Reviewerly/exHarmony huggingface dataset:
EDM3: Event Detection as Multi-task Text Generation

Event detection refers to identifying event occurrences in a text and comprises of two subtasks; event identification and classification. We present EDM3, a novel approach for Event Detection that formulates three generative tasks: identification, classification, and combined detection. We show that EDM3 helps to learn transferable knowledge that can be leveraged to perform Event Detection and its subtasks concurrently, mitigating the error propagation inherent in pipelined approaches. Unlike previous dataset- or domain-specific approaches, EDM3 utilizes the existing knowledge of language models, allowing it to be trained over any classification schema. We evaluate EDM3 on multiple event detection datasets: RAMS, WikiEvents, MAVEN, and MLEE, showing that EDM3 outperforms 1) single-task performance by 8.4% on average and 2) multi-task performance without instructional prompts by 2.4% on average. We obtain SOTA results on RAMS (71.3% vs. 65.1% F-1) and competitive performance on other datasets. We analyze our approach to demonstrate its efficacy in low-resource and multi-sentence settings. We also show the effectiveness of this approach on non-standard event configurations such as multi-word and multi-class event triggers. Overall, our results show that EDM3 is a promising approach for Event Detection that has the potential for real-world applications.
Think Before You Act: Decision Transformers with Internal Working Memory

Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model's performance on previous tasks. In contrast to LLMs' implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Thus inspired, we propose an internal working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in both Atari games and meta-world object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.
Federated Neural Compression Under Heterogeneous Data

We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation. We propose a distributed source model that encompasses both characteristics, and naturally suggests a compressor architecture that uses analysis and synthesis transforms shared by clients. Inspired by personalized federated learning methods, we employ an entropy model that is personalized to each client. This allows for a global latent space to be learned across clients, and personalized entropy models that adapt to the clients' latent distributions. We show empirically that this strategy outperforms solely local methods, which indicates that learned compression also benefits from a shared global representation in statistically heterogeneous federated settings.
Stock Price Prediction of PT. Kimia Farma, Tbk Using Bayesian Ridge Algorithm

&lt;p&gt;&lt;em&gt;Stocks are proof of ownership of capital/funds in the company, a paper that clearly states the nominal value, and company name and is followed by an explanation of the rights and obligations to each shareholder, enough shares to be sold. Many companies from various sectors market their shares, one of which is PT. Kimia Farma, Tbk. This study aims to determine the performance of the Bayesian Ridge Regression algorithm to predict the closing price of PT Kimia Farma, Tbk's stock price. The data for these stocks was collected through the website id.investing.com with a data period of 2015 - 2022. Based on the test results, the Bayesian Ridge Regression algorithm can be used to predict stock prices with a good value where the R2 value is 0.9968 and RMSE value of 49.199 when modelling, R2 value is 0.9972 and RSME value is 45.400 when testing with new/testing data.&lt;/em&gt;&lt;/p&gt;
Streaming the Video Presentation Slides based on Frame Distance Technique

The lockdown of the society that caused by COVID 19 couldn’t affect the education sector as there is an alternative option that the students can take their education in online classes. Online classes start been widely used over the Internet as all the education sectors start using the e-learning system. E-learning system use the online tools to stream the contents of the lecture, such contents will increase the traffic load on the networks. The load of the networks will affect the smoothness of the online classes, as there is a possibility that the video frames could be lost or corrupted.&#x0D; Frame distance technique is proposed to reduce the traffic on the networks, by dropping the redundancy slides that are related to the same display time segment. Three different frame distance technique are considered in this study (low, medium and high) to show the amount of bandwidth that are required to stream the video to a group of students. From the outcomes it can be found from that, adaption will be necessary to reduce the video traffic load on the networks that will have a positive effect on the online classes.
CORRELATION BETWEEN SPEAKING ANXIETY AND SPEAKING ACHIEVEMENT OF THE EIGHTH GRADE STUDENTS

Speaking becomes a crucial ability that must be mastered in communication. This is quantitative research with a correlational design which aimed to find out whether or not there was a significant correlation between students’ speaking anxiety and their speaking achievement and the contribution of speaking anxiety to their speaking achievement. The population of this research was all of the eighth-grade students of SMPN 1 Tanjung Raja with a total number of 128 students. However, there were only 32 students involved as the sample in this research taken by using stratified random sampling. By using oral Proficiency categories,­­­ students‘ speaking achievement was scored in terms of the six sub-skills of grammar, vocabulary, fluency, pronunciation, comprehension, and task. Furthermore, Public Speaking Classroom Anxiety Scale (PSCAS) questionnaire developed used to measure speaking anxiety containing 17 items. Pearson Product-Moment was used to find out the correlation between variables. The result showed that there was no significant correlation between students speaking anxiety and their speaking achievement with r= (.177) lower than r-table (.349) and the level of probability (p) significance (.332) was more than (.05). It indicated that H0 was accepted and Ha was rejected. This study could have implications for English language teachers, students, and future researchers.
Parameters for Prediction of ICUs Mortality using Organ Dysfunction and Diseases Severity Scoring System

Objectives: The aim of this article is to find the mortality rate of the critical patient admitted in ICU depends on how severe or acute of their diseases and how actively are there organ functioning.Methods: There are various surgeries are critical in nature among them one such surgery is CABG which requires a few days of ICU stay. The type of ICU stay recommended for various CABG patients depends on the nature of operation done and the type of observation, care needed for them. Like for some patients ICU is recommended, for some ICU along with ventilation is suggested.Findings: Approximately thousand patients suffering from different types of disease severity or organ dysfunctions were taken into consideration and the increment of SOFA scores >= 2 were considered for the comparison. After that a follow up period, three hundred patients regarding death and discharge from the care unit are considered. It was found that 46% were discharged after recovery and remaining 54% are suffering from their sickness in the possibility of hospital stay. It is found that calculated mean for age is 46.9 ± 19.4 years with seniority 52% of patients older than 46 years. Peoples who suffer from illness in care unit were significantly (ρ=0.0352) mature 48.2 ± 18.4 years than peoples who were properly released. In this study a greater number of male patients 56% are identified compared to female patients. The LOS in the ICU after critical surgery is an important variable with other major factors for effective use of the critical resources.Novelty: The main aim of this study is to find a better and more effective scoring system, at a low cost, so that the patients admitted in hospitals get better treatment along with accurate diagnosis. Last but not the least the author wishes everyone a healthy and happy life.
The method of building 3D map by the reconfigurable computing environment for unmanned aerial vehicles, drones

Unmanned aerial vehicle (UAV) is a flying device that is integrated with appropriate hardware and software, besides it is also a device to collect and process data to meet the needs and purposes of users. Currently, most drones use GPS technology to determine the flight path, and other sensors on it are used to determine distance, avoid collisions. However, with small, narrow spaces, dangerous environments that humans cannot go into that space, GPS not working, small drones with small hardware devices placed on it to map themselves and navigation would be a suitable solution. In this paper, a method to build a high-performance reconfigurable computing environment model to map drone as well as UAV flying in 3D space is proposed. Mapping based on Boolean formulas with reconfigurable structure and adjustable automatons will generate high-precision maps for efficient robot navigation.
An Experience with Augmented Math Trails and Service-Learning in Initial Teacher Training

We present the ongoing project developed at the Universidad Autónoma de Madrid (Spain) using math trails in Initial Teacher Training with the aim that they can incorporate this educational resource into their future professional career. The learning experience is an integrated project focused on the creation of math trails by prospective students as a valuable outdoor problem-posing activity. In addition, it includes a two-step evaluation process that, first, allows prospective teachers to assess each other’s routes, and secondly, they are tested by students as a service-learning activity. The math trails used in the project include mobile technology as an enriching element, combining the experience of using MathCityMap and the Augmented Reality tools of GeoGebra within the same project.
Optimization The Utility Of E-Learning Platform Through Integrating Smart Emotional Recognition Feature

Educational applications of image processing have emerged due to data collection tools development. Education is vital field in human life where highly accurate performance is required. Integrating image processing and deep learning with the education will help to optimize the performance of entire system. It is possible now to make out the student’s emotional status through study the features from facial images taken for a group of students. That reduces the time and cost of the education by providing a facility similar to the regular classrooms environments. Which may help plenty of people who are unable to access regular educational facilities due to intolerable cost. In this paper, automatic emotional detection is being performed using neural network. Two models are used namely artificial neural network and CNN neural network. The models are tested using emotional images data. Results are reported 96.7 % and 99.2 % accuracies from bother artificial neural network and CNN respectively.
Agile Functional Requirements Modeling from Goal-Oriented Requirements Engineering: A Systematic Mapping Study

During the last two decades, the application of Goal-Oriented Requirements Engineering has increased in software projects, mainly because this approach is useful during the requirements analysis process and supports the realization of tasks such as requirements elaboration, validation, and planning, and can be used as part of the system life cycle, e.g. architecture and design of behavioral processes, coding, testing, monitoring, adaptation and evolution. Several approaches addressing goal-oriented requirements have already been proposed. Currently, there are several concerns about how to correctly adopt and implement it in current industrial contexts, such as agile approaches and, more specifically, its integration with user stories and acceptance criteria. This study presents a compilation of the current knowledge on adopting GORE in agile requirements specification through user stories and their acceptance criteria. A systematic mapping study guided by ten research questions was conducted, and nine primary studies were selected considering the selection criteria. The results obtained show some initiatives as goal-oriented modelling languages in different proposals for transforming goal-oriented models into user stories, among them: i* (i-star), Tropos, KAOS, GRL, AOM, and Goal Net. This study provides a solid basis for classifying existing and future approaches in the application of GORE. Researchers and practitioners can use this mapping to identify existing technical/research gaps to better address and assess their own contributions or understand existing ones.
Recommending Bug Assignment Approaches for Individual Bug Reports: An Empirical Investigation

Multiple approaches have been proposed to automatically recommend potential developers who can address bug reports. These approaches are typically designed to work for any bug report submitted to any software project. However, we conjecture that these approaches may not work equally well for all the reports in a project. We conducted an empirical study to validate this conjecture, using three bug assignment approaches applied on 2,249 bug reports from two open source systems. We found empirical evidence that validates our conjecture, which led us to explore the idea of identifying and applying the best-performing approach for each bug report to obtain more accurate developer recommendations. We conducted an additional study to assess the feasibility of this idea using machine learning. While we found a wide margin of accuracy improvement for this approach, it is far from achieving the maximum possible improvement and performs comparably to baseline approaches. We discuss potential reasons for these results and conjecture that the assignment approaches may not capture important information about the bug assignment process that developers perform in practice. The results warrant future research in understanding how developers assign bug reports and improving automated bug report assignment
Make-A-Voice: Unified Voice Synthesis With Discrete Representation

Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, the majority of voice synthesis models currently rely on annotated audio data, but it is crucial to scale them to self-supervised datasets in order to effectively capture the wide range of acoustic variations present in human voice, including speaker identity, emotion, and prosody. In this work, we propose Make-A-Voice, a unified framework for synthesizing and manipulating voice signals from discrete representations. Make-A-Voice leverages a "coarse-to-fine" approach to model the human voice, which involves three stages: 1) semantic stage: model high-level transformation between linguistic content and self-supervised semantic tokens, 2) acoustic stage: introduce varying control signals as acoustic conditions for semantic-to-acoustic modeling, and 3) generation stage: synthesize high-fidelity waveforms from acoustic tokens. Make-A-Voice offers notable benefits as a unified voice synthesis framework: 1) Data scalability: the major backbone (i.e., acoustic and generation stage) does not require any annotations, and thus the training data could be scaled up. 2) Controllability and conditioning flexibility: we investigate different conditioning mechanisms and effectively handle three voice synthesis applications, including text-to-speech (TTS), voice conversion (VC), and singing voice synthesis (SVS) by re-synthesizing the discrete voice representations with prompt guidance. Experimental results demonstrate that Make-A-Voice exhibits superior audio quality and style similarity compared with competitive baseline models. Audio samples are available at https://Make-A-Voice.github.io
AdANNS: A Framework for Adaptive Semantic Search

Web-scale search systems learn an encoder to embed a given query which is then hooked into an approximate nearest neighbor search (ANNS) pipeline to retrieve similar data points. To accurately capture tail queries and data points, learned representations typically are rigid, high-dimensional vectors that are generally used as-is in the entire ANNS pipeline and can lead to computationally expensive retrieval. In this paper, we argue that instead of rigid representations, different stages of ANNS can leverage adaptive representations of varying capacities to achieve significantly better accuracy-compute trade-offs, i.e., stages of ANNS that can get away with more approximate computation should use a lower-capacity representation of the same data point. To this end, we introduce AdANNS, a novel ANNS design framework that explicitly leverages the flexibility of Matryoshka Representations. We demonstrate state-of-the-art accuracy-compute trade-offs using novel AdANNS-based key ANNS building blocks like search data structures (AdANNS-IVF) and quantization (AdANNS-OPQ). For example on ImageNet retrieval, AdANNS-IVF is up to 1.5% more accurate than the rigid representations-based IVF at the same compute budget; and matches accuracy while being up to 90x faster in wall-clock time. For Natural Questions, 32-byte AdANNS-OPQ matches the accuracy of the 64-byte OPQ baseline constructed using rigid representations -- same accuracy at half the cost! We further show that the gains from AdANNS translate to modern-day composite ANNS indices that combine search structures and quantization. Finally, we demonstrate that AdANNS can enable inference-time adaptivity for compute-aware search on ANNS indices built non-adaptively on matryoshka representations. Code is open-sourced at https://github.com/RAIVNLab/AdANNS.
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

A number of recent benchmarks seek to assess how well models handle natural language negation. However, these benchmarks lack the controlled example paradigms that would allow us to infer whether a model had learned how negation morphemes semantically scope. To fill these analytical gaps, we present the Scoped Negation NLI (ScoNe-NLI) benchmark, which contains contrast sets of six examples with up to two negations where either zero, one, or both negative morphemes affect the NLI label. We use ScoNe-NLI to assess fine-tuning and in-context learning strategies. We find that RoBERTa and DeBERTa models solve ScoNe-NLI after many shot fine-tuning. For in-context learning, we test InstructGPT models and find that most prompt strategies are not successful, including those using step-by-step reasoning. To better understand this result, we extend ScoNe with ScoNe-NLG, a sentence completion test set that embeds negation reasoning in short narratives. Here, InstructGPT is successful, which reveals the model can correctly reason about negation, but struggles to do so on prompt-adapted NLI examples outside of its core pretraining regime.
Control of quantum coherence of photons exploiting quantum entanglement

Accurately controlling the quantum coherence of photons is pivotal for their applications in quantum sensing and quantum imaging. Here, we propose the utilization of quantum entanglement and local phase manipulation techniques to control the higher-order quantum coherence of photons. By engineering the spatially varying phases in the transverse plane, we can precisely manipulate the spatial structure of the second-order coherence function of entangled photon pairs without changing the photon intensity distribution of each photon. Our approach can readily be extended to higher-order quantum coherence control. These results could potentially stimulate new experimental research and applications of optical quantum coherence.
LIV: Language-Image Representations and Rewards for Robotic Control

We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations. Exploiting a novel connection between dual reinforcement learning and mutual information contrastive learning, the LIV objective trains a multi-modal representation that implicitly encodes a universal value function for tasks specified as language or image goals. We use LIV to pre-train the first control-centric vision-language representation from large human video datasets such as EpicKitchen. Given only a language or image goal, the pre-trained LIV model can assign dense rewards to each frame in videos of unseen robots or humans attempting that task in unseen environments. Further, when some target domain-specific data is available, the same objective can be used to fine-tune and improve LIV and even other pre-trained representations for robotic control and reward specification in that domain. In our experiments on several simulated and real-world robot environments, LIV models consistently outperform the best prior input state representations for imitation learning, as well as reward specification methods for policy synthesis. Our results validate the advantages of joint vision-language representation and reward learning within the unified, compact LIV framework.
HHT: An Approach for Representing Temporally-Evolving Historical Territories

The notion of territory plays a major role in human and social sciences. Representation of this spatio-temporal object and computation of the changes occurring have been tackled in various ways. However, in an historical context, most approaches are irrelevant as they rely on geometric data, which is not available. In order to represent historical territories, we conceived the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. This approach relies on a notion of building blocks to replace polygonal geometry. This representation is further used to provide an algorithm to detect and characterize territorial changes in a knowledge graph. Said algorithm creates a knowledge graph of changes at multiple levels encompassing basic changes occurring in a single territory, and composite changes, which are the abstraction of several smaller changes into a large change. The approach was followed to produce 3 knowledge graphs available online. Each of these graphs allowed to set up an analysis of the evolution of the territories during the historical period they cover.