Reviewerly/exHarmony huggingface dataset:
An Updated Experimental Evaluation of Graph Bipartization Methods

We experimentally evaluate the practical state-of-the-art in graph bipartization (Odd Cycle Transversal (OCT)), motivated by the need for good algorithms for embedding problems into near-term quantum computing hardware. We assemble a preprocessing suite of fast input reduction routines from the OCT and Vertex Cover (VC) literature and compare algorithm implementations using Quadratic Unconstrained Binary Optimization problems from the quantum literature. We also generate a corpus of frustrated cluster loop graphs, which have previously been used to benchmark quantum annealing hardware. The diversity of these graphs leads to harder OCT instances than in existing benchmarks. In addition to combinatorial branching algorithms for solving OCT directly, we study various reformulations into other NP-hard problems such as VC and Integer Linear Programming (ILP), enabling the use of solvers such as CPLEX. We find that for heuristic solutions with time constraints under a second, iterative compression routines jump-started with a heuristic solution perform best, after which point using a highly tuned solver like CPLEX is worthwhile. Results on exact solvers are split between using ILP formulations on CPLEX and solving VC formulations with a branch-and-reduce solver. We extend our results with a large corpus of synthetic graphs, establishing robustness and potential to generalize to other domain data. In total, over 8,000 graph instances are evaluated, compared to the previous canonical corpus of 100 graphs. Finally, we provide all code and data in an open source suite, including a Python API for accessing reduction routines and branching algorithms, along with scripts for fully replicating our results.
Improving Collaborative Filtering

In this paper, we experiment with a combination of metrics to calculate the similarity when creating collaborative filtering. The Otai Coefficient and Euclidean Distance are used, resulting in a recommender system that produces a satisfactory result.
Hierarchical Multi-Label Ship Recognition in Remote Sensing Images Using Label Relation Graphs

Hierarchical multi-label classification (HMC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. In the application of ship recognition in remote sensing images, a ship can own coarse-to-fine hierarchical labels, e.g., the military ship, aircraft carrier, and nimitz class aircraft carrier. In this paper, we propose to combine two forms of loss functions to solve the HMC problem based on the neural network. The first probabilistic classification loss is to encode the hierarchical knowledge by introducing hierarchy and exclusion (HEX) graphs to impose constraints on hierarchical labels. The second cross-entropy loss imposes the softmax normalization on leaf nodes in the hierarchy to discriminate fine-grained classes. We evaluate our method on the high resolution satellite image dataset for ship recognition (HRSC), in which hierarchical labels are organized as the three-level tree. The proposed method shows comparative results compared to state-of-art HMC models.
Cryptography: Recent research trends of encrypting mathematics

In today's world where technology and mathematics are progressing hand in hand there are so many things to be considered and thought of when it comes to network security. Cryptography plays a prominent and an important role when it comes to securing the system from unwanted hazardous attacks. As the Internet has made great strides in different domains it has impacted the human life in several ways and hence data security has become the question of the hour. In such a scenario cryptography plays a big role as it ensures that the data transmitted is received as it is without any alteration or modification. However, we have discussed the algorithms and various methods required in order to maintain the security. In the subsequent sections we shall be discussing about theoretical computer science, cryptography attacks, algorithms and its application in everyday life.
Handling Difficult Labels for Multi-label Image Classification via Uncertainty Distillation

Multi-label image classification aims to predict multiple labels for a single image. However, the difficulties of predicting different labels may vary dramatically due to semantic variations of the label as well as the image context. Direct learning of multi-label classification models has the risk of being biased and overfitting those difficult labels, e.g., deep network based classifiers are over-trained on the difficult labels, therefore, lead to false-positive errors of those difficult labels during testing. To handle difficult labels of multi-label image classification, we propose to calibrate the model, which not only predicts the labels but also estimates the uncertainty of the prediction. With the new calibration branch of the network, the classification model is trained with the pick-all-labels normalized loss and optimized pertaining to the number of positive labels. Moreover, to improve performance on difficult labels, instead of annotating them, we leverage the calibrated model as the teacher network and teach the student network about handling difficult labels via uncertainty distillation. Our proposed uncertainty distillation teaches the student network which labels are highly uncertain through prediction distribution distillation, and locates the image regions that cause such uncertain predictions through uncertainty attention distillation. Conducting extensive evaluations on benchmark datasets, we demonstrate that our proposed uncertainty distillation is valuable to handle difficult labels of multi-label image classification.
Achieving Hard Real-Time Capability for 3D Human Pose Estimation Systems

In the industrial domain, the application of any system as a safety function has to follow strict rules and requirements, defined in safety standards such as ISO 13849 [1] and ISO 13855 [2]. Two core requirements are an extremely low rate of dangerous errors and an upper limit for the response time of the system (hard real-time requirement). Current approaches in the field of human pose estimation achieve neither of both.In our work we approach the second requirement by introducing a general procedure to achieve hard real-time capability for interchangeable 3D human pose estimation systems. We use the detections of the pose estimation to model the human as 3D volume consisting of spheres and spherical cones. To bridge the time between arriving detections, the volume is adjusted in a way that ensures coherence, continuity and, most important, conformance with necessary surcharges from safety standard ISO 13855 [2]. Low and fixed computational cost for the adaption makes the procedure real-time capable. Our modelling approach using spheres and spherical cones also allows distance calculations at low and fixed computational costs, e.g. between human and robot. We show the benefit of our approach via human-robot distance calculation experiments, outperforming a safety-certified laser scanner for most of the time.
Self-adaptive mobile web service discovery framework for Dynamic Mobile Environment

This paper proposes a self-adaptive mobile web service (MWS) discovery framework for a dynamic mobile environment (DME) to deal with MWS proliferation, dynamic context, and irrelevant MWS discovery challenges. The main contribution of this research includes an improvement of the matchmaking algorithm, enhanced MWS categorization approach, and extensible meta-context ontology that represents the context information in DME. This was achieved by enabling the self-adaptive matchmaker to learn MWS relevance using a Modified-Negative Selection Algorithm (M-NSA) and retrieve the most relevant MWS based on the current context of the discovery. To assess the proposed framework, series of experiments was carried out using publicly-available datasets. The performance of the framework is evaluated against the state-of-the-art frameworks. It was found that the proposed framework is more effective and attained better binary and graded relevance when subjected to context variations which are prevalent in DME. This is useful for service-based application designers and other MWS clients.
Method for the Microstructural Characterisation of Unidirectional Composite Tapes

The outstanding properties of carbon fibre-reinforced polymer composites are affected by the development of its microstructure during processing. This work presents a novel approach to identify microstructural features both along the tape thickness and through the thickness. Voronoi tessellation-based evaluation of the fibre volume content on cross-sectional micrographs, with consideration of the matrix boundary, is performed. The method is shown to be robust and is suitable to be automated. It has the potential to discriminate specific microstructural features and to relate them to processing behaviour removing the need for manufacturing trials.
A Brief Survey on Concept Drifted Data Stream Regression

With the recent advancements in the internet, mobile, sensor, and IoT technologies, the amount of high speed and unbounded data generated is enormous. This sequential and continuous data is known as a data stream. Mining such data streams have recently emerged as an active research area to discover knowledge. One of the challenges in data stream analysis is the unpredictable data distribution change caused due to changes in the hidden context, known as concept drift. Due to this evolving nature and drift, the prediction model based on conventional machine learning algorithms designed on the premise of independent and identically distribution tends to fail or gives poor performance. Due to the wide range of applicability in the real world, the data stream regression task of finding the relationship between a set of input variables and a real-valued output variable has become an emerging interest in multidisciplinary domains. Many of the research efforts focus on data stream classification and clustering. However, regression problems have not received much attention and effort due to their updating model complexity in drifting scenarios. This paper presents a brief survey of recent research related to regression problems for drifted data streams. Finally, we also provide a set of existing synthetic and real data sets to start, evaluate, and compare the different approaches.
Exploring Pathologist Knowledge for Automatic Assessment of Breast Cancer Metastases in Whole-slide Image

Automatic assessment of breast cancer metastases plays an important role to help pathologist reduce the time-consuming work in histopathological whole-slide image diagnosis. From the utilization of knowledge point of view, the low-magnification level and high-magnification level are carefully checked by the pathologists for tumor pattern and cell tumor characteristic. In this paper, we propose a novel automatic patient-level tumor segmentation and classification method, which makes full use of the diagnosis knowledge clues from pathologists. For tumor segmentation, a multi-level view DeepLabV3+ (MLV-DeepLabV3+) is designed to explore the distinguishing features of cell characteristics between tumor and normal tissue. Furthermore, the expert segmentation models are selected and integrated by Pareto-front optimization to imitate the expert consultation to get perfect diagnosis. For wholeslide classification, multi-level magnifications are adaptive checked to focus on the effective features in different magnification. The experimental results demonstrate that our pathologist knowledge-based automatic assessment of whileslide image is effective and robust on the public benchmark dataset.
AM-RRT*: Informed Sampling-based Planning with Assisting Metric

In this paper, we present a new algorithm that extends RRT* and RT-RRT* for online path planning in complex, dynamic environments. Sampling-based approaches often perform poorly in environments with narrow passages, a feature common to many indoor applications of mobile robots as well as computer games. Our method extends RRT-based sampling methods to enable the use of an assisting distance metric to improve performance in environments with obstacles. This assisting metric, which can be any metric that has better properties than the Euclidean metric when line of sight is blocked, is used in combination with the standard Euclidean metric in such a way that the algorithm can reap benefits from the assisting metric while maintaining the desirable properties of previous RRT variants - namely probabilistic completeness in tree coverage and asymptotic optimality in path length. We also introduce a new method of targeted rewiring, aimed at shortening search times and path lengths in tasks where the goal shifts repeatedly. We demonstrate that our method offers considerable improvements over existing multi-query planners such as RT-RRT* when using diffusion distance as an assisting metric: finding near-optimal paths with a decrease in search time of several orders of magnitude. Experimental results demonstrate our method offers a reduction of 99.5% in planning times and 9.8% in path lengths over RT-RRT* in a variety of environments.
A fuzzy content recommendation system using similarity analysis, content ranking and clustering

Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm (TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy.
Improving More Instance Segmentation and Better Object Detection in Remote Sensing Imagery Based on Cascade Mask R-CNN

In this paper, our approach is high-quality instance segmentation contains object detection in Remote Sensing imagery. In instance segmentation cross-entropy used as a loss function and intersection-over-union (IoU) used as a network performance measurement metric while in object detection, intersection over union (IoU) is often used to describe pos-itive/negative thresholds. Using IoU as a loss function can solve the problem between the loss function and the metric of the evaluation. We proposed a max-batch soft IoU training approach that eliminates the fixed IoU loss. randomness of the initial max-batch gradient descent (GD) technique. It resolves the IoU loss function's instability. However, our proposed method Cascade Mask R-CNN with max-batch soft IoU produces better results on the NWPU VHR-10 dataset for object detection and instance segmentation.
Iterative Image Translation for Unsupervised Domain Adaptation

In this paper, we propose an image-translation-based unsupervised domain adaptation approach that iteratively trains an image translation and a classification network using each other. In Phase A, a classification network is used to guide the image translation to preserve the content and generate images. In Phase B, the generated images are used to train the classification network. With each step, the classification network and generator improve each other to learn the target domain representation. Detailed analysis and the experiments are testimony of the strength of our approach.
Event-triggered smoothing for hidden Markov models: Risk-sensitive and MMSE results

An event-triggered smoothing problem for hidden Markov models (HMMs) is investigated in this paper. The transmission of the measurements is jointly determined by a stochastic event-triggering condition and a Gilbert–Elliott communication channel. Firstly, the event-triggered risk-sensitive smoothed estimate is characterized by constructing an augmented processing of the smoothed information state, which is given by the product of the forward recursive information state and the backward recursive information state under a reference measure. Secondly, the risk-neutral smoothed estimate (namely, the MMSE smoother) is proved to be a special case of the obtained risk-sensitive one when the risk-sensitive parameter approaches zero. The implementation issues of the obtained results are discussed by introducing an alternative smoothing algorithm that is numerically equivalent to the original algorithm. The effectiveness of the proposed results is evaluated through a numerical example and comparative simulations with a naive risk-sensitive smoother that treats unreceived information as packet dropout.
No-Reference Video Quality Assessment with Heterogeneous Knowledge Ensemble

Blind assessment of video quality is still challenging even in this deep learning era. The limited number of samples in existing databases is insufficient to learn a good feature extractor for video quality assessment (VQA), while manually labeling a larger database with subjective perception is very labor-intensive and time-consuming. To relieve such difficulty, we first collect 3589 high-quality video clips as the reference and build a large VQA dataset. The dataset contains more than 300K samples degraded by various distortion types due to compression and transmission error, and provides weak labels for each distorted sample with several full-reference VQA algorithms. To learn effective representation from the weakly labeled data, we alleviate the bias of single weak label (i.e., single knowledge) via learning from multiple heterogeneous knowledge. To this end, we propose a novel no-reference VQA (NR-VQA) method with HEterogeneous Knowledge Ensemble (HEKE). Comparing to learning from single knowledge, HEKE can theoretically reach a lower infimum, and learn richer representation due to the heterogeneity. Extensive experimental results show that the proposed HEKE outperforms existing NR-VQA methods, and achieves the state-of-the-art performance. The source code will be available at https://github.com/Sissuire/BVQA-HEKE.
A Model Describing the Required Digital and Green Competences of VET Educators for Practical Use

The study focuses on the development of a new combined model of digital and green competences for vocational training educators with the aim of its subsequent integration into a digital platform. The model meets the requirements of a green economy and is built in accordance with the DigCompEdu standard, considering specific digital competences determined by the requirements of the industry and relevant aspects of the e-CF standard.
Environmental Hotspot Identification in Limited Time with a UAV Equipped with a Downward-Facing Camera

Our work is motivated by environmental monitoring tasks, where finding the global maxima (i.e., hotspot) of a spatially varying field is crucial. We investigate the problem of identifying the hotspot for fields that can be sensed using an Unmanned Aerial Vehicle (UAV) equipped with a downward-facing camera. The UAV has a limited time budget which it can use for learning the unknown field and identifying the hotspot. Our contribution is to show how this problem can be formulated as a novel multi-fidelity variant of the Gaussian Process (GP) multi-armed bandit problem. The novelty is two-fold: (i) unlike standard multi-armed bandit settings, the rewards of the arms are correlated with each other; and (ii) unlike standard GP regression, the measurements in our problem are images (i.e., vector measurements) whose quality depends on the altitude of the UAV. We present a strategy for finding the sequence of UAV sensing locations and empirically compare it with several baselines. Experimental results using images gathered onboard a UAV are also presented and the scalability of the proposed methodology is assessed in a large-scale simulated environment.
CARTMAN: Complex Activity Recognition Using Topic Models for Feature Generation from Wearable Sensor Data

The recognition of complex activities such as "having dinner" or "cooking" from wearable sensor data is an important problem in various healthcare, security and context-aware mobile and ubiquitous computing applications. In contrast to simple activities such as walking that involve single, indivisible repeated actions, recognizing complex activities such as "having dinner" is a harder sub-problem that may be composed of multiple interleaved or concurrent simple activities with different orderings each time. Most of prior work has focused on recognizing simple activities, used hand-crafted features, or did not perform classification using a state-of-the-art neural networks model. In this paper, we propose CARTMAN, a complex activity recognition method that uses Latent Dirichlet allocation (LDA) topic models to generate smartphone sensor features that capture the latent representation of complex activities. These LDA features are then classified using a DeepConvLSTM neural network with self-attention. DeepConvLSTM auto-learns the spatio-temporal features from the sensor data while the self-attention layer identifies and focuses on the predictive points within the time-series sensor data. Our CARTMAN approach outperforms the current state-of-the-art complex activity models and baseline models by 6-23% in macro and weighted F1-scores.
Project-Based Learning Approach Applied in the Course “Digital Electronics” for Studying the Topic “Binary Multipliers”

research-article Share on Project-Based Learning Approach Applied in the Course “Digital Electronics” for Studying the Topic “Binary Multipliers” Authors: Adriana Borodzhieva University of Ruse, Bulgaria University of Ruse, BulgariaView Profile , Ivanka Tsvetkova University of Ruse, Bulgaria University of Ruse, BulgariaView Profile , Snezhinka Zaharieva University of Ruse, Bulgaria University of Ruse, BulgariaView Profile , Dimitar Dimitrov ࿽࿽࿽Georgi Benkovski࿽࿽࿽ Air Force Academy, Bulgaria ࿽࿽࿽Georgi Benkovski࿽࿽࿽ Air Force Academy, BulgariaView Profile , Valentin Mutkov University of Ruse, Bulgaria University of Ruse, BulgariaView Profile Authors Info & Claims CompSysTech '21: International Conference on Computer Systems and Technologies '21June 2021 Pages 212–217https://doi.org/10.1145/3472410.3472446Online:07 October 2021Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access