Reviewerly/exHarmony huggingface dataset:
Improving Token-Based World Models with Parallel Observation Prediction

Motivated by the success of Transformers when applied to sequences of discrete symbols, token-based world models (TBWMs) were recently proposed as sample-efficient methods. In TBWMs, the world model consumes agent experience as a language-like sequence of tokens, where each observation constitutes a sub-sequence. However, during imagination, the sequential token-by-token generation of next observations results in a severe bottleneck, leading to long training times, poor GPU utilization, and limited representations. To resolve this bottleneck, we devise a novel Parallel Observation Prediction (POP) mechanism. POP augments a Retentive Network (RetNet) with a novel forward mode tailored to our reinforcement learning setting. We incorporate POP in a novel TBWM agent named REM (Retentive Environment Model), showcasing a 15.4x faster imagination compared to prior TBWMs. REM attains superhuman performance on 12 out of 26 games of the Atari 100K benchmark, while training in less than 12 hours. Our code is available at \url{https://github.com/leor-c/REM}.
Denoising CSAMT signals in the time domain base on long short-term memory

Abstract Controlled-Source Audio Frequency Magnetotellurics (CSAMT) is an artificial-source electromagnetic technique that partially mitigates the limitations of weak natural field signals. However, practical field surveys inevitably encounter strong interference, severely affecting signal quality. Traditional methods like Fourier transformation, which directly computes apparent resistivity from frequency-domain information, are inadequate for this context, so we need alternative denoising approaches. However, research on CSAMT denoising is currently limited. Given the excellent performance of Long Short-Term Memory (LSTM) neural networks in the processing of Magnetotelluric (MT) data, as demonstrated by previous studies, this paper proposes the use of LSTM to denoise CSAMT signals in the time domain. Unlike traditional denoising methods, we aim to directly extract the target frequency signal from the time series data for denoising. For MT data, target frequency signals and noise are all mixed together, so noise suppression can only be achieved by identifying noise characteristics in the time series. However, unlike MT data, CSAMT data has an artificial transmitting source, and the frequency of the valid signal is fixed within a time interval. This allows for the direct extraction of target frequency signals without considering the complex characteristics of noise. In this study, we developed a neural network based on bidirectional LSTM to accomplish the task of noise suppression. After conducting both simulated and measured data tests, this method was able to, on average, improve the signal-to-noise ratio (SNR) of CSAMT data by approximately 20dB and partially address the challenge of denoising when the data's SNR falls below 0dB.
A Conceptual Instructional Design Model for Executive Education

This paper presents a conceptual instructional design (ID) model for executive education derived from business school good practice and education theory. A review of executive education literature finds that today’s executive education offerings are required to be real-world oriented, closely aligned with the demands of the post-pandemic workplace, attuned to the changing needs and circumstances of adult learners and sufficiently able to deliver immersive learning. A reconnaissance investigation of 14 top-ranked business schools identifies the most frequently adopted teaching and learning approaches in executive education considered as good practice for the sector. These include experiential learning, collaborative learning, and technology-enabled learning. The proposed conceptual instructional design model is rooted in these approaches alongside three key principles that underpin experiential learning, namely authenticity, reflection and collaboration. Moreover the model proposes ADDIE supported teaching approaches to ensure the realisation of these principles within an appropriate constructive learning environment (CLE). Informed by activity theory, an ‘activity system’ is presented as the outer shell of the model to ensure that an executive education-oriented CLE is informed by learner needs and authentic context.
Analysis of Social Interaction of Deaf Children in Special Schools

&#x0D; &#x0D; &#x0D; &#x0D; Deaf is a term used to describe the symptoms and symptoms of someone who is hearing impaired or deaf. Deaf children interact through a variety of methods, including sign language, facial expressions, and body, as well as the use of hearing aid technology. They can also utilize visual communication approaches, peer support, and participation in group activities to strengthen social skills. Social interaction is a dynamic in which individuals or groups communicate, act, or influence each other in a particular environment or situation. Social interaction involves the exchange of information, emotions, ideas, or behaviors between individuals or groups. This research makes a valuable contribution to improving our understanding of the benefits of social interaction for deaf children in Special Schools, opening up opportunities for the development of more inclusive educational strategies and supporting their holistic development. This study aimed to analyze social interactions between deaf children. An observational approach and interview, this study explores the dynamics of communication, information exchange, and emotional understanding in interactions between both groups of children. Qualitative analysis methods are used to identify unique communication patterns and understand the challenges faced by deaf children in communicating. The subjects in this study were interviewing homeroom teachers and counseling teachers at the school, the number of subjects to be studied were children with deafness. The results highlight adaptation efforts and communication strategies developed by deaf children to interact. The results of these findings can provide valuable insights for educators, parents, and health practitioners in supporting the development of social skills of deaf children so that they can engage more effectively in society.&#x0D; &#x0D; &#x0D; &#x0D;
Research on Chinese Animal Disease Text Named Entity Recognition Based on Data Augmentation

Animal disease-related information serves as an important reference. This paper focuses on the study of Named Entity Recognition (NER) in animal disease texts using a dataset constructed from animal disease books. However, the dataset exhibits imbalanced distribution of different types of entities, which affects the learning effectiveness of the models. In this method, firstly, a small amount of data was enhanced based on the template data augmentation method, and then the overall data was enhanced based on the improved EDA method. On the enhanced dataset, the accuracy of NER based on the BiLSTM network and CRF model increased by 3%. The recall rate saw a significant improvement of 17%, and the F1 value increased by 10%. The experimental findings suggest that the data augmentation technique presented in this paper can improve the model’s learning effectiveness on the dataset related to livestock and poultry diseases.
AI Assistance for UX: A Literature Review Through Human-Centered AI

Recent advancements in HCI and AI research attempt to support user experience (UX) practitioners with AI-enabled tools. Despite the potential of emerging models and new interaction mechanisms, mainstream adoption of such tools remains limited. We took the lens of Human-Centered AI and presented a systematic literature review of 359 papers, aiming to synthesize the current landscape, identify trends, and uncover UX practitioners' unmet needs in AI support. Guided by the Double Diamond design framework, our analysis uncovered that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the valuable empathy-building process. Furthermore, focusing solely on individual UI screens without considering interactions and user flows reduces the system's practical value for UX designers. Based on these findings, we call for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics, for HCI and AI communities to collaboratively work toward effective AI support for UX.
Learn To be Efficient: Build Structured Sparsity in Large Language  Models

Large Language Models (LLMs) have achieved remarkable success with their billion-level parameters, yet they incur high inference overheads. The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference. Existing methods only focus on utilizing this naturally formed activation sparsity, overlooking the potential for further amplifying this inherent sparsity. In this paper, we hypothesize that LLMs can learn to be efficient by achieving more structured activation sparsity.To achieve this, we introduce a novel algorithm, Learn-To-be-Efficient (LTE), designed to train efficiency-aware LLMs to learn to activate fewer neurons and achieve a better trade-off between sparsity and performance. Furthermore, unlike SOTA MoEfication methods, which mainly focus on ReLU-based models, LTE can also be applied to LLMs like GPT and LLaMA with soft activation functions. We evaluate LTE on four models and eleven datasets. The experiments show that LTE achieves a better trade-off between sparsity and task performance. For instance, LTE with LLaMA provides a 1.83x-2.59x FLOPs speed-up on language generation tasks, outperforming the state-of-the-art methods.
Determining the upper bound of code distance of quantum stabilizer codes  through Monte Carlo method based on fully decoupled belief propagation

Code distance is an important parameter for quantum stabilizer codes (QSCs). Directly precisely computing it is an NP-complete problem. However, the upper bound of code distance can be computed by some efficient methods. In this paper, employing the idea of Monte Carlo method, we propose the algorithm of determining the upper bound of code distance of QSCs based on fully decoupled belief propagation. Our algorithm shows high precision - the upper bound of code distance determined by the algorithm of a variety of QSCs whose code distance is known is consistent with actual code distance. Besides, we explore the upper bound of logical X operators of Z-type Tanner-graph-recursive-expansion (Z-TGRE) code and Chamon code, which is a kind of XYZ product code constructed by three repetition codes. The former is consistent with the theoretical analysis, and the latter implies the code distance of XYZ product codes can very likely achieve $O(N^{2/3})$, which supports the conjecture of Leverrier et al..
On the Out-Of-Distribution Generalization of Multimodal Large Language  Models

We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks. We evaluate their zero-shot generalization across synthetic images, real-world distributional shifts, and specialized datasets like medical and molecular imagery. Empirical results indicate that MLLMs struggle with generalization beyond common training domains, limiting their direct application without adaptation. To understand the cause of unreliable performance, we analyze three hypotheses: semantic misinterpretation, visual feature extraction insufficiency, and mapping deficiency. Results identify mapping deficiency as the primary hurdle. To address this problem, we show that in-context learning (ICL) can significantly enhance MLLMs' generalization, opening new avenues for overcoming generalization barriers. We further explore the robustness of ICL under distribution shifts and show its vulnerability to domain shifts, label shifts, and spurious correlation shifts between in-context examples and test data.
Musical Systems with $\mathbb{Z}_n$ -- Cayley Graphs

We apply geometric group theory to study and interpret known concepts from Western music. We show that chords, the circle of fifths, scales and certain aspects of the first species of counterpoint are encoded in the Cayley graph of the group $\mathbb{Z}_{12}$, generated by $3$ and $4$. Using $\mathbb{Z}_{12}$ as a model, we extend the above music concepts to a particular class of groups $\mathbb{Z}_{n}$, which displays geometric and algebraic features similar to $\mathbb{Z}_{12}$. We identify a weaker form of counterpoint which, in particular leads to Fux's dichotomy in $\mathbb{Z}_{12}$, and to consonant sets in $\mathbb{Z}_n$. Using Maple software, we implement these new constructions and show how to experiment with them musically.
Designing NLP-based solutions for requirements variability management:  experiences from a design science study at Visma

Context and motivation: In this industry-academia collaborative project, a team of researchers, supported by a software architect, business analyst, and test engineer explored the challenges of requirement variability in a large business software development company. Question/problem: Following the design science paradigm, we studied the problem of requirements analysis and tracing in the context of contractual documents, with a specific focus on managing requirements variability. This paper reports on the lessons learned from that experience, highlighting the strategies and insights gained in the realm of requirements variability management. Principal ideas/results: This experience report outlines the insights gained from applying design science in requirements engineering research in industry. We show and evaluate various strategies to tackle the issue of requirement variability. Contribution: We report on the iterations and how the solution development evolved in parallel with problem understanding. From this process, we derive five key lessons learned to highlight the effectiveness of design science in exploring solutions for requirement variability in contract-based environments.
EmoWear: Exploring Emotional Teasers for Voice Message Interaction on  Smartwatches

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored "Emotional Teasers"-pre-retrieval cues offering a glimpse into an awaiting message's emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders' choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.
3D Gaussian as a New Vision Era: A Survey

3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of Computer Graphics, offering explicit scene representation and novel view synthesis without the reliance on neural networks, such as Neural Radiance Fields (NeRF). This technique has found diverse applications in areas such as robotics, urban mapping, autonomous navigation, and virtual reality/augmented reality, just name a few. Given the growing popularity and expanding research in 3D Gaussian Splatting, this paper presents a comprehensive survey of relevant papers from the past year. We organize the survey into taxonomies based on characteristics and applications, providing an introduction to the theoretical underpinnings of 3D Gaussian Splatting. Our goal through this survey is to acquaint new researchers with 3D Gaussian Splatting, serve as a valuable reference for seminal works in the field, and inspire future research directions, as discussed in our concluding section.
Unsupervised Discovery of Object-Centric Neural Fields

We study inferring 3D object-centric scene representations from a single image. While recent methods have shown potential in unsupervised 3D object discovery from simple synthetic images, they fail to generalize to real-world scenes with visually rich and diverse objects. This limitation stems from their object representations, which entangle objects' intrinsic attributes like shape and appearance with extrinsic, viewer-centric properties such as their 3D location. To address this bottleneck, we propose Unsupervised discovery of Object-Centric neural Fields (uOCF). uOCF focuses on learning the intrinsics of objects and models the extrinsics separately. Our approach significantly improves systematic generalization, thus enabling unsupervised learning of high-fidelity object-centric scene representations from sparse real-world images. To evaluate our approach, we collect three new datasets, including two real kitchen environments. Extensive experiments show that uOCF enables unsupervised discovery of visually rich objects from a single real image, allowing applications such as 3D object segmentation and scene manipulation. Notably, uOCF demonstrates zero-shot generalization to unseen objects from a single real image. Project page: https://red-fairy.github.io/uOCF/
Parameterizations for Gradient-based Markov Chain Monte Carlo on the  Stiefel Manifold: A Comparative Study

Orthogonal matrices play an important role in probability and statistics, particularly in high-dimensional statistical models. Parameterizing these models using orthogonal matrices facilitates dimension reduction and parameter identification. However, establishing the theoretical validity of statistical inference in these models from a frequentist perspective is challenging, leading to a preference for Bayesian approaches because of their ability to offer consistent uncertainty quantification. Markov chain Monte Carlo methods are commonly used for numerical approximation of posterior distributions, and sampling on the Stiefel manifold, which comprises orthogonal matrices, poses significant difficulties. While various strategies have been proposed for this purpose, gradient-based Markov chain Monte Carlo with parameterizations is the most efficient. However, a comprehensive comparison of these parameterizations is lacking in the existing literature. This study aims to address this gap by evaluating numerical efficiency of the four alternative parameterizations of orthogonal matrices under equivalent conditions. The evaluation was conducted for four problems. The results suggest that polar expansion parameterization is the most efficient, particularly for the high-dimensional and complex problems. However, all parameterizations exhibit limitations in significantly high-dimensional or difficult tasks, emphasizing the need for further advancements in sampling methods for orthogonal matrices.
From Uncertainty to Precision: Enhancing Binary Classifier Performance  through Calibration

The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's inherent uncertainty, especially when dealing with sensitive decision-making domains, such as finance or healthcare. Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. In our study, we analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Calibration Score. Comparing recalibration methods, we advocate for local regressions, emphasizing their dual role as effective recalibration tools and facilitators of smoother visualizations. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration during performance optimization.
Aya Model: An Instruction Finetuned Open-Access Multilingual Language  Model

Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions in 101 languages of which over 50% are considered as lower-resourced. Aya outperforms mT0 and BLOOMZ on the majority of tasks while covering double the number of languages. We introduce extensive new evaluation suites that broaden the state-of-art for multilingual eval across 99 languages -- including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance. Furthermore, we conduct detailed investigations on the optimal finetuning mixture composition, data pruning, as well as the toxicity, bias, and safety of our models. We open-source our instruction datasets and our model at https://hf.co/CohereForAI/aya-101
Secured Auto Encryption and Authentication Process for Cloud Computing Security

In the IT sector, cloud computing is a rapidly expanding technology. Users are being offered more and more flexible and appropriate services by cloud service providers (CSP). Cloud computing users are increasing at a steady pace. Users and CSP are looking for appropriate security measures as cloud computing becomes a need in modern life. A large number of researchers are studying cloud computing security issues. This work introduces an appropriate cryptography and secure user authentication system, which includes auto encryption and a mechanism for updating keys on a cloud site that is not initially accessible to users. After a predetermined amount of time, this operation will recur frequently on the cloud site. This protocol can be carried out manually by CSP at any time, or it can run automatically after a predetermined amount of time. This will guarantee additional data security. Additionally, this procedure adds a level to the user's credentials. Even with legitimate credentials, these feathers will ensure that hackers cannot obtain original files or data. The suggested protocol is emulated by CloudSim, and we saw that there is an additional step requiring user credentials, requiring some time to access files or data for the first time. Although this small amount of time is insignificant and can be disregarded, it significantly improves security.
Forecasting high-impact research topics via machine learning on evolving  knowledge graphs

The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy, and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.