Reviewerly/exHarmony huggingface dataset:
Generating Anchor Boxes Based on Attention Mechanism for Object Detection in Remote Sensing Images

Nowadays, object detection methods based on deep learning are applied more and more to the interpretation of optical remote sensing images. However, the complex background and the wide range of object sizes in remote sensing images increase the difficulty of object detection. In this paper, we improve the detection performance by combining the attention information, and generate adaptive anchor boxes based on the attention map. Specifically, the attention mechanism is introduced into the proposed method to enhance the features of the object regions while reducing the influence of the background. The generated attention map is then used to obtain diverse and adaptable anchor boxes using the guided anchoring method. The generated anchor boxes can match better with the scene and the objects, compared with the traditional proposal boxes. Finally, the modulated feature adaptation module is applied to transform the feature maps to adapt to the diverse anchor boxes. Comprehensive evaluations on the DIOR dataset demonstrate the superiority of the proposed method over the state-of-the-art methods, such as RetinaNet, FCOS and CornerNet. The mean average precision of the proposed method is 4.5% higher than the feature pyramid network. In addition, the ablation experiments are also implemented to further analyze the respective influence of different blocks on the performance improvement.
Minimizing Embedding Impact for H.264 Steganography by Progressive Trellis Coding

This paper proposes a novel coding strategy to achieve distortion minimization for H.264 steganography with quantized discrete cosine transform (QDCT) coefficients. Currently, with the help of syndrome-trellis codes (STCs), state-of-the-art image steganography embeds messages while minimizing a heuristically defined distortion function. However, this concept cannot be directly ported to steganography using compressed video as the cover media. According to the intra prediction principle, an H.264 QDCT coefficient block is predicted and coded based on previously encoded blocks, so even a slight embedding change will set off a chain reaction in the remaining cover blocks. Considering the cover block dependency, we make necessary changes to the standard trellis coding structure so as to be applicable for the joint compression embedding scenario. During the coding/embedding procedure, we maintain multiple contexts corresponding to possible optimal routes, and retrace each route periodically to determine how each cover block should be modified. After each modification, the remaining cover blocks, as well as their embedding costs, are re-evaluated, and each context is updated to reflect the embedding effect. In this way, the global optimality can be approached progressively in a block-by-block manner, so our proposed method is named progressive trellis coding (PTC). Extensive experiments have been conducted, and corresponding results show that the adoption of PTC brings about a significant gain in embedding performance.
A Novel Approach to Steganography: Enhanced Least Significant Bit Substitution Algorithm Integrated with Self-Determining Encryption Feature

One of the most well-known and used algorithms for Steganography is Least Significant Bit (LSB) substitution. Although LSB has several advantages such as simplicity, efficiency, and easy-to-do implementation, it has some dis... | Find, read and cite all the research you need on Tech Science Press
Enhanced Human Action Recognition Using Fusion of Skeletal Joint Dynamics and Structural Features

In this research work, we propose a method for human action recognition based on the combination of structural and temporal features. The pose sequence in the video is considered to identify the action type. The structural variation features are obtained by detecting the angle made between the joints during the action, where the angle binning is performed using multiple thresholds. The displacement vector of joint locations is used to compute the temporal features. The structural variation features and the temporal variation features are fused using a neural network to perform action classification. We conducted the experiments on different categories of datasets, namely, KTH, UTKinect, and MSR Action3D datasets. The experimental results exhibit the superiority of the proposed method over some of the existing state-of-the-art techniques.
A Machine Translation System from Hindi to Sanskrit Language using Rule based Approach

Machine Translation is an area of Natural Language Processing which can replace the laborious task of manual translation. Sanskrit language is among the ancient Indo-Aryan languages. There are numerous works of art and literature in Sanskrit. It has also been a medium for creating treatise of philosophical work as well as works on logic, astronomy and mathematics. On the other hand, Hindi is the most prominent language of India. Moreover,it is among the most widely spoken languages across the world. This paper is an effort to bridge the language barrier between Hindi and Sanskrit language such that any text in Hindi can be translated to Sanskrit. The technique used for achieving the aforesaid objective is rule-based machine translation. The salient linguistic features of the two languages are used to perform the translation. The results are produced in the form of two confusion matrices wherein a total of 50 random sentences and 100 tokens (Hindi words or phrases) were taken for system evaluation. The semantic evaluation of 100 tokens produce an accuracy of 94% while the pragmatic analysis of 50 sentences produce an accuracy of around 86%. Hence, the proposed system can be used to understand the whole translation process and can further be employed as a tool for learning as well as teaching. Further, this application can be embedded in local communication based assisting Internet of Things (IoT) devices like Alexa or Google Assistant.
Introduction. Generating Murmurations for an Actionable Coherence

This project truly stands on the shoulders of giants and we are so grateful for the foundation they built for us.Many thanks to the original Lifespan Writing Development Group for their decades of individual research and for their work as a collective that makes it possible for us to move forward.We're especially grateful to Deb Brandt and Steve Graham for their ongoing
Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources

In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face’s similarity/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, while the other parts shared their parameters. A multi-batch algorithm is utilized for training. The training process takes a few hours on a single GPU. The proposed approach achieves near-human accuracy (98.9%) on the Labeled Faces in the Wild (LFW) benchmark, which is competitive with other techniques that are presented in the literature. It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). To handle the cluster quality challenge, a novel post-clustering optimization procedure is proposed. It outperforms popular clustering approaches, like K-Means and spectral by 0.098 and up to 0.344 according to F1-measure.
On improving training mechanisms teaching staff and prospects for targeted learning in the interests of universities

The article reveals the main trends and staffing impediments in higher educational institutions based on the analysis of legislation and standards, academic literature, statistics and the available management experience of educational activities in engineering training areas. The targeted training attributes as a framework for vocational training management are also specified in the paper. Shortage of academic staff capable to use the efficient advanced technologies for rebuilding the training process quickly under changing both labor market environment and students' interests is one of the main factors that today compromises the quality of highly qualified personnel training for the national economy causing serious concern of higher education authorities and educational institutions administration. The lack of interest of academic staff to conduct scientific researches due to on the one hand, by the increased competition for grants, targeted subsidies for fundamental and applied research and the inability (lack of skills) of many applicants to apply for funding correctly and timely, on the other hand contributes to low indicators of the research activities. The authors have concluded that the targeted training, which allows organizing the training process for young professionals starting from high school graduates, could be the main framework for recruiting them to teaching and researching positions that is undoubtedly to the benefit of an educational institution.
A Teaching Model for College Learners of Japanese Based on Online Learning

Online learning has not been fully implemented by college Japanese teach-ers, due to the lack of supports from intelligent technology and the poor de-cision-making of college management. This paper attempts to develop a teaching model that effectively integrates online learning to enhance the learning ability of Japanese among college students. To begin with, the au-thors summarized the status quo, impacts, and problems of online learning among Japanese learners in colleges. Through expert survey and literature review, an index system was established to measure the online learning abil-ity of Japanese among college students. The weight of each index was de-termined by analytic hierarchy process (AHP). Based on index system, a grey relational analysis (GRA) model was set up to evaluate the online learn-ing ability of Japanese among college students. The research results provide a good reference for the online learning of other language majors.
Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery

The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%.
Optimization of Music Teaching Methods Based on Multimedia Computer-aided Technology

With the development and progress of society, consumers are more and more inclined to choose personalized and rich products.In order to meet the needs of people's increasingly rich material and cultural products, the designed products must not only have functional practicality and beautiful appearance, but also be able to meet the internal personalized needs of consumers.The introduction of experiential teaching in computer-aided graphic design courses is mainly for students to design products with rich connotation and personalization based on mastering the technology of computer-aided graphic design and combining with practice.In experiential teaching, the starting point and end point of our teaching reform is to closely combine classroom teaching of design performance techniques with professors of design theory knowledge, while focusing on cultivating and enhancing students' practical innovation ability and personalized design level.And then achieve the purpose of training high-quality composite design talents.
Exploring the Relation between Technical Debt Principal and Interest: An Empirical Approach

The cornerstones of technical debt (TD) are two concepts borrowed from economics: principal and interest. Although in economics the two terms are related, in TD there is no study on this direction so as to validate the strength of the metaphor. We study the relation between Principal and Interest, and subsequently dig further into the 'ingredients' of each concept (since they are multi-faceted). In particular, we investigate if artifacts with similar levels of TD Principal exhibit a similar amount of TD Interest, and vice-versa. To achieve this goal, we performed an empirical study, analyzing the dataset using the Mantel test. Through the Mantel test, we examined the relation between TD Principal and Interest, and identified aspects that are able to denote proximity of artifacts, with respect to TD. Next, through Linear Mixed Effects (LME) modelling we studied the generalizability of the results. The results of the study suggest that TD Principal and Interest are related, in the sense that classes with similar levels of TD Principal tend to have similar levels of Interest. Additionally, we have reached the conclusion that aggregated measures of TD Principal or Interest are more capable of identifying proximate artifacts, compared to isolated metrics. Finally, we have provided empirical evidence on the fact that improving certain quality properties (e.g., size and coupling) should be prioritized while ranking refactoring opportunities in the sense that high values of these properties are in most of the cases related to artifacts with higher levels of TD Principal. The findings shed light on the relations between the two concepts, and can be useful for both researchers and practitioners: the former can get a deeper understanding of the concepts, whereas the latter can use our findings to guide their TD management processes such as prioritization and repayment.
Accurate 3-D Reconstruction Under IoT Environments and Its Applications to Augmented Reality

With the remarkable development of sensor devices and the Internet of Things (IoT), today's researchers can easily know what changes have taken place in the real world by acquiring a 3-D model. Conversely, a large amount of image data promotes the development of perceptual computing technology. In this article, we focus on modeling 3-D scenes from the multisource image data obtained from the IoT with cameras. Although great progress has been made in 3-D reconstruction, it is still challenging to recover the 3-D model from IoT data because the captured images are usually noisy, incomplete, varying scale, and with repetitive structures or features. In this article, we propose an accurate 3-D reconstruction method under IoT environments for perceptual computing of the scene. This method consists of sparse, dense, and surface reconstruction processes, which can gradually recover high-quality geometric models from the image data and efficiently deal with various repetitive structures. By analyzing the reconstructed model, we can detect the changes of scenes. We evaluate the proposed method on the benchmark data sets (i.e., tanks and temples) and publicly available data sets(in which samples usually contain repeated structures, lighting change, and different scales). Experimental results show that the proposed method outperforms the state-of-the-art methods according to the standard evaluation metric. We also use our method to enhance the real scenes with virtual objects, thus producing promising results.
Maximal-steered-coherence protection by quantum reservoir engineering

We show that the effects of decoherence on quantum steering ellipsoids can be controlled by a specific reservoir manipulating, in both Markovian and non-Markovian realms. Therefore, the so-called maximal steered coherence could be protected through reservoir engineering implemented by coupling auxiliary qubits to the reservoir.
Seq2KG: An End-to-End Neural Model for Domain Agnostic Knowledge Graph (not Text Graph) Construction from Text

Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsupervised pipelines, using carefully crafted linguistic and statistical patterns to extract co-occurred noun phrases as triples, essentially constructing text graphs rather than true knowledge graphs. In this research, for the first time, in the same flavour as Collobert et al.'s seminal work of "Natural language processing (almost) from scratch" in 2011, we propose a Seq2KG model attempting to achieve "Knowledge graph construction (almost) from scratch". An end-to-end Sequence to Knowledge Graph (Seq2KG) neural model jointly learns to generate triples and resolves entity types as a multi-label classification task through deep learning neural networks. In addition, a novel evaluation metric that takes both semantic and structural closeness into account is developed for measuring the performance of triple extraction. We show that our end-to-end Seq2KG model performs on par with a state of the art rule-based system which outperformed other neural models and won the first prize of the first Knowledge Graph Contest in 2019. A new annotation scheme and three high-quality manually annotated datasets are available to help promote this direction of research.
Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint

The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an increasingly important role in the success of many graph-based representations and algorithms in the field of machine learning and graph signal processing. In this paper, we propose a novel graph learning framework that incorporates the node-side and observation-side information, and in particular the covariates that help to explain the dependency structures in graph signals. To this end, we consider graph signals as functions in the reproducing kernel Hilbert space associated with a Kronecker product kernel, and integrate functional learning with smoothness-promoting graph learning to learn a graph representing the relationship between nodes. The functional learning increases the robustness of graph learning against missing and incomplete information in the graph signals. In addition, we develop a novel graph-based regularisation method which, when combined with the Kronecker product kernel, enables our model to capture both the dependency explained by the graph and the dependency due to graph signals observed under different but related circumstances, e.g. different points in time. The latter means the graph signals are free from the i.i.d. assumptions required by the classical graph learning models. Experiments on both synthetic and real-world data show that our methods outperform the state-of-the-art models in learning a meaningful graph topology from graph signals, in particular under heavy noise, missing values, and multiple dependency.
Occlusion-Aware Motion Planning at Roundabouts

In this work, we present a motion planning framework for automated vehicles to drive safely through intersections despite occlusions and the uncertain behavior of the surrounding vehicles. A context representation based on probably-free gaps is proposed as a means to provide, in occluded scenes, richer information to the motion planner compared to representations only based on observed objects. Our solution builds upon the path-velocity decomposition approach. Path planning is performed with state-of-the-art techniques, while a novel trajectory abstraction is used to reason about speed profiles without explicitly generating sequences of accelerations. To efficiently identify the best safe speed profile for every path candidate, a reachability-based analysis is also formulated. The proposed planning workflow is evaluated in roundabout scenarios. Our simulation study shows that the proposed context representation facilitates the decision-making in occluded scenes and that the reachability-based planning strategy is robust, computationally efficient, and outperforms a simpler reactive strategy.
Tree Framework With BERT Word Embedding for the Recognition of Chinese Implicit Discourse Relations

Currently, discourse relation recognition (DRR), which is not directly marked with connectives, is a challenging task. Traditional approaches for implicit DRR in Chinese have focused on exploring the concepts and features of words; however, these approaches have only yielded slow progress. Moreover, the lack of Chinese labeled data makes it more difficult to complete this task with high accuracy. To address this issue, we propose a novel hybrid DRR model combining a pretrained language model, namely bidirectional encoder representations from transformers (BERT), with recurrent neural networks. We use BERT as a text representation and pretraining model. In addition, we apply a tree structure to the implicit DRR in Chinese to produce hierarchical classes. The 19-class F1 score of our proposed method can reach 74.47% on the HIT-CIR Chinese discourse relation corpus. The attained results showed that the use of BERT and the proposed tree structure forms a novel and precise method that can automatically recognize the implicit relations of Chinese discourse.
Simple Reductions from Formula-SAT to Pattern Matching on Labeled Graphs and Subtree Isomorphism

The CNF formula satisfiability problem (CNF-SAT) has been reduced to many fundamental problems in P to prove tight lower bounds under the Strong Exponential Time Hypothesis (SETH).Recently, the works of Abboud, Hansen, Vassilevska W. and Williams (STOC'16), and later, Abboud and Bringmann (ICALP'18) have proposed basing lower bounds on the hardness of general boolean formula satisfiability (Formula-SAT).Reductions from Formula-SAT have two advantages over the usual reductions from CNF-SAT:(1) conjectures on the hardness of Formula-SAT are arguably much more plausible than those of CNF-SAT, and (2) these reductions give consequences even for logarithmic improvements in a problem's upper bounds.Here we give tight reductions from Formula-SAT to two more problems: pattern matching on labeled graphs (PMLG) and subtree isomorphism.Previous reductions from Formula-SAT were to sequence alignment problems such as Edit Distance, LCS, and Frechet Distance and required some technical work.This paper uses ideas similar to those used previously, but in a decidedly simpler setting, helping to illustrate the most salient features of the underlying techniques.