Reviewerly/exHarmony huggingface dataset:
Monte Carlo Planning in Hybrid Belief POMDPs

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online Partially Observable Markov Decision Processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?

Global explanations of a reinforcement learning (RL) agent's expected behavior can make it safer to deploy. However, such explanations are often difficult to understand because of the complicated nature of many RL policies. Effective human explanations are often contrastive, referencing a known contrast (policy) to reduce redundancy. At the same time, these explanations also require the additional effort of referencing that contrast when evaluating an explanation. We conduct a user study to understand whether and when contrastive explanations might be preferable to complete explanations that do not require referencing a contrast. We find that complete explanations are generally more effective when they are the same size or smaller than a contrastive explanation of the same policy, and no worse when they are larger. This suggests that contrastive explanations are not sufficient to solve the problem of effectively explaining reinforcement learning policies, and require additional careful study for use in this context.
An online algorithm for contrastive Principal Component Analysis

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive learning. However, the performance of cPCA is sensitive to hyper-parameter choice and there is currently no online algorithm for implementing cPCA. Here, we introduce a modified cPCA method, which we denote cPCA*, that is more interpretable and less sensitive to the choice of hyper-parameter. We derive an online algorithm for cPCA* and show that it maps onto a neural network with local learning rules, so it can potentially be implemented in energy efficient neuromorphic hardware. We evaluate the performance of our online algorithm on real datasets and highlight the differences and similarities with the original formulation.
Meta-Learning of Neural State-Space Models Using Data From Similar Systems

Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected from the actual system under consideration, despite the likely existence of operational data from similar systems which have previously been deployed in the field. In this paper, we propose the use of model-agnostic meta-learning (MAML) for constructing deep encoder network-based SSMs, by leveraging a combination of archived data from similar systems (used to meta-train offline) and limited data from the actual system (used for rapid online adaptation). We demonstrate using a numerical example that meta-learning can result in more accurate neural SSM models than supervised- or transfer-learning, despite few adaptation steps and limited online data. Additionally, we show that by carefully partitioning and adapting the encoder layers while fixing the state-transition operator, we can achieve comparable performance to MAML while reducing online adaptation complexity.
Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models

With the rise of large, publicly-available text-to-image diffusion models, text-guided real image editing has garnered much research attention recently. Existing methods tend to either rely on some form of per-instance or per-task fine-tuning and optimization, require multiple novel views, or they inherently entangle preservation of real image identity, semantic coherence, and faithfulness to text guidance. In this paper, we propose an optimization-free and zero fine-tuning framework that applies complex and non-rigid edits to a single real image via a text prompt, avoiding all the pitfalls described above. Using widely-available generic pre-trained text-to-image diffusion models, we demonstrate the ability to modulate pose, scene, background, style, color, and even racial identity in an extremely flexible manner through a single target text detailing the desired edit. Furthermore, our method, which we name $\textit{Direct Inversion}$, proposes multiple intuitively configurable hyperparameters to allow for a wide range of types and extents of real image edits. We prove our method's efficacy in producing high-quality, diverse, semantically coherent, and faithful real image edits through applying it on a variety of inputs for a multitude of tasks. We also formalize our method in well-established theory, detail future experiments for further improvement, and compare against state-of-the-art attempts.
Prompting Language Models for Linguistic Structure

Although pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable linguistic understanding versus surface-level lexical patterns. To test this, we present a structured prompting approach for linguistic structured prediction tasks, allowing us to perform zero- and few-shot sequence tagging with autoregressive PLMs. We evaluate this approach on part-of-speech tagging, named entity recognition, and sentence chunking, demonstrating strong few-shot performance in all cases. We also find that while PLMs contain significant prior knowledge of task labels due to task leakage into the pretraining corpus, structured prompting can also retrieve linguistic structure with arbitrary labels. These findings indicate that the in-context learning ability and linguistic knowledge of PLMs generalizes beyond memorization of their training data.
Characterizing the Spectrum of the NTK via a Power Series Expansion

Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the network. We observe faster decay of the Hermite coefficients leads to faster decay in the NTK coefficients and explore the role of depth. Using this series, first we relate the effective rank of the NTK to the effective rank of the input-data Gram. Second, for data drawn uniformly on the sphere we study the eigenvalues of the NTK, analyzing the impact of the choice of activation function. Finally, for generic data and activation functions with sufficiently fast Hermite coefficient decay, we derive an asymptotic upper bound on the spectrum of the NTK.
Positive Matrix Factorization of Large Aerosol Mass Spectrometry Datasets Using Error-Weighted Randomized Hierarchical Alternating Least Squares

Weighted positive matrix factorization (PMF) has been used by scientists to find small sets of underlying factors in environmental data. However, as the size of the data has grown, increasing computational costs have made it impractical to use traditional methods for this factorization. In this paper, we present a new weighting method to dramatically decrease computational costs for these traditional algorithms. We then apply this weighting method with the Randomized Hierarchical Alternating Least Squares (RHALS) algorithm to a large environmental dataset, where we show that interpretable factors can be reproduced using these methods. We show this algorithm results in a computational speedup of 38, 67, and 634 compared to the Multiplicative Update (MU), deterministic Hierarchical Alternating Least Squares (HALS), and non-negative Alternating Least Squares (ALS) algorithms, respectively. We also investigate rotational ambiguity in the solution, and present a simple ``pulling'' method to rotate a set of factors. This method is shown to find alternative solutions, and in some cases, lower the weighted residual error of the algorithm.
GESTÃO PÚBLICA ALIADA À TECNOLOGIA DA INFORMAÇÃO: ANÁLISE DA APLICAÇÃO DA TECNOLOGIA DA INFORMAÇÃO NA GESTÃO PÚBLICA NA PREFEITURA DA CIDADE DO RIO DE JANEIRO

The central Idea of this chapter is to list some aspects of Agroecology, considered one of the most sustainable agricultural systems in the world and which is aimed at maintaining production systems that can contribute to sustainable development by integrating environmentally, economically and socially sustainable practices, and also contribute to food security and sovereignty.The aim of this study is to analyze the issue of Brazilian food sovereignty, presenting possible alternatives for a more sovereign society, with emphasis on the Ecovida Agroecology Network, which is made up of family farmers from the three states of the southern region of Brazil and the state of São Paulo.
Impact of COVID-19 Pandemic to Finance Shared Services Employees’ Performance of a Government Contracting Company in Dubai, United Arab Emirates

This study assessed the implications of the COVID-19 pandemic to the work lifestyle (work prioritization, work satisfaction, and work performance), and psychological and physical health of the finance shared services department workforce of a government contracting company in Dubai, United Arab Emirates. The data were collected through a survey questionnaire sent via Google form to 20 finance shared services employees. The findings of the study revealed that majority of the respondents are female, with ages from 41-50 years old, single, working on either accounts payables or accounts receivables, regular employees, and have been staying with the company for 6 years and above. As for the work lifestyle, the employees generally disagreed on the indicators for work prioritization, work satisfaction, and work performance. Further, as for the psychological impact, employees indicated that they worry that they might get sick or their loved ones due to the virus. Similarly, they showed constant worry regarding their physical health. The study also found out that there is no significant correlation between work performance and work satisfaction to the respondents’ demographic profile as well as having no correlation of employees’ psychological and physical health to respondents’ demographic profile. On the other hand, there is a significant correlation between the work performance and work satisfaction to employees’ psychological and physical health.
Online Direct Learning Strategies of Mandarin as a Foreign Language Learners During COVID-19 Pandemic Period in a Malaysian University

This study seeks to identify the patterns of the use of direct learning strategies of learners of Mandarin as a foreign language in an online setting during the spread of the Covid-19 Pandemic where educational institutions had shifted to online distance learning.This study also examined at how gender difference in strategy preference were represented in various situations, as well as student preferences for language learning strategies.A simple random sampling was conducted to investigate the third language learners' direct learning strategies (memory, cognitive, compensation) in Mandarin as a foreign language based on the Strategy Inventory for Language Learning (SILL) Questionnaire developed by (Oxford, 1990).A total of 445 Malay undergraduates who were undertaking the basic Mandarin course participated by answering questionnaires which were prepared as a data collection method.All the participants were non-native Mandarin speakers.Descriptive statistics and significance tests were utilised in the study.The significant differences in direct learning strategies used by male and female degree students were determined using the Independent Sample t-Test.The results revealed that memory strategy is the most preferred direct learning strategies used by the undergraduates, followed by compensation strategy and cognitive strategy.Besides, memory strategy also gained the most preferred learning strategy regardless of gender.However, the results also showed that the male and female degree students had statistically significant differences in using compensation learning strategy.The findings of the study would be important to Mandarin as a Foreign Language instructors when selecting strategies, teaching materials and activities to be carried out in their language classroom.The implications for Mandarin as a foreign language instructor are to improve the teaching and learning by preparing materials and tasks that are suitable for online learning.Therefore, it is recommended that future research should integrate quantitative and qualitative methodologies to gather more detailed information about the use of strategies or study the effects of the direct language learning strategy (LLS) on students' success in the Mandarin course.It will be preferable if a longitudinal study is undertaken to determine whether the effect of direct LLS increases over time as learners progress in learning Mandarin.
The Analysis and Development of an XAI Process on Feature Contribution Explanation

&lt;p&gt;Explainable Artificial Intelligence (XAI) research focuses on effective explanation techniques to understand and build AI models with trust, reliability, safety, and fairness. Feature importance explanation summarizes feature contributions for end-users to make model decisions. However, XAI methods may produce varied summaries that lead to further analysis to evaluate the consistency across multiple XAI methods on the same model and data set. This paper defines metrics to measure the consistency of feature contribution explanation summaries under feature importance order and saliency map. Driven by these consistency metrics, we develop an XAI process oriented on the XAI criterion of feature importance, which performs a systematical selection of XAI techniques and evaluation of explanation consistency. We demonstrate the process development involving twelve XAI methods on three topics, including a search ranking system, code vulnerability detection and image classification. Our contribution is a practical and systematic process with defined consistency metrics to produce rigorous feature contribution explanations.&lt;/p&gt;
YORO -- Lightweight End to End Visual Grounding

We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task. This task involves localizing, in an image, an object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by embracing a single-stage design, without CNN backbone. YORO consumes natural language queries, image patches, and learnable detection tokens and predicts coordinates of the referred object, using a single transformer encoder. To assist the alignment between text and visual objects, a novel patch-text alignment loss is proposed. Extensive experiments are conducted on 5 different datasets with ablations on architecture design choices. YORO is shown to support real-time inference and outperform all approaches in this class (single-stage methods) by large margins. It is also the fastest VG model and achieves the best speed/accuracy trade-off in the literature.
A Dataset and Model for Crossing Indian Roads

Roads in medium-sized Indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross Indian roads. Yet, we currently lack datasets collected on Indian roads from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in Indian conditions. We introduce INDRA (INdian Dataset for RoAd crossing), the first dataset capturing videos of Indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional neural networks (CNNs). The best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant running DilatedRoadCrossNet, which can help the blind cross Indian roads in real-time. The project webpage is http://roadcross-assistant.github.io/Website/.
The Relationship between Employee Engagement, Job Fit and Job Performance: An Explorative Study

This study was conducted to examine what contributes to a satisfied employee by examining a number of factors such as job fit, good communication, appreciation and clear objectives. The study also looked at the roles of the organization and individual in employee satisfaction. This study discusses employee engagement, job fit, and how to develop a successful employee engagement program. Job performance, another key success factor for organizations, was also examined. The link between job fit and performance findings has been expounded and recommendations on what can be done to improve the variable relationship has been provided. This study provides compelling information that will help to understand the advantages of having satisfied and high performing employees as well as using the power of employee engagement to ensure business sustainability. Keywords: Employee Engagement, Job fit and Job Performance
An Overview on Controllable Text Generation via Variational Auto-Encoders

Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language. The employment of deep neural architectures has been largely explored in a multitude of context and tasks to fulfill various user needs. On one hand, producing textual content that meets specific requirements is of priority for a model to seamlessly conduct conversations with different groups of people. On the other hand, latent variable models (LVM) such as variational auto-encoders (VAEs) as one of the most popular genres of generative models are designed to characterize the distributional pattern of textual data. Thus they are inherently capable of learning the integral textual features that are worth exploring for controllable pursuits. \noindent This overview gives an introduction to existing generation schemes, problems associated with text variational auto-encoders, and a review of several applications about the controllable generation that are instantiations of these general formulations,\footnote{A detailed paper list is available at \url{https://github.com/ImKeTT/CTG-latentAEs}} as well as related datasets, metrics and discussions for future researches. Hopefully, this overview will provide an overview of living questions, popular methodologies and raw thoughts for controllable language generation under the scope of variational auto-encoder.
Git going with LEGO

Git, an open source version control software, is difficult to both teach and learn because it conceals its work in a hidden directory and is often deployed using a command line interface. Yet, version control is an essential tool of reproducible research. Two librarians formed a playful partnership to experiment with teaching Git using LEGO, an approach inspired by our Carpentries Instructor certifications and one’s LEGO Serious Play certification. Our method combines novice-centric Carpentries pedagogy with theories of play, constructionism, constructivism, conceptual metaphor, and the hand-mind connection that are foundational to LEGO Serious Play. We share details of developing, testing, and assessing this Git-LEGO combination. Our preliminary study indicates that our LEGO activity may increase learners' short-term retention of Git commands. We discuss future directions for our research and our experiences as academic librarians in a playful partnership.
Reported speech in Aviation English: an analysis through two specific corpora

Aviation English is a specialized language and, as such, features some specific structures that should be carefully analyzed to be dealt with appropriately. Reported Speech (RS) constitutes an essential communicative function for pilots and controllers because they must often relay information to different parties in complex communication scenarios. Regarding the teaching and learning of RS, the most traditional orientation is that it should observe tense backshifting - the possibilities of not shifting back the tense seem to be treated as exceptions, even in specialized coursebooks. This study discusses the use of RS in aeronautical communications by analyzing the occurrences of this structure in two specialized corpora – CORPAC, the Corpus of Pilot and ATC Communications and RTPEC, the Radiotelephony and Plain English Corpus, examining reporting verbs used in actual conversation samples and strictures after the verbs ‘said’ and ‘told’. The main findings show the most used reporting verbs and suggest that around 50% of the indirect reported clauses in aviation maintain the original tense, which seems to be evidence that pilots and ATCs choose to report no changes in the scenario when relaying information in a similar proportion to choosing to backshift. Accordingly, teaching and learning resources like specialized coursebooks or tailordesigned materials should factor the real communication features in their activities.
Complete monogamy of the multipartite quantum mutual information

Quantum mutual information (QMI) not only displays the mutual information in the system but also demonstrates some quantum correlation beyond entanglement. We explore here the two alternatives of multipartite quantum mutual information (MQMI) based on the von Neumann entropy according to the framework of the complete measure of multi-particle quantum system. We show that these two MQMI are complete, monogamous on pure states, and one of them is not only completely monogamous but also tightly complete monogamous while another one is not. Moreover, we present another two MQMI by replacing the von Neumann entropy with the Tasllis $q$-entropy from the former two ones. It is proved that one of them displays some degree of ``completeness'' as a measure of multi-particle quantum system, but the other one is not even non-negative and thus it can not be a alternative of MQMI. We also discuss the triangle relation for these three alternatives of MQMI. It is shown that the triangle inequalities hold for the former two MQMI as that of entanglement measure but the later one fails. By comparison, we found that the von Neumann entropy is better than other versions of entropy as desired when we characterize the quantum correlation in multi-particle system.