Reviewerly/exHarmony huggingface dataset:
An expanding pipeline: 20 + years of Acentech internships

Acoustics interns at Acentech, a multidisciplinary acoustics, technology, and noise &amp; vibration control consultancy based in Cambridge, Massachusetts, USA, have included undergraduate and graduate students as well as graduates in many fields of engineering, physics, and architecture. Acentech interns work on a deliberate mix of both project work and internal research and development, often inspired by research presented by academics in our field. For a time, interns were typically from one of approximately a dozen US-based graduate programs in acoustic, and more rarely, from one or two undergraduate programs with acoustics concentrations. In recent years, successful interns have hailed from an increasingly wide array of university programs, and the results have exceeded expectations: an increasingly diverse pipeline of skilled, intellectually curious individuals with a passion for music, buildings, and acoustical design.
Optimizing medical personnel speech recognition models using speech synthesis and reinforcement learning

Text-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons. Speech can be synthesized by mimicking different accents, dialects, and speaking styles in a medical language. Reinforcement Learning (RL), in the context of ASR, can be used to optimize a model. A model can be trained to minimize errors in speech-to-text transcription, especially for technical medical terminology. In this case, the “reward” to the RL model can be negatively proportional to the number of transcription errors. The paper presents a method and experimental study from which it is concluded that the combination of TTS and RL can enable the creation of a speech recognition model suited to the specific needs of medical personnel, helping to expand the training data and optimize the model to minimize transcription errors. The learning process used reward functions based on Mean Opinion Score (MOS), a subjective metric for assessing speech quality, and Word Error Rate (WER), which evaluates the quality of speech-to-text transcription. [The Polish National Center for Research and Development (NCBR) supported the project: “ADMEDVOICE- Adaptive intelligent speech processing system of medical personnel with the structuring of test results and support of therapeutic process,” no. INFOSTRATEG4/0003/2022.]
A theory of vowel dispersion based on probabilistic modeling of optimized speakers and listeners

Dispersion Theory (Liljencrants and Lindblom, 1972; Lindblom, 1986) holds that optimal vowel systems maximize perceptual contrast by maximizing psycho-acoustic distance between vowels, successfully predicting much of the acoustic typology of natural languages. Here we explore probabilistic models of optimized speakers and listeners to see if they might yield more accurate and principled predictions about vowel dispersion. Given a set of vowel distributions and a set of categorical perceptual boundaries optimized for those distributions, the optimal set of vowel distributions maximizing the percentage of correctly perceived tokens is not in general the same as the original set of distributions. This motivates an iterated confusion minimization algorithm that can model dispersion as an incremental diachronic process that achieves globally optimized vowel systems by locally optimizing each vowel category against its own perceptual boundaries at each generation. We show this model provides a principled explanation for several nuanced patterns observed in cross-linguistic corpora, including greater distances in F2 than F1 and even spacing of F1 in log(Hz) rather than auditory-based scales (Becker-Kristal, 2010). A new prediction of this model is that lower-probability vowel pairs should be less dispersed; survey work now in progress so far confirms this prediction.
Using virtual sound reproduction for studying L1/L2 speech perception in varying acoustic environments

Studying the effect of varying acoustic environments on speech perception is often challenging due to the logistical difficulties in moving participants undertaking listening tests between environments as well as lack of controllability and reproducibility in terms of the acoustics of the test environments. In addition, when the study requires to compare first and second language listeners, it often becomes infeasible due to the availability of participants with required language background in a single geographical location where the study is conducted. Virtual sound reproduction could address the challenge thanks to its ability to reproduce the acoustics of arbitrary spaces at multiple geographic locations in a controllable manner. However, it has not been studied well if the results collected using virtual sound reproduction would be a valid alternative to the results collected in real spaces. This talk will introduce a recent study that investigated the difference of speech perception in varying acoustic environments between first and second language New Zealand English listeners using virtual sound reproduction technology. The talk will particularly focus on how the results collected under virtual acoustic environments assimilates to that collected in the original real acoustic environments between first and second language listeners.
Consistent physical frequencies in time-frequency analysis

Wavelet transforms have been studied extensively for a wide variety of applications such as signal compression and signal denoising. Wavelet transforms have also been examined for the detection of transient signals. However, wavelet levels (and corresponding pseudo-frequencies) are inherently dependent on the sampling rate of the analyzed data. The work presented herein examines how wavelet analysis can produce inconsistent data representations for the same analytical signal digitized at different sampling rates. Results are compared to time-octave and other time-frequency representations to identify methodologies which produce consistent characterization of physical frequencies. Similarities and difference between approaches are discussed.
An automated pop song mashup system using drum swapping

Music mashups blend elements from two or more songs. They are extremely popular among both composers and listeners since they carry the emotional power of the original songs, plus delightful new surprises. A few automated systems have appeared on the mashup scene, but the results are often heavy-handed and muddy, since they usually combine everything from both songs, and therefore, lack the nuance and clarity of hand-crafted mashups by professional composers. We propose a mashup system “Rhythm Fusion” that allows users to swap the drum track from one song into another. We use a beat-tracking algorithm to synchronize and align the songs. In our preliminary subjective tests, 10 songs were used to generate 100 combinations. Of these, 27% of the variations were rated more musically satisfying than the original. These results indicate that drum swapping alone has much promise for music mashups, and will guide our future work on swapping both drums and bass stems from different songs in automated music mashups, and eventually other components as well.
Jet oscillation model for flute-like instruments allowing overall deflection

A new jet oscillation model for flute-like instruments is proposed. This is based on a conventional semi-empirical model for a jet in half space disturbed by an acoustic cross flow. The proposed model, however, allows a jet to deflect and to oscillate as a whole with no phase delay in response to the volume flow through the instrument's window (embouchure hole or mouth). The overall jet deflection is essential to match the volume flow swept by the lateral motion of the jet to that through the window. The complete jet oscillation is the sum of the overall oscillation and that caused by fluid dynamical sinuous instability. The latter grows exponentially with a phase delay as the jet travels. The amplitude of acoustic volume velocity triggering this oscillation is much smaller than that through the window because most of it has already contributed to the overall deflection. The proposed model reasonably reproduces the recorder's head reflection coefficient measured in [Price et al., J. Acoust. Soc. Am. 138(5), 3282–3292 (2015)]. Synthesis of recorder sounds using this model is underway.
Examining the voice verification system resistance developed for banking to attacks employing the voice cloning

The developed system for verifying the speaker identity in banking branches is based on voice biometrics employing DeepSpeaker neural network model. The testing of its resistance to a potential attack using voice cloning also employed neural networks, such as SV2TTS, Tacotron, WaveRNN, and GE2E. Subjective listening tests indicated that people might easily get confused and point out cloned recordings as an original sample. Nearly 50% of respondents pointed to the wrong answer, confusing the synthesized recording with the original one. Subjects in most cases declared that the quality of cloned and original recordings is similar (Good or Fair according to ITU-R BS.1534 recommendation) and, in some cases, even better than the original (graded as Good for synthesized sample, Fair for original one). Meanwhile, nearly all verification attempts with cloned samples failed (98.8% of samples were rejected). It proves that voice biometrics based on deep neural networks can identify cloned samples better than human listeners. Methods and results of testing the resistance of the developed voice biometrics system to voice cloning attacks are discussed in thepaper. This research was funded from the budget of project No.POIR.01.01.01-0092/19 subsidized by the Polish National Centre for Research and Development (NCBR).
Lost and Found: University Teachers about the Change of Higher Education Quality

The article considers last three years’ changes of higher education quality from the perspective of university teachers as key actors of an educational process. The study focuses on the main factors infl uencing the quality of higher education and the nature of changes in the post-pandemic educational environment, in particular, through the use of digital solutions. The authors deliver primary (general) results of Russian university teachers’ poll regarding the triggers for changing the quality of higher education, the eff ectiveness of educational and pedagogical activities in the digital environment, as well as points to be paid attention at in ensuring the quality of higher education. The results of the poll show signifi cant changes in higher education over the period studied, especially in applied teaching practices and in lesson formats, these shifts contributing to the change of education quality in general. Global, institutional, and individual infl uencing factors together give multidirectional impact on the quality of education, thus forming diff erent experiences and perceptions of higher education quality dynamics. University teachers, however, are quite diff erently engaged in the university education quality assessment. Due to understanding themselves as key stakeholders in education quality assurance, university teachers identify their course load and additional non-curricular functions as the main barriers in developing their own eff ectiveness. The article is addressed to heads of academic programs and educational institutions, to executive authorities responsible for quality assurance and assessment in higher education, as well as to the representatives of research community in Russian higher education.
Learning from Code

As a software engineer, one of the most valuable resources for learning is the code itself. It becomes particularly beneficial if the code base you work with is new at least every six months. This ensures you don't become too rooted in one method of coding, a trap that's easy to avoid if you're rotating to a different team within a company regularly or if you're a consultant.
Rotation

Long-term focus on a single project or specific area of code can hinder your career progress as a software engineer. Despite potentially gaining considerable expertise in your area of responsibility, your broader knowledge may become limited. Fresh challenges and ideas are vital for continued learning, motivation, and professional growth. The remedy for this stagnation is regular rotation, meaning that you are rotated on a new code base. Although frequently changing code responsibilities or projects might seem counterproductive, I would argue that the benefits of rotation outweigh the drawbacks. Let's discuss the advantages and disadvantages and explore effective strategies for rotation.
Career Change

Having long-term career goals is useful. They act as a guiding North Star in our career journey, enabling us to periodically assess if we're heading in the right direction. For many professionals, especially those in senior roles, setting dramatically different career goals can be challenging. I know several individuals who, after five to ten years, have reached senior or staff software engineer positions and are content with continuing in that role. They have no desire to transition into solution architects, tech leads, or CTO roles; their passion lies in development.
Feedback

Navigating the software engineering field can be tough. It's important to constantly check if what you're doing is right. Self-reflection is helpful, but it's easy to overlook something from your own point of view. That's why I rely heavily on giving and receiving feedback. Feedback is great for learning, and research shows it significantly boosts learning outcomes [1]. And having a culture where everyone gives and gets feedback helps the team as it builds trust and reminds us we're all here to learn.
Hierarchical Text Classification Using Language Models with Global Label-Wise Attention Mechanisms

Hierarchical text classification (HTC) is a natural language processing task with the objective of categorising text documents into a set of classes from a structured class hierarchy. Recent HTC approaches combine encodings of the class hierarchy with the language understanding capabilities of pre-trained language models (PLMs) to improve classification performance. Furthermore, label-wise attention mechanisms have been shown to improve performance in HTC tasks by placing larger weight on more important parts of the document for each class individually to obtain label-specific representations of the document. However, using label-wise attention mechanisms to fine-tune PLMs for downstream HTC tasks has not been comprehensively investigated in previous work. In this paper, we evaluate the performance of a HTC approach which adds label-wise attention mechanisms, along with a label-wise classification layer, to a PLM which is subsequently fine-tuned on the downstream HTC dataset. We evaluate several existing label-wise attention mechanisms and propose an adaptation to one of the approaches which separates the attention mechanisms for the different levels of the hierarchy. Our proposed approach allows the prediction task at a certain level to leverage the information gained from the predictions performed at all ancestor levels. We compare the different label-wise attention mechanisms on three HTC benchmark datasets and show that our proposed approach generally outperforms the other label-wise attention mechanisms. Furthermore, we show that without using the complex techniques proposed in recent HTC approaches, our relatively simple approach outperforms state-of-the-art approaches on two of the three benchmark datasets.
Seamless Crime Scene Reconstruction in Mixed Reality for Investigation Training: A Design and Evaluation Study

Investigation training in the real crime scene is a critical component of forensic science education. However, bringing young investigators to real crime scenes is costly and faces significant challenges. Mixed Reality (MR) is one of the most evolving technologies that provide unlimited possibilities for practical activities in the education sector. This paper aims to propose and evaluate a novel design of an MR system using Microsoft HoloLens 2.0 and it is tailored to work in a spatial 3D scanned and reconstructed crime scene. The system was designed to be a costly-effective experience that helps young Kuwait police officers to enhance their investigation skills. The proposed system has been evaluated through system usability, user interaction and performance metrics quantitatively via 44 young police officers, and qualitatively using the Think-aloud protocols via a group of experts. Both groups showed positive levels of usability, user interaction and overall satisfaction with minimal negative feedback. Based on the positive feedback, the system will be taken into the commercialisation stage in the future. Despite the high cost of the MR device, it was stated by experts that the system is needed to be provided as an essential tool for crime scene education and investigation practices.
A Comparative Study of Over-Sampling Techniques as Applied to Seismic Events

The likelihood that an earthquake will occur in a specific location, within a specific time frame, and with ground motion intensity greater than a specific threshold is known as a seismic hazard. Predicting these types of hazards is crucial since doing so can enable early warnings, which can lessen the negative effects. Research is currently being executed in the field of machine learning to predict seismic events based on previously recorded incidents. However, because these events happen so infrequently, this presents a class imbalance problem to the machine learning or deep learning learners. As a result, this study provided a comparison of the performance of popular over-sampling techniques that seek to even out class imbalance in seismic events data. Specifically, this work applied SMOTE, SMOTENC, SMOTEN, BorderlineSMOTE, SVMSMOTE, and ADASYN to an open source Seismic Bumps dataset then trained several machine learning classifiers with stratified K-fold cross-validation for seismic hazard detection. The SVMSMOTE algorithm was the best over-sampling method as it produced classifiers with the highest overall accuracy, F1 score, recall, and precision of 100%, respectively, whereas the ADASYN over-sampling methodology showed the lowest performance in all the reported metrices of all the models. To our understanding, no research has been done comparing the effectiveness of the aforementioned over-sampling techniques for tasks involving seismic events.
RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of play-testing agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in the MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline. The open-source environment is available at a GitHub repository.
Study of the advantages of the combination of predicate logic, fuzzy c-means and the ontological model to increase the efficiency of data processing in organizational and technical complexes

International scientific conference “MININGMETALTECH 2023 – The mining and metals sector: integration of business, technology and education” : conference proceedings (November 29–30, 2023. Riga, the Republic of Latvia). Riga, Latvia : “Baltija Publishing”, 2023. Vol. 1. 272 pages.
Pengaruh Manajemen Kelas Terhadap Motivasi Belajar Siswa di MTsN 1 Jombang

Teachers are required to have knowledge and skills enabling them to effectively make a conducive learning environment, design learning procedures, develop good relationships with students and increase attention to the academic activities they manage to motivate students to learn, so that learning objectives are achieved. This study aims to determine whether or not there is an influence between classroom management and student learning motivation. This research method is a quantitative method. In collecting the data, the researcherused questionnaires, then the data analyzed by simple linear regression. The results showed that there is a significant influence between classroom management and student motivation, the results of t countedis greater than t table with 98 degrees of freedom and significance level of 5%, is (19.156&gt;1.66055). The influenceof classroom management toward students’ motivation is 78.9%.
열망과 현실의 간극 – 한국 전도부인의 삶을 통해 살펴본 네비어스 선교방법의 문제점 (1890년대~1930년대)

This study aims to provide a comprehensive understanding of the Nevius Methods, a Protestant mission policy that aimed to promote self-reliance among local churches, by uncovering the everyday struggles faced by Bible women in the Korean mission field from the 1890s to the 1930s. Despite the prevailing narrative that often credits the achievement of Bible women, this study suggests that the plight of Bible women was closely linked to the Nevius Methods, which encouraged local churches to self-support their church workers. Although these methods were believed to be the key to fast-growing local churches, when these churches faced financial difficulties, Bible women, situated at the lowest end of the church hierarchy, often faced unfavorable treatment. Relying heavily on primary sources, this study delves into Bible women’s experiences and addresses the stark contrast between missionaries’ aspirations for the rapid growth of indigenous churches and the harsh realities of a poverty-stricken mission field.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS BLOG UNTUK MENINGKATKAN MOTIVASI BELAJAR X SMAN 1 TANJUNG BUMI

The application of technology in learning as a learning medium will make it easier for students to learn according to their characteristics and can help teachers when overcoming difficulties in conveying learning material. This study aims to determine the procedure for blog-based learning media, and whether or not the learning media is appropriate, as well as to analyze the development or increase in student motivation after the introduction of class X blog-based learning media at SMAN 1 Tanjung Bumi. This study uses research and development methods using the ADDIE model. Using data sources from teachers and students at SMAN 1 Tanjung Bumi, with research data on the development of learning media. Data collection using a questionnaire. Data analysis techniques are qualitative and quantitative data analysis, qualitative data analysis of media is carried out according to the scores of material experts, media experts, economic learning practitioners, and student response questionnaires. The results of research on the development of blog-based learning media in class X OJK lessons with the satisfaction of using blog-based learning media are very suitable for use in learning; student responses experienced an increase in motivation to take part in learning