While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box" solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set labeled with propaganda techniques. To help human experts annotate natural language sentences with these features, RhetAnn, a web application, was specifically designed to minimize an otherwise considerable mental effort. Finally, a small set of annotated data was used to fine-tune GPT-3.5, a generative large language model (LLM), to annotate the remaining data while optimizing for financial cost and classification accuracy. This study demonstrates how combining a small number of human annotated examples with GPT can be an effective strategy for scaling the annotation process at a fraction of the cost of traditional annotation relying solely on human experts. The results are on par with the best performing model at the time of writing, namely GPT-4, at 10x less the cost. Our contribution is a set of features, their properties, definitions, and examples in a machine-readable format, along with the code for RhetAnn and the GPT prompts and fine-tuning procedures for advancing state-of-the-art interpretable propaganda technique detection.

We introduce an approach to identifying speaker names in dialogue transcripts, a crucial task for enhancing content accessibility and searchability in digital media archives. Despite the advancements in speech recognition, the task of text-based speaker identification (SpeakerID) has received limited attention, lacking large-scale, diverse datasets for effective model training. Addressing these gaps, we present a novel, large-scale dataset derived from the MediaSum corpus, encompassing transcripts from a wide range of media sources. We propose novel transformer-based models tailored for SpeakerID, leveraging contextual cues within dialogues to accurately attribute speaker names. Through extensive experiments, our best model achieves a great precision of 80.3\%, setting a new benchmark for SpeakerID. The data and code are publicly available here: \url{https://github.com/adobe-research/speaker-identification}

Universal sound separation (USS) is a task of separating mixtures of arbitrary sound sources. Typically, universal separation models are trained from scratch in a supervised manner, using labeled data. Self-supervised learning (SSL) is an emerging deep learning approach that leverages unlabeled data to obtain task-agnostic representations, which can benefit many downstream tasks. In this paper, we propose integrating a self-supervised pre-trained model, namely the audio masked autoencoder (A-MAE), into a universal sound separation system to enhance its separation performance. We employ two strategies to utilize SSL embeddings: freezing or updating the parameters of A-MAE during fine-tuning. The SSL embeddings are concatenated with the short-time Fourier transform (STFT) to serve as input features for the separation model. We evaluate our methods on the AudioSet dataset, and the experimental results indicate that the proposed methods successfully enhance the separation performance of a state-of-the-art ResUNet-based USS model.

Thanks to a partnership with CNN Chile, our analyses were aired every Monday as part of a weekly program devoted to Plataforma Telar, with more details posted on our website and social media accounts. Our results were regularly met with high engagement, shared by media companies and personalities, and even by convention members.a Plataforma Telar thus had a noticeable impact on how people understood the convention (for an analysis of how data-driven political communication impacts public opinion, see Daud2). Given the diversity, scale and dynamics of the data, our cloud infrastructure was increasingly becoming unwieldy, with relevant information about particular entities (for example, convention members) scattered around different tables. In order to better structure these data, we structured these data as a knowledge graph, called TelarKG, which could then be queried using MillenniumDB: an open-source graph database also developed within the IMFD.

This paper approaches the problem of separating the notes from a quantized symbolic music piece (e.g., a MIDI file) into multiple voices and staves. This is a fundamental part of the larger task of music score engraving (or score typesetting), which aims to produce readable musical scores for human performers. We focus on piano music and support homophonic voices, i.e., voices that can contain chords, and cross-staff voices, which are notably difficult tasks that have often been overlooked in previous research. We propose an end-to-end system based on graph neural networks that clusters notes that belong to the same chord and connects them with edges if they are part of a voice. Our results show clear and consistent improvements over a previous approach on two datasets of different styles. To aid the qualitative analysis of our results, we support the export in symbolic music formats and provide a direct visualization of our outputs graph over the musical score. All code and pre-trained models are available at https://github.com/CPJKU/piano_svsep

Chord diagrams are used by guitar players to show where and how to play a chord on the fretboard. They are useful to beginners learning chords or for sharing the hand positions required to play a song.However, the diagrams presented on guitar learning toolsare usually selected from an existing databaseand rarely represent the actual positions used by performers.In this paper, we propose a tool which suggests a chord diagram for achord label,taking into account the diagram of the previous chord.Based on statistical analysis of the DadaGP and mySongBook datasets, we show that some chord diagrams are over-represented in western popular musicand that some chords can be played in more than 20 different ways.We argue that taking context into account can improve the variety and the quality of chord diagram suggestion, and compare this approach with a model taking only the current chord label into account.We show that adding previous context improves the F1-score on this task by up to 27% and reduces the propensity of the model to suggest standard open chords.We also define the notion of texture in the context of chord diagrams andshow through a variety of metrics that our model improves textureconsistencywith the previous diagram.

Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it is assumed that deep learning methods are not the optimal solution for application in this field. However, excluding the entire -- and prevalent -- group of methods seems rather rash given the progress that has been made in recent years in its development. For this reason, the following paper is the first to present an approach to natural language data stream classification using the sentence space method, which allows for encoding text into the form of a discrete digital signal. This allows the use of convolutional deep networks dedicated to image classification to solve the task of recognizing fake news based on text data. Based on the real-life Fakeddit dataset, the proposed approach was compared with state-of-the-art algorithms for data stream classification based on generalization ability and time complexity.

The interplay between humans and Generative AI (Gen AI) draws an insightful parallel with the dynamic relationship between giraffes and acacias on the African Savannah. Just as giraffes navigate the acacia's thorny defenses to gain nourishment, humans engage with Gen AI, maneuvering through ethical and operational challenges to harness its benefits. This paper explores how, like young giraffes that are still mastering their environment, humans are in the early stages of adapting to and shaping Gen AI. It delves into the strategies humans are developing and refining to help mitigate risks such as bias, misinformation, and privacy breaches, that influence and shape Gen AI's evolution. While the giraffe-acacia analogy aptly frames human-AI relations, it contrasts nature's evolutionary perfection with the inherent flaws of human-made technology and the tendency of humans to misuse it, giving rise to many ethical dilemmas. Through the HHH framework we identify pathways to embed values of helpfulness, honesty, and harmlessness in AI development, fostering safety-aligned agents that resonate with human values. This narrative presents a cautiously optimistic view of human resilience and adaptability, illustrating our capacity to harness technologies and implement safeguards effectively, without succumbing to their perils. It emphasises a symbiotic relationship where humans and AI continually shape each other for mutual benefit.

Controllable music generation promotes the interaction between humans and composition systems by projecting the users' intent on their desired music. The challenge of introducing controllability is an increasingly important issue in the symbolic music generation field. When building controllable generative popular multi-instrument music systems, two main challenges typically present themselves, namely weak controllability and poor music quality. To address these issues, we first propose spatiotemporal features as powerful and fine-grained controls to enhance the controllability of the generative model. In addition, an efficient music representation called REMI_Track is designed to convert multitrack music into multiple parallel music sequences and shorten the sequence length of each track with Byte Pair Encoding (BPE) techniques. Subsequently, we release BandControlNet, a conditional model based on parallel Transformers, to tackle the multiple music sequences and generate high-quality music samples that are conditioned to the given spatiotemporal control features. More concretely, the two specially designed modules of BandControlNet, namely structure-enhanced self-attention (SE-SA) and Cross-Track Transformer (CTT), are utilized to strengthen the resulting musical structure and inter-track harmony modeling respectively. Experimental results tested on two popular music datasets of different lengths demonstrate that the proposed BandControlNet outperforms other conditional music generation models on most objective metrics in terms of fidelity and inference speed and shows great robustness in generating long music samples. The subjective evaluations show BandControlNet trained on short datasets can generate music with comparable quality to state-of-the-art models, while outperforming them significantly using longer datasets.

Our rapid immersion into online life has made us all ill. Through the generation, personalization, and dissemination of enchanting imagery, artificial technologies commodify the minds and hearts of the masses with nauseating precision and scale. Online networks, artificial intelligence (AI), social media, and digital news feeds fine-tune our beliefs and pursuits by establishing narratives that subdivide and polarize our communities and identities. Meanwhile those commanding these technologies conquer the final frontiers of our interior lives, social relations, earth, and cosmos. In the Attention Economy, our agency is restricted and our vitality is depleted for their narcissistic pursuits and pleasures. Generative AI empowers the forces that homogenize and eradicate life, not through some stupid "singularity" event, but through devaluing human creativity, labor, and social life. Using a fractured lens, we will examine how narratives and networks influence us on mental, social, and algorithmic levels. We will discuss how atomizing imagery -- ideals and pursuits that alienate, rather than invigorate the individual -- hijack people's agency to sustain the forces that destroy them. We will discover how empires build digital networks that optimize society and embolden narcissists to enforce social binaries that perpetuate the ceaseless expansion of consumption, exploitation, and hierarchy. Structural hierarchy in the world is reified through hierarchy in our beliefs and thinking. Only by seeing images as images and appreciating the similarity shared by opposing narratives can we facilitate transformative action and break away from the militaristic systems plaguing our lives.