Reviewerly/exHarmony huggingface dataset:
Node Feature Augmentation Vitaminizes Network Alignment

Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their effectiveness is not without additional information such as prior anchor links and/or node features, which may not always be available due to privacy concerns or access restrictions. To tackle this challenge, we propose Grad-Align+, a novel NA method built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers a part of node pairs until all node pairs are found. In designing Grad-Align+, we account for how to augment node features in the sense of performing the NA task and how to design our NA method by maximally exploiting the augmented node features. To achieve this goal, Grad-Align+ consists of three key components: 1) centrality-based node feature augmentation (CNFA), 2) graph neural network (GNN)-aided embedding similarity calculation alongside the augmented node features, and 3) gradual NA with similarity calculation using aligned cross-network neighbor-pairs (ACNs). Through comprehensive experiments, we demonstrate that Grad-Align+ exhibits (a) the superiority over benchmark NA methods, (b) empirical validations as well as our theoretical findings to see the effectiveness of CNFA, (c) the influence of each component, (d) the robustness to network noises, and (e) the computational efficiency.
TraceFixer: Execution Trace-Driven Program Repair

When debugging unintended program behavior, developers can often identify the point in the execution where the actual behavior diverges from the desired behavior. For example, a variable may get assigned a wrong value, which then negatively influences the remaining computation. Once a developer identifies such a divergence, how to fix the code so that it provides the desired behavior? This paper presents TraceFixer, a technique for predicting how to edit source code so that it does not diverge from the expected behavior anymore. The key idea is to train a neural program repair model that not only learns from source code edits but also exploits excerpts of runtime traces. The input to the model is a partial execution trace of the incorrect code, which can be obtained automatically through code instrumentation, and the correct state that the program should reach at the divergence point, which the user provides, e.g., in an interactive debugger. Our approach fundamentally differs from current program repair techniques, which share a similar goal but exploit neither execution traces nor information about the desired program state. We evaluate TraceFixer on single-line mistakes in Python code. After training the model on hundreds of thousands of code edits created by a neural model that mimics real-world bugs, we find that exploiting execution traces improves the bug-fixing ability by 13% to 20% (depending on the dataset, within the top-10 predictions) compared to a baseline that learns from source code edits only. Applying TraceFixer to 20 real-world Python bugs shows that the approach successfully fixes 10 of them.
SAPHIR: A Pluricultural Authoring Tool to Produce Resources in Support of Education for Sustainable Development

In this paper, we present SAPHIR, a multilingual authoring tool producing a Progressive Web App, usable on computers, tablets, and smartphones, online or offline. We presented our design process, the architecture of the system, the model on which it is based, and its main parts: SAPHIR it-self is the main software proposing activities to children to learn and play; MINE is the authoring tool used by pedagogical designers and resources translators to create and translate resources without requiring any programming skills; TAILLE is dedicated to teachers to whom he provides educational explanations to use SAPHIR with their learners. The different parts were used with both pedagogical designers and students.
Loss and Reward Weighing for increased learning in Distributed Reinforcement Learning

This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gradient merger. The R/L weighted methods replace standard practices for training multiple agents, such as summing or averaging the gradients. The core of our methods is to scale the gradient of each actor based on how high the reward (for R-Weighted) or the loss (for L-Weighted) is compared to the other actors. During training, each agent operates in differently initialized versions of the same environment, which gives different gradients from different actors. In essence, the R-Weights and L-Weights of each agent inform the other agents of its potential, which again reports which environment should be prioritized for learning. This approach of distributed learning is possible because environments that yield higher rewards, or low losses, have more critical information than environments that yield lower rewards or higher losses. We empirically demonstrate that the R-Weighted methods work superior to the state-of-the-art in multiple RL environments.
Flexible constraint compilation in the parity architecture

We present tools and methods to generalize parity compilation to digital quantum computing devices with arbitrary connectivity graphs and construct circuit implementations for the constraint Hamiltonian of higher-order constrained binary optimization problems. In particular, we show how even non-local constraints can be efficiently implemented without expensive SWAP gates. We show how the presented tools can be used to optimize the total circuit depth and CNOT count of the quantum approximate optimization algorithm in the parity architecture and highlight the advantages of the flexible compilation using various examples. We derive the relation between the developed gate sequences and the traditional approach that uses SWAP gates. The result can be applied to improve the implementation of many other non-local operators.
Discovering Graph Generation Algorithms

We provide a novel approach to construct generative models for graphs. Instead of using the traditional probabilistic models or deep generative models, we propose to instead find an algorithm that generates the data. We achieve this using evolutionary search and a powerful fitness function, implemented by a randomly initialized graph neural network. This brings certain advantages over current deep generative models, for instance, a higher potential for out-of-training-distribution generalization and direct interpretability, as the final graph generative process is expressed as a Python function. We show that this approach can be competitive with deep generative models and under some circumstances can even find the true graph generative process, and as such perfectly generalize.
Quantum Gaussian Process Regression for Bayesian Optimization

Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits. By employing a hardware-efficient feature map and careful regularization of the Gram matrix, we demonstrate that the variance information of the resulting quantum Gaussian process can be preserved. We also show that quantum Gaussian processes can be used as a surrogate model for Bayesian optimization, a task that critically relies on the variance of the surrogate model. To demonstrate the performance of this quantum Bayesian optimization algorithm, we apply it to the hyperparameter optimization of a machine learning model which performs regression on a real-world dataset. We benchmark the quantum Bayesian optimization against its classical counterpart and show that quantum version can match its performance.
N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models

Understanding the function of individual neurons within language models is essential for mechanistic interpretability research. We propose $\textbf{Neuron to Graph (N2G)}$, a tool which takes a neuron and its dataset examples, and automatically distills the neuron's behaviour on those examples to an interpretable graph. This presents a less labour intensive approach to interpreting neurons than current manual methods, that will better scale these methods to Large Language Models (LLMs). We use truncation and saliency methods to only present the important tokens, and augment the dataset examples with more diverse samples to better capture the extent of neuron behaviour. These graphs can be visualised to aid manual interpretation by researchers, but can also output token activations on text to compare to the neuron's ground truth activations for automatic validation. N2G represents a step towards scalable interpretability methods by allowing us to convert neurons in an LLM to interpretable representations of measurable quality.
Escaping the sentence-level paradigm in machine translation

It is well-known that document context is vital for resolving a range of translation ambiguities, and in fact the document setting is the most natural setting for nearly all translation. It is therefore unfortunate that machine translation -- both research and production -- largely remains stuck in a decades-old sentence-level translation paradigm. It is also an increasingly glaring problem in light of competitive pressure from large language models, which are natively document-based. Much work in document-context machine translation exists, but for various reasons has been unable to catch hold. This paper suggests a path out of this rut by addressing three impediments at once: what architectures should we use? where do we get document-level information for training them? and how do we know whether they are any good? In contrast to work on specialized architectures, we show that the standard Transformer architecture is sufficient, provided it has enough capacity. Next, we address the training data issue by taking document samples from back-translated data only, where the data is not only more readily available, but is also of higher quality compared to parallel document data, which may contain machine translation output. Finally, we propose generative variants of existing contrastive metrics that are better able to discriminate among document systems. Results in four large-data language pairs (DE$\rightarrow$EN, EN$\rightarrow$DE, EN$\rightarrow$FR, and EN$\rightarrow$RU) establish the success of these three pieces together in improving document-level performance.
Exploring the parameter space of the modified rhyme test to improve efficiency

The modified rhyme test (MRT) is a words-in-noise test that is used as a part of screening and testing procedures within the United States military. Each trial has a carrier phrase followed by a target word (“Please select the word CAT”). Listeners choose the target from a set of six options. Competing words differ only in the starting or ending consonant sound (e.g., “BAT,” “VAT,” “SAT,” “RAT,” and “THAT”). The present study had two goals. The first goal was to improve the efficiency of the MRT by eliminating the carrier phrase and providing only the test word. This should quicken data collection. The second goal was to determine the effect of reducing the number of alternatives. Reducing the number of alternatives should reduce response time, but may affect data quality. Aside from the primary goals, other parameters were adjusted: signal-to-noise ratio, presentation level, noise type, and filtering condition. Over 3000 service members participated in the study from multiple sites. The MRT was administered on calibrated tablet and headphones. Findings will be discussed. [The views expressed in this abstract are those of the authors and do not necessarily reflect the official policy of the Department of Defense or the U.S. Government.]
Statistical regularities of accented speech influence listeners’ own speech productions

Conversation partners’ speech acoustics sometimes converge, providing evidence for the transfer of information from speech perception to one’s own speech productions. The nature of this phonetic convergence has remained elusive, with variable experimental findings regarding the contexts under which phonetic convergence emerges, the acoustic speech features it affects, and the gender of talkers most influenced. Here, we approach phonetic convergence through the lens of dimension-based statistical learning whereby the statistical regularities of short-term speech input impact the perceptual weight of acoustic dimensions in speech categorization. Participants passively listened to a randomly ordered sequence of 4 pier and 4 beer utterances, sampled across voice onset time (VOT) and fundamental frequency (F0) in a manner that aligned with English norms (Canonical) or departed from typical English pronunciations as an ‘accent’ (Reverse). Immediately after, participants categorized and repeated aloud an ambiguous test stimulus varying only in F0, with ambiguous VOT. The Reverse input regularities led both male and female participants to down-weight F0 in both perceptual categorization and in the acoustics of word repetitions. The results indicate that statistical learning across passive listening to another speaker’s voice can lead to detailed acoustic-phonetic adjustments in one’s own speech productions.
Do we really need to do this?

In the acoustical consulting profession, our task is usually to provide recommendations based on our expertise and experience on projects involving existing problems or new facilities looking to avoid problems and achieve sonic success. This does not occur in a vacuum as practicalities and budget usually loom in the shadows (or out in the open). Inevitably, clients often question and sometimes even push back on our guidance. This presentation will discuss certain areas where these situations are prone to arise, with examples, and will endeavor to explore the nuances of the question: “do we really need to do this?”
Steelpan building and tuning: General basics and personal observations

In this presentation, I will share the steps I use to build and tune steelpans, as well as some insights and observations from my experience as a builder/tuner. Building includes welding, sinking (stretching the steel into a concave surface), note shaping, and steel smoothing. After that, the steelpan is burned and then tuned. Tuning is done using hand hammering and occasional heating. It involves tuning multiple modes of vibration independently in a single note panel, adjusting the timbre, and getting the desired interactions between notes. Oftentimes, the tuning experience and final product is somewhat unpredictable. Small differences in the steel shape can result in large changes to the sound, and operations that will work on one note might not for another. Furthermore, there are many problems that arise from detrimental note-note interactions and note-skirt interactions. All this results in a seemingly mysterious nature to steelpans, requiring a degree of trial and error to get the desired qualities. My hope is that a dialogue between tuners and researchers can strengthen our understanding of the instrument from both viewpoints.
Within- versus between-speaker acoustic variability in Thai

This paper continues our studies of acoustic variability in voice between and within speakers. Previous work indicates that acoustic variability is characterized by the balance between high-frequency harmonic and inharmonic energy in the voice (measured using cepstral peak prominence) and by formant dispersion, regardless of the speaker’s sex, native language, or speaking style. Our recent investigation of the language effect on voice variability surveyed three languages, English, Korean, and White Hmong, which form an organized subset with respect to their linguistic use of F0 and the phonological status of phonation quality. The cross-linguistic comparisons revealed a second tier of language-specific variability that reflects features of the phonology of each language. The present paper adds a tone language, Thai, to the mix. Samples of read speech from fifty Thai speakers (33F, 17M) were evaluated as in our previous studies. Results revealed that variability in Thai voices is primarily accounted for by the same biologically relevant measures observed globally, and further shaped by the language-specific use of F0. These data further support our hypothesis that acoustic variability in voice is governed first by biological factors, secondly by language-specific, cultural factors, and finally by idiosyncrasies of an individual’s anatomy and personal speaking style.
Hadza liquid production and acoustics

Liquid systems in the world’s languages are relatively poorly understood. This study employs 3D/4D ultrasound methods to investigate articulatory characteristics of Hadza liquids produced by a single male speaker in a laboratory, analyzing simultaneously collected acoustic signals to determine relations between articulation and acoustics. Articulatory-acoustic relations are then compared to acoustic data obtained from other speakers collected in the field. Hadza is a language isolate spoken by approximately 1200 people (Brian Wood, pc.) in north-central Tanzania. Hadza is reported to have a single liquid phoneme which varies allophonically between a lateral approximant [l] occurring word-initially, and a tap [ɾ], occurring intervocalically (Tucker et al., 1977; Sands et al., 1996; Sands, 2013). The results of the current study confirm the production of both lateral approximant [l] and tap [ɾ] allophones; however, the results also show substantial variation between speakers. Furthermore, some speakers exhibit a categorical distinction between the allophones in given phonological environments, while others indicate a very similar articulation in both, and demonstrate no such distinction.
A comparison of the timbral characteristics of steel and wooden drum sets

Although it is quite common to construct snare drum shells from various metals, it is still rare for full drum sets to have shells made from metal. Several manufacturers have produced metal drum sets intermittently since the 1970s. However, they still remain a niche version of the instrument that has received less attention than their wooden counterparts. This paper compares the timbral characteristics of a drum set made from steel against a maple drum set. Audio samples of the two drum sets are meticulously recorded in a recording studio with high grade equipment. The configurations of the two drum sets are made to match as closely as possible. Then the recorded audio samples are analyzed using computational methods—looking at both time-domain and spectral features for both instruments. In this way we observe how the different construction material and methods result in contrasting timbral characteristics.
Modeling the sound radiation of gamelan gongs using closed-form rigid spherical models

The structural modes of gamelan gongs have clear connections with the gongs' far-field radiated patterns. However, the instruments' unique geometry and modal characteristics limit the applicability of simple theoretical closed-form models, such as a radially vibrating cap on a sphere, for understanding their radiation. This work develops and applies two different models, a vibrating cap on a spherical shell with a circular aperture and a vibrating cap with imposed mode shapes, to better understand the gongs' directional characteristics. The models agree with acoustical measurements, predicting dipole and cardioid-like patterns and lobes formed from constructive and destructive interference.
Breathing patterns of conversation partners

Previous research has shown that breathing patterns of conversation partners can become synchronized. For example, when one conversation partner is speaking, the listening partner's breathing patterns more closely resemble speech breathing than tidal breathing (McFarland, 2001). The present study aims to replicate and extend these findings using an unconventional instrumental setup consisting of a single inductive plethysmograph machine shared by both conversation partners. In addition to scripted and unscripted conversational speech, single-subject quiet breathing and speech breathing tasks of increasing length and complexity were also recorded using conventional inductive plethysmography, and used as baseline data for interpretating the conversational speech breathing data.
The Assessment of Technology Integration into Science Lesson Textbooks

Textbooks are still accepted as one of the most influential materials used during instruction today. On the other hand technological devices and processes have been used more during instruction as in every field in our lives. Therefore, this study aims to assess technology integration into textbooks for students’ motivation. In this context, science lesson textbooks from 3rd to 8th grades were examined in terms of included technological concepts or technology use in the activities and handled digital competences. The study is carried out with the document analysis method in a qualitative research and it is found that technology, television, telephone and computer are the most used concepts in textbooks. And also it is found that “information and data literacy” and “communication and collaboration” competences identified in the European Digital Competency Framework are used most in the textbook activities. “Digital content creation” and “problem solving” competences are included less in the textbook activities. Depending on the data obtained from the study, it is seen that the use of only QR codes in textbooks is not sufficient to improve the students’ digital competency. For this reason, it is suggested that different technological methods and techniques should be used more.
Layers of the Internet: The Challenge of the Dark Web and the Need for an International Legal Framework

The Dark Web and Dark Net, or Darknet, have become buzzwords in recent years, particularly around cyber-crime and illicit activities. They refer to a hidden part of the internet that is not easily accessible by traditional search engines or browsers. The Dark Web operates through encrypted networks, such as Tor, which allow users to browse the internet anonymously. The Dark Web has been associated with illicit activities such as the sale of illegal drugs, firearms, and even human trafficking. These activities are facilitated through the use of cryptocurrency transactions, making it difficult for law enforcement agencies to track the movements of money and identify the individuals involved in such transactions.Activities on the Dark Web however are not all illegal. There are legitimate uses such as political dissent and whistle-blowing. Many journalists and activists use the Dark Web to communicate and share information securely. The Dark Web also has the potential to be a tool for good, such as providing a safe space for marginalized communities to connect and organize, away from the scrutiny of those who seek to oppress them.This paper discusses the legal framework for the Dark Web activities. The Dark Web still remains a source of illicit activity, and law enforcement agencies around the world are working to dismantle criminal networks and apprehend those responsible for facilitating illegal activities on the Dark Web. As technology continues to evolve, it is likely that the Dark Web will continue to be a challenge for law enforcement and a platform for criminal activity, both nationally and internationally.