Reviewerly/exHarmony huggingface dataset:
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident variations caused by the stochastic nature of our world. These outcomes are governed by the characteristic kinematics of objects that dictate their overall motion caused by a local interaction. Conversely, the movement of an object provides crucial information about its underlying distinctive kinematics and the interdependencies between its parts. This two-way relation motivates learning a bijective mapping between object kinematics and plausible future image sequences. Therefore, we propose iPOKE – invertible Prediction of Object Kinematics – that, conditioned on an initial frame and a local poke, allows to sample object kinematics and establishes a one-to-one correspondence to the corresponding plausible videos, thereby providing a controlled stochastic video synthesis. In contrast to previous works, we do not generate arbitrary realistic videos, but provide efficient control of movements, while still capturing the stochastic nature of our environment and the diversity of plausible outcomes it entails. Moreover, our approach can transfer kinematics onto novel object instances and is not confined to particular object classes. Our project page is available at https://bit.ly/3dJN4Lf.
Fixing the inconsistencies in fuzzy spatiotemporal RDF graph

Since an increasing number of applications have urgent requirements for the management of fuzzy spatiotemporal information, it is challenging to model fuzzy spatiotemporal data. On the other hand, because Resource Description Framework (RDF) benefits greatly from web data, linked data, and knowledge graph, it has been widely accepted and has rapidly become a new choice to model and represent data in many application domains. The advent of RDF seems to provide an opportunity for dealing with fuzzy spatiotemporal data. However, RDF data is subject to continuously changing operations so that they may violate the integrity constraints and cause inconsistencies. In order to solve these problems, we propose an extended RDF model that can represent fuzzy spatiotemporal data, and then present the structure of fuzzy spatiotemporal data in RDF graph. After studying the constraint conditions of fuzzy spatiotemporal data in RDF graph, we propose the corresponding fixing rules and algorithms to detect and repair the fuzzy spatiotemporal RDF graph. Finally, the experimental results demonstrate the effectiveness of our approach which can significantly solve the inconsistency problem of fuzzy spatiotemporal RDF graphs.
Restrictive deterrence and the scope of hackers’ reoffending: Findings from two randomized field trials

Extensive criminological research has investigated the effectiveness of law enforcement efforts to prevent offenders' reoffending and effect changes in criminal behavior patterns. However, no research has quantified the influence of gossip regarding law enforcement operations and its effect on repeat offending. Moreover, only scant research has studied these relationships in the cyber-environment. The current work addresses this gap by studying the effect of online messages sent to active hackers that describe law enforcement efforts against website defacement activity. In two field experiments, we randomly assigned self-identified hackers with active Facebook accounts into control and treatment groups. We then sent subjects in the treatment group a gossip message (via Facebook) alerting them to law enforcement efforts in cyberspace. Following the intervention, we compared the control and treatment groups in terms of changes in the proportion of reoffending, number of website defacements, and severity of website defacements. Findings reveal that when the gossip was sent to a hacker's private inbox, it prompted a reduction in the proportion of hackers who reoffend, the frequency at which they reoffend, and the severity of attacks they generate. However, posting similar gossip on the hacker's Facebook wall was ineffective in restricting malicious hacking activity. Theoretical implications and practical recommendations for law enforcement operations in cyberspace are discussed.
Integration of Landsat 8, gravity, and field data for exploration of gold mineralization in Gamba District, Northern Cameroon

Remote sensing is a robust and useful tool for providing high‐resolution image data and enabling reliable geological mapping during the initial stage of mineral exploration. One of its main applications is the extraction of lineaments and to locate alteration areas to target gold exploration. It has been long used in the Pan‐African belt of Cameroon to identify a hydrothermal alteration and a great number of lineaments associated with mineralizations. The study area located in the Pan‐African belt hosts numerous alluvial gold deposits where the primary mineralization was still largely poorly unknown until now, due to deep weathering. Therefore, remote sensing combined with field data is useful for targeting potential zones of primary gold resources involved in the hydrothermal and lineament systems. In this study, remote sensing data from Landsat 8 imagery were selected to map the distribution of hydrothermal minerals, and gravity data were interpreted for highlighting structural patterns related to the control of high‐potential zone for gold mineralization, generating a mineral prospect map. The lineaments network shows directions ranging from ENE‐WSW to E‐W, with main direction N45° and a secondary striking N275°. Image enhancement/processing techniques included the application of band ratio and principal component analysis that were helpful to demarcate potential alteration zones marked by iron oxide/hydroxides in which haematite and pyrite are used as proximal alterations and hydroxyl‐bearing minerals in which sericite (muscovite) is used as a marker of proximal alteration, while chlorite, epidote, biotite, quartz, and calcite are used as distal alteration zone, as described by field and petrographic data. The identified alteration zones display a high consistency with the known locations of gold occurrences (mining sites) and closely concordant with large‐scale gold mineralization in the study area. This study presents an integrated approach of Landsat 8 imagery with gravity data and field data for discovering primary mineral resources in a deep weathering area.
Analysis of Artificial Neural Networks for Forecasting Photovoltaic Energy Generation with Solar Irradiance

HIGHLIGHTS The input GHI variable increased the prediction accuracy of the ANN models in 10%. The meteorological variables do not influence the prediction errors statistically. ANNs with 10 neurons demonstrated lower accuracy than those with 30 and 60 neurons. ANNs with one hidden layer showed similar or higher accuracy than those with two layers.
Gaining IIoT insights by leveraging ontology-based modelling of raw data and Digital Shadows

Along a production value chain, huge amounts of diverse data are generated. A proper collection, transfer and modeling of this data is essential for its meaningful use in following engineering tools and applications. Data engineering is thereby complex due to a high variety of possible use cases and a lot of different interpretations of the same data. In this paper, we present an approach to a use case sensitive modeling of raw data by the methods of ontology-based data access (OBDA) and the concept of Digital Shadows. The developed approach is based on an industrial use case of glass production in the Saint-Gobain Sekurit enterprise. Existing data modeling structures of Saint-Gobain Sekurit are extended by an ontology-based application for building use case specific data templates. The aim of this paper is to highlight potentials of semantic data modeling to apply the concept of Digital Shadows for data engineering in the production environment.
A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation

Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy.
Error messages in relational database management systems: A comparison of effectiveness, usefulness, and user confidence

The database and the database management system (DBMS) are two of the main components of any information system. Structured Query Language (SQL) is the most popular query language for retrieving data from the database, as well as for many other data management tasks. During system development and maintenance, software developers use a considerable amount of time to interpret compiler error messages. The quality of these error messages has been demonstrated to affect software development effectiveness, and correctly formulating queries and fixing them when needed is an important task for many software developers. In this study, we set out to investigate how participants (N=152) experienced the qualities of error messages of four popular DBMSs in terms of error message effectiveness, perceived usefulness for finding and fixing errors, and error recovery confidence. Our results show differences between the DBMSs by three of the four metrics, and indicate a discrepancy between objective effectiveness and subjective usefulness. The results suggest that although error messages have perceived differences in terms of usefulness for finding and fixing errors, these differences may not necessarily result in differences in query fixing success rates.
Event Occurrence Date Estimation based on Multivariate Time Series Analysis over Temporal Document Collections

Real world events are quite often mentioned in texts. Estimating the occurrence time of event mentions has many applications in IR, QA, general document understanding and downstream NLP tasks. In this paper we propose an approach to temporal profiling of event mentions in text. Our method utilizes a news article archival collection for collecting temporal as well as textual information containing contemporary and retrospective event references. As we demonstrate in our experiments, the recent method which relies on secondary data sources like Wikipedia is insufficient to correctly estimate the event time, especially, for minor or less well-known events that happened in the past. Our method then harnesses news article archives to effectively infer the occurrence time of past events, and is able to estimate the time at different temporal granularities (e.g., day, week, month, or year). As evidenced through extensive experiments, the proposed model outperforms the existing methods by a large margin at all granularities. We also demonstrate that our approach helps to answer arbitrary questions about past events, when incorporated into a QA framework operating over news article archives.
Learning Two-Layer ReLU Networks Is Nearly as Easy as Learning Linear Classifiers on Separable Data

Neural networks with non-linear rectified linear unit (ReLU) activation functions have demonstrated remarkable performance in many fields. It has been observed that a sufficiently wide and/or deep ReLU network can accurately fit the training data, with a small generalization error on the testing data. Nevertheless, existing analytical results on provably training ReLU networks are mostly limited to over-parameterized cases, or they require assumptions on the data distribution. In this paper, training a two-layer ReLU network for binary classification of linearly separable data is revisited. Adopting the hinge loss as classification criterion yields a non-convex objective function with infinite local minima and saddle points. Instead, a modified loss is proposed which enables (stochastic) gradient descent to attain a globally optimal solution. Enticingly, the solution found is globally optimal for the hinge loss too. In addition, an upper bound on the number of iterations required to find a global minimum is derived. To ensure generalization performance, a convex max-margin formulation for two-layer ReLU network classifiers is discussed. Connections between the sought max-margin ReLU network and the max-margin support vector machine are drawn. Finally, an algorithm-dependent theoretical quantification of the generalization performance is developed using classical compression bounds. Numerical tests using synthetic and real data validate the analytical results.
Multiresolution Reservoir Graph Neural Network

Graph neural networks are receiving increasing attention as state-of-the-art methods to process graph-structured data. However, similar to other neural networks, they tend to suffer from a high computational cost to perform training. Reservoir computing (RC) is an effective way to define neural networks that are very efficient to train, often obtaining comparable predictive performance with respect to the fully trained counterparts. Different proposals of reservoir graph neural networks have been proposed in the literature. However, their predictive performances are still slightly below the ones of fully trained graph neural networks on many benchmark datasets, arguably because of the oversmoothing problem that arises when iterating over the graph structure in the reservoir computation. In this work, we aim to reduce this gap defining a multiresolution reservoir graph neural network (MRGNN) inspired by graph spectral filtering. Instead of iterating on the nonlinearity in the reservoir and using a shallow readout function, we aim to generate an explicit k -hop unsupervised graph representation amenable for further, possibly nonlinear, processing. Experiments on several datasets from various application areas show that our approach is extremely fast and it achieves in most of the cases comparable or even higher results with respect to state-of-the-art approaches.
Automated detection of tumor regions from oral histological whole slide images using fully convolutional neural networks

The diagnosis of different types of cancer, including oral cavity-derived cancer, is made by a pathologist through complex and time-consuming microscopic analysis of tissue samples. This paper presents a method based on a fully convolutional neural network to localize and perform refined segmentation of oral cavity-derived tumor regions in H&E-stained histological whole slide images. The proposed method uses color features in the HSV color model to identify tissue regions in a pre-processing step to remove background and nonrelevant areas. The identified tissue regions are then transformed into the CIE L*a*b* color model and split into image-patches. The method was applied in a WSI dataset of oral squamous cell carcinoma tissue samples. In addition, for further validation and comparison with other proposals, we also applied the proposed method in a WSI dataset of sentinel lymph nodes with breast cancer metastases. Experimental evaluations were performed using a total of 85,621 image-patches of size 640 × 640 pixels and the proposed method achieved good results in different cancer-derived datasets with images of different tumors. The results revealed that the proposal is robust and capable to localize and perform refined segmentation, achieving accuracy results up to 97.6%, specificity up to 98.4%, and sensitivity up to 92.9%. The influence of different color spaces and different image-patch sizes in the proposed method also were explored.
echidna-parade: a tool for diverse multicore smart contract fuzzing

Echidna is a widely used fuzzer for Ethereum Virtual Machine (EVM) compatible blockchain smart contracts that generates transaction sequences of calls to smart contracts. While Echidna is an essentially single-threaded tool, it is possible for multiple Echidna processes to communicate by use of a shared transaction sequence corpus. Echidna provides a very large variety of configuration options, since each smart contract may be best-tested by a non-default configuration, and different faults or coverage targets within a single contract may also have differing ideal configurations. This paper presents echidna-parade, a tool that provides pushbutton multicore fuzzing using Echidna as an underlying fuzzing engine, and automatically provides sophisticated diversification of configurations. Even without using multiple cores, echidna-parade can improve the effectiveness of fuzzing with Echidna, due to the advantages provided by multiple types of test configuration diversity. Using echidna-parade with multiple cores can produce significantly better results than Echidna, in less time.
STSRNet: Deep Joint Space–Time Super-Resolution for Vector Field Visualization

We propose STSRNet, a joint space-time super-resolution deep learning based model for time-varying vector field data. Our method is designed to reconstruct high temporal resolution and high spatial resolution vector fields sequence from the corresponding low-resolution key frames. For large scale simulations, only data from a subset of time steps with reduced spatial resolution can be stored for post hoc analysis. In this article, we leverage a deep learning model to capture the nonlinear complex changes of vector field data with a two-stage architecture: the first stage deforms a pair of low spatial resolution (LSR) key frames forward and backward to generate the intermediate LSR frames, and the second stage performs spatial super-resolution to output the high-resolution sequence. Our method is scalable and can handle different datasets. We demonstrate the effectiveness of our framework with several datasets through quantitative and qualitative evaluations.
Practical Wide-Angle Portraits Correction with Deep Structured Models

Wide-angle portraits often enjoy expanded views. However, they contain perspective distortions, especially noticeable when capturing group portrait photos, where the background is skewed and faces are stretched. This paper introduces the first deep learning based approach to remove such artifacts from freely-shot photos. Specifically, given a wide-angle portrait as input, we build a cascaded network consisting of a LineNet, a ShapeNet, and a transition module (TM), which corrects perspective distortions on the background, adapts to the stereographic projection on facial regions, and achieves smooth transitions between these two projections, accordingly. To train our network, we build the first perspective portrait dataset with a large diversity in identities, scenes and camera modules. For the quantitative evaluation, we introduce two novel metrics, line consistency and face congruence. Compared to the previous state-of-the-art approach, our method does not require camera distortion parameters. We demonstrate that our approach significantly outperforms the previous state-of-the-art approach both qualitatively and quantitatively.
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems

Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount of interest in the past few decades, while one of the most critical operations in these systems is the perception of the environment. Deep learning and, especially, the use of Deep Neural Networks (DNNs) provides impressive results in analyzing and understanding complex and dynamic scenes from visual data. The prediction horizons for those perception systems are very short and inference must often be performed in real time, stressing the need of transforming the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Our goal in this work is to investigate best practices for appropriately applying novel weight sharing techniques, optimizing the available variables and the training procedures towards the significant acceleration of widely adopted DNNs. Extensive evaluation studies carried out using various state-of-the-art DNN models in object detection and tracking experiments, provide details about the type of errors that manifest after the application of weight sharing techniques, resulting in significant acceleration gains with negligible accuracy losses.
Learning An Explicit Weighting Scheme for Adapting Complex HSI Noise

An efficient approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements. Such weighting scheme, however, largely depends on the prior understanding or subjective distribution assumption on HSI noises, making them easily biased to complicated real noises, and hardly generalizable to diverse practical scenarios. Against this issue, this paper proposes a new scheme aiming to capture general weighting principle in a data-driven manner. Specifically, such weighting principle is delivered by an explicit function, called hyper-weight-net (HWnet), mapping from an input noisy image to its properly imposed weights. A Bayesian framework as well as a variational inference algorithm for inferring HWnet parameters is elaborately designed, expecting to extract the latent weighting rule for general diverse and complicated noisy HSIs. Comprehensive experiments substantiate that the learned HWnet can be not only finely generalized to different noise types from those used in training, but also effectively transferred to other weighted models. Besides, as a sounder guidance, HWnet can help to more faithfully and robustly achieve deep hyperspectral prior(DHP). The extracted weights by HWnet are verified to be able to effectively capture complex noise knowledge underlying input HSI, revealing its working insight in experiments.
Fast, High-Quality Dual-Arm Rearrangement in Synchronous, Monotone Tabletop Setups

Rearranging objects on a planar surface arises in a variety of robotic applications, such as product packaging. Using two arms can improve efficiency but introduces new computational challenges. This paper studies the structure of dual-arm rearrangement for synchronous, monotone tabletop setups and develops an optimal mixed integer model. It then describes an efficient and scalable algorithm, which first minimizes the cost of object transfers and then of moves between objects. This is motivated by the fact that, asymptotically, object transfers dominate the cost of solutions. Moreover, a lazy strategy minimizes the number of motion planning calls and results in significant speedups. Theoretical arguments support the benefits of using two arms and indicate that synchronous execution, in which the two arms perform together either transfers or moves, introduces only a small overhead. Experiments support these points and show that the scalable method can quickly compute solutions close to the optimal for the considered setup.