Reviewerly/exHarmony huggingface dataset:
Discovering Creative Commons Sounds in Live Coding

This article reports on a study to identify the new sonic challenges and opportunities for live coders, computer musicians and sonic artists using MIRLCa, a live-coding environment powered by an artificial intelligence (AI) system. MIRLCa works as a customisable worldwide sampler, with sounds retrieved from the collective online Creative Commons (CC) database Freesound. The live-coding environment was developed in SuperCollider by the author in conversation with the live-coding community through a series of workshops and by observing its use by 16 live coders, including the author, in work-in-progress sessions, impromptu performances and concerts. This article presents a qualitative analysis of the workshops, work-in-progress sessions and performances. The findings identify (1) the advantages and disadvantages, and (2) the different compositional strategies that result from manipulating a digital sampler of online CC sounds in live coding. A prominent advantage of using sound samples in live coding is its low-entry access suitable for music improvisation. The article concludes by highlighting future directions relevant to performance, composition, musicology and education.
Live Coding Poetry: The narrative of code in a hybrid musical/poetic context

Live coding is a celebrated practice that is used in many areas, combined with a variety of artistic fields. Code poetry is a form of poetry with many variations, all of which have a common rule: the code that is or produces the poem must compile without errors. The meeting point of live coding and code poetry seems to have not yet been thoroughly explored, leaving space for experimentation and research. Certain attempts have already been made, where live coding is either approached through natural language or used to break up and merge chunks of existing poems, forming new ones. Computer code has also been used to write deterministic opera librettos, following the code poetry paradigm. This article focuses on the literary and artistic attributes of code, on code poetry and on the existing attempts to combine it with live coding. It also highlights the narrative attribute of musical live coding to formulate a rationale for combining live coding with code poetry in a musical context. The goal is to examine the possibilities of this combination, as well as how this can be achieved, from a technical point of view.
Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living

While Human Activity Recognition systems may benefit from Active Learning by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in activity recognition systems, which utilises semantic similarity measures and a dataset of natural language descriptions of common activities (which we make publicly available). Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply this approach to two active learning scenarios: (i) using an existing CASAS dataset, demonstrating long-term usage; and (ii) using an online activity recognition system, which tackles the issue of online segmentation and labelling. We demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) quantitatively, as an efficient way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to compare our approach and an established method. Results show the potential of our approach as a hands-free interface for annotation of sensor data as part of an active learning system. We provide insights into the challenges of active learning for activity recognition under real-world conditions and identify potential ways to address them.
Moderating mental health: Addressing the human–machine alignment problem through an adaptive logic of care

Covid-19 deepened the need for digital-based support for people experiencing mental ill-health. Discussion platforms have long filled gaps in health service provision and access, offering peer-based support usually maintained by a mix of professional and volunteer peer moderators. Even on dedicated support platforms, however, mental health content poses difficulties for human and machine moderation. While automated systems are considered essential for maintaining safety, research is lagging in understanding how human and machine moderation interacts when addressing mental health content. Working with three digital mental health services, we examine the interaction between human and automated moderation of discussion platforms, contrasting ‘reactive’ and ‘adaptive’ moderation practices. Presenting ways forward for improving digital mental health services, we argue that an integrated ‘adaptive logic of care’ can help manage the interaction between human and machine moderators as they address a tacit ‘risk matrix’ when dealing with sensitive mental health content.
Mixing detection on Bitcoin transactions using statistical patterns

Abstract Cryptocurrencies, particularly Bitcoin, have garnered attention for their potential in anonymous transactions. However, their anonymity has often been compromised by deanonymization attacks. To counter this, mixing services have been introduced. While they enhance privacy, they obscure fund traceability. This study seeks to demystify transactions linked to these services, shedding light on pathways of concealed and laundered money. We propose a method to identify and classify transactions and addresses of major mixing services in Bitcoin. Unlike previous research focusing on older techniques like CoinJoin, we emphasize modern mixing services. We gathered labelled data by transacting with three prominent mixers (MixTum, Blemder, and CryptoMixer) and identified recurring patterns. Using these patterns, an algorithm was created to pinpoint mixing transactions and distinguish mixer‐related addresses. The algorithm achieved a remarkable recall rate of 100%. Given the lack of clear ground truth and the vast number of unlabelled transactions, ensuring accuracy was a challenge. However, by analyzing a set of non‐mixing transactions with our model, it was confirmed that the high recall rate was not misleading. This work provides a significant advancement in monitoring mixing transactions, presenting a valuable tool against fraud and money laundering in cryptocurrency networks.
Data re-uploading with a single qudit

Abstract Quantum two-level systems, i.e., qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantum systems constitute a promising alternative and are increasingly explored in theory and practice. Here, we explore the capabilities of multi-level quantum systems, so-called qudits, for their use in a quantum machine learning context. We formulate classification and regression problems with the data re-uploading approach and demonstrate that a quantum circuit operating on a single qudit is able to successfully learn highly non-linear decision boundaries of classification problems such as the MNIST digit recognition problem. We demonstrate that the performance strongly depends on the relation between the qudit states representing the labels and the structure of labels in the training data set. Such a bias can lead to substantial performance improvement over qubit-based circuits in cases where the labels, the qudit states, and the operators employed to encode the data are well-aligned. Furthermore, we elucidate the influence of the choice of the elementary operators and show that a squeezing operator is necessary to achieve good performances. We also show that there exists a trade-off for qudit systems between the number of circuit-generating operators in each processing layer and the total number of layers needed to achieve a given accuracy. Finally, we compare classification results from numerically exact simulations and their equivalent implementation on actual IBM quantum hardware. The findings of our work support the notion that qudit-based algorithms exhibit attractive traits and constitute a promising route to increasing the computational capabilities of quantum machine learning approaches.
Improving concave point detection to better segment overlapped objects in images

Abstract This study presents a method to improve state-of-the-art concave point detection methods as the first step towards effectively segmenting overlapping objects in images. The approach relies on analysing the curvature of the object contour. This method comprises three main steps. First, the original image is preprocessed to obtain the curvature value at each contour point. Second, the regions with higher curvatures are selected and a recursive algorithm is applied to refine previously selected regions. Finally, a concave point is obtained for each region by analysing the relative position of their neighbourhood. Furthermore, the experimental results indicate that improving the detection of concave points leads to better division of clusters. To evaluate the quality of the concave point detection algorithm, a synthetic dataset was constructed to simulate the presence of overlapping objects. This dataset includes the precise location of concave points, which serve as the ground truth for evaluation. As a case study, the performance of a well-known application, such as the splitting of overlapping cells in images of peripheral blood smears samples from patients with sickle cell anaemia, was evaluated. We used the proposed method to detect concave points in cell clusters and then separated these clusters by ellipse fitting.
Big Data technologies in criminal investigations: The frames of the members of Judiciary Police in Portugal

Big Data has been increasingly implemented in police departments. In the Judiciary Police in Portugal, although it is at an early stage of implementation, there are no studies on the topic. Based on 16 interviews with members of the Judiciary Police, their frames that portray Big Data’s benefits and harms in criminal investigations are explored. The benefits portrayed are related to its capabilities to help fight organized and transnational crimes, advance criminal investigations, and expand the availability of sets of information. The harms focus on the lack of regulatory documents, threats to human rights, and the probability of obtaining erroneous conclusions. A critical analysis of these frames may contribute to reflecting on their role to inform technology developments in policing settings, with implications for inequalities, and crime control.
Evaluating the detection range of microwave quantum illumination radar

Abstract Based on quantum illumination (QI) protocol, microwave quantum radar has been developed, which has attracted great interest from radar researchers. While, according to the QI protocol, the quantum advantage of QI radar is related to the power of generated signal, which may lead to a trade‐off between its quantum advantage and the maximum detection range. However, the maximum detection range is an important indicator to measure the performance of the radar. Therefore, it is necessary to quantitatively analyse the maximum detection range and quantum advantages of microwave QI radar, which will help us understand its performance from the perspective of radar applications and further evaluate the potential application scenarios and future research directions of it. The microwave quantum radar equation is developed, by the authors, based on the classical radar equation with the consideration of the quantum properties of extremely weak signals. Then, the maximum detection range of QI radar and classical two mode noise radar in different microwave bands is comprehensively compared and analysed. The possible applications of microwave QI radars in different bands are also discussed. In addition, the authors demonstrate that the symmetrical quantum limited amplification actually has no contribution to improving the detection range of a QI radar.
Improving Students’ Reading Ability Through Reciprocal Teaching Method

This study aims to determine the implementation of reciprocal teching method forimprovingthestudents reading ability at MTS Nurul Huda GondolBuleleng Bali. This is one of the method that can be applied by the teacher in teaching learning process. Through a reading ability that must be mastered by the students in learning English, it is important to be applied by the creative method while teaching learning process. This research conducts in Classroom Action Research (CAR) which consists of two cycles, each cycle included planning, implementing, observing, and reflecting. The cycle finishes when the indicators of success had achieved the completeness of learning set by the school. Completenessof learning for grade VIII subjects is 75%.The results show an increase of students' reading ability its learning outcomes in each cycle. The increase in learning outcomesshows in the pretest results with the percentage of completeness of learning 13,64% and there was an increase in the results of the post test cycle 1 to 40,91%, and an increase again in the results of the post test cycle 2 to 90,90 %. The increase in learning outcomes from the results of the pre-test , post-test cycle I to post-test cycle II was due to improvements in the reciprocal teaching method after evaluating the learning process activities and learning outcomes.From the results of observations in the learning process of students’ reading ability in the VIII grade of MTS Nurul Huda GondolBuleleng Bali, the students show a good response. Itconclude that learning using reciprocal teaching method.
Association measures for collocation extraction

Abstract In this study, we propose a new evaluation scheme to assess the strengths and limitations of collocation extraction measures and explore type-sensitive methods for extracting collocations. We introduced the pooling strategy widely used in Information Retrieval and automated the evaluation process using online dictionaries. Sixteen well-known metrics are evaluated based on their effectiveness and then distributional and linguistic compared. The results show that Group A methods (e.g. z-score, Dice, PMI) are more effective in extracting low-frequency collocations with relatively small extraction scales. In contrast, Group B methods (e.g. t-test, LMI, LLR) perform better at finding high-frequency collocations, most of which outperform Group A methods as the extraction scale increases. Moreover, Group A prefers NN collocations, while Group B identifies collocations with a wide range of syntactic structures. This study provides suggestions for studies to identify hybrid extraction methods as well as for language educators and dictionary compilers.
Rule 1: Remember the human. A socio-cognitive discourse study of a Reddit forum banned for promoting hate based on identity

Detecting internet hate speech automatically is an important but difficult task that is recognised as ethically problematic. In comparison to typical computer science approaches, the current study focuses on psychologically meaningful aspects of language, and not on terms pre-defined as hateful. Data consists of the naturally occurring discourse of a gender critical feminist group banned from the Reddit discussion platform for promoting hate based on identity; this is compared with discourse of a feminist group from Reddit, that has not been banned. Notable psychologically meaningful terms of the gender critical group include third-person plural pronouns, and metonymic acronyms that reference the gender critical outgroup, which may represent outgroup derogation, and outgroup homogeneity. It is noted that the banned forum, which is shown to be an online community, may be responding to threats to identity in recognised ways. It is concluded that a socio-cognitive discourse approach to hate speech detection may help address related ethical concerns, including potential social injustice.
Keypoint Changes for Fast Human Activity Recognition

Abstract Human activity recognition has been an open problem in computer vision for almost 2 decades. During this time, there have been many approaches proposed to solve this problem, but very few have managed to solve it in a way that is sufficiently computationally efficient for real-time applications. Recently, this has changed, with keypoint-based methods demonstrating a high degree of accuracy with low computational cost. These approaches take a given image and return a set of joint locations for each individual within an image. In order to achieve real-time performance, a sparse representation of these features over a given time frame is required for classification. Previous methods have achieved this using a reduced number of keypoints, but this approach gives a less robust representation of the individual’s body pose and may limit the types of activity that can be detected. We present a novel method for reducing the size of the feature set, by calculating the Euclidian distance and the direction of keypoint changes across a number of frames. This allows for a meaningful representation of the individuals movements over time. We show that this method achieves accuracy on par with current state-of-the-art methods, while demonstrating real-time performance.
Learning from Other Communities: Organising Collective Action in a Grassroots Food-sharing Initiative

Abstract This paper illustrates the work of creating, infrastructuring, and organising a food-sharing community from the ground up. Drawing on Participatory Action Research (PAR) and a three-year engagement with FoodSharing Stockholm, the paper shows how the processes of starting up a grassroots initiative are shaped by participants’ direct experience and knowledge of similar initiatives. The analysis draws attention to: (1) how central activities such as recruiting volunteers, choosing digital tools, and establishing partnerships with food donors are conceived and organised, (2) the concrete challenges of sharing surplus food, such as adopting a distribution model, and negotiating fairness, and (3) how governance and decision-making models are adopted and (re)negotiated over time. The paper introduces the term Collective histories of organising to capture the impact that learning from previous experiences can have on communities’ efforts to set up and run; and re-orient design visions towards the consideration and adoption of existing sociotechnical practices, rather than always aiming at novel digital explorations. We outline three emerging dimensions that can characterise “Collective histories of organising” as a concept, (1) configuring capacities, (2) configuring sociotechnical practices, and (3) configuring participation. The paper contributes practical sensitivities to build, sustain, and infrastructure surplus food-sharing initiatives, where these three dimensions are discussed as central concerns designers and other food-sharing communities could learn from.
Lodestar: Supporting rapid prototyping of data science workflows through data-driven analysis recommendations

Keeping abreast of current trends, technologies, and best practices in visualization and data analysis is becoming increasingly difficult, especially for fledgling data scientists. In this paper, we propose lodestar, an interactive computational notebook that allows users to quickly explore and construct new data science workflows by selecting from a list of automated analysis recommendations. We derive our recommendations from directed graphs of known analysis states, with two input sources: one manually curated from online data science tutorials, and another extracted through semi-automatic analysis of a corpus of over 6000 Jupyter notebooks. We validated Lodestar through three separate user studies: first a formative evaluation involving novices learning data science using the tool. We used the feedback from this study to improve the tool. This was followed by a summative study involving both new and returning participants from the formative evaluation to test the efficacy of our improvements. We also engaged professional data scientists in an expert review assessing the utility of the different recommendations. Overall, our results suggest that both novice and professional users find Lodestar useful for rapidly creating data science workflows.
ACRE: Actor-Critic with Reward-Preserving Exploration

Abstract While reinforcement learning (RL) algorithms have generated impressive strategies for a wide range of tasks, the performance improvements in continuous-domain, real-world problems do not follow the same trend. Poor exploration and quick convergence to locally optimal solutions play a dominant role. Advanced RL algorithms attempt to mitigate this issue by introducing exploration signals during the training procedure. This successful integration has paved the way to introduce signals from the intrinsic exploration branch. ACRE algorithm is a framework that concretely describes the conditions for such an integration, avoiding transforming the Markov decision process into time varying, and as a result, making the whole optimization scheme brittle and susceptible to instability. The key distinction of ACRE lies in the way of handling and storing both extrinsic and intrinsic rewards. ACRE is an off-policy, actor-critic style RL algorithm that separately approximates the forward novelty return. ACRE is shipped with a Gaussian mixture model to calculate the instantaneous novelty; however, different options could also be integrated. Using such an effective early exploration, ACRE results in substantial improvements over alternative RL methods, in a range of continuous control RL environments, such as learning from policy-misleading reward signals. Open-source implementation is available here: https://github.com/athakapo/ACRE .
Semantic segmentation of textured mosaics

This paper investigates deep learning (DL)-based semantic segmentation of textured mosaics. Existing popular datasets for mosaic texture segmentation, designed prior to the DL era, have several limitations: (1) training images are single-textured and thus differ from the multi-textured test images; (2) training and test textures are typically cut out from the same raw images, which may hinder model generalization; (3) each test image has its own limited set of training images, thus forcing an inefficient training of one model per test image from few data. We propose two texture segmentation datasets, based on the existing Outex and DTD datasets, that are suitable for training semantic segmentation networks and that address the above limitations: SemSegOutex focuses on materials acquired under controlled conditions, and SemSegDTD focuses on visual attributes of textures acquired in the wild. We also generate a synthetic version of SemSegOutex via texture synthesis that can be used in the same way as standard random data augmentation. Finally, we study the performance of the state-of-the-art DeepLabv3+ for textured mosaic segmentation, which is excellent for SemSegOutex and variable for SemSegDTD. Our datasets allow us to analyze results according to the type of material, visual attributes, various image acquisition artifacts, and natural versus synthetic aspects, yielding new insights into the possible usage of recent DL technologies for texture analysis.