Reviewerly/exHarmony huggingface dataset:
Modeling of Autonomous Grasping Method for UAV Guided by Vision

For the current problems of separation of flight control and gripping control of the gripping UAV and redundancy of UAV degrees of freedom and robotic arm degrees of freedom. In this paper, the characteristics of multi-rotor UAV with high mobility and multiple degrees of freedom are used to replace the traditional mechanical gripper structure. At the same time, binocular vision technology is used for target information acquisition, the target position is converted into UAV flight attitude control commands, and conduct mathematical modeling and analysis of a multi-rotor grasping UAV.
Research on Deadline Division and Scheduling of Batch Scientific Workflow in Cloud Environment

Data intensive batch processing scientific workflow is a typical application model in the era of big data. A reasonable scheduling method can improve the resource utilization rate and reduce the rental cost on the premise of meeting the deadline requirements. In this paper, an iterative floating interval allocation method suitable for batch scientific workflow is proposed in the deadline allocation stage, and then in the resource mapping stage, a task scheduling algorithm considering the utilization of time gaps and minimizing the cost of renting virtual machines is proposed, which weighted the expected utilization of time slices. Experiments show that compared with similar scheduling algorithms, in the specified deadline time, it can not only better solve the data transmission bottleneck, but also better improve the execution efficiency and reduce the total rental cost.
Challenges of Promoting Open Science within the NI4OS-Europe Project in Hungary

National Initiatives for Open Science in Europe (NI4OS-Europe) is a Horizon 2020 project related to the European Open Science Cloud (EOSC). One of the project objectives is promoting EOSC and open science in 15 Central and East European EU states and EU-associated countries. This paper describes the variety of promoting activities carried out in Hungary as part of the NI4OS-Europe project by the Governmental Agency for IT Development (KIFÜ). Identifying good practices will give us the chance to find the best communication channels and methods to promote open science and to manage expectations of funders, researchers and librarians. The audience diversity of organized NI4OS events was analyzed in this study. The anonymized dataset based on registration forms was filtered by profession. Results suggest that events are generally visited by more librarians than researchers. The only exception is the third forum where the main Hungarian research fund as co-organizer might have attracted researchers’ attention. This suggests that librarians are considered to be in charge of open science issues in general. Usage data of the open science news feed were also studied. The 130 posts between May 2021 and April 2022 and 2500 visitors until the end of June 2022 give us the chance to learn about the characteristics of the most visited posts. We can conclude that the focus of communication is on open and FAIR data management, while other areas receive less attention. The results show that despite more international posts being published, the target group is more interested in local information.
Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks

Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are robust (i.e., physically-realizable) and universally malicious, and hence represent a severe security threat to real-world DNN-based systems. We propose Jujutsu, a two-stage technique to detect and mitigate robust and universal adversarial patch attacks. We first observe that adversarial patches are crafted as localized features that yield large influence on the prediction output, and continue to dominate the prediction on any input. Jujutsu leverages this observation for accurate attack detection with low false positives. Patch attacks corrupt only a localized region of the input, while the majority of the input remains unperturbed. Therefore, Jujutsu leverages generative adversarial networks (GAN) to perform localized attack recovery by synthesizing the semantic contents of the input that are corrupted by the attacks, and reconstructs a ``clean'' input for correct prediction. We evaluate Jujutsu on four diverse datasets spanning 8 different DNN models, and find that it achieves superior performance and significantly outperforms four existing defenses. We further evaluate Jujutsu against physical-world attacks, as well as adaptive attacks.
Based on the ISM-MICMAC model, the risk path identification and factor analysis of the capital operation of small and medium-sized real estate enterprises

In small and medium-sized real estate enterprises, the accurate identification of the main factors affecting the operation of enterprise project funds and the analysis of their influencing process significantly impact the survival and development of small and medium-sized enterprises. Analyze the influence of financing, investment and construction activities on the operation of project funds and identify 16 major risk factors. Based on the calculation of the adjacency matrix and reachable matrix, the interpretive structure model and hierarchical structure diagram (ISM) were constructed, and divide the influencing factors into four categories, namely independent cluster, dependent cluster, linkage cluster, spontaneous cluster, combined with cross influence matrix multiplication (MICMAC). The results show that the driving force of quality management is low, but the dependence is vital. The macro background of financing, investment management, investment policy, and organizational coordination have low driving force and strong dependence. Quality management is an essential factor affecting the regular operation of enterprise project funds. The macro background of financing, investment management, investment policy, and organization coordination are the direct reasons to keep the project fund risk stable. The research of this paper will contribute to the healthy development of SMEs and play a positive role in China's social and economic life.
THE CASE OF REMITTANCES INFLOW AND CAPITAL MARKET SUSTAINABILITY IN NIGERIA

Owing to the continuous increase in the volume of remittances inflow to Nigeria and the seeming synergy between capital market and economic growth, this study examines the role of remittances in fostering sustainability of the Nigeria capital market for the period 1981 to 2020. The study used Auto-Regressive Distributed Lag (ARDL) modeling approach to co-integration analysis and based on relevant theory, the model was estimated. Estimates showed that remittances significantly and positively impact capital market in the long run. The result also shows that the capital market adjusts speedily to the tune of 64 percent towards convergence in any displacement. Other diagnostics analyses conducted, such as the causality test, affirmed the existence of unidirectional causality running from remittances to the capital market. CUSUM and CUSUMQ tests attest to the stability of the model, which makes it amenable for policy prediction. The findings bring the policy implications and recommendations to the fore.
SOSIALISASI LITERASI DASAR BAGI MAHASISWA BARU UIN KH ABDURRAHMAN WAHID PEKALONGAN

College libraries have the task of supporting the implementation of the Tridharma of Higher Education, one of which is community service. In this case the library also carries out the task of collecting, processing, storing, presenting and disseminating information to the public. That is, the library has a role in building the literate generation. An effort is needed to program activities that can improve capabilities in the field of information retrieval. It is on this basis that this library-based community service activity is carried out. This activity was carried out with the literacy socialization method as well as a library tour or library tour targeting all new students in the UIN KH Abdurrahman Wahid Pekalongan environment with the aim of gaining basic literacy skills in their academic life. From the results of the service, it was found that this activity was well received and had benefits that could be felt directly by the participants of the activity. This means that efforts to increase literacy actually need to be framed in an integrated movement, not partial, individually or determined by certain groups but must be collaborative
Jamming Source Localization Using Augmented Physics-Based Model

Monitoring interferences to satellite-based navigation systems is of paramount importance in order to reliably operate critical infrastructures, navigation systems, and a variety of applications relying on satellite-based positioning. This paper investigates the use of crowdsourced data to achieve such detection and monitoring at a central node that receives the data from an arbitrary number of agents in an area of interest. Under ideal conditions, the pathloss model is used to compute the Cram\'er-Rao Bound of accuracy as well as the corresponding maximum likelihood estimator. However, in real scenarios where obstructions and reflections are common, the signal propagation is far more complex than the pathloss model can explain. We propose to augment the pathloss model with a data-driven component, able to explain the complexities of the propagation channel. The paper shows a general methodology to jointly estimate the interference location and the parameters of the augmented model, showing superior performances in complex scenarios such as those encountered in urban environments.
Reliable Measures of Spread in High Dimensional Latent Spaces

Understanding geometric properties of natural language processing models' latent spaces allows the manipulation of these properties for improved performance on downstream tasks. One such property is the amount of data spread in a model's latent space, or how fully the available latent space is being used. In this work, we define data spread and demonstrate that the commonly used measures of data spread, Average Cosine Similarity and a partition function min/max ratio I(V), do not provide reliable metrics to compare the use of latent space across models. We propose and examine eight alternative measures of data spread, all but one of which improve over these current metrics when applied to seven synthetic data distributions. Of our proposed measures, we recommend one principal component-based measure and one entropy-based measure that provide reliable, relative measures of spread and can be used to compare models of different sizes and dimensionalities.
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems

Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model's pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time.
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs

Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.
Meeting Summarization: A Survey of the State of the Art

Information overloading requires the need for summarizers to extract salient information from the text. Currently, there is an overload of dialogue data due to the rise of virtual communication platforms. The rise of Covid-19 has led people to rely on online communication platforms like Zoom, Slack, Microsoft Teams, Discord, etc. to conduct their company meetings. Instead of going through the entire meeting transcripts, people can use meeting summarizers to select useful data. Nevertheless, there is a lack of comprehensive surveys in the field of meeting summarizers. In this survey, we aim to cover recent meeting summarization techniques. Our survey offers a general overview of text summarization along with datasets and evaluation metrics for meeting summarization. We also provide the performance of each summarizer on a leaderboard. We conclude our survey with different challenges in this domain and potential research opportunities for future researchers.
Improving self-supervised representation learning via sequential adversarial masking

Recent methods in self-supervised learning have demonstrated that masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision. However, existing approaches apply random or ad hoc masking strategies that limit the difficulty of the reconstruction task and, consequently, the strength of the learnt representations. We improve upon current state-of-the-art work in learning adversarial masks by proposing a new framework that generates masks in a sequential fashion with different constraints on the adversary. This leads to improvements in performance on various downstream tasks, such as classification on ImageNet100, STL10, and CIFAR10/100 and segmentation on Pascal VOC. Our results further demonstrate the promising capabilities of masking-based approaches for SSL in computer vision.
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation

The estimation of spatiotemporal data from limited sensor measurements is a required task across many scientific disciplines. The sensor selection problem, which aims to optimize the placement of sensors, leverages innovations in greedy algorithms and low-rank subspace projection to provide model-free, data-driven estimates. Alternatively, Kalman filter estimation balances model-based information and sparsely observed measurements to collectively make an estimation, with many related optimization algorithms developed for selecting optimal sensors. The majority of methods have been developed for stationary sensors, with relatively limited work estimating spatiotemporal data using mobile sensors that leverage both Kalman filtering and low-rank features. We show that mobile sensing along dynamic trajectories can achieve the equivalent performance of a larger number of stationary sensors, with performance gains related to three distinct timescales: (i) the timescale of the spatio-temporal dynamics, (ii) the velocity of the sensors, and (iii) the rate of sampling. Taken together, these timescales strongly influence how well-conditioned the estimation task is. Mobile sensing is particularly effective for spatio-temporal data that contain spatially localized structures, whose features are captured along dynamic trajectories. We draw connections between the Kalman filter performance and the observability of the state space model, and propose a greedy path planning algorithm based on minimizing the condition number of the observability matrix. Through a series of examples of increasing complexity, we show that mobile sensing improves Kalman filter performance in terms of better limiting estimation and faster convergence.
Safe Evaluation For Offline Learning: Are We Ready To Deploy?

The world currently offers an abundance of data in multiple domains, from which we can learn reinforcement learning (RL) policies without further interaction with the environment. RL agents learning offline from such data is possible but deploying them while learning might be dangerous in domains where safety is critical. Therefore, it is essential to find a way to estimate how a newly-learned agent will perform if deployed in the target environment before actually deploying it and without the risk of overestimating its true performance. To achieve this, we introduce a framework for safe evaluation of offline learning using approximate high-confidence off-policy evaluation (HCOPE) to estimate the performance of offline policies during learning. In our setting, we assume a source of data, which we split into a train-set, to learn an offline policy, and a test-set, to estimate a lower-bound on the offline policy using off-policy evaluation with bootstrapping. A lower-bound estimate tells us how good a newly-learned target policy would perform before it is deployed in the real environment, and therefore allows us to decide when to deploy our learned policy.
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering

Most TextVQA approaches focus on the integration of objects, scene texts and question words by a simple transformer encoder. But this fails to capture the semantic relations between different modalities. The paper proposes a Scene Graph based co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We created a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To make explicit teaching of the relations between the two modalities, we proposed and integrated two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conducted extensive experiments on two benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperformed existing ones because of the scene graph and its attention modules.
An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems

Monitoring machine learning systems post deployment is critical to ensure the reliability of the systems. Particularly importance is the problem of monitoring the performance of machine learning systems across all the data subgroups (subpopulations). In practice, this process could be prohibitively expensive as the number of data subgroups grows exponentially with the number of input features, and the process of labelling data to evaluate each subgroup's performance is costly. In this paper, we propose an efficient framework for monitoring subgroup performance of machine learning systems. Specifically, we aim to find the data subgroup with the worst performance using a limited number of labeled data. We mathematically formulate this problem as an optimization problem with an expensive black-box objective function, and then suggest to use Bayesian optimization to solve this problem. Our experimental results on various real-world datasets and machine learning systems show that our proposed framework can retrieve the worst-performing data subgroup effectively and efficiently.
Can We Find Strong Lottery Tickets in Generative Models?

Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network pruning is considered the main cornerstone of model compression for reducing the costs of computation and memory. Unfortunately, pruning a generative model has not been extensively explored, and all existing pruning algorithms suffer from excessive weight-training costs, performance degradation, limited generalizability, or complicated training. To address these problems, we propose to find a strong lottery ticket via moment-matching scores. Our experimental results show that the discovered subnetwork can perform similarly or better than the trained dense model even when only 10% of the weights remain. To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably. Our code and supplementary materials are publicly available.
MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models &amp; Tasks

Vision and language models (VL) are known to exploit unrobust indicators in individual modalities (e.g., introduced by distributional biases) instead of focusing on relevant information in each modality. That a unimodal model achieves similar accuracy on a VL task to a multimodal one, indicates that so-called unimodal collapse occurred. However, accuracy-based tests fail to detect e.g., when the model prediction is wrong, while the model used relevant information from a modality. Instead, we propose MM-SHAP, a performance-agnostic multimodality score based on Shapley values that reliably quantifies in which proportions a multimodal model uses individual modalities. We apply MM-SHAP in two ways: (1) to compare models for their average degree of multimodality, and (2) to measure for individual models the contribution of individual modalities for different tasks and datasets. Experiments with six VL models -- LXMERT, CLIP and four ALBEF variants -- on four VL tasks highlight that unimodal collapse can occur to different degrees and in different directions, contradicting the wide-spread assumption that unimodal collapse is one-sided. Based on our results, we recommend MM-SHAP for analysing multimodal tasks, to diagnose and guide progress towards multimodal integration. Code available at \url{https://github.com/Heidelberg-NLP/MM-SHAP}.