Reviewerly/exHarmony huggingface dataset:
Table Detection Using Boundary Refining via Corner Locating

Table detection based on bounding-box method has achieved remarkable results. However, there still exists inaccurate table boundary locating. In this paper, a table detection method is proposed. Firstly, coarse table detection is implemented through Faster R-CNN. Secondly, corner locating is implemented through RPN and refined through Fast R-CNN. Corner grouping and filtering are implemented through post-processing algorithms. Therefore, unreliable corners are filtered. Thirdly, table boundaries are refined via reliable corners. Experimental results show that the proposed method obviously improves the precision of table boundary locating. We test on ICDAR2017 POD dataset, our method achieves an F-measure of 95.3%. Compared to Faster R-CNN method, the proposed method significantly increases by 3.2% in F-measure. Moreover, our method increases by 3.3% at pixel-level localization.
Pixel-wise classification using support vector machine for binarization of degraded historical document image

&lt;span lang="EN-GB"&gt;Binarization&lt;/span&gt;&lt;span lang="EN-GB"&gt; of historical documents nowadays is very important as digital archiving has become the best and preferred solution for the retrieval and storage of valuable archives. However, the process becomes more challenging due to the degradation of historical documents. Hence, this paper described a method on binarization of historical documents using the learning concept. Support vector machine (SVM) learning was used as a classifier in this work. After training some images with the help of ground truth images, a model was developed. Testing images then used the model to segregate each pixel as text or non-text. The grey level and RGB values were chosen as descriptors for a particular pixel and comparisons were made between these two descriptors. The intensities of the local neighbourhood for every pixel were used in the experiment. To compare these descriptors, standard dataset HDIBCO2014, DIBCO2012 and DIBCO2016 were used in the training and testing phase. The results from the experiment clearly showed that grey level values gave better performance compared to RGB values.&lt;/span&gt;
On the Multi-scale Real-Time Object Detection Using ResNet

Real-time target detection and location has important values in video surveillance. Aimed at the low accuracy of existing real-time object detection algorithms, this paper proposes a multi-scale real-time target detection algorithm based on residual convolution neural network. Firstly, the residual convolutional neural network is introduced into the YOLOv3-Tiny algorithm. The jump connection of the low-level and high-level networks forms the residual module in the YOLOv3-Tiny algorithm to effectively prevent network degradation while increasing the depth of the neural network. Secondly, a new prediction layer is added to the neural network to improve the results of small target detection. Finally, the trained model is tested on the Pascal VOC public dataset. The experimental results show that the proposed algorithm achieves 64.6% accuracy on the validation dataset, and the speed of 60FPS in the video detection. The detection accuracy is improved to a higher level at a small cost of a little lower speed still meeting the real-time detection requirements, and small targets in the image can be effectively detected. The algorithm is effective and robust.
Markov and improved particle swarm optimization-based privacy preservation algorithm for user space geographical location

With the development of location-based service, the problem of personal privacy information security is becoming more and more serious. Users’ personal privacy information is used without authorization, personal privacy information security is facing various challenges, and personal property security is likely to be violated. Based on HMM model, this paper analyses the location and the location transition probability, and then predicts the location probability by AFMO algorithm. Privacy location information can be predicted by AFMO algorithm, which can improve the accuracy of location prediction. Finally, the experimental results show that the HMM-AFMO algorithm is superior to the HMM model in terms of execution time and effect. In the position prediction, the similarity advantage is obvious, which fully reflects the advantages of the algorithm proposed in this paper.
SOP based Fault Type Identification of Substation using ANN

After AlphaGo, there has been a move to apply AI advancements and self-learning to substation fault determination system to transition to an automatic fault recovery system. In order to fault recovery, fault type identification and fault location determination must be preceded. Artificial Neural Network (ANN) with smart advantage has recently been increasing interest due to the advancement of computer hardware and software platform.BR In this study, ANN is used to identify fault type in substation. First, we made the structure of ANN using the components of substation and the fault types of Standard Operation Procedure (SOP). Then the learning pattern was included considering the steady state and the 15 fault types specified in SOP. After learning through Back Propagation (BP), the ANN for identifying fault type of substation presented as a test pattern was tested. Finally, the proposed technique was evaluated under various simulation conditions.
Joint Trajectory Planning Based on Minimum Euclidean Distance of Joint Angles of a Seven-Degrees-of-Freedom Manipulator for a Sequential Reaching Task

When a nuclear power disaster occurs at a nuclear power plant, it is hazardous for humans to enter the plant. If robots could remove radioactive substances adhering to a plane such as a plant wall, humans would be able to enter the plant to investigate the situation and to work. In this study, to efficiently remove radioactive substances from a wall with a manipulator, we examined joint trajectory planning based on the minimum Euclidean distance of joint angles of a seven-degrees-of-freedom (7-DOF) serial link manipulator for a sequential reaching task on a plane. We demonstrate the planning for the sequential reaching task, which is an iterative point-to-point reaching movement between positions on a plane. The joint angles for each target position were obtained based on the inverse kinematics for an arm angle, and the optimal arm angles within the constraints of the joint angles were computed by the sequential quadratic programming method. The optimal trajectories for the arm angles were compared with the trajectories of the joint angles that were the eight inverse kinematic solutions for a fixed arm angle. The result showed that through optimal planning, an efficient trajectory within the movable ranges of the joint angles could be obtained for the sequential reaching task.
A Hybrid Framework for Improving Diversity and Long Tail Items in Recommendations

In today’s information overloaded era, recommender system is a necessity and it is widely used in most of the domains of e-commerce. Over the years, recommender system is improved to meet the main purpose of achieving better user experience, where accuracy is considered as one of the important aspects in its design. However, other aspects such as diversity, long tail item recommendation, novelty and serendipity are equally important while providing recommendations to the users. Research to improve above mentioned aspects is limited. In this paper, we propose an efficient approach to improve diversity and long tail item recommendations. The experiments are conducted on two real world movie rating datasets namely, MovieLens and Netflix. Experimental analysis shows that the proposed method outperforms the state-of-the art approaches in recommending diverse and long tail items.
A Deep Network for Automatic Video-Based Food Bite Detection

Past research has now provided compelling evidence pointing towards correlations among individual eating styles and the development of (un)healthy eating patterns, obesity and other medical conditions. In this setting, an automatic, non-invasive food bite detection system can be a really useful tool in the hands of nutritionists, dietary experts and medical doctors in order to explore real-life eating behaviors and dietary habits. Unfortunately, the automatic detection of food bites can be challenging due to occlusions between hands and mouth, use of different kitchen utensils and personalized eating habits. On the other hand, although accurate, manual bite detection is time-consuming for the annotator, making it infeasible for large scale experimental deployments or real-life settings. To this regard, we propose a novel deep learning methodology that relies solely on human body and face motion data extracted from videos depicting people eating meals. The purpose is to develop a system that can accurately, robustly and automatically identify food bite instances, with the long-term goal to complement or even replace manual bite-annotation protocols currently in use. The experimental results on a large dataset reveal the superb classification performance of the proposed methodology on the task of bite detection and paves the way for additional research on automatic bite detection systems.
Lung image segmentation by generative adversarial networks

Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks. We employed a variety of generative adversarial networks and use its capability of image translation to perform image segmentation. The generative adversarial networks was employed to translate the original lung image to the segmented image. The generative adversarial networks based segmentation method was test on real lung image data set. Experimental results shows that the proposed method is effective and outperform state-of-the art method.
Classification of vector lines and fields by geometric and numerical methods

Abstract Geometric and numerical methods of classification of vector lines and vector fields are proposed. The division of vector lines of arbitrary vector fields into potential and spiral lines is carried out. The division of vortex vector fields into layered and spiral vector fields is carried out. It is shown that any vector field can be represented as a superposition of layered vector fields. Conditions of existence of so-called forceless spiral vector fields are established. The mathematical conditions to be satisfied by the magnetic fields of magnetic clouds of the solar wind are formulated.
An Intelligent Vision System to Navigate a Robot Without On-Board Sensors

This paper presents a smart system to navigate a robot without sensors using a vertical camera and real-time data processing to identify static and dynamic obstacles. To identify the obstacles, image processing techniques are used such as background subtraction and morphological operations in real-time. After detecting the obstacles, the optimal path is determined, and control commands are sent to the robot accordingly. The navigation path of the robot from the start point (robot current location) to the destination point is determined based on graph theory. A graph is created to cover all possible routes in the work area, then A-Star algorithm is utilized to find the optimal path. The optimal path is updated continuously to account for any moving obstacles that could obstruct the robot's path.
Apprenticeship Learning for a Predictive State Representation of Anesthesia

Objective: In this paper, we present an original decision support algorithm to assist the anesthesiologists delivery of drugs to maintain the optimal Depth of Anesthesia (DoA). Methods: Derived from a Transform Predictive State Representation algorithm (TPSR), our model learned by observing anesthesiologists in practice. This framework, known as apprenticeship learning, is particularly useful in the medical field as it is not based on an exploratory process - a prohibitive behavior in healthcare. The model only relied on the four commonly monitored variables: Heart Rate (HR), the Mean Blood Pressure (MBP), the Respiratory Rate (RR) and the concentration of anesthetic drug (AAFi). Results: Thirty-one patients have been included. The performances of the model is analyzed with metrics derived from the Hamming distance and cross entropy. They demonstrated that low rank dynamical system had the best performances on both predictions and simulations. Then, a confrontation of our agent to a panel of six real anesthesiologists demonstrated that 95.7% of the actions were valid. Conclusion: These results strongly support the hypothesis that TPSR based models convincingly embed the behavior of anesthesiologists including only four variables that are commonly assessed to predict the DoA. Significance: The proposed novel approach could be of great help for clinicians by improving the fine tuning of the DoA. Furthermore, the possibility to predict the evolutions of the variables would help preventing side effects such as low blood pressure. A tool that could autonomously help the anesthesiologist would thus improve safety-level in the surgical room.
TIAD Shared Task 2019: orthonormal explicit topic analysis for translation inference across dictionaries

The task of inferring translations can be achieved by the means of comparable corpora and in this paper we apply explicit topic modelling over comparable corpora to the task of inferring translation candidates. In particular, we use the Orthonormal Explicit Topic Anal- ysis (ONETA) model, which has been shown to be the state-of-the-art explicit topic model through its elimination of correlations between top- ics. The method proves highly effective at selecting translations with high precision.
Quantum algorithm for spectral projection by measuring an ancilla iteratively

We propose a quantum algorithm for projecting a quantum system to eigenstates of any Hermitian operator, provided one can access the associated control-unitary evolution for the ancilla and the system, as well as the measurement of the controlling ancillary qubit. Such a Hadamard-test-like primitive is iterated so as to achieve the spectral projection, and the distribution of the projected eigenstates obeys the Born rule. This algorithm can be used as a subroutine in the quantum annealing procedure by measurement to drive the system to the ground state of a final Hamiltonian, and we simulate this for quantum many-body spin chains.
MMA: Motion Memory Attention Network for Video Object Detection

Modern object detection frameworks such as Faster R-CNN achieve good performance on static images, benefiting from the powerful feature representations. However, it is still challenging to detect tiny, vague and deformable objects in videos. In this paper, we propose a Motion Memory Attention (MMA) network to tackle this issue by considering the motion and temporal information. Specifically, our network contains two main parts: the dual stream and the memory attention module. The dual stream is designed to improve the detection of tiny object, which is composed of an appearance stream and a motion stream. Our motion stream can be embedded into any video object detection framework. In addition, we also introduce the memory attention module to handle the issue of vague and deformable objects by utilizing the temporal information and distinguishing features. Our experiments demonstrate that the detection performance can be significantly improved when integrating the proposed algorithm with Faster R-CNN and YOLO$$_{v2}$$.
Real Time Palm and Finger Detection for Gesture Recognition Using Convolution Neural Network

Hand motion is one of the methods for communicating with the PC to perform colossal application. The goal is to apply the machine learning algorithm for quicker motion route acknowledgment. This application pursues the learning of motions and recognizing them accurately. At first Convolution Neural Network (CNN) model is prepared with various pictures of various hand signals for different people for grouping the letters in order. The initial step is realizing, where the data is stacked and in pre-handling step, edge, mass locators, twofold thresholding, limit box recognitions are used to extract the features. By utilizing ConvNet, which is a machine learning algorithm, the input picture’s features are cross confirmed. The accuracy is found to be 98% and this methodology is effective to address the impact of various problems. The assessment of this strategy shows to performing hand gesture acknowledgment.
Real-Time Workflows Oriented Hybrid Scheduling Approach With Balancing Host Weighted Square Frequencies in Clouds

High energy consumption in cloud data centers has become one of the main obstacles to green cities, and an urgent problem to be solved.So far, a large number of scheduling algorithms have been developed to reduce energy consumption for executing workflows.However, most existing algorithms have obvious defects in energy and resource efficiency, because they schedule workflow tasks to hosts directly and ignore that the host is so powerful that a single workflow task cannot make full use of its resources.To resolve the issue, a new scheduling architecture is designed for cloud data centers.Then, two principles are derived: one suggests that hybrid scheduling tasks from different workflows is helpful for improving resource utilization; the other one deduces that balancing host weighted square frequencies can minimize total power consumption of active hosts under a given resource requirement.On the basis of the scheduling architecture and the two principles, an oNline schEduling AlgoriThm, namely NEAT, is proposed to schedule dynamic workflows with deadlines.Furthermore, three strategies for dynamically adjusting the available virtual machines (VMs) and active hosts are proposed and integrated into NEAT to improve the energy and resource efficiency for cloud data centers.Finally, the proposed NEAT is compared with three existing algorithms using real-world workflow traces to demonstrate its superior performance with respect to energy and resource efficiency while guaranteeing the timing requirements of workflows.Compared with baseline algorithms, NEAT is capable of reducing energy consumption for cloud data centers by an average of 40% in the range from 34.0% to 45.67%.
Lucciola: Presenting Aerial Images by Generating a Fog Screenat any Point in the Same 3D Space as a User

In this paper, we propose a method of presenting an aerial image at any point in the same three-dimensional space as a user. In the existing method, presenting an image to an arbitrary point in 3D space is difficult because the presentation position on the device is fixed. Therefore, in this study, particles with scattering properties remain to an arbitrary point in space and used as a screen. Specifically, we focus on the phenomenon of vortex rings that can stably transport particles, and we generate an aerial screen by colliding vortex rings ejected from air cannons at multiple points in the air. In a prototyping experiment, we generated a screen at a specified point in space and confirmed that aerial image presentation was possible by projection. We also determined potential issues.
Path-induced closure operators on graphs for defining digital Jordan surfaces

Abstract Given a simple graph with the vertex set X , we discuss a closure operator on X induced by a set of paths with identical lengths in the graph. We introduce a certain set of paths of the same length in the 2-adjacency graph on the digital line ℤ and consider the closure operators on ℤ m ( m a positive integer) that are induced by a special product of m copies of the introduced set of paths. We focus on the case m = 3 and show that the closure operator considered provides the digital space ℤ 3 with a connectedness that may be used for defining digital surfaces satisfying a Jordan surface theorem.
Understanding Developer Commenting in Code Reviews

Modern code review is a well-known practice to assess the quality of software where developers discuss the quality in a web-based review tool. However, this lightweight approach may risk an inefficient review participation, especially when comments becomes either excessive (i.e., too many) or underwhelming (i.e., too few). In this study, we investigate the phenomena of reviewer commenting. Through a large-scale empirical analysis of over 1.1 million reviews from five OSS systems, we conduct an exploratory study to investigate the frequency, size, and evolution of reviewer commenting. Moreover, we also conduct a modeling study to understand the most important features that potentially drive reviewer comments. Our results find that (i) the number of comments and the number of words in the comments tend to vary among reviews and across studied systems; (ii) reviewers change their behaviours in commenting over time; and (iii) human experience and patch property aspects impact the number of comments and the number of words in the comments.
Irreducibility of iterates of post-critically finite quadratic polynomials over $\mathbb {Q}$

In this paper, we classify, up to three possible exceptions, all monic, post-critically finite quadratic polynomials $f(x)\in \mathbb {Z}[x]$ with an iterate reducible module every prime, but all of whose iterates are irreducible over $\mathbb {Q}$. In particular, we obtain infinitely many new examples of the phenomenon studied by Jones. While doing this, we also find, up to three possible exceptions, all integers $a$ such that all iterates of the quadratic polynomial ${(x+a)^2-a-1}$ are irreducible over $\mathbb {Q}$, which answers a question posed in by Ayad and McQuillan, except for three values of $a$. Finally, we make a conjecture that suggests a necessary and sufficient condition for the stability of any monic, post-critically finite quadratic polynomial over any field of characteristic $\neq 2$.
Utilización del smartphone por estudiantes del nivel superior

Definitely, smartphones have penetrated people's lives; Mexico is not the exception. At a growing rate, in recent times they are being used for various purposes, in addition to speaking and sending messages, for information searches, games, etc. Its use is so widespread that it has become addiction in many cases. The purpose of this study is to analyze the use of smartphone in Faculty of Informatics of Autonomous University of Sinaloa’s Students. The use has been measured with the help of an instrument designed with 23 items divided into five categories and was administered to 23 second-grade students of Bachelor of Information Systems Engineering at the Autonomous University of Sinaloa, Mazatlan campus; of which five belong to the female gender and 18 to the male gender. The results show that, for the most part, students use the smartphone for school activities, such as researching (74%), performing academic tasks (70%), downloading study material (65%), accessing class slides (61%), watch tutorial videos (74%). However, 19 of the 23 students consider that Never or Almost never can use their phone to receive advice from teachers; and 15 of the 23 say that Never or Almost never teachers use the smartphone to support teaching. The above reveals an academic use of this mobile device by students, but they do not perceive that their instructors rely on it as an educational tool.