Reviewerly/exHarmony huggingface dataset:
Delay-Sensitive Local Plasticity in Echo State Networks

Time delays are inherently present in any physical or biological network. However, the role of delays in echo state networks (ESNs) has only been touched upon. In recent years, the use of local plasticity has been explored in the field of reservoir computing, and specifically in ESNs. In this paper, we investigate the role of distance dependent inter-neuron delays in adaptive reservoirs. We introduce a novel ESN design called adaptive distance-based delay network (ADDN), that combines inter-neuron delays with local synaptic plasticity in the reservoir weights using a delay sensitive version of the Bienenstock-Cooper-Munro (BCM) rule. We show that ADDNs perform better on prediction tasks compared to ESNs, regular distance-based delay networks, and ESNs with conventional BCM connections. We optimized the hyperparameters of ADDNs and each of the baseline models using covariance matrix adaptation evolution strategy (CMA-ES). We prove that with ADDNs, we can evolve a single set of hyperparameters that can generate networks which, after unsupervised adaptation, can obtain good performance on different Mackey-Glass sequences with a range of different time constants. By adapting its reservoir weights to the dynamics of the input data, ADDNs can generalize between versions of the same “class” of tasks.
Graph Representation for Weakly-Supervised Spatio-Temporal Action Detection

Spatio-temporal action recognition and localization are crucial in several computer vision applications including video surveillance, video captioning to name a few. However, most of the existing action recognition and localization approaches are for offline use, perform well only on trimmed action clips. Also, they need precise annotations at the clip, frame, and pixel levels which is labor-intensive and thus undermines their usage for real-world large-scale scenarios. In this paper, we propose a weakly-supervised spatio-temporal action recognition and localization based on graph representation in untrimmed videos. More specifically, we propose an efficient graph representation of videos using only the clip level annotations, while existing approaches are either supervised or unsupervised learning approach. For graph construction, the local actions are determined based on the key interesting demeanor in an action clip and assigned the class label the same as that of the clip. This weak annotation impacts both action recognition and localization significantly because the local actions have considerable intra-class variability and inter-class similarity. To handle the intra-class variability and inter-class similarity, we use a weakly-supervised deep multiple instance ranking framework on the local action descriptors. To classify a graph of local actions into one of the action classes, we use a support vector machine along with a graph kernel and then localize the recognized action as a non-cubic shaped-portion of the video based on local actions in the graph. The experimental results show that the proposed approach outperforms the state-of-the-art methods on the three benchmark datasets, namely, THUMOS14, UCF-Sports, and JHMDB-21.
Predicting CNN Learning Accuracy Using Chaos Measurement

Learning on entropy coded data has many benefits. First, it avoids decoding, but also it allows one to process compact data. However, this type of learning has been overlooked due to the chaos introduced by entropy coding functions. Indeed, convolution widely used in learning algorithm requires that the encoding function preserves the distance between pixel positions (spatial closeness), and the distance between pixel values (semantic closeness). Even if these two properties are not satisfied by entropy coding, we had shown previously that learning on entropy coded data is possible and that the accuracy depends on spatial and semantic closeness. In this paper, we quantify this dependence, and introduce a new metric, that measures the chaos in the data representation. This measure is easy to compute as it depends on the encoded data only. Moreover, this measures allows one to predict the accuracy of the learning algorithm, that process entropy coded data.
Upaya Meningkatkan Pemahaman Siswa terhadap Materi Membaca Menggunakan Metode Resitasi pada Siswa Kelas IV SDN 10 Way Lima Pesawaran Tahun Ajaran 2021/2022

: The purpose of this study was to improve student learning outcomes by applying the recitation method to reading material for grade 4 students SDN 10 Way Lima Pesawaran. The approach used in this study was a scientific approach with the recitation method. As for the steps of the recitation method, namely : (1) planning, (2) implementation, (3) observation and (4) reflection. Data collection techinques using observation sheets and students learning outcomes test. The data analysis technique used the formula for analyzing students learning outcomes and the percentage of completeness in student learning outcomes. The results of this study indicate that the apllication of the recitation method can improve students learning outcomes. Seen from the initial observatations with the number of students 17 students. Students learninf outcomes can increase thin can be seen from the student laerning outcomes test cycle 1 the average value at the first meeting is 5,23% and the second semester is 6,29%. Then in cycle 2 the implementation was only carried out once in a meeting with the test results cery clearly seen the improvement and had exceeded the standard of complete learning that had been determined by the Indonesian language education curriculum, which was an average of 6.70%.
DISASTER: Blockchain-Enabled Token Trading Game for Supply Chain Management

This paper describes the blockchain-enabled token trading game for supply chain management, a web-based supply chain simulation. The game uses blockchain technology’s concepts to create virtual markets for supplier capacity trading among retailers, in which participants take on the role of a retailer, have different valuations for products, and submit an order before knowing their demand; after demand realization, participants trade tokens (claims on the supplier’s capacity) among themselves using virtual markets to maximize their profits. The game provides participants with firsthand experience with how blockchain technology can be used in practice, thus serving as an interactive pedagogical tool. More generally, the game is intended to help supply chain management and blockchain technology students and executives understand the challenges of serving uncertain customer demand and the role of virtual markets in providing an effective remedy for reconciling supply and demand, thus improving supply chain performance. Supplemental Material: The supplemental material is available at https://doi.org/10.1287/ited.2023.0015 . The Teaching Note and PowerPoint presentation are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .
Neonatologie in tijden van big data, machine learning en artificiële intelligentie: potentiële toepassingen gebruikmakend van continu gemonitorde vitale parameters. Een systematische review.

Neonatology in times of big data, machine learning and artificial intelligence: potential applications using continuously monitored vital signs. A systematic review Background: At the neonatal intensive care unit vital signs are continuously monitored, which yields a huge amount of values. At the moment, these are mainly used in a real-time bedside manner when values cross fixed boundaries triggering an alarm, also general trends are followed. Subtle patterns which may be a first sign of disease or clinical condition can be missed. By using scientific revolutions as big data, machine learning and artificial intelligence it may be possible to extract much more information from this amount of data. Objective: We make an overview of possible applications more thoroughly using continuously monitored vital signs in the NICU, developed by using scientific evolutions as big data, machine learning or artificial intelligence. Methods: A systematic search is carried out in four databases, PRISMA-guidelines are followed. The retained articles are analysed regarding the topic, the used methods, the results and possible implementation. Results: We included 35 articles. The investigated topics and used methods were very diverse. Only a minority of possible applications are already studied enough to enable use in daily practice. Conclusion: Applications using continuously monitored vital signs, developed by using scientific evolutions as big data, machine learning and artificial intelligence are promising and a lot of research is being carried out. Nevertheless, implementation is still very limited.
Research Methodology and Design: Types, Methods, Tools, Tests and Analysis

Research methodology is the systematic steps that research takes to solve a research question.Research methodology includes research methods covering all methods and techniques used to conduct research.The choice of both methodology and method is important as it determines the quality of the research process and its results.This narrative and theoretical review discusses the methodologies applied to a particular research project and the factors that determine the choice of methodology.It also covers sample sizing, sampling techniques, data collection, and analysis methods across a range of qualitative, quantitative, and mixed research methods.
Towards Learning and Generating Audience Motion from Video

There has recently been an explosion of interest in creating large-scale shared virtual spaces for multiplayer content. However, rendering player-controllable avatars in real-time creates latency issues when scaling to thousands of players. We introduce a human audience video dataset to support applications in deep learning-based 2D video audience simulation, bypassing the need for background 3D virtual humans. This dataset consists of YouTube videos that depict audiences with diverse lighting conditions, color, dress, and movement patterns. We describe the dataset statistics, our implicit data collection strategy, and audience video extraction pipeline. We apply deep learning tasks on this data based on video prediction techniques, and propose a novel method for 2D audience simulations.
Denoising-Aware Adaptive Sampling for Monte Carlo Ray Tracing

Monte Carlo rendering is a computationally intensive task, but combined with recent deep-learning based advances in image denoising it is possible to achieve high quality images in a shorter amount of time. We present a novel adaptive sampling technique that further improves the efficiency of Monte Carlo rendering combined with deep-learning based denoising. Our proposed technique is general, can be combined with existing pre-trained denoisers, and, in contrast with previous techniques, does not itself require any additional neural networks or learning. A key contribution of our work is a general method for estimating the variance of the outputs of a neural network whose inputs are random variables. Our method iteratively renders additional samples and uses this novel variance estimate to compute the sample distribution for each subsequent iteration. Compared to uniform sampling and previous adaptive sampling techniques, our method achieves better equal-time error in all scenes tested, and when combined with a recent denoising post-correction technique, significantly faster error convergence is realized.
Improving Across-Dataset Brain Tissue Segmentation Using Transformer

Despite achieving compelling performance, many deep learning automated brain tissue segmentation solutions struggle to generalize to new datasets due to properties inherent to MRI scans. We propose TABS, a new transformer-based deep learning architecture that achieves state-of-the-art-performance, generalization, and consistency. We tested TABS on three datasets of differing field strands and acquisition parameters. TABS outperformed RAUnet on our performance testing and remained consistent across test-retest repeated scans from a separate dataset. Moreover, TABS achieved impressive generality performance and even improved in performance across datasets. We believe TABS represents a generalized and accurate brain tissue segmentation alternative.
The D-WISE Tool Suite: Multi-Modal Machine-Learning-Powered Tools Supporting and Enhancing Digital Discourse Analysis

This work introduces the D-WISE Tool Suite (DWTS), a novel working environment for digital qualitative discourse analysis in the Digital Humanities (DH).The DWTS addresses limitations of current DH tools induced by the ever-increasing amount of heterogeneous, unstructured, and multi-modal data in which the discourses of contemporary societies are encoded.To provide meaningful insights from such data, our system leverages and combines state-of-the-art machine learning technologies from Natural Language Processing and Computer Vision.Further, the DWTS is conceived and developed by an interdisciplinary team of cultural anthropologists and computer scientists to ensure the tool's usability for modern DH research.Central features of the DWTS are: a) import of multi-modal data like text, image, audio, and video b) preprocessing pipelines for automatic annotations c) lexical and semantic search of documents d) manual span, bounding box, time-span, and frame annotations e) documentation of the research process.
Four Approaches to Low-Resource Multilingual NMT: The Helsinki Submission to the AmericasNLP 2023 Shared Task

The Helsinki-NLP team participated in the AmericasNLP 2023 Shared Task with 6 submissions for all 11 language pairs arising from 4 different multilingual systems.We provide a detailed look at the work that went into collecting and preprocessing the data that led to our submissions.We explore various setups for multilingual Neural Machine Translation (NMT), namely knowledge distillation and transfer learning, multilingual NMT including a high-resource language (English), languagespecific fine-tuning, and a system with a modular architecture.Our multilingual Model B ranks first in 4 out of the 11 language pairs.
Dealing with Semantic Underspecification in Multimodal NLP

Intelligent systems that aim at mastering language as humans do must deal with its semantic underspecification, namely, the possibility for a linguistic signal to convey only part of the information needed for communication to succeed.Consider the usages of the pronoun they, which can leave the gender and number of its referent(s) underspecified.Semantic underspecification is not a bug but a crucial language feature that boosts its storage and processing efficiency.Indeed, human speakers can quickly and effortlessly integrate semanticallyunderspecified linguistic signals with a wide range of non-linguistic information, e.g., the multimodal context, social or cultural conventions, and shared knowledge.Standard NLP models have, in principle, no or limited access to such extra information, while multimodal systems grounding language into other modalities, such as vision, are naturally equipped to account for this phenomenon.However, we show that they struggle with it, which could negatively affect their performance and lead to harmful consequences when used for applications.In this position paper, we argue that our community should be aware of semantic underspecification if it aims to develop language technology that can successfully interact with human users.We discuss some applications where mastering it is crucial and outline a few directions toward achieving this goal.
Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals

We present a human-in-the-loop dashboard tailored to diagnosing potential spurious features that NLI models rely on for predictions.The dashboard enables users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions.Additionally, users can receive feedback from a trained NLI model on how challenging the newly created example is and make refinements based on the feedback.Through our investigation, we discover several categories of spurious correlations that impact the reasoning of NLI models, which we group into three categories: Semantic Relevance, Logical Fallacies, and Bias.Based on our findings, we identify and describe various research opportunities, including diversifying training data and assessing NLI models' robustness by creating adversarial test suites.
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction

Link prediction is the task of inferring missing links between entities in knowledge graphs.Embedding-based methods have shown effectiveness in addressing this problem by modeling relational patterns in triples.However, the link prediction task often requires contextual information in entity neighborhoods, while most existing embedding-based methods fail to capture it.Additionally, little attention is paid to the diversity of entity representations in different contexts, which often leads to false prediction results.In this situation, we consider that the schema of knowledge graph contains the specific contextual information, and it is beneficial for preserving the consistency of entities across contexts.In this paper, we propose a novel Schema-augmented Multi-level contrastive LEarning framework (SMiLE) to conduct knowledge graph link prediction.Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information.Then we fine-tune our model under the supervision of individual triples to learn subtler representations for link prediction.Extensive experimental results on four knowledge graph datasets with thorough analysis of each component demonstrate the effectiveness of our proposed framework against state-of-theart baselines.The implementation of SMiLE is available at https://github.com/GKNL/SMiLE.
An Empirical Study on the Transferability of Transformer Modules in Parameter-efficient Fine-tuning

Parameter-efficient fine-tuning approaches have recently garnered a lot of attention.Having considerably lower number of trainable weights, these methods can bring about scalability and computational effectiveness.In this paper, we look for optimal subnetworks and investigate the capability of different transformer modules in transferring knowledge from a pre-trained model to a downstream task.Our empirical results suggest that every transformer module in BERT can act as a winning ticket: fine-tuning each specific module while keeping the rest of the network frozen can lead to comparable performance to the full fine-tuning.Among different modules, LayerNorms exhibit the best capacity for knowledge transfer with limited trainable weights, to the extent that, with only 0.003% of all parameters in the layer-wise analysis, they show acceptable performance on various target tasks.On the reasons behind their effectiveness, we argue that their notable performance could be attributed to their high-magnitude weights compared to that of the other modules in the pre-trained BERT.The code for this paper is freely available at https://github.com/m-tajari/transformer-transferability.
SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks.Opting for a more thorough understanding of their capabilities and inner workings, researchers have established the extend to which they capture lower-level knowledge like grammaticality, and mid-level semantic knowledge like factual understanding.However, there is still little understanding of their knowledge of higher-level aspects of language.In particular, despite the importance of sociodemographic aspects in shaping our language, the questions of whether, where, and how PLMs encode these aspects, e.g., gender or age, is still unexplored.We address this research gap by probing the sociodemographic knowledge of different single-GPU PLMs on multiple English data sets via traditional classifier probing and information-theoretic minimum description length probing.Our results show that PLMs do encode these sociodemographics, and that this knowledge is sometimes spread across the layers of some of the tested PLMs.We further conduct a multilingual analysis and investigate the effect of supplementary training to further explore to what extent, where, and with what amount of pre-training data the knowledge is encoded.Our overall results indicate that sociodemographic knowledge is still a major challenge for NLP.PLMs require large amounts of pre-training data to acquire the knowledge and models that excel in general language understanding do not seem to own more knowledge about these aspects.
Unsupervised Domain Adaptation for Joint Information Extraction

Joint Information Extraction (JIE) aims to jointly solve multiple tasks in the Information Extraction pipeline (e.g., entity mention, event trigger, relation, and event argument extraction).Due to their ability to leverage task dependencies and avoid error propagation, JIE models have presented state-of-the-art performance for different IE tasks.However, an issue with current JIE methods is that they only focus on standard supervised learning setting where training and test data comes from the same domain.Cross-domain/domain adaptation learning with training and test data in different domains have not been explored for JIE, thus hindering the application of this technology to different domains in practice.To address this issue, our work introduces the first study to evaluate performance of JIE models in unsupervised domain adaptation setting.In addition, we present a novel method to induce domain-invariant representations for the tasks in JIE, called Domain Adaptation for Joint Information Extraction (DA4JIE).In DA4JIE, we propose an Instance-relational Domain Adaptation mechanism that seeks to align representations of task instances in JIE across domains through a generalized version of domain-adversarial learning approach.We further devise a Context-invariant Structure Learning technique to filter domain-specialized contextual information from induced representations to boost performance of JIE models in new domains.Extensive experiments and analyses demonstrate that DA4JIE can significantly improve out-of-domain performance for current state-of-the-art JIE systems for all IE tasks.
ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge

Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images.Recently, transformer-based TIS models (such as DALL-E) have been proposed using the encoder-decoder architectures.Yet, these billion-scale TIS models are difficult to tune and deploy in resource-constrained environments.In addition, there is a lack of language-specific TIS benchmarks for Chinese, together with high-performing models with moderate sizes.In this work, we present ARTIST, A tRansformer-based Chinese Textto-Image SynThesizer for high-quality image generation.In ARTIST, the rich linguistic and relational knowledge facts are injected into the model to ensure better model performance without the usage of ultra-large models.We further establish a large-scale Chinese TIS benchmark with the re-production results of state-of-the-art transformer-based TIS models.Results show ARTIST outperforms previous approaches.