Reviewerly/exHarmony huggingface dataset:
Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling

The presented problem concerns the route planning of a UAV fleet carrying out deliveries to spatially dispersed customers in a highly dynamic and unpredictable environment within a specified timeframe. The developed model allows for predictive (i.e., taking into account forecasted changing weather conditions) and reactive (i.e., enabling contingency UAVs rerouting) delivery mission planning (i.e., NP-hard problem) in terms of the constraint satisfaction problem. Due to the need to implement an emergency return of the UAV to the base or handling ad hoc ordered deliveries, sufficient conditions have been developed. Checking that these conditions are met allows cases to be eliminated if they do not guarantee acceptable solutions, thereby allowing the calculations to be sped up. The experiments carried out showed the usefulness of the proposed approach in DSS-based contingency planning of the UAVs’ mission performed in a dynamic environment.
Efficient Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach

Abstract Reliability-based mission planning of off-road autonomous ground vehicles (AGVs) aims to identify an optimal path under uncertain and deformable terrain environment, while satisfying specific mission mobility reliability (MMR) constraints. The repeated evaluation of MMR during path planning poses computational challenges for practical applications. This paper presents an efficient reliability-based mission planning using an outcrossing approach that has a similar computational complexity compared to deterministic mission planning. A Gaussian random field is employed to represent the spatially dependent uncertainty sources in the terrain environment. The latter are then used in conjunction with a vehicle mobility model to generate a stochastic mobility map. Based on the stochastic mobility map, outcrossing rate maps are generated using the outcrossing concept which is widely used in time-dependent reliability. Integration of the outcrossing rate map with a rapidly exploring random tree (RRT*) algorithm allows for efficient path planning of AGVs subject to MMR constraints. A reliable RRT* algorithm using the outcrossing approach (RRT*-OC) is developed to implement the proposed efficient reliability-based mission planning. Results of a case study with two scenarios verify the accuracy and efficiency of the proposed algorithm.
New Challenges for Deep Neural Networks in Automotive Radar Perception

Radar sensors are a key component of automated vehicles. The requirements for radar perception modules are growing more demanding. At the same time, the radar sensors themselves are becoming increasingly sophisticated. Both developments lead to the progression of very complex algorithms. In the field of machine learning, increased task difficulty is often managed by using various types of deep neural networks (DNN). Deeper and more complex network structures allow for achieving results that had, until recently, been considered unattainable. In order to make use of this new set of machine learning algorithms, particular attention must be paid to the quality of the input data. This article gives an overview of some of the most promising ideas that will define the near to mid-term future in the field of DNNs in automotive radar perception. Contrary to image- or lidar-based approaches, the main challenge towards using DNNs on automotive radar data is information sparsity at a perception level. This currently prevents riding on the wave of recent successes in the object detection area. Another difficulty is the need for large amounts of labeled data. For information sparsity, important solutions such as high resolution processing or the utilization of low-level data layers and polarimetric radars are discussed. Furthermore, the annotation problem is derived using an example and a practical solution for the realization of an auto-labeling system is described.
Bidirectional Symmetry Network with Dual-Field Cyclic Attention for Multi-Temporal Aerial Remote Sensing Image Registration

Multi-temporal remote sensing image registration is a geometric symmetry process that involves matching a source image with a target image. To improve the accuracy and enhance the robustness of the algorithm, this study proposes an end-to-end registration network—a bidirectional symmetry network based on dual-field cyclic attention for multi-temporal remote sensing image registration, which mainly improves feature extraction and feature matching. (1) We propose a feature extraction framework combining an attention module and a pre-training model, which can accurately locate important areas in images and quickly extract features. Not only is the dual receptive field module designed to enhance attention in the spatial region, a loop structure is also used to improve the network model and improve overall accuracy. (2) Matching has not only directivity but also symmetry. We design a symmetric network of two-way matching to reduce the registration deviation caused by one-way matching and use a Pearson correlation method to improve the cross-correlation matching and enhance the robustness of the matching relation. In contrast with two traditional methods and three deep learning-based algorithms, the proposed approach works well under five indicators in three public multi-temporal datasets. Notably, in the case of the Aerial Image Dataset, the accuracy of the proposed method is improved by 39.8% compared with the Two-stream Ensemble method under a PCK (Percentage of Correct Keypoints) index of 0.05. When the PCK index is 0.03, accuracy increases by 46.8%, and increases by 18.7% under a PCK index of 0.01. Additionally, when adding the innovation points in feature extraction into the basic network CNNGeo (Convolutional Neural Network Architecture for Geometric Matching), accuracy is increased by 36.7% under 0.05 PCK, 18.2% under 0.03 PCK, and 8.4% under 0.01 PCK. Meanwhile, by adding the innovation points in feature matching into CNNGeo, accuracy is improved by 16.4% under 0.05 PCK, 9.1% under 0.03 PCK, and 5.2% under 0.01 PCK. In most cases, this paper reports high registration accuracy and efficiency for multi-temporal remote sensing image registration.
New differentially 4-uniform permutations from modifications of the inverse function

Finding permutations with good cryptographic parameters is a good research topic about constructing a secure S-box in substitution-permutation networks. In particular constructing differentially 4-uniform permutations has made considerable progress in recent years. In this paper, we present new differentially 4-uniform permutations from the inverse function composed by disjoint cycles. Our new differentially 4-uniform permutations have high nonlinearity and low differential-linear uniformity. We give the differential spectrum and the extended Walsh spectrum of some of our differentially 4-uniform permutations, and then we can see that they are CCZ-inequivalent to some permutations whose differential spectrum and extended Walsh spectrum are known.
Self-Supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging

Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any lesion changes. Our training automatically synthesises lesion changes in serial images, introducing detection and localisation pseudo-labels that are used to self-supervise the training of our model. Given the rarity of these lesion changes in the synthesised images, we train the model with the imbalance robust focal Tversky loss. When compared to supervised models trained on different datasets, our method shows competitive performance in the detection and localisation of new demyelinating lesions on longitudinal magnetic resonance imaging in multiple sclerosis patients. Code for the models will be made available at https://github.com/toson87/MSChangeDetection.
Continuously Non-malleable Secret Sharing: Joint Tampering, Plain Model and Capacity

We study non-malleable secret sharing against joint leakage and joint tampering attacks. Our main result is the first threshold secret sharing scheme in the plain model achieving resilience to noisy-leakage and continuous tampering. The above holds under (necessary) minimal computational assumptions (i.e., the existence of one-to-one one-way functions), and in a model where the adversary commits to a fixed partition of all the shares into non-overlapping subsets of at most \(t-1\) shares (where t is the reconstruction threshold), and subsequently jointly leaks from and tampers with the shares within each partition.
Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry

Abstract Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO 3 ), Ca, Na, Cl, Mg, and sulfate (SO 4 ). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO 3 , Ca, Na, Cl, Mg, SO 4 , pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
Form and Function: A Study on the Distribution of the Inflectional Endings in Italian Nouns and Adjectives

Inflectional values, such as singular and plural, sustain agreement relations between constituents in sentences, allowing sentence parsing and prediction in online processing. Ideally, these processes would be facilitated by a consistent and transparent correspondence between the inflectional values and their form: for example, the value of plural should always be expressed by the same ending, and that ending should only express plural. Experimental research reports higher processing costs in the presence of a non-transparent relation between forms and values. While this effect was found in several languages, and typological research shows that consistency is far from common in morphological paradigms, it is still somewhat difficult to precisely quantify the transparency degree of the inflected forms. Furthermore, to date, no accounts have quantified the transparency in inflection with regard to the declensional classes and the extent to which it is expressed across different parts of speech, depending on whether these act as controllers of the agreement (e.g., nouns) or as targets (e.g., adjectives). We present a case study on Italian, a language that marks gender and number features in nouns and adjectives. This work provides measures of the distribution of forms in the noun and adjective inflection in Italian, and quantifies the degree of form-value transparency with respect to inflectional endings and declensional classes. In order to obtain these measures, we built Flex It, a dedicated large-scale database of inflectional morphology of Italian, and made it available, in order to sustain further theoretical and empirical research.
Credible Web Service Composition based on Improved Multi-objective Particle Swarm Optimization

In the field of Web service composition, since the published value of Quality of Service (QoS) is not necessarily reliable, the research on the credibility of Web services has become a research hotspot. However, the current credibility research cannot well describe the difference between the published value and the actual operating value. At the same time, the algorithm used to solve the Web service composition scheme generally has the problem of poor solution effect. Based on this, this paper proposes a credible Web service composition method based on improved multi-objective particle swarm optimization (IMOPSO). Firstly, this paper proposes a similarity-based credibility calculation model, and combines it with the QoS published value through the inverse of the similarity, so that the QoS of the Web service meets the actual operation effect. Secondly, this paper proposes an improved multi-objective particle swarm optimization algorithm, which solves the problem of premature convergence by expanding the population diversity, and the solution effect is significantly improved. Finally, the improved multi-objective particle swarm algorithm is used to solve the Web service composition problem based on credible QoS. Simulation experiments on the QWS_V2 data set and the Synthetic data set prove that the method proposed in this paper can effectively improve the credibility of the service composition scheme, and has higher composition efficiency and better composition effect.
Visualizing Uncertainty in Probabilistic Graphs with Network Hypothetical Outcome Plots (NetHOPs)

Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.
An obstacle avoidance algorithm for space hyper-redundant manipulators using combination of RRT and shape control method

Abstract This paper proposes a kinematic obstacle avoidance algorithm for Space hyper-redundant manipulators, and its basic idea is to use a static and a dynamic curve to constrain the macroshape of the manipulators simultaneously. The static curve is constructed based on a traditional rapidly exploring random tree algorithm, and a backbone curve is utilized as the dynamic curve. For these two curves, two novel shape control methods are proposed to accomplish the shape constraining process. Finally, we verify the reliability and effectiveness of our algorithm through simulations.
Gesture based Home appliance control system for Disabled People

Home appliances can be controlled using gesture recognition for the disabled people. Gestures can be the movements or motions of human body parts. The gesture recognition can be used for many applications such as automation sign language recognition, human interactions and much more. Mostly hand gestures are used for controlling home appliances for better functionality. The proposed system shows that the hand gestures can be used to easily control the electronic home appliances. Here concepts like deep learning, artificial intelligence, automation is used to achieve the target. These techniques are used to control the home appliances via the camera without the usage of sensors. Thus, the result of our project is to help the disabled people to operate their home appliances from a given fixed position with ease via the camera with the help of deep learning algorithms, artificial intelligence without the use of sensors and internet connectivity.
Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning

Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data. Nevertheless, the goal of human-level intelligence is to enable a model to quickly obtain an in-depth understanding given a small number of samples, especially with heterogeneity in the multi-modal scenarios such as visual question answering and image captioning. In this paper, we study the few-shot visual-semantic learning and present the Hierarchical Graph ATtention network (HGAT). This two-stage network models the intra- and inter-modal relationships with limited image-text samples. The main contributions of HGAT can be summarized as follows: 1) it sheds light on tackling few-shot multi-modal learning problems, which focuses primarily, but not exclusively on visual and semantic modalities, through better exploitation of the intra-relationship of each modality and an attention-based co-learning framework between modalities using a hierarchical graph-based architecture; 2) it achieves superior performance on both visual question answering and image captioning in the few-shot setting; 3) it can be easily extended to the semi-supervised setting where image-text samples are partially unlabeled. We show via extensive experiments that HGAT delivers state-of-the-art performance on three widely-used benchmarks of two visual-semantic learning tasks.
Maxima-Based Defuzzification Methods Proposed for Fuzzy Systems

Defuzzification is the last step of fuzzy systems. It converts fuzzy output set into crisp value. This paper illustrates fuzzy inference process and gives review of existing defuzzificaion methods. Two maxima-based methods are proposed which select element from core as output of defuzzificaion. The proposed methods also exhibit property of continuity. These methods are computationally efficient and easy to understand. Results of proposed methods are compared with existing methods such as COG, COA, FOM, LOM, and MeOM. Results of proposed methods are close to most commonly used COG method. The proposed methods may be more suitable for fuzzy systems.
LargeGraph

Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph . Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach , which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).
Mood Based Food Recommendation System

The constantly expanding volume of online information is a strategic approach to managing the excessive amount of data. The importance of recommender systems cannot be emphasized, considering their ubiquitous use in many online applications and their ability to alleviate many problems associated with over-choice. All potential parts of enterprise are the rising usage of technology that demands usage of IT. The hotel and restaurant industry nowadays are one of the most expanding businesses and has greatly contributed to the economy of the nation. Existing restaurant recommendation does not consider user's current point of view or is not personalized. The proposed system is completely personalized for users, this system recommends food and available restaurants based on user's current mood. The dataset from Zomato is taken to locate the restaurants based on location of user. A website is designed where the user must enter their basic personal details for developing their personalized system, and then select their current mood out of the options provided. Based on given inputs the application recommends user with food items and restaurant. Multiple options are provided to the user along with restaurant rating to give better experience. Total 9 restaurants are recommended to a customer out of which top-3 are the best recommendations and 6 are other recommendations. This model is developed using PyCharm, the restaurants are grouped by location using KNN algorithm. Flask is used to create website which is user friendly. This application can be used when a customer doesn't understand what to eat when they are in the moods.