Reviewerly/exHarmony huggingface dataset:
Use of deep learning algorithms for real-time detection of vessels in confined spaces using the Tensorflow framework

Abstract Over 4515 small boat accidents were registered in the United State of America in 2012, resulting in 651 causalities and 22% of the accidents took place between two boats. It is, therefore, one of the most interesting applications for image analysis and recognition using deep learning, collision avoidance in passenger boats. Advances in parallel computing, graphic processing unit technology and deep learning have facilitated real-time image processing. The main objective of this study was to compare the performance metrics for different deep learning algorithms using pre-trained data sets. The algorithms used were: faster region-based convolutional neural networks, region-based fully convolutional network, and single shot multibox detector using the feature extractors: residual neural network, inception and convolutional neural networks for mobile vision applications to detect generic boats in confined waterways. These models were coded in Python programming language, using the framework Tensorflow and OpenCV library for image processing. The algorithms were pre-trained using the free images database posted on the web, Microsoft COCO. The use of these pre-trained models allowed making use of computers without graphic processing unit. As a result, it was found that the faster region-based convolutional neural networks and region-based fully convolutional network method compared to the single shot multibox detector method offer a small advantage precision if speed detection is not required, but the single shot multibox detector method is useful for case detectors in real time, however it did not perform as accurate when detecting small objects.
A Genetic Approach for Trajectory Optimization Applied to a Didactic Robot

This work presents a genetic and adaptive genetic algorithm for generating optimized trajectories for a didactic robot. Manipulator robots are used in industrial environments to perform repetitive tasks, unhealthy and dangerous jobs, among others. Optimization in the execution of these tasks is essential to make the use of these technologies compatible with the industry 4.0 requirements. The trajectory that a robot performs during its operation is directly related to its wear, consumption, time and even space of security that should be delimited inside of the workspace. The artificial intelligence popularization parallels the expansion of its applications and technologies, such as genetic algorithms, fuzzy systems, reinforcement learning and convolutional networks. By modeling the kinematics of a commercial didactic robot, an adaptive genetic algorithm used to optimize the trajectories is developed, presenting trajectories generated by polynomial interpolation, traditional and adaptive genetic algorithm, thus analyzing the possibility of reducing the total distance covered and other metrics by the technique adopted. Simulation results illustrate the main characteristics of the proposed algorithms.
OWL RL to Framework for Ontological Knowledge Integration Preliminary Transformation

FOKI is a formally defined framework, proposed by authors, which addresses storing, processing, and integrating ontologies. Its model is based on a mathematical apparatus but lacks a concrete syntax. These features make difficult to use standardized benchmark datasets, usually expressed in OWL2, during experimental verification of FOKI’s validity. To enable a practical usage of FOKI, a set of bidirectional transformation rules (defined at the abstract syntax level) between the OWL2 RL and the framework is needed. However, due to major differences in base assumptions it is impossible to provide a straightforward translation between FOKI and OWL. Therefore, the aim of the paper is to identify which elements of OWL syntax can be transformed into FOKI formalism (on its current state of development) and which of these rules are bi-directional. The defined rules are illustrated with some overall examples. The paper also provides a short discussion about different approaches to transformation definitions.
Paragraph-Parallel based Neural Machine Translation Model with Hierarchical Attention

Abstract Neural Machine Translation (NMT) has achieved great developments in recent years, but we still have to face two challenges: establishing a high-quality corpus and exploring optimal parameters of models for long text translation. In this paper, we first attempt to set up a paragraph-parallel corpus based on English and Chinese versions of the novels and then design a hierarchical attention model for it to handle these two challenges. Our encoder and decoder take segmented clauses as input to process the words, clauses, paragraphs at different levels, particularly with a two-layer transformer to capture the context from both source and target languages. The output of the model based on the original transformer is used as another level of abstraction, conditioning on its own previous hidden states. During this process, inter-clause and intra-clause contexts from source and target sides are introduced for translation prediction and both encoder and decoder can profit from contexts in complementary ways. Experimental results show that our hierarchical attention model significantly outperforms six competitive baselines, including ensembles.
Skew Detection and Ortho-rectification for System Corrected Landsat TM Images

We have been working on the development of geometrical correction method combining the Fourier phase only correlation method (POC) and an ortho-rectification method based on affine transform for system-corrected Landsat TM images. In this paper, in order to suppress image destruction by interpolation and improve registration accuracy, the bilinear interpolation method used for image interpolation was changed to the bicubic convolution interpolation method, and this method was applied to a system corrected Landsat TM image. As a result, it was found that the registration accuracy was improved to 9.0 m in x-direction and 37.3 m in y direction, respectively (in UTM coordinates). And it was found that the system-corrected Landsat TM image contains small skew distortion. After correcting the skew distortion, as a result of re-applying our improved geometric correction method, the registration accuracy became 3.4m in x-direction and 2.5 m in y-1 direction, which is dramatically improved.
Named Entity Recognition for Russian Historical Texts

With the raise of big data, machine learning and crowdsourcing, the volume of existing datasets for different machine learning problems have greatly increased. The natural language processing field is not an exception; so, as a result, most of the researches have transitioned into investigating and applying different deep architectures for it. One of the main issues of this trend is as follows: it is hard to adopt such approaches for somewhat poorly studied languages, which do not have training data enough as for natural language processing perspective. In this paper, we investigate some modern approaches to named entity recognition as for Russian language and show that for historical texts their results are much lower than for general ones. In addition, we propose our own algorithm that improves the results of for these historical texts.
3D Single Image Face Reconstruction Approaches With Deep Neural Networks

3D facial reconstruction is an emerging and interesting application in the field of computer graphics and computer vision. It is difficult and challenging to reconstruct the 3D facial model from a single photo because of arbitrary poses, non-uniform illumination, expressions, and occlusions. Detailed 3D facial models are difficult to reconstruct because every algorithm has some limitations related to profile view, fine detail, accuracy, and speed. The major problem is to develop 3D face with texture of large poses, wild faces, large training data, and occluded faces. Mostly algorithms use convolution neural networks and deep learning frameworks to create facial model. 3D face reconstruction algorithms used for application such as 3D printing, 3D VR games and facial recognition. Different issues, problems and their proposed solutions are discussed. Different facial dataset and facial 3DMM used for 3D face reconstructing from a single photo are explained. The recent state of art 3D facial reconstruction and 3D face learning methods developed in 2019 is briefly explained.
Saralinsky Graben—Devonian Rift of the Kuznetsk-Minusinsk Zone, Altai–Sayan Folded Area

The first studies of the Devonian grabens of the northeastern part of the Kuznetsk Alatau were obtained by the Krasnoyarskgeologiya and Zapsibgeologiya Geological Surveys in the 1950s–80s. The research was aimed at characterizing the grabens, and other structures of the region, and any associated ore mineralization and geochemical anomalies. Summaries of this early research are reported in Mustafin et al. (1966), Kryukov et al. (1969) and Turchenko (1975). Subsequent research has provided additional insights, and in this chapter we review the current understanding of these Devonian grabens.
Malware Containment in Cloud

Malware is pervasive and poses serious threats to normal operation of business processes in cloud. Cloud computing environments typically have hundreds of hosts that are connected to each other, often with high risk trust assumptions and/or protection mechanisms that are not difficult to break. Malware often exploits such weaknesses, as its immediate goal is often to spread itself to as many hosts as possible. Detecting this propagation is often difficult to address because the malware may reside in multiple components across the software or hardware stack. In this scenario, it is usually best to contain the malware to the smallest possible number of hosts, and it's also critical for system administration to resolve the issue in a timely manner. Furthermore, resolution often requires that several participants across different organizational teams scramble together to address the intrusion. In this vision paper, we define this problem in detail. We then present our vision of decentralized malware containment and the challenges and issues associated with this vision. The approach of containment involves detection and response using graph analytics coupled with a blockchain framework. We propose the use of a dominance frontier for profile nodes which must be involved in the containment process. Smart contracts are used to obtain consensus amongst the involved parties. The paper presents a basic implementation of this proposal. We have further discussed some open problems related to our vision.
An analytical framework for formulating metrics for evaluating multi-dimensional time-series data

This paper proposes a visual analytics framework for formulating metrics for evaluating multi-dimensional time-series data. Multidimensional time-series data has been collected and utilized in different domains. We believe evaluation metrics play an important role in utilizing those data, such as decision making and labeling training data used in machine learning. However, it is a difficult task for even domain experts to formulate metrics. To support the process of formulating metrics, the proposed framework represents metrics as a linear combination of data attributes, and provides a means for formulating it through interactive data exploration. A prototype interface that visualizes target data as an animated scatter plot was implemented. Through this interface, several visualized objects can be directly manipulated: a node and a trajectory of an instance, and a convex hull as the group of nodes and trajectories. Linear combinations of attributes are adjusted in accordance with the manipulation of different objects' types by the user. The effectiveness of the proposed framework was demonstrated through two application examples with real-world data.
Solving for Inverse-Like Dynamic Matrices of Variables and Derivatives Using Zhang Neural Dynamics (ZND) Equivalency

Solving for inverse-like dynamic matrices of variables and derivatives is exciting and challenging. Although it may be encountered in the fields of industrial control or scientific research, it has not been widely studied because of its complexity and difficulty. In this work, to solve this problem, the Zhang neural dynamics (ZND, or called, zeroing neural dynamics) method is employed to the equivalent transformation of the original problem, and a model termed ZND equivalency (ZE) model is proposed and investigated. Meanwhile, a derivative dynamics (DD) model is proposed for comparison purposes. In order to facilitate the implementation of digital hardware, a five- instant Zhang et al discretization (ZeaD) formula is presented. Thus, two discrete models termed discrete-time ZE model and discrete-time DD model (in short DZE model and DDD model, respectively) are proposed by using the presented ZeaD formula. Theoretical analysis and numerical experimental results indicate the good performance, accuracy and superiority of the DZE model.
Consumer Trust Recommendation in Online Social Commerce

In online society, understanding the methods of trust is essential. In addition, issues of trust are necessary to several solving problems corporate responsibility, online shopping, and the social system. There are many trust value calculations have been proposed direct ratings to or propagate. However, in other web-based social networks, which is no direct rating value where users only have binary relationships. In this paper, calculate weight for comments and texts shared by the online users via text-mining method with Improved TF-IDF (Term Frequency Inverse Document Frequency) and time-weight and credit calculation with Improved PageRank algorithm. This propose technique will be of much help in any direct rating to calculate the trust correctness values in online social network (OSNs).
Detecting Characters From Digital Image With Chinese Text Based on Grid Lines

For neatness, many Chinese and international students write on paper with grid lines in horizontal and vertical directions. The lines have a few styles, such as composition format, post format and so on. The approaches of Chinese text detection in natural scenes focus on complex and changeable objects, paying less attention to grid lines. Therefore, the results of their application are mostly unsatisfactory. In Chinese text image with grid lines, characters are surrounded by similar rectangular boundary lines. This paper proposes an approach based on grid lines to detect characters. The approach can effectively avoid the omissions and overlaps that easily occurs in other approaches. The image is firstly converted into a binary edge one using adaptive Canny edge detection. Horizontal and vertical lines are extracted from the binary edge image using Hough transform. Rectangles bounding characters are identified from the transformed lines. The experimental results show that this presented approach can accurately detect characters from different formats of Chinese text images with the grid lines.
Managerial Competencies of an Investigator and the Specifics of Their Implementation in Crime Investigation

The authors describe the contents of managerial work of an investigator that includes the following elements: analysis of the investigative situation; provision of investigative leads; planning; taking investigative or other procedural actions; organization of interaction not only with the departments involved in investigative and criminalistic work, but also with other government and non-government bodies, public organizations and community members; making a decision to end preliminary investigation. They also describe the specific features of managerial work of an investigator while taking into account the variability of investigative situations and their contents, legal status of the participants of criminal proceedings, the deficit of time and the boundaries of investigative and other procedural actions, the necessity of psychological support of an investigator’s work, the presence of prosecutor's supervision that makes it possible to identify drawbacks in an investigator's work in time and quickly correct them, as well as of court control that underlines the importance of procedural actions that can only be sanctioned by a court decision. The opinions of scholars regarding the concept of «managerial competencies» and its contents are presented. The authors show that it is necessary to form and further improve the managerial competencies of an investigator on the basis of a competence approach used in public service, and to introduce a special complex of psychological techniques that show if an investigator possesses or lacks the competencies necessary for his work, in the process of psychological support of an investigator’s managerial activities. They also describe the correlations between the competencies of people that manage an investigation and the effectiveness of the work of agencies responsible for the pre-trial criminal proceedings in general. The specific features of implementing the competencies of an investigator in investigating crimes are described.
Access pattern-based high-performance main memory system for graph processing on single machines

With the increasing complexity of graph structures, the current real-world large-scale graphs are being represented by a considerable amount of vertex and edge data. Furthermore, the analysis of a large number of computing nodes has become a very complicated job that requires a large amount of hardware resources. Moreover, in large-scale graph processing, the vertex and edge data show random and sequential memory access patterns at the same time, and this is a major bottleneck in graph processing. In this paper, we present a high-capacity main memory system with an intelligent pattern-aware prefetching engine to overcome the scalability problem and the memory inefficiency of single-machine graph processing. The proposed intelligent pattern-aware prefetching engine is designed to predict and handle sequential or regular patterns and random-access patterns simultaneously. Experimental results demonstrated that the proposed model exhibited performance improvements of 60% over conventional DRAM models, approximately 40% over the existing prefetch models, and about 12.5% over the latest prefetch models.
Generic Software Architecture for Semantic and Visual Queries

Software architecture is a very important part in the development and life cycle of large and complex software. Large companies and research institutes generate software architectures for very specific uses. However, these can be hardly adopted for other purposes and uses. Therefore, there is a lack of generic architectures that can be easily used in any type of problem and these can be sufficiently flexible to be implemented in different contexts, mainly in the context of systems with databases with scientific information. In this paper, we present the design and implementation of a generic architecture that allows semantic and visual queries from a web interface. This architecture is easily scalable, because it is divided into separate layers that can be interchangeable and each layer has specific purposes. This generic software architecture is composed of three important features: Semantic, Rich Interaction and Control. Therefore, the system implemented with this architecture can be easily adaptable to different contexts and it can be evolved and scalable over time. In this paper, we describe in detail all the layers involved and their purpose. So this architecture can be implemented in all types of systems that need a visual interaction of their information. The contribution of this generic architecture is that it can be adapted to provide new types of interaction with different types of scientific information.
PARAMETER ESTIMATION SPACE FOR UNKNOWN INTERNAL EVOLUTION ON IOT DOMOTIC SYSTEMS

This paper describes the parameter estimation modeling concerning a domotic designer bot system with internet of things (IoT) assistance using the probabilistic operator based on the stochastic parameter estimation through the moments and the recursive conditions. Light, CCTV, presence, and temperature are IoT data monitored, shared, and accessed by the internet for a smart office designer performance that evolves based on historical web data. The relationship established by Wiener between covariance and variance found the parameter time evolution by observing through the time. The development is viewed in the visible results between non-recursive and recursive mathematical structures. In both cases, the convergence rate is based on probabilistic estimation, the functional error presents a high convergence rate which is viewed as an effect of the function of a density function. The estimate considered a non-invasive perspective, and it helps in different applications such as health diagnosis in humans and animals with internal problems, or systems which are unknown for internal evolution such as for IoT model adoption. Therefore, our objective is to propose a black box, inner approximation through the parameter estimation without a no invasive stochastic method based in Wiener approximation.
Learning Spatiotemporal Representation Based on 3D Autoencoder for Anomaly Detection

Because of ambiguous definition of anomaly and the complexity of real data, anomaly detection in videos is of utmost importance in intelligent video surveillance. We approach this problem by learning a novel 3D convolution autoencoder architecture to capture informative spatiotemporal representation, and an 2D convolutional autoencoder to learn the pixel-wise correspondences of appearance and motion information to boost the performance. Experiments on some publicly available datasets demonstrate the effectiveness and competitive performance of our method on anomaly detection in videos.
UAV-Multispectral Sensed Data Band Co-Registration Framework

Precision agriculture (PA) has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farmers to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among them, which makes the alignment process complex. In this paper, is proposed a new framework for the band co-registration process based on the premise that natural misalignment is an attribute of the camera, so it does not change during the acquisition process. The results were compared with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of 0.425 pixels, this result being 335% better than the evaluated frameworks.
Broad Learning System-Based Learning Controller for Course Control of Marine Vessels

In this paper, a Broad Learning System (BLS)-based learning controller is proposed for course control problem of marine vessels. The training data set of BLS comes from a PID controller, the learning control method is proposed to improve the control performance using the learned knowledge. Simulation studies are performed to demonstrate the proposed scheme can achieve the better control performance with smaller tracking error.