-qubit gates is a predetermined swap strategy which applies layers of SWAP gates simultaneously executable on the coupling map. A good initial mapping for the swap strategy reduces the number of required swap gates. However, even when a circuit consists of commuting gates, e.g., as in the Quantum Approximate Optimization Algorithm (QAOA) or trotterized simulations of Ising Hamiltonians, finding a good initial mapping is a hard problem. We present a SAT-based approach to find good initial mappings for circuits with commuting gates transpiled to the hardware with swap strategies. Our method achieves a 65% reduction in gate count for random three-regular graphs with 500 nodes. In addition, we present a heuristic approach that combines the SAT formulation with a clustering algorithm to reduce large problems to a manageable size. This approach reduces the number of swap layers by 25% compared to both a trivial and random initial mapping for a random three-regular graph with 1000 nodes. Good initial mappings will therefore enable the study of quantum algorithms, such as QAOA and Ising Hamiltonian simulation applied to sparse problems, on noisy quantum hardware with several hundreds of qubits.
REAP: A Large-Scale Realistic Adversarial Patch Benchmark

Machine learning models are known to be susceptible to adversarial perturbation. One famous attack is the adversarial patch, a sticker with a particularly crafted pattern that makes the model incorrectly predict the object it is placed on. This attack presents a critical threat to cyber-physical systems that rely on cameras such as autonomous cars. Despite the significance of the problem, conducting research in this setting has been difficult; evaluating attacks and defenses in the real world is exceptionally costly while synthetic data are unrealistic. In this work, we propose the REAP (REalistic Adversarial Patch) benchmark, a digital benchmark that allows the user to evaluate patch attacks on real images, and under real-world conditions. Built on top of the Mapillary Vistas dataset, our benchmark contains over 14,000 traffic signs. Each sign is augmented with a pair of geometric and lighting transformations, which can be used to apply a digitally generated patch realistically onto the sign. Using our benchmark, we perform the first large-scale assessments of adversarial patch attacks under realistic conditions. Our experiments suggest that adversarial patch attacks may present a smaller threat than previously believed and that the success rate of an attack on simpler digital simulations is not predictive of its actual effectiveness in practice. We release our benchmark publicly at https://github.com/wagner-group/reap-benchmark.
Vocabulary Learning Strategies of Indonesian EFL Learners in Malaysia: A Focus on Gender

Vocabulary learning is vital for EFL students. Students face difficulties in learning new English vocabulary. Meanwhile, few studies have investigated the strategies used by Indonesian English as a Foreign Language (EFL) learners to learn new words and the relationship with their gender differences. And there need to be more studies that examine the vocabulary learning strategies (VLSs) of Indonesian students studying abroad in the ESL context. Considering the increasing number of Indonesian students studying in ESL communities, this study aims to identify the vocabulary learning strategies employed by Indonesian university students who are pursuing their studies at universities in Malaysia. There were 183 students (89 males and 94 females) enrolled in various degree programmes randomly selected in this study. The Vocabulary Learning Strategies Questionnaire from(Almabrouk, 2017), based on (Schmitt, 1997)’s taxonomy, was adopted as the data collection instrument. The findings showed that the most frequent strategy employed by the respondents was 'trying to develop vocabulary knowledge by watching English TV channels (e.g. movies, songs, documentaries from the metacognitive category. Meanwhile, the least frequent strategy employed by the respondents was 'ask the lecturer for translation of the new word into an L1 translation' from the social category. The results also indicated that there were no statistically significant differences in the use of VLSs based on gender.
Errors on Efl Students’ Paragraph Writings

It is common for EFL students to commit grammatical errors in the early stage of learning writing, especially in paragraph writing. Therefore, this research aimed to identify the types of errors and the most dominant ones committed in descriptive paragraph writing exercises. A qualitative research design was applied to this study. Writing exercises were used as research instruments to collect the data. Twenty-six students of the English Study Program were used as participants in this study. Error Analysis Method (EAM) based on Surface Structure Taxonomy and percentage error analysis was used to analyze the data. The findings showed that there were four types of errors committed referring to misformation with a frequency of error was 47 (0,14 %), omission with a frequency of error 81 (0,24%), misordering with a frequency of error 153 (0,47%), and addition with the frequency of error was 45 (0,13%). In this case, a misordering mistake was the dominant one, and an addition error was the least dominant one committed by the students. In conclusion, the researchers assume that some factors affect students' mistakes, such as lack of knowledge and competence in English grammar. The error analysis method was a very suitable way to identify types of errors using simple present tense in descriptive paragraph writing.
Students’ Strategies in English Learning at Home During the Covid-19 Pandemic

English is an international language, and there are many people or students in the world learning English. All the students have different abilities or strategies for learning English. Without a plan and the students cannot improve their English skills well. So when the students want to learn English should have a strategy because it is will helpful. This research is to determine the method and obstacles English students face in learning English at home during the COVID-19 pandemic at students' universities. This is descriptive, using a qualitative approach with data collection techniques using interviews. The sample in this study was 5 participants from the English Department in the 7th semester of the academic year 2020./2021. The researcher found that of the five participants, the most widely used strategy by students was the first social strategy; most of them practiced English with their parents, family, or friends at home during the pandemic. The second is a compensation strategy; in this strategy, students learn English by listening to songs or reading books to improve their vocabulary. The third is a memory strategy; in this strategy, students learn by listening to songs, radio, or watching on youtube instead of taking vocabulary notes, and they memorize them during the pandemic. Constraints faced by students when learning English at home during the pandemic. Many obstacles faced by students, namely lack of money, not being able to buy internet quota, laziness to move because of the pandemic, unable to accept and laziness to take all the courses given by lecturers, and not having learning tools such as laptops, English dictionaries to help them learn English at University.
Introduction to Blockchain Technologies

The energy field is developing rapidly in the direction of a distributed, low-carbon, digital, and intelligent energy Internet. With its characteristics of decentralization, group collaboration, peer-to-peer (P2P) communication, smart contracts, anti-counterfeiting and tamper-proofing, openness, and transparency, blockchain technology fits the development direction of the energy industry to improve quality, efficiency, innovation, and reform. At present, the energy industry of many countries has explored related applications, especially in the business fields of power trading, power metering, microgrid, and renewable energy. As the energy industry pays more attention to energy conservation and energy efficiency, blockchain applications are also attracting attention for optimizing the energy industry. The International Renewable Energy Agency (IRENA) also lists blockchain technology as one of the key innovations in the field of renewable energy. IRENA pointed out in a report that to accelerate the development of low-cost renewable energy, the world needs at least 30 innovative technology tools and enables them to benefit from the scale of renewable energy. Blockchain technology is one of them. Blockchain technology is a distributed network data management and data computing technology that uses cryptography technology and distributed consensus protocols to ensure network transmission and access security and realizes multi-party data maintenance, cross-validation, uniformity across the network, and resistance to tampering. As an important evolution of a new generation of information and communication technology, the characteristics 236of non-tampering, transparent, and traceable data make the blockchain technology an infrastructure to solve the mutual trust of industry chain participants.
English Language Proficiency through the TOEFL Test Taken by UMNAW Postgraduate Students

The purpose of this research was to find out how the English proficiency of UMNAW postgraduate students was measured with the TOEFL test consisted of three main sections; Listening Comprehension, Structure and Written Expression, and Reading Comprehension. The researcher used a descriptive qualitative research design in which the researcher described and analyzed students’ English proficiency based on the TOEFL test. The fourth-year postgraduate students of the Indonesian Language Education Study Program in the Academic Year 2021/2022 total of 10 people the population of the research. Based on the research findings, it can be classified that in Listening Comprehension Section one student got the highest score 28 and one students got the lowest score of 18. In the Structure and Written Expression Section, one student got the highest score 24 and one student got the lowest score of 15. In Reading Comprehension Section, one student got the highest score 28 and one student got the lowest score of 19. After being converted, one student got the highest score 453 and one student got the lowest score 440. Furthermore, it can be classified that UMNAW postgraduate students taking the TOEFL test are at a Low Intermediate level. This research is then expected to be a reference for further research and can be more perfectly developed.
NFNet: Non-interacting Fermion Network for Efficient Simulation of Large-scale Quantum Systems

We present NFNet, a PyTorch-based framework for polynomial-time simulation of large-scale, continuously controlled quantum systems, supporting parallel matrix computation and auto-differentiation of network parameters. It is based on the non-interacting Fermionic formalism that relates the Matchgates by Valiant to a physical analogy of non-interacting Fermions in one dimension as introduced by Terhal and DiVincenzo. Given an input bit string $\boldsymbol{x}$, NFNet computes the probability $p(\boldsymbol{y}|\boldsymbol{x})=\langle x|U_{\theta}^\dagger \Pi_y U_\theta |x\rangle$ of observing the bit string $\boldsymbol{y}$, which can be a sub or full-system measurement on the evolved quantum state $U_{\mathbf{\theta}}|x\rangle$, where $\mathbf{\theta}$ is the set of continuous rotation parameters, and the unitary $U_{\mathbf{\theta}}$'s underlying Hamiltonians are not restricted to nearest-neighbor interactions. We first review the mathematical formulation of the Matchgate to Fermionic mapping with additional matrix decomposition derivations, and then show that on top of the pair-wise circuit gates documented in Terhal and DiVincenzo, the Fermionic formalism can also simulate evolutions whose Hamiltonians are sums of arbitrary two-Fermion-mode interactions. We then document the design philosophy of NFNet, its software structure, and demonstrate its usage in various quantum system simulation, benchmarking, and quantum learning tasks involving 512+ qubits. As NFNet is both an efficient large-scale quantum simulator, and a quantum-inspired classical computing network structure, many more exciting topics are worth exploring, such as its connection to recurrent neural networks, discrete generative learning and discrete normalizing flow. NFNet source code can be found at https://github.com/BILLYZZ/NFNet.
Automated Variable Renaming: Are We There Yet?

Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support developers in using meaningful identifiers, several (semi-)automatic techniques have been proposed, mostly being data-driven (e.g. statistical language models, deep learning models) or relying on static code analysis. Still, limited empirical investigations have been performed on the effectiveness of such techniques for recommending developers with meaningful identifiers, possibly resulting in rename refactoring operations. We present a large-scale study investigating the potential of data-driven approaches to support automated variable renaming. We experiment with three state-of-the-art techniques: a statistical language model and two DL-based models. The three approaches have been trained and tested on three datasets we built with the goal of evaluating their ability to recommend meaningful variable identifiers. Our quantitative and qualitative analyses show the potential of such techniques that, under specific conditions, can provide valuable recommendations and are ready to be integrated in rename refactoring tools. Nonetheless, our results also highlight limitations of the experimented approaches that call for further research in this field.
Direct Speech-to-speech Translation without Textual Annotation using Bottleneck Features

Speech-to-speech translation directly translates a speech utterance to another between different languages, and has great potential in tasks such as simultaneous interpretation. State-of-art models usually contains an auxiliary module for phoneme sequences prediction, and this requires textual annotation of the training dataset. We propose a direct speech-to-speech translation model which can be trained without any textual annotation or content information. Instead of introducing an auxiliary phoneme prediction task in the model, we propose to use bottleneck features as intermediate training objectives for our model to ensure the translation performance of the system. Experiments on Mandarin-Cantonese speech translation demonstrate the feasibility of the proposed approach and the performance can match a cascaded system with respect of translation and synthesis qualities.
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasing popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and finetuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy. Code is available at https://github.com/Vision.