Reviewerly/exHarmony huggingface dataset:
Two-dimensional playability maps for single-reed woodwind instruments

One method to analyze musical instrument playability is by visualizing a two-dimensional subspace of the musician's control parameters. This hasbeen widely used in the form of Schelleng diagrams for bowed string instruments. There, it is possible to identify regions within the bow force—bowing position subspace where Helmholtz motion is achieved. Such diagrams may be populated either on the basis of experimental measurements, or via physical modeling. In fact, physical modeling is particularly suited to this task, since playing parameters can be directly controlled. It has been recently suggested (Woodhouse, in Proc. SMAC 2023) that similar diagrams may be used for analyzing wind instrument playability. This study aims at exploring this direction using a physical model which has been previously validated against experimental measurements. Initially, an informed decision is made on which parameters should be chosen for plotting an equivalent playability diagram. Subsequently, various diagram versions are generated, focusing on different aspects of the generated tones. As already pointed out by Woodhouse, similarities appear between the diagrams for bowed-string and woodwind instruments. It remains to examine how valuable information regarding woodwind playability may be extracted from such studies.
Architectural acoustics for non-acousticians

The great majority of professionals who design our everyday environments, as well as our special spaces, have a very limited understanding of acoustics. Those who may be interested in learning more about acoustics must overcome the vast oceans of misinformation available on the web, and often feel “allergic” to math and physics. This presentation will focus on the methods and approaches for providing useful information that they can use and remember, in a meaningful and organized manner, as based on decades of “brown bag” lunches, meetings, professional society presentations, webinars, short courses, and 3-credit classes.
Stochastic Adder Circuits with Improved Entropy Output

Random pulse computing (RPC), the third paradigm along with digital and quantum computing, draws inspiration from biology, particularly the functioning of neurons. Here, we study information processing in random pulse computing circuits intended for the summation of numbers. Based on the information-theoretic merits of entropy budget and relative Kolmogorov–Sinai entropy, we investigate the prior art and propose new circuits: three deterministic adders with significantly improved output entropy and one exact nondeterministic adder that requires much less additional entropy than the previous art. All circuits are realized and tested experimentally, using quantum entropy sources and reconfigurable logic devices. Not only the proposed circuits yield a precise mathematical result and have output entropy near maximum, which satisfies the need for building a programmable random pulse computer, but also they provide affordable hardware options for generating additional entropy.
Evaluation of Satellite-based Rainfall Data in Flood Prediction

Rainfall-runoff transformation is a solution to the difficulty of obtaining observed discharge data in flood prediction analysis. Rainfall-runoff transformation requires observed rainfall data with a high rate of accuracy spatially. However, observed rainfall data is also often not available. Satellite rainfall data is commonly used to replace observed rainfall data. However, the accuracy of satellite rainfall data still needs to be tested. This study applied rainfall-runoff transformation to the observed rainfall data and the PERSIANN, GPM, and GSMaP satellite rainfall data in the Opak Watershed using GAMA I SUH method, which were then compared with the observed hydrograph at the AWLR Kretek during the flood event that occurred in Yogyakarta Province due to Cyclone Cempaka to evaluate their accuracy. The results showed that the GPM data generated a hydrograph that is the closest to the observed hydrograph, both the shape and the peak of the hydrograph.
A Diceless Game of the Classic and Finite Hyper Dice Backgammon: A New Class of Partizan Combinatorial Games

We introduce the notion of a combinatorial game defined on a finite hyper dice backgammon (which is a generalization of the standard backgammon) by omitting the dice from it and call it Diceless or No Chance hyper dice backgammon. Instead of a random element, players choose their own numbers under certain constraints in full view of the opponent. The idea for this new game is simple and converts a non-combinatorial game (a game of chance) into an extended Partizan Combinatorial Game.
Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.
Am I Fighting Well? Fighting Game Commentary Generation With ChatGPT

This paper presents a new approach for leveraging ChatGPT in fighting game commentary generation task. Commentary generation often relies on deep learning techniques, which typically demand extensive data to achieve effectiveness. Large language models (LLMs) have become essential due to their remarkable ability to process data efficiently, thanks to their extensive training on vast datasets. Our proposed approach integrates the use of LLMs, specifically the GPT-3.5 model, for generating commentaries through the utilization of various prompts with data from the open-source fighting game, DareFightingICE. Four prompt variants are employed to assess the effectiveness of each prompt components. Objective evaluation using natural language metrics reveals that different prompt components significantly affect the generated commentaries. Additionally, subjective evaluation through a questionnaire reveals that prompts without parameter definitions received the highest preference from human evaluators. These results suggest that LLMs exhibit versatility in generating fighting game commentaries and hold promise for broader applications.
Predicting Knee Angles from Video: An Initial Experiment with Machine Learning

Machine Learning (ML) has drawn a lot of attention these days due to its capability to automate processes that where very complicated and/or only performed by humans before. It is done by not having the need to write hard coded rules to solve problems, letting the machine find the rules by itself, and for being able to universally approximate functions with certain algorithms. On the other side, motion capture systems are quite expensive, making it more difficult to health professionals and physicians to have a more precise way to analyze the gait of patients and the diseases related to it. Having that said, this paper aimed to show that is possible to predict (generate) the angles of the right knee of patients directly from a common video of their walks, reducing costs and opening the opportunity for a more complete and affordable system to realize motion capture. Thus, this study is an initial experiment and the first step towards a possible future more complete product. As an initial study, the data of three patients were acquired and the resulting model, a Multilayer Perceptron Artificial Neural Network (MLP), acquainted a score of 92.2% when predicting the angles of the right knee for unknown data.
New Ways of Knowing Ourselves. BCI Facilitating Artistic Exploration of Our Biology

As rapidly advancing technologies become more widely available, having access to tools that collect biometric data and in particular BCI technology, is providing artists with new ways of exploring our biological selves as well as creating new modes of audience interaction. Brainlight is a large illuminated interactive sculpture that integrates biology, lighting design and BCI technology to explore the hidden aspects of our minds. The installation is controlled with a wireless EMOTIV EPOC+ EEG headset that detects live neural activity which is translated into a light display within the brain sculpture. In real time it visualises the brain frequencies of Theta (3.5–7.5 Hz) as green light, Alpha (7.5–13 Hz) as blue light, and Beta (16–32 Hz) as red light. Previously, in more traditional art, when an audience views an artwork their own psychological process would be a passive, hidden, private experience. The aim of Brainlight is to harness the brain as the creator of an interactive art experience where no physical interplay is required except for the electrical activity of the mind. The project exposes some key developments in the use of BCI technology for artistic purposes, such as how to accurately collect and process EEG data aesthetically, and what license the artist can take with this data in order to facilitate meaning or allow space for the audience to bring their own meaning to the work. This chapter will explore these developments and outline the collaborative process behind the research and development of the work and the contexts in which it has subsequently been exhibited and used by the public.
Adaptive Workflow Design Based on Blockchain

Increasingly, organizational processes have become more complex. There is a need for the design of workflows to focus on how organizations adapt to emergent processes while balancing the need for decentralization and centralization goal. The advancement in new technologies especially blockchain provides organizations with the opportunity to achieve the goal. Using blockchain technology (i.e. smart contract and blocks of specified consensus for deferred action), we leverage the theory of deferred action and a coordination framework to conceptually design a workflow management system that addresses organizational emergence (e-WfMS). Our artifact helps managers to predict and store the impact of deferred actions. We evaluated the effectiveness of our system against a complex adaptive system for utility assessment.
Cognitive Systems and Robotics

Cognitive systems are able to monitor and analyze complex processes, which also provides them with the ability to make the right decisions in unplanned or unfamiliar situations. Fraunhofer experts are employing machine learning techniques to harness new cognitive functions for robots and automation solutions. To do this, they are equipping systems with technologies that are inspired by human abilities, or imitate and optimize them. This report describes these technologies, illustrates current example applications, and lays out scenarios for future areas of application.
Salient Object Detection with CNNs and Multi-scale CRFs

Recent CNNs based salient object detection approaches tend to embed a fully connected Conditional Random Field (CRF) layer to refine the saliency maps from CNNs for post processing. Due to the significant performance enhancement by the CRF layer, in this paper, we propose a more flexible CRF refinement framework by embedding the CRF inference to multiple levels of side outputs from CNNs for multi-scale saliency refinement. A fully convolutional neural networks based on the simple yet effective encoder-decoder architecture with only three scales of side output maps is pre-trained. Then, the CRF layers are embedded to each scale of the side output respectively to complement the defects of each side output maps. Finally, the refined side output maps are fused and refined by another CRF inference for the final saliency map. The proposed multi-scale CRFs model (MCRF) is trained with low computational costs and shows competitive performance over four datasets in comparison with the existing state-of-the-art saliency models.
Sheared multi-scale weight sharing for multi-spectral superresolution

Deep learning approaches to single-image superresolution typically use convolutional neural networks. Convolutional layers introduce translation invariance to neural networks. However, other spatial invariants appear in imaging data. Two such invariances are scale invariance, similar features at multiple spacial scales, and shearing invariance. We investigate these invariances by using weight sharing between dilated and sheared convolutional kernels in the context of multi-spectral imaging data. Traditional pooling methods can extract features at coarse spacial levels. Our approach explores a finer range of scales. Additionally, our approach offers improved storage efficiency because dilated and sheared convolutions allows single trainable kernels to extract information at multiple spacial scales and shears without the costs of training and storing many filters, especially in multi-spectral imaging where data representations are complex.
Shannon mutual information of a qutrit in cascade model

Quantum sensing is an important application of Quantum Information (QI) techniques and usually entangled Qubits are used for this task. The sensor sensitivity depends on QI metrics like Shannon Mutual Information (SMI). In this work we present a mathematical model for calculating 1-Qutrit SMI and compare that with 1-Qubit SMI. Qutrit in cascade configuration is found to have higher SMI and so it a better Quantum Sensor for constant fields.
Analysis of student’s final qualification theses using text loans detection systems

In this paper there are results of the bachelor and master theses citing analysis. These students graduated from the Higher mathematics chair of the Russian Technological University in the summer of 2018. In this comparative analysis the dependencies of thesis loan percent on parameters of students, statistical values of their theses are explored. This research is actual because of the progress and development of new informational technologies used in the educational system. Popularity of the text loan detection systems increases. Automatic plagiarism detection systems are intended to make educational process better, the text drawing search easier, to support the copyright laws and academical honesty. The percentage is given by two main Russian plagiarism detection systems: Antiplagiat and Rucontext. Connections between thesis parameters are explored. Advantages of each text loan detection systems are described. In this research there are the results of the pedagogical experiment aimed to analyze statistically the dependencies of the bachelor’s and master’s theses loan percentage which have been got from Antiplagiat and Rucontext systems on the author’s parameters, statistical values describing thesis text. The comparison between statistical results of these systems have been made. The conclusions about their advantages have been presented in the paper. In order to make the comparison methods of the mathematical statistics have been used. Numerical experiment has been provided by means of the packages of the R statistical language. The difference between text loan percentages in the Antiplagiat and Rucontext systems has been analyzed. It has been shown that it grows when length of the text becomes larger. The dependencies of the text loan percentage on the available parameters of the thesis author and text parameters have been presented. The dependencies types are the same for the both systems. Scale of the coefficients in the statistical dependencies is also the same. The difference is in the very set of the parameters: the Rucontext percentage is better described statistically with the sex of the author, the Antiplagiat percentage is described with the type of the higher education (bachelor’s or master’s thesis). Also the dependency of the text loan percentage on the length of the thesis text differs: the Antiplagiat percentage is better described statistically with the number of words but the Rucontext percentage is described with the number of characters. It seems that these differences can be explained with different text search and analyze algorithms. The dependencies between the Rucontext percentage and the Antiplagiat text loan percentage is presented.
ML Polymorphism of Linear Lambda Calculus with First-class Continuations

The Curry-Howard isomorphism shows that an intuitionistic deductive system is similar to a formal computational system; propositions correspond to types, proofs to lambda-terms, and a proof normalization procedure to an evaluation strategy. Furthermore, the duality between computation values and continuations is discovered under the Curry-Howard isomorphism. In the traditional lambda calculus, duplication and erasing of values are allowed but those of continuations prohibited. On the other hand, in the lambda calculus with first-class continuations, both values and continuations are permitted to be duplicated and erased. In our previous paper, we proposed a linear lambda calculus with first-class continuations, in which we cannot duplicate and erase values but can do continuations.
Deep Product Quantization for Large-Scale Image Retrieval

Product quantization (PQ) is a promising technique for big data retrieval. It generates compact codebooks by decomposing the high-dimensional feature space into the Cartesian product of the low-dimensional subspace. Due to the imbalance of the subspace variance and the excessive variance of the subspace, the existing methods based on PQ face inefficient codebook learning and quantization distortions. In this work, we propose an end-to-end PQ method based on deep learning to optimally divide the subspace and reduce the quantization distortion while learning shorter codebooks. A convolutional neural network model is trained by minimizing a well-designed loss function, which maps image data into clustering features, reducing and balancing the variance of each subspace. Compared with state-of-the-art approaches, our method significantly improves performance on the benchmark datasets.
Fedriani, Chiara, &amp; Sansò, Andrea (Eds.). 2017. <i>Pragmatic Markers, Discourse Markers and Modal Particles</i>

Abstract This article reviews Pragmatic Markers, Discourse Markers and Modal Particles 978 90 272 5951 6.
Method for Generalization of Fuzzy Sets

We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both “gaps” and “offshoots”. The method globally minimizes a penalty combining head subjects and gaps and offshoots. We apply this to extract research tendencies from a collection of about 18000 research papers published in Springer journals on data science. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System 2012 (ACM-CCS). We find fuzzy clusters of leaf topics over the text collection and use thematic clusters’ head subjects to make some comments on the tendencies of research.
Research on a New Clustering Validity Index Based on Data Mining

Clustering analysis has made great achievements in the development process, but there are still many problems in it. In this paper, the question on determining optimal number of clusters in cluster analysis is studied mainly. The KMIBCP (K-means Intra-Between-cluster partition) in K-means clustering algorithm is proposed. KMIBCP algorithm uses IBCP (Intra-Between-cluster partition) validity index to analyze the validity of clustering results produced by K-means clustering algorithm, and to determine optimal number of clusters. The experimental results on artificial datasets verify the effectiveness of the proposed algorithm.
Comments on “Unconditionally Secure, Universally Composable Privacy Preserving Linear Algebra”

In this paper, we discuss the security of a distributed inner product (DIP) protocol [IEEE Trans. Inf. Forensics Security, 11.1, 59, (2016)]. We show information leakage in this protocol that does not happen in an ideal execution of the DIP functionality. In some scenarios, this information leakage enables one of the parties to completely learn the other party's input. We will give examples of such scenarios.
Towards image and video super-resolution for improved analytics from overhead imagery

In this work, we address the problem of losing details in the overhead remote sensing image acquisition and generation process due to sensor resolution and distance to target by leveraging state-of-the-art deep neural network architectures. The goal is to recover such details by super-resolving the images acquired by overhead imaging sensors in order for human analysts to interpret data more accurately, and consequentially, for automated visual exploitation algorithms to be applied more effectively. We have developed a super-resolution framework operating on overhead full motion video (FMV) and still imagery (e.g. satellite images). Our framework consists of a neural network capable of learning the mapping between low and high resolution images in order to produce plausible details about the scene. Our framework combines Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) to process low resolution signals both spatially and, in the case of FMV, temporally. We have applied the output of our system to several visual perception tasks, including object detection, object tracking, and semantic segmentation. We have also applied our methods to data from different geographical areas, sensors, and even modalities to demonstrate broad and generalized applicability.
Development and application of online monitoring software for pollutants on oil platforms

The purpose of this study is to improve the efficiency of on-line monitoring of pollutants on oil platform and the economic benefits of oil exploration and development companies. In this study, firstly, several technologies used in the development of this system are introduced, namely, Common Gatew ay Interface (CGI), Active Server Page (ASP), Document Object Model (DOM) and Internet Information Server (IIS). Secondly, the overall structure of oil anti-fouling statistics system is divided into three functional modules: news module, anti-fouling data processing module and user management module. Finally, the overall workflow of the system, user workflow, system security and database are designed. The results show that the system can realize the rapid and automatic exchange and information sharing of system information, and provide information service for related departments. In addition, it can also improve the comprehensive information query ability, and provide leaders and competent departments with the latest information at any time, so as to provide information support for planning and decision-making. Therefore, through this research, it is possible to speed up the collection and processing of information, improve the comprehensive utilization of information resources, and provide a reliable basis for scientific decision-making.