Reviewerly/exHarmony huggingface dataset:
EVALUATION OF THE IMPACT OF A TRAINING INTERVENTION IN THE PROMOTION OF EVIDENCE-BASED PRACTICE

Goal: The provision of evidencebased health care is an imperative of contemporary societies in a context of more complex clinical conditions, demanding high quality and safety responses, in a context of high resource constraints, implying higher levels of effectiveness, efficiency and effectiveness of interventions.In this context, it is pertinent to assess the impact of promoting evidence-based practice through the promotion of a short-term intensive educational/training intervention.Methods: A quasi-experimental study in which the impact of an educational intervention was evaluated on 264 subjects, which corresponds to 63.8% of the total number of participants in the training sessions (N = 414).For data analysis, descriptive statistics and inferential statistics were used, specifically the t test for independent samples.A p-value < 0.05 was considered statistically significant.The following instruments were used to monitor and evaluate the impact: BRUS -Barriers to Research Utilization Scale; QABPBE-26 -Questionnaire of Attitudes and Barriers to Evidence-Based Practice and the QECPBE-20 -Questionnaire of Clinical Efficacy and Evidence-Based Practice.All instruments had psychometric properties indicative of their validity for use in the present study.Results: There are statistically significant differences at the level of the "Practice" subscale of the QECPBE-20 and the four subscales that make up the BRUS scale.Regarding the "Practices" dimension of the QECPBE-20, lower mean values of evidence-based practice perception were registered after the intervention was carried out.Regarding the perception of barriers in relation to EBP, in the four dimensions higher average values are observed.Final considerations: A short-term training program is an intervention that can be used by organizational leaders in order to provide nurses with greater skills, involving them in the dynamics of EBP, and therefore, albeit in a limited way, relevant.
Penggunaan DTM Presisi dari Fotogrametri UAV untuk Analisa Bencana Longsor Menggunakan Sistem Informasi Geografis

The morphologies of the Pandansari Village (Ngantang District, Malang Regency, Indonesia) are vulnerable to landslide disasters that may damage human properties, infrastructures, and even fatalities. Landslide disaster mitigation can be carried out by conducting disaster-prone mapping utilizing Unmanned Aerial Vehicle (UAV) photogrammetry along with geographic information systems (GIS) to produce precise Digital Elevation Model/Digital Terrain Model (DEM/DTM). The purpose of this study is to analyze areas prone to landslides using precision DTM data from UAV technology integrated with geospatial data. DEM is widely used for disaster mapping applications in the form of DTM, representing the ground surface. DTM can be generated from UAV images with photogrammetric processing and additional procedures for removing non-ground objects. This study utilizes PCI Geomatics software to remove vegetation and human-made objects off the ground surfaces semi-automatically. The evaluation revealed that LE 90% of the DTM has only deviated at approximately 0.81 m. This value follows the introductory map geometric accuracy provisions according to BIG No.15 of 2014 for a scale of 1:2500 in class 2. The landslide hazard map classifications using the landslide estimation Puslittanak are dominated by a high classification landslide hazard level with an area of 20.1 ha (48%). In addition, the validation of the landslide-prone map using the accuracy assessment method obtained a percentage of 83%.
Miqat Application in Determining the Qibla Direction of the Mosque

In this all-digital era, the use of Android devices is no longer just a medium for communication, but on your smartphone device, there are already many applications circulating on the Play store developed by software companies and astronomers that are easy to use to assist you in determining. qibla direction. To fulfill the need for information on the Qibla direction of a place, you have to activate the technology on your smartphone that provides real-world Qibla direction information, namely by using the miqat application. This research is to find out the method of using the miqat application in determining the Qibla direction and knowing how the Qibla direction of the Sultan Mahmud Badarudin Jayo Wikramo mosque and Al-Islam Muhammad Cheng Ho Mosque is using the Miqat application. This research is classified as field research, namely research conducted by direct observation of the object studied in the field, aiming to determine the level of accuracy of the use of the miqat application in determining the Qibla direction. The use of the Miqat application in determining the Qibla direction is a method that is very easy to use wherever you are, but before using the application, it is better if you first calibrate the magnetic compass on your smartphone by shaking the phone so that the needle on the compass is stable. The accuracy of the Qibla direction of the Sultan Mahmud Badarudin Jayo Wikramo Mosque and the Al-Islam Muhammad Cheng Ho Mosque with the Miqat application, that in terms of whether or not this application is accurate, in principle, depends on usage. If used in places that contain metal it will make the magnetic sensor on the compass inaccurate which results in degree deviations on the compass being affected by the magnetic field and the metal around it which will result in a larger deviation of degrees on the compass, this application should be used outside the building because in principle, the compass on the cellular relies on magnetic sensors and GPS on the smartphone itself.
Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future

Recurrent models are frequently being used in online tasks such as autonomous driving, and a comprehensive study of their vulnerability is called for. Existing research is limited in generality only addressing application-specific vulnerability or making implausible assumptions such as the knowledge of future input. In this paper, we present a general attack framework for online tasks incorporating the unique constraints of the online setting different from offline tasks. Our framework is versatile in that it covers time-varying adversarial objectives and various optimization constraints, allowing for a comprehensive study of robustness. Using the framework, we also present a novel white-box attack called Predictive Attack that `hallucinates' the future. The attack achieves 98 percent of the performance of the ideal but infeasible clairvoyant attack on average. We validate the effectiveness of the proposed framework and attacks through various experiments.
Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data

Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world datasets. As a result, many existing causal discovery methods can fail under limited data. In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian method to estimate mutual information (MI), based on which we propose a robust MI based independence test; 2) Secondly, we consider the Bayesian estimation of hypothesis likelihood and incorporate it into a well-defined statistical test, resulting in a robust statistical testing based independence test. We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples. Experiments show significant performance improvement in terms of both accuracy and efficiency over SOTA methods.
Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits

In many domains, worst-case guarantees on the performance (e.g. prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial. In this work we develop a method to quantify the robustness of decision functions with respect to credal Bayesian networks, formal parametric models of the environment where uncertainty is expressed through credal sets on the parameters. In particular, we address the maximum marginal probability (MARmax) problem, that is, determining the greatest probability of an event (such as misclassification) obtainable for parameters in the credal set. We develop a method to faithfully transfer the problem into a constrained optimization problem on a probabilistic circuit. By performing a simple constraint relaxation, we show how to obtain a guaranteed upper bound on MARmax in linear time in the size of the circuit. We further theoretically characterize this constraint relaxation in terms of the original Bayesian network structure, which yields insight into the tightness of the bound. We implement the method and provide experimental evidence that the upper bound is often near tight and demonstrates improved scalability compared to other methods.
Non-Cheating Teaching Revisited: A New Probabilistic Machine Teaching Model

Over the past decades in the field of machine teaching, several restrictions have been introduced to avoid ‘cheating’, such as collusion-free or non-clashing teaching. However, these restrictions forbid several teaching situations that we intuitively consider natural and fair, especially those ‘changes of mind’ of the learner as more evidence is given, affecting the likelihood of concepts and ultimately their posteriors. Under a new generalised probabilistic teaching, not only do these non-cheating constraints look too narrow but we also show that the most relevant machine teaching models are particular cases of this framework: the consistency graph between concepts and elements simply becomes a joint probability distribution. We show a simple procedure that builds the witness joint distribution from the ground joint distribution. We prove a chain of relations, also with a theoretical lower bound, on the teaching dimension of the old and new models. Overall, this new setting is more general than the traditional machine teaching models, yet at the same time more intuitively capturing a less abrupt notion of non-cheating teaching.
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (Extended Abstract)*

We present a framework for analysing the impact of AI on occupations. This framework maps 59 generic tasks from different occupational datasets to 14 cognitive abilities and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity in AI. The use of cognitive abilities as an intermediate layer allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly programmed. We provide insights into the abilities through which AI is most likely to affect jobs, and we show how some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies.
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to graph classes with limited labeled graphs. Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set. However, existing methods generally ignore task correlations among meta-training tasks while treating them independently. Nevertheless, such task correlations can advance the model generalization to the target task for better classification performance. On the other hand, it remains non-trivial to utilize task correlations due to the complex components in a large number of meta-training tasks. To deal with this, we propose a novel few-shot learning framework FAITH that captures task correlations via constructing a hierarchical task graph at different granularities. Then we further design a loss-based sampling strategy to select tasks with more correlated classes. Moreover, a task-specific classifier is proposed to utilize the learned task correlations for few-shot classification. Extensive experiments on four prevalent few-shot graph classification datasets demonstrate the superiority of FAITH over other state-of-the-art baselines.
Hidden 1-Counter Markov Models and How to Learn Them

We introduce hidden 1-counter Markov models (H1MMs) as an attractive sweet spot between standard hidden Markov models (HMMs) and probabilistic context-free grammars (PCFGs). Both HMMs and PCFGs have a variety of applications, e.g., speech recognition, anomaly detection, and bioinformatics. PCFGs are more expressive than HMMs, e.g., they are more suited for studying protein folding or natural language processing. However, they suffer from slow parameter fitting, which is cubic in the observation sequence length. The same process for HMMs is just linear using the well-known forward-backward algorithm. We argue that by adding to each state of an HMM an integer counter, e.g., representing the number of clients waiting in a queue, brings its expressivity closer to PCFGs. At the same time, we show that parameter fitting for such a model is computationally inexpensive: it is bi-linear in the length of the observation sequence and the maximal counter value, which grows slower than the observation length. The resulting model of H1MMs allows us to combine the best of both worlds: more expressivity with faster parameter fitting.
Leveraging Class Abstraction for Commonsense Reinforcement Learning via Residual Policy Gradient Methods

Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in open-source knowledge graphs to abstract away from specific objects. We develop a residual policy gradient method that is able to integrate knowledge across different abstraction levels in the class hierarchy. Our method results in improved sample efficiency and generalisation to unseen objects in commonsense games, but we also investigate failure modes, such as excessive noise in the extracted class knowledge or environments with little class structure.
Composing Neural Learning and Symbolic Reasoning with an Application to Visual Discrimination

We consider the problem of combining machine learning models to perform higher-level cognitive tasks with clear specifications. We propose the novel problem of Visual Discrimination Puzzles (VDP) that requires finding interpretable discriminators that classify images according to a logical specification. Humans can solve these puzzles with ease and they give robust, verifiable, and interpretable discriminators as answers. We propose a compositional neurosymbolic framework that combines a neural network to detect objects and relationships with a symbolic learner that finds interpretable discriminators. We create large classes of VDP datasets involving natural and artificial images and show that our neurosymbolic framework performs favorably compared to several purely neural approaches.
Early Diagnosis of Lyme Disease by Recognizing Erythema Migrans Skin Lesion from Images Utilizing Deep Learning Techniques

Lyme disease is one of the most common infectious vector-borne diseases in the world. We extensively studied the effectiveness of convolutional neural networks for identifying Lyme dis-ease from images. Our research contribution includes dealing with lack of data, multimodal learning incorporating expert opinion elicitation, and automation of skin hair mask generation.
Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts

Long range forecasts are the starting point of many decision support systems that need to draw inference from high-level aggregate patterns on forecasted values. State of the art time-series forecasting methods are either subject to concept drift on long-horizon forecasts, or fail to accurately predict coherent and accurate high-level aggregates. In this work, we present a novel probabilistic forecasting method that produces forecasts that are coherent in terms of base level and predicted aggregate statistics. We achieve the coherency between predicted base-level and aggregate statistics using a novel inference method based on KL-divergence that can be solved efficiently in closed form. We show that our method improves forecast performance across both base level and unseen aggregates post inference on real datasets ranging three diverse domains. (Project URL)
PACE: Predictive and Contrastive Embedding for Unsupervised Action Segmentation

Action segmentation, inferring temporal positions of human actions in an untrimmed video, is an important prerequisite for various video understanding tasks. Recently, unsupervised action segmentation (UAS) has emerged as a more challenging task due to the unavailability of frame-level annotations. Existing clustering- or prediction-based UAS approaches suffer from either over-segmentation or overfitting, leading to unsatisfactory results. To address those problems,we propose Predictive And Contrastive Embedding (PACE), a unified UAS framework leveraging both predictability and similarity information for more accurate action segmentation. On the basis of an auto-regressive transformer encoder, predictive embeddings are learned by exploiting the predictability of video context, while contrastive embeddings are generated by leveraging the similarity of adjacent short video clips. Extensive experiments on three challenging benchmarks demonstrate the superiority of our method, with up to 26.9% improvements in F1-score over the state of the art.
Exchangeability-Aware Sum-Product Networks

Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence. On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable. Exchangeability, which arises naturally in relational domains, has not been considered for efficient representation and inference in SPNs yet. The contribution of this paper is a novel probabilistic model which we call Exchangeability-Aware Sum-Product Networks (XSPNs). It contains both SPNs and MEVMs as special cases, and combines the ability of SPNs to efficiently learn deep probabilistic models with the ability of MEVMs to efficiently handle exchangeable random variables. We introduce a structure learning algorithm for XSPNs and empirically show that they can be more accurate than conventional SPNs when the data contains repeated, interchangeable parts.
Automatically Gating Multi-Frequency Patterns through Rectified Continuous Bernoulli Units with Theoretical Principles

Different nonlinearities are only suitable for responding to different frequency signals. The locally-responding ReLU is incapable of modeling high-frequency features due to the spectral bias, whereas the globally-responding sinusoidal function is intractable to represent low-frequency concepts cheaply owing to the optimization dilemma. Moreover, nearly all the practical tasks are composed of complex multi-frequency patterns, whereas there is little prospect of designing or searching a heterogeneous network containing various types of neurons matching the frequencies, because of their exponentially-increasing combinatorial states. In this paper, our contributions are three-fold: 1) we propose a general Rectified Continuous Bernoulli (ReCB) unit paired with an efficient variational Bayesian learning paradigm, to automatically detect/gate/represent different frequency responses; 2) our numerically-tight theoretical framework proves that ReCB-based networks can achieve the optimal representation ability, which is O(m^{η/(d^2)}) times better than that of popular neural networks, for a hidden dimension of m, an input dimension of d, and a Lipschitz constant of η; 3) we provide comprehensive empirical evidence showing that ReCB-based networks can keenly learn multi-frequency patterns and push the state-of-the-art performance.
Multi-policy Grounding and Ensemble Policy Learning for Transfer Learning with Dynamics Mismatch

We propose a new transfer learning algorithm between tasks with different dynamics. The proposed algorithm solves an Imitation from Observation problem (IfO) to ground the source environment to the target task before learning an optimal policy in the grounded environment. The learned policy is deployed in the target task without additional training. A particular feature of our algorithm is the employment of multiple rollout policies during training with a goal to ground the environment more globally; hence, it is named as Multi-Policy Grounding (MPG). The quality of final policy is further enhanced via ensemble policy learning. We demonstrate the superiority of the proposed algorithm analytically and numerically. Numerical studies show that the proposed multi-policy approach allows comparable grounding with single policy approach with a fraction of target samples, hence the algorithm is able to maintain the quality of obtained policy even as the number of interactions with the target environment becomes extremely small.
A Weighting-Based Tabu Search Algorithm for the p-Next Center Problem

The p-next center problem (pNCP) is an extension of the classical p-center problem. It consists of locating p centers from a set of candidate centers and allocating both a reference and a backup center to each client, to minimize the maximum cost, which is the length of the path from a client to its reference center and then to its backup center. Among them, the reference center is the closest center to a client and serves it under normal circumstances, while the backup center is the closest center to the reference center and serves the client when the reference center is out of service. In this paper, we propose a weighting-based tabu search algorithm called WTS for solving pNCP. WTS optimizes the pNCP by solving its decision subproblems with given assignment costs with an efficient swap-based neighborhood structure and a hierarchical penalty strategy for neighborhood evaluation. Extensive experimental studies on 413 benchmark instances demonstrate that WTS outperforms the state-of-the-art methods in the literature. Specifically, WTS improves 12 previous best known results and matches the optimal results for all remaining 401 ones in a much shorter time than other algorithms. More importantly, WTS reaches the lower bounds for 10 instances for the first time.