Reviewerly/exHarmony huggingface dataset:
Co-segmentation inspired attention module for video-based computer vision tasks

Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing pre-trained models to perform object detection, object segmentation and/or object pose estimation. Although using pre-trained models is a viable approach, it has several limitations in the need for an exhaustive annotation of object categories, a possible domain gap between datasets and a bias that is typically present in pre-trained models. In this work, we propose to utilize the common rationale that a sequence of video frames capture a set of common objects and interactions between them, thus a notion of co-segmentation between the video frame features may equip the model with the ability to automatically focus on task-specific salient regions and improve the underlying task’s performance in an end-to-end manner. In this regard, we propose a generic module called “Co-Segmentation inspired Attention Module” (COSAM) that can be plugged in to any CNN model to promote the notion of co-segmentation based attention among a sequence of video frame features. We show the application of COSAM in three video-based tasks namely: (1) Video-based person re-ID, (2) Video captioning, & (3) Video action classification and demonstrate that COSAM is able to capture the task-specific salient regions in video frames, thus leading to notable performance improvements along with interpretable attention maps for a variety of video-based vision tasks, with possible application to other video-based vision tasks as well.
LEAPME: Learning-based Property Matching with Embeddings

Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings. Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME. We also show that our approach is even effective when training data from another domain (transfer learning) is used.
Kinematic estimation of fracture mechanics parameter with automatic crack-tip identification

Digital Image Correlation (DIC) is probably one of the most used optical methods for displacements and strains measurement and has been used both in its standard form and in its integrated variant to extract the fracture mechanics parameters from a set of images of the crack. However, differently from photoelasticity, where stress data is directly available because the imaged fringes are proportional to σ1−σ2, in the DIC case the fracture mechanics parameters result from a reverse calibration procedure involving the least-squares fit of a theoretical displacement model to the experimental data. Thus, the accuracy of the computed parameters critically depends on the exact identification of the crack location and orientation. This problem is well known, and a partial solution has been provided by Réthoré et al. (2008), who proposed an approach for computing the distance from the estimated and the true location of the crack tip in the longitudinal direction using the first hypersingular functions (i.e., the 1st negative term of William’s series). However, no hint is provided to estimate the crack orientation. This work wants to fill the gap, describing an automatic algorithm for the complete identification of the crack tip location.
Visualizing Data in Software Cities

The city metaphor for visualizing software systems in 3D has been widely explored and it has led to many diverse implementations and approaches. However, when looking at software systems in general, and when using specifically a city approach, it is evident that something is missing: The data. Indeed, software systems are intrinsically driven by data, which is usually managed using databases or often also simply stored in files coming in a variety of formats, such as CSV, XML, and JSON. While such data files are part of a project’s file system and can thus be easily retrieved, the situation is different for databases: A database is usually not contained in the file system, and its presence can only be inferred from the source code which contains the database accesses.We present an extension of the CodeCity implementation, M3TRICITY2, with two new contributions: First, we consider data files and use simple metrics to integrate them in the visualization seamlessly. Second, we present a novel way to add a database to the visualization by making use of the one remaining space left unused: the sky and the underground. We present our contributions and illustrate them on various software systems.
PCTBagging: From inner ensembles to ensembles. A trade-off between discriminating capacity and interpretability

The use of decision trees considerably improves the discriminating capacity of ensemble classifiers. However, this process results in the classifiers no longer being interpretable, although comprehensibility is a desired trait of decision trees. Consolidation (consolidated tree construction algorithm, CTC) was introduced to improve the discriminating capacity of decision trees, whereby a set of samples is used to build the consolidated tree without sacrificing transparency. In this work, PCTBagging is presented as a hybrid approach between bagging and a consolidated tree such that part of the comprehensibility of the consolidated tree is maintained while also improving the discriminating capacity. The consolidated tree is first developed up to a certain point and then typical bagging is performed for each sample. The part of the consolidated tree to be initially developed is configured by setting a consolidation percentage. In this work, 11 different consolidation percentages are considered for PCTBagging to effectively analyse the trade-off between comprehensibility and discriminating capacity. The results of PCTBagging are compared to those of bagging, CTC and C4.5, which serves as the base for all other algorithms. PCTBagging, with a low consolidation percentage, achieves a discriminating capacity similar to that of bagging while maintaining part of the interpretable structure of the consolidated tree. PCTBagging with a consolidation percentage of 100% offers the same comprehensibility as CTC, but achieves a significantly greater discriminating capacity.
Accelerating Distributed Deep Learning using Multi-Path RDMA in Data Center Networks

Data center networks (DCNs) have widely deployed RDMA to support data-intensive applications such as machine learning. While DCNs are designed with rich multi-path topology, current RDMA (hardware) technology does not support multi-path transport. In this paper we advance Maestro- a purely software-basedmulti-path RDMA solution - to effectively utilize the rich multi-path topology for load balancing and reliability. As a "middleware" operating at the user-space, Maestro is [email protected] software-defined:Maestro decouples path selection and load balancing mechanisms from hardware features, and allows DCN operators and applications to make flexible decisions by employing the best mechanisms as needed. As such, Maestro can be readily deployed using existing RDMA hardware (NICs) to support distributed deep learning (DDL) applications. Our experiments show that Maestro is capable of fully utilizing multiple paths with negligible CPU overheads, thereby enhancing the performance of DDL applications.
Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.
Knowledge &amp; Learning-based Adaptable System for Sensitive Information Identification and Handling

Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can lead to data leaks. To handle this problem we present Knowledge and Learning-based Adaptable System for Sensitive InFormation Identification and Handling (KLASSIFI) which is an end to end system capable of identifying and redacting sensitive information present in diagnostic data. KLASSIFI is highly customizable, allowing it to be used for various different business use cases by simply changing the configuration. KLASSIFI ensures that the output file is useful by retaining the metadata which is used by various debugging tools. Various optimizations have been done to improve the performance of KLASSIFI. Empirical evaluation of KLASSIFI shows that it is able to process large files (128 GB) in 84 minutes and its performance scales linearly with varying factors. This points to practicability of KLASSIFI.
Uraninite chemistry of the Central Mineral Belt, Labrador, Canada: Application of grain-scale unsupervised machine-learning

The Central Mineral Belt (CMB) in Labrador hosts several enigmatic U ± base ± precious metal showings, prospects, and deposits. Multiple mineralization styles occur within different host rocks, which has led to a variety of ore system models to be proposed. Here, unsupervised machine-learning (principal component (PCA) and targeted cluster analyses) applied to quantitative LA-ICP-MS trace element maps of uraninite were used to understand genesis the U systems in the CMB. Trace element mapping, PCA and cluster analysis indicate that: i) the largest source of data variance in uraninite from the occurrences is related to Th, REE, Zr, Hf, As, V and Ba contents, and ii) there are distinct uraninite compositions at the Two-Time deposit, Near Miss and Anomaly No. 7 mineral occurrences, not recognized from petrographic studies or major element chemistry. Trace element chemistry indicates that uraninite precipitated from fluids under (1) high-temperature magmatic and/or metasomatic conditions (> 350 °C; Dandy prospect; U/Th = 107 and ΣREE = 0.9 wt%), (2) low-temperature (< 350 °C) and locally oxidizing hydrothermal vein-type environments (e.g., Two-Time and Anomaly No. 7; U/Th ≥104 and ΣREE ≤0.1 to 3.6 wt%), and (3) complex environments were precursor uraninite was overprinted by presumably lower temperature hydrothermal fluids (e.g., Jacques Lake and Nash deposits; U/Th = 102 to 105 and ΣREE ≤0.1 to 1.4 wt%). Hydrothermal alteration caused LREE enrichment and/or increased U/Th ratios of the primary uraninite and locally, U remobilization into microfractures. Recognizing high- and low-temperature uraninite from the same mineral occurrences provides new evidence for multiple stages of hydrothermal fluid influx. Therefore, our results further support previous studies indicating significant variation in the trace element contents of uraninite due to hydrothermal alteration. In addition to the genetic constraints, PCA results and normalized REE patterns also show that the CMB uraninites have distinct geochemical signatures. Combining petrographic studies, trace element mapping, and unsupervised machine-learning unravelled cryptic chemical variations in key minerals that can guide future mineral exploration in the district. In the CMB, these chemical signatures provide insights on the evolution of U-rich fluids, in particular the presence of multiple fluid sources that evolved through a complex tectono-magmatic history.
On MILP-Based Automatic Search for Bit-Based Division Property for Ciphers with (Large) Linear Layers

With the introduction of the division trail, the bit-based division property (BDP) has become the most efficient method to search for integral distinguishers. The notation of the division trail allows us to automate the search process by modelling the propagation of the DBP as a set of constraints that can be solved using generic Mixed-integer linear programming (MILP) and SMT/SAT solvers. The current models for the basic operations and Sboxes are efficient and accurate. In contrast, the two approaches to model the propagation of the BDP for the non-bit-permutation linear layer are either inaccurate or inefficient. The first approach relies on decomposing the matrix multiplication of the linear layer into COPY and XOR operations. The model obtained by this approach is efficient, in terms of the number of the constraints, but it is not accurate and might add invalid division trails to the search space, which might lead to missing the balanced property of some bits. The second approach employs a one-to-one map between the valid division trails through the primitive matrix represented the linear layer and its invertible sub-matrices. Despite the fact that the current model obtained by this approach is accurate, it is inefficient, i.e., it produces a large number of constraints for large linear layers like the one of Kuznyechik. In this paper, we address this problem by utilizing the one-to-one map to propose a new MILP model and a search procedure for large non-bit-permutation layers. As a proof of the effectiveness of our approach, we improve the previous 3- and 4-round integral distinguishers of Kuznyechik and the 4-round one of PHOTON's internal permutation (\(P_{288}\)). We also report, for the fist time, a 4-round integral distinguisher for Kalyna block cipher and a 5-round integral distinguisher for PHOTON's internal permutation (\(P_{288}\)).
On the (In)Security of ElGamal in OpenPGP

Roughly four decades ago, Taher ElGamal put forward what is today one of the most widely known and best understood public key encryption schemes. ElGamal encryption has been used in many different contexts, chiefly among them by the OpenPGP standard. Despite its simplicity, or perhaps because of it, in reality there is a large degree of ambiguity on several key aspects of the cipher. Each library in the OpenPGP ecosystem seems to have implemented a slightly different "flavour" of ElGamal encryption. While --taken in isolation-- each implementation may be secure, we reveal that in the interoperable world of OpenPGP, unforeseen cross-configuration attacks become possible. Concretely, we propose different such attacks and show their practical efficacy by recovering plaintexts and even secret keys.
Heterogeneous Attention Nested U-Shaped Network for Blur Detection

With the popularity of image sensors in various mobile devices, image blurring caused by hand shaking or out of focus becomes ubiquitous, which deteriorates image quality and poses challenges for vision tasks, including object detection, image classification and image segmentation. Designing an efficient blur detection algorithm which can automatically detect and locate blurred regions becomes necessary. In this letter, we design an end-to-end convolution neural network called heterogeneous attention nested U-shaped network (HANUN) for blur detection. We introduce pyramid pooling into encoders to enhance the feature extraction at different scales and reduce the gradual information loss. Inspired by the nested network design, small U-shaped networks are embedded into our decoders to increase the network depth and promote feature fusion with different receptive field scales. In addition, we incorporate a channel attention mechanism in the proposed network to highlight the informative features for detecting the blurry regions. Experimental results show that HANUN outperforms other state-of-the-art algorithms for blur detection tasks on public datasets and real-world images.
Robust Video Watermarking in Frequency Domain for Copyright Protection

The problem of illegal video piracy can be overcome by the video watermarking method. Therefore, this paper presents a robust video watermarking technique for copyright protection based on redundant discrete wavelet transform (RDWT) and Schur transform. A grayscale watermark image is concealed into the 1-level LL sub-band of the video frame’s luminance channel (Y). The matrices, namely peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and normalized cross-correlation (NC) are used to assess the imperceptibility and robustness. The experimental results validate the performance of the proposed method.
Enhanced Movie Recommendation using Knowledge Graph and Particle Filtering

In today’s very busy world, recommendation systems are becoming increasingly crucial. In a 24-hour period, people are always pressed for time because of the many activities they must do. Deshalb, the Movie recommendation systems are vital, as they help cinephiles find the finest movies to watch without having to waste their cognitive resources which are limited. This project aims to propose a movie recommendation system using the concept of knowledge graph and particle filtering. While every existing movie recommendation system depends on machine learning and clustering algorithms. This research work proposes a novel movie recommender system that operates directly on the database and is extremely efficient by utilizing the expressive potential of Knowledge Graphs and provides the user with the best recommendations. Using particle filtering and a knowledge graph database, this movie recommendation system provides the user with the best suitable movies depending on numerous parameters such as the user’s favourite Directors, genre, etc. Unlike other systems, this recommendation system does not keep track of the browser history for future recommendations.
Weakly Supervised Instance Segmentation by Exploring Entire Object Regions

Weakly supervised instance segmentation with image-level class supervision is a challenging task as it associates the highest-level instances to the lowest-level appearance. Previous approaches for the task utilize classification networks to obtain rough discriminative parts as seed regions and use distance as a metric to cluster pixels of the same instances. Unlike previous approaches, we provide a novel self-supervised joint learning framework as the basic network and consider the clustering problem as calculating the probability that pixels belong to each instance. To this end, we propose our self-supervised joint learning two-stream network (SJLT Net) to finish this task. In the first stream, we leverage a joint learning framework to implement image-level supervised semantic segmentation with self-supervised saliency detection. In the second stream, we propose a Center Detection Network to detect different instances’ centers with the gaussian loss function to cluster instances pixels. Besides, an integration module is utilized to combine information of both streams and get precise pseudo instances labels. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. Our model achieves excellent performance on the PASCAL VOC 2012 dataset, surpassing the best baseline trained with the same labels by 4.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP^r_{50}$</tex-math></inline-formula> on the train set and 2.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP^r_{50}$</tex-math></inline-formula> on the validation set.
A Multi-Goal Global Dynamic Path Planning Method for Indoor Mobile Robot

For the requirements of indoor mobile robot planning global optimal path, traversing multi goals and dynamic obstacle avoidance, a multi-goal global dynamic path planning method is proposed in this paper, which is combined improved A * algorithm, traveling salesman problem (TSP), and dynamic window approach (DWA). The improved A* is used to plan the optimal collision-free path between two goals, and a key node selection scheme is used to remove the redundant nodes. And the TSP distance search square matrix is formed by using the optimal path length between the two goals, and the optimal order of traversing all goals is determined by ant colony algorithm. Then DWA evaluation function is constructed, which is considered with the global optimal path and speed, and real-time obstacle avoidance. On the basis of ensuring the global optimality of the planned path, all goals are traversed at the optimal speed, and the path is smoother, the ability of dynamic obstacle avoidance is improved. The simulation results demonstrate that compared with traditional methods, the proposed method is more efficient, smoother path, real-time dynamic obstacle avoidance, which has applications in autonomous navigation of multi targets mobile robots.