Reviewerly/exHarmony huggingface dataset:
Multimedia ontology population through semantic analysis and hierarchical deep features extraction techniques

Abstract The rapid increase of available data in different complex contexts needs automatic tasks to manage and process contents. Semantic Web technologies represent the silver bullet in the digital Internet ecosystem to allow human and machine cooperation in achieving these goals. Specific technologies as ontologies are standard conceptual representations of this view. It aims to transform data into an interoperability format providing a common vocabulary for a given domain and defining, with different levels of formality, the meaning of informative objects and their possible relationships. In this work, we focus our attention on Ontology Population in the multimedia realm. An automatic and multi-modality framework for images ontology population is proposed and implemented. It allows the enrichment of a multimedia ontology with new informative content. Our multi-modality approach combines textual and visual information through natural language processing techniques, and convolutional neural network used the features extraction task. It is based on a hierarchical methodology using images descriptors and semantic ontology levels. The results evaluation shows the effectiveness of our proposed approach.
Barriers and Driving Factors for Sustainable Development of CO2 Valorisation

Mitigating CO2 emissions has become a top question in international and national arenas, likewise on the city level. To initiate and maintain transformative policies related to climate neutrality, an evident-based multi-sectoral forecasting model needs to be timely and effectively deployed. Decarbonisation solutions should be considered from the economic, environmental, and social perspectives. The resulting complexity constitutes an essential barrier to implementing CO2 valorisation projects. This study aims to analyse barriers and driving factors for the sustainable development of CO2 valorisation options. In order to reach the research goal, a methodological approach based on the combination of strengths, weaknesses, opportunities, and threats analysis, Geographical Information System and Fuzzy Logic Cognitive Analysis method was used. The method has been applied to a case study in Latvia
User Experience Research in the Work Context: Maps, Gaps and Agenda

User experience (UX) research has been criticized for focusing on leisure contexts and overlooking the work contexts. Moreover, researchers have been drawing attention to how UX at work differs from UX in leisure contexts, requiring development of domain specific knowledge. Inspired by these discussions, our motivation is to see the current state of UX at work research. Based on the systematic review of 52 papers, our results revealed that UX at work research is still immature. It is run by dichotomies; the conceptualizations such as the definition and the dimensions of UX at work are ambiguous; the variety of the studied work domains are limited and do not contribute to the understanding of UX at work; and the consideration of contextual factors are arbitrary and coincidental. To advance this important field of research, we indicate the research gaps and suggest a research agenda introducing areas for future research.
Spatiotemporal tubelet feature aggregation and object linking for small object detection in videos

Abstract This paper addresses the problem of exploiting spatiotemporal information to improve small object detection precision in video. We propose a two-stage object detector called FANet based on short-term spatiotemporal feature aggregation and long-term object linking to refine object detections. First, we generate a set of short tubelet proposals. Then, we aggregate RoI pooled deep features throughout the tubelet using a new temporal pooling operator that summarizes the information with a fixed output size independent of the tubelet length. In addition, we define a double head implementation that we feed with spatiotemporal information for spatiotemporal classification and with spatial information for object localization and spatial classification. Finally, a long-term linking method builds long tubes with the previously calculated short tubelets to overcome detection errors. The association strategy addresses the generally low overlap between instances of small objects in consecutive frames by reducing the influence of the overlap in the final linking score. We evaluated our model in three different datasets with small objects, outperforming previous state-of-the-art spatiotemporal object detectors and our spatial baseline.
Automatic Video Summarization for Cricket Match Highlights using Convolutional Neural Network

The technique of condensing a raw video into a more compact form without losing much information is known as video summarizing. This proposed system will summarize the sports match video to generate the highlights automatically. This tool will help the user to generate the video on giving an input of a sports match. Video analysis is the technique used by Video editors to edit, cut and merge the pieces of videos into fully summarized video this requires highly professional tools and professional knowledge of editing the video. Proposed system change the scenario of manual highlight generation to automatic highlight generation in some time and with more accuracy. Manual highlight generation necessitates expert editing abilities and is a time-consuming procedure, necessitating the development of a system capable of automatically generating sporting event highlights. Therefore, to distinguish and clip crucial events in a cricket match, it offer a system that takes into account both event-based and excitement-based attributes. Cues used to capture such events include audio cues, scoreboards, scene understanding, and player celebrations. The accuracy of proposed system to detect four is around 84.7%.
An Overview on Deployment Strategies for Global Quantum Key Distribution Networks

We present a comprehensive literature review and comparative study on the deployment strategies of quantum key distribution (QKD) networks for global coverage. The state-of-the-art deployment strategies, including terrestrial QKD via optical fibers, free-space QKD via ground-based fixed links and ground-to-air dynamic links, and satellite QKD, are reviewed and compared in terms of channel loss, interference, distance limit, connection topology, and deployment cost. Selection criteria and deployment strategies are developed to enable a global coverage of QKD networks from intercontinental, long-haul, metro, to access networks.
Self-Attention based fine-grained cross-media hybrid network

Due to the heterogeneity gap, the data representations of different types of media are inconsistent. It is challenging to measure the fine-grained gap between different media. To this end, we propose a self-attention-based hybrid network to learn the common representations of different media data. Specifically, we first utilize a local self-attention layer to learn the common attention space between different media data. Then we propose a similarity concatenation method to understand the content relationship between features. To further improve the robustness of the model, we also learn a local position encoding to capture the spatial relationships between features. Therefore, our proposed approach can effectively reduce the gap between different feature distributions on cross-media retrieval tasks. Extensive experiments and ablation studies demonstrate that our proposed method achieves state-of-the-art performance. The source code and models are publicly available at: https://github.com/NUST-Machine-Intelligence-Laboratory/SAFGCMHN.
Re‐examination of the 1928 Parral, Mexico earthquake (M6.3) using a new multiplatform graphical vectorization and correction software for legacy seismic data

Abstract Modern analysis of earthquakes depends on digital time series; however, only about 30% of the timespan of recorded seismicity is available in digital format. During the first half of the 20th century, most of the earthquake ground motions in Mexico were recorded by Wiechert mechanical instruments on smoked paper. In this work, we developed and use Tiitba , a new portable multiplatform graphical user interface (GUI) open‐source software coded on python, to vectorize and correct old analogue seismograms. Using this software, we vectorized the 11/1/1928 Parral (M6.3) earthquake seismograms to obtain the constant time‐interval digitized time series for each component and station of the seismogram. Then, we obtained its source focal mechanism using a genetic algorithm methodology. Also, with an auxiliary Tiitba module, we constructed a SEISAN S‐file to relocate the hypocentre of this earthquake. The obtained relocation is about 125 km south of a recently recorded seismic swarm that occurred in 2013, which has a similar focal mechanism to the one that we obtained in this work.
The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection

Credit card fraud is a major problem in today’s financial world. It induces severe damage to financial institutions and individuals. There has been an exponential increase in the losses due to fraud in recent years. Hence, effectively detecting fraudulent behavior is of vital importance for either financial institutions or individuals. Since credit fraud events account for a small proportion of all transaction events in real life, the datasets about credit fraud are usually imbalanced. Some common classifiers, such as decision tree and naïve Bayes, are unable to detect fraud. Furthermore, in some cases, traditional strategies for dealing with an imbalanced problem, such as the synthetic minority oversampling technique (SMOTE), are not effective for the fraud detection datasets. To accurately detect fraud behavior, this study uses anomaly detection on imbalanced data, as well as Isolation Forest (IForest) with kernel principal component analysis. A one-class support vector machine (OCSVM) with AdaBoost is used as two models to detect outliers which significantly improves detection accuracy and efficiency. The model achieved 96% accuracy, 100% precision, 96% recall, and 98% <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="bold-italic">F</mi> <mn mathvariant="bold">1</mn> </math> score, respectively. The proposed model is expected to become a helpful tool for finding credit card fraud detection, and the analysis presented in this study will provides useful insights into credit card fraud detection mechanisms.
Mexican traffic sign detection and classification using deep learning

Automatic detection and classification of traffic signs is challenging to support a driver’s safety and even assist in autonomous driving. This paper aims to propose a methodology for detecting and classifying Mexican traffic signs using deep learning. The methodology consists of the creation of a new Mexican traffic sign data set, the training, testing, and comparing of two sign detectors (the Region-based Convolutional Neural Network (R-CNN) and the You Only Look Once (YOLO v3)), and the use of a modified Residual Neural Network (ResNet-50) for classification. According to the detection results, the combination R-CNN/ResNet-50 yielded a mean Average Precision (mAP) of 95.33%, while the YOLO v3/ResNet-50 yielded 90.33%. The overall classification accuracy was 99.00%. Our results are competitive to those presented in the literature. We demonstrated the robustness of our proposal by conducting a test to classify images that do not contain traffic signs. The accuracy for the R-CNN/ResNet-50 was 99.5% and 99.77% for the YOLO v3/ResNet-50. We also obtained satisfactory classification results with traffic signs occluded and inserted in random positions in the scene. Finally, an ablation study regarding the data set and the batch size was conducted.
The Go programming language and environment

Released as open source in November 2009, Go has become the foundation for critical infrastructure at every major cloud provider. Its creators look back on how Go got here and why it has stuck around.
A Time-Efficient Method to Avoid Collisions for Collision Cones: An Implementation for UAVs Navigating in Dynamic Environments

This paper presents a methodology that can be used to avoid collisions of aerial drones. Even though there are many collision avoidance methods available in literature, collision cone is a proven method that can be used to predict a collision beforehand. In this research, we propose an algorithm to avoid a collision in a time-efficient manner for collision cone based aerial collision avoidance approaches. Furthermore, the paper has considered all possible scenarios including heading change, speed change and combined heading and speed change, to avoid a collision. The heading-based method was mathematically proven to be the most time-efficient method out of the three. The proposed heading-based method was compared with other work presented in the literature and validated with both simulations and experiments. A Matrice 600 Pro hexacopter is used for the collision avoidance experiments.
Text detection and recognition based on a lensless imaging system

Lensless cameras are characterized by several advantages (e.g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras. However, they have not been extensively employed due to their poor image clarity and low image resolution, especially for tasks that have high requirements on image quality and details such as text detection and text recognition. To address the problem, a framework of deep-learning-based pipeline structure was built to recognize text with three steps from raw data captured by employing lensless cameras. This pipeline structure consisted of the lensless imaging model U-Net, the text detection model connectionist text proposal network (CTPN), and the text recognition model convolutional recurrent neural network (CRNN). Compared with the method focusing only on image reconstruction, U-Net in the pipeline was able to supplement the imaging details by enhancing factors related to character categories in the reconstruction process, so the textual information can be more effectively detected and recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed lensless images. By performing experiments on datasets of different complexities, the applicability to text detection and recognition on lensless cameras was verified. This study reasonably demonstrates text detection and recognition tasks in the lensless camera system, and develops a basic method for novel applications.
3D Perception based Imitation Learning under Limited Demonstration for Laparoscope Control in Robotic Surgery

Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking without considering information hidden in surgical scenes are not intelligent enough, while the latest supervised imitation learning (IL)-based methods require expensive sensor data and suffer from distribution mismatch issues caused by limited demonstrations. In this paper, we propose a novel Imitation Learning framework for Laparoscope Control (ILLC) with reinforcement learning (RL), which can efficiently learn the control policy from limited surgical video clips. Specially, we first extract surgical laparoscope trajectories from unlabeled videos as the demonstrations and reconstruct the corresponding surgical scenes. To fully learn from limited motion trajectory demonstrations, we propose Shape Preserving Trajectory Augmentation (SPTA) to augment these data, and build a simulation environment that supports parallel RGB-D rendering to reinforce the RL policy for interacting with the environment efficiently. With adversarial training for IL, we obtain the laparoscope control policy based on the generated rollouts and surgical demonstrations. Extensive experiments are conducted in unseen reconstructed surgical scenes, and our method outperforms the previous IL methods, which proves the feasibility of our unified learning-based framework for laparoscope control.
How to deal with negative preferences in recommender systems: a theoretical framework

Negative information plays an important role in the way we express our preferences and desires. However, it has not received the same attention as positive feedback in recommender systems. Here we show how negative user preferences can be exploited to generate recommendations. We rely on a logical semantics for the recommendation process introduced in a previous paper and this allows us to single out three main conceptual approaches, as well as a set of variations, for dealing with negative user preferences. The formal framework provides a common ground for analysis and comparison. In addition, we show how existing approaches to recommendation correspond to alternatives in our framework.
Enhanced Load Balancing in Kubernetes Cluster By Minikube

Kubernetes is a container orchestration tool for configuring and managing containerized applications in different deployment environments. The orchestration means that defined workflow procedure that leads to running of applications in various isolated environments. This containerizing environment leads to an OS level virtualization and from that we can run multiple services on single host. This Kubernetes helps to scheduling and managing clusters. that act as an ecosystem for managing cluster of containers. This Kubernetes environment have a manual load balancing for providing high availability.so efforts should be taken to manage load balancing of cluster and monitoring of each deployment.
Addressing domain shift in neural machine translation via reinforcement learning

Domain Adaptation (DA) has been a well-known transfer learning algorithm used in Neural Machine Translation (NMT) task. Adding domain-related corpora in training data to train the model boosts the performance of NMT. Many low resource language pairs such as Hindi–Nepali and Spanish–Portuguese, for which sufficient parallel data are not available to train the model, face in-domain data scarcity issue. Non-availability of in-domain training data leads to the use of unrelated corpus in training the model, which degrades the NMT performance. This domain mismatch between training and test data results in a domain shift problem. To tackle the challenge of the domain shift problem in NMT, we propose REINFORCE-based Sentence Selection and Weighting (RSSW) method, which selects pseudo in-domain sentences from out-of-domain data and learns their weights based on Reinforcement Learning. The proposed method leverages the similarity between language pairs by encoding the source and target languages into a common encoding script for language model training. RSSW uses minimum risk training and maximum likelihood estimation as an objective function to train NMT on selected pseudo in-domain sentences. Furthermore, we employ multi-domain and fine-tuning approaches to compare proposed method on Hindi↔Nepali and Hindi↔Marathi language pairs through extensive experimental analysis. From the experimental results, we find that the proposed method outperforms the state-of-the-art and baseline approaches by ∼2 BLEU points in all translation directions of language pairs.
Event Transition Planning for Open-ended Text Generation

Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context.The openended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays.Despite these neural models are good at producing human-like text, it is difficult for them to arrange causalities and relations between given facts and possible ensuing events.To bridge this gap, we propose a novel two-stage method which explicitly arranges the ensuing events in open-ended text generation.Our approach can be understood as a specially-trained coarse-to-fine algorithm, where an event transition planner provides a "coarse" plot skeleton and a text generator in the second stage refines the skeleton.Experiments on two open-ended text generation tasks demonstrate that our proposed method effectively improves the quality of the generated text, especially in coherence and diversity.The code is available at: https://github.com/
Understanding AR Activism: An Interview Study with Creators of Augmented Reality Experiences for Social Change

The rise of consumer augmented reality (AR) technology has opened up new possibilities for interventions intended to disrupt and subvert cultural conventions. From defacing corporate logos to erecting geofenced digital monuments, more and more people are creating AR experiences for social causes. We sought to understand this new form of activism, including why people use AR for these purposes, opportunities and challenges in using it, and how well it can support activist goals. We conducted semi-structured interviews with twenty people involved in projects that used AR for a social cause across six different countries. We found that AR can overcome physical world limitations of activism to convey immersive, multilayered narratives that aim to reveal invisible histories and perspectives. At the same time, people experienced challenges in creating, maintaining, and distributing their AR experiences to audiences. We discuss open questions and opportunities for creating AR tools and experiences for social change.
Relation Discovery in Nonlinearly Related Large-Scale Settings

Causal inquiries provide crucial insight into the advancement of scientific discoveries. In real-world studies like climatology, sensory data acquired from nodal measurements are nonlinearly related and complex. At the same time, they have information from millions of sensors with only a few decades’ temporal samples, which leads to the curse of dimensionality in large-scale systems. Despite a rich literature on causal discovery, the problem is challenging for largescale datasets. We put forth a novel method that utilizes a radial basis function (RBF) to tackle curse-of-dimensionality in complex systems. The proposed method is probabilistic, encompasses nonlinear relations, and is suitable for large-scale data in two steps. Extensive simulations on synthetic data of different sizes and real-world climatology data show that our method outperforms all other methods when nodal observations are temporally scarce.
Connecting Targets via Latent Topics And Contrastive Learning: A Unified Framework For Robust Zero-Shot and Few-Shot Stance Detection

Zero-shot and few-shot stance detection (ZFSD) aims to automatically identify the users’ stance toward a wide range of continuously emerging targets without or with limited labeled data. Previous works on in-target and cross-target stance detection typically focus on extremely limited targets, which is not applicable to the zero-shot and few-shot scenarios. Additionally, existing ZFSD models are not good at modeling the relationship between seen and unseen targets. In this paper, we propose a unified end-to-end framework with a discrete latent topic variable that implicitly establishes the connections between targets. Moreover, we apply supervised contrastive learning to enhance the generalization ability of the model. Comprehensive experiments on the ZFSD task verify the effectiveness and superiority of our proposed method.