Reviewerly/exHarmony huggingface dataset:
Online cooperative path planning for multi-quadrotors in an unknown dynamic environment

The online cooperative path planning problem is discussed for multi-quadrotor maneuvering in an unknown dynamic environment. Based on the related basic concepts, typical three-dimensional obstacle models, such as spherical and cubic, and their collision checking criteria are presented in this article. An improved rapidly exploring random tree (RRT) algorithm with goal bias and greed property is proposed based on the heuristic search strategy to overcome the shortcomings of the classical RRT algorithm. Not only are the kinematic constraints of the quadrotor established but the time and space coordination strategy matching with the improved RRT algorithm is also presented in this article. Furthermore, a novel online collision avoidance strategy according to the partial information of the surrounding environment is proposed. On the basis of the above work, a distributed online path planning strategy is proposed to obtain the feasible path for each quadrotor. Numerical simulation results show that the improved RRT algorithm has better search efficiency than the classical RRT algorithm. And the satisfactory path planning and path tracking results prove that the above model and related planning strategies are reasonable and effective.
A New Method of Group Information Recommendation Based on the User Dynamic Profile Information Optimization

Group information recommendation not only satisfies the preferences of the individuals, but also maximizes the satisfaction of the whole group. This paper firstly analyzes the related research of group information recommendation, and points out the problems of the current group information recommendation. Secondly, aiming at the problems of low accuracy of similar user calculation and incomplete consideration of expert users, this paper proposes a new group information recommendation method based on the user dynamic profile information optimization. Finally, the paper carries out the experiment to test and verify the high efficiency of the new algorithm using the data of Zhihu.
Research on E-commerce Oral English Blended Teaching

With the continuous development of The Belt and Road Initiative, higher requirements are put forward for the oral ability of E-commerce major. Based on the analysis of the current situation and problems of business application oral English teaching and training, an oral teaching and training model is proposed based on blended teaching around the application scenario of business English. The blended teaching mode effectively integrates online video learning and offline classroom learning based on the Internet, and makes full use of the characteristics of wide distribution area and abundant information resources of the Internet. It can expand the learning space and the learning content, optimize the learning mode and improve the teaching level of oral English, and improve the quality of e-commerce personnel training effectively. This model aims at improving the ability of oral English application, innovating teaching objectives, enriching teaching content, optimizing curriculum implementation, implementing process evaluation, and promoting the reform of E-commerce oral English teaching in an all-round way. After one year's pilot practice, it was found that the new model could effectively improve the teaching effect of the course and the oral application ability.
The 10 best practices for remote software engineering

Focusing on the human element of remote software engineer productivity.
Analysis on Handwriting Using Pen-Tablet for Identification of Person and Handedness

Human handwriting has some unique properties to express the behaviors and personality of any person. The state-of-the-art systems analyze the handwriting on paper manually and demand human expertness. Moreover, it is very difficult to recognize a person by analyzing the handwriting manually. Thus, this paper proposes a system for person identification along with another system to identify the handedness of individuals using handwriting data analysis. The used handwritten texts for these studies have been collected by the pen-tablet. Six parameters are captured and distinguishable features are extracted to identify the person's handwriting attributes. To identify the person and handedness using extracted features, two different classification algorithms, i.e., Support Vector Machine with linear kernel and Random Forest Classifier, are used. The proposed systems show 69.39% and 72.44 % accuracy using SVM and random forest classifier respectively for person identification and 97.25% accuracy for handedness identification after using SVM.
Active and Compact Entropy Search for High-Dimensional Bayesian Optimization

Entropy search and its derivative methods are one class of Bayesian Optimization methods that achieve active exploration of black-box functions. They maximize the information gain about the position in the input space where the black-box function gets the global optimum. However, existing entropy search methods suffer from harassment caused by high dimensional optimization problems. On the one hand, the computation for estimating entropies increases exponentially as dimensions increase, which limits the applicability of entropy search to high dimensional problems. On the other hand, many high-dimensional problems have the property that a large number of dimensions have little influence on the objective function, but currently there is no compress mechanism to exclude these redundant dimensions. In this work, we propose Active Compact Entropy Search (AcCES) to fix these defects. Under the guidance of historical evaluation, AcCES actively explores the prevalent inter-dimensional correlations by maximizing the linear or non-linear relationships that may exist between dimensions in the acquisition function, which is ignored by existing Bayesian Optimization methods. In order to build a more compact input space, redundant dimensions are compressed by exploiting inter-dimensional correlations. Experiments demonstrate that AcCES achieves higher query efficiency and optimal results than existing entropy search methods.
Estimation of Various Parameters for AES, DES, and RSA

Securing the data from unauthorized access is turn out to be a major challenge nowadays. Large volume of data is available in every field for which the security is most crucial. To secure the data, the security experts in last few decades implement various techniques. The main domain, which provide data security, is known as cryptography. This provide security to data against the unauthorized access. Encryption and decryption are two basic terms in cryptography. With the evolution of techniques and intelligence, the chances of getting unauthorized access on data are increased with multifold scenario. To overcome with this, the researchers use various security algorithms. In this research paper, a practical analysis between AES, DES, and RSA is implemented to assess their performance parameters in respect of their space complexity, encryption time, and throughput. Experiments are carried out on various type of data files so that the performance of three can be analyzed.
Deep Homography-Based Video Stabilization

Video stabilization is fundamental for providing good Quality of Experience for viewers and generating suitable content for video applications. In this scenario, Digital Video Stabilization (DVS) is convenient and economical for casual or amateur recording because it neither requires specific equipment nor demands knowledge of the device used for recording. Although DVS has been a research topic for decades, with a number of proposals from industry and academia, traditional methods tend to fail in a number of scenarios, e.g. with occlusion, textureless areas, parallax, dark areas, amongst others. On the other hand, defining a smooth camera path is a hard task in Deep Learning scenarios. This paper proposes a video stabilization system based on traditional and Deep Learning methods. First, we leverage Spatial Transformer Networks (STNs) to learn transformation parameters between image pairs, then utilize this knowledge to stabilize videos: we obtain the motion parameters between frame pairs and then smooth the camera path using moving averages. Our approach aims at combining the strengths of both Deep Learning and traditional methods: the ability of STNs to estimate motion parameters between two frames and the effectiveness of moving averages to smooth camera paths. Experimental results show that our system outperforms state-of- the-art proposals and a commercial solution.
Secure Data Storage Using Erasure-Coding In Cloud Environment

In present era, protection has become a much more beneficial move. This article expresses how cloud computing is offered services with protection. The developed system is mainly focused in third-party authentication (TPA), in this verification process, where administrator behaves as an interface between the customer and the TPA. Many storage technologies leverage storage space by third parties. The main objective of this paper is by using the digital exclusion coding method shown in the data center to safeguard data. Throughout this methodology, to use a unique dynamic secure key, as well as every block of data is shielded. The TPA verifies the information with the corresponding privacy key when the server program uses some process in the system or information at the instance so that the data is much more confidential and private.
A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning

We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning. Due to the high inter-class similarity in reference sentences describing remote sensing data, jointly encoding the sentences and images encourages prediction of captions that are semantically more precise than the ground truth in many cases. To this end, we introduce an Actor Dual-Critic training strategy where a second critic model is deployed in the form of an encoder-decoder RNN to encode the latent information corresponding to the original and generated captions. While all actor-critic methods use an actor to predict sentences for an image and a critic to provide rewards, our proposed encoder-decoder RNN guarantees high-level comprehension of images by sentence-to-image translation. We observe that the proposed model generates sentences on the test data highly similar to the ground truth and is successful in generating even better captions in many critical cases. Extensive experiments on the benchmark Remote Sensing Image Captioning Dataset (RSICD) and the UCM-captions dataset confirm the superiority of the proposed approach in comparison to the previous state-of-the-art where we obtain a gain of sharp increments in both the ROUGE-L and CIDEr measures.
Sequential Nonparametric Detection of Anomalous Data Streams

We study a nonparametric search problem to detect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> anomalous streams from a finite set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ S$</tex-math></inline-formula> data streams. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> anomalous streams are real-valued independent and identically distributed (i.i.d.) sequences drawn from the distribution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ q$</tex-math></inline-formula> , while the remaining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S-L$</tex-math></inline-formula> data streams are i.i.d. sequences drawn from the distribution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ p$</tex-math></inline-formula> . The distributions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ p$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ q$</tex-math></inline-formula> are assumed to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arbitrary</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown</i> , but distinct. We consider two cases: one where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L = 1$</tex-math></inline-formula> , and the other where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0 \leq L \leq A$</tex-math></inline-formula> . In both cases, we propose universal distribution-free sequential tests that are consistent. For the first case, we also: (1) show that the test is universally exponentially consistent and stops in finite time almost surely, and (2) bound the limiting growth rate of the expected stopping time as the probability of error decreases to zero. Simulations show that the performance of the proposed test is better than that of the fixed sample size test.
Episodic Training for Domain Generalization Using Latent Domains

Domain generalization (DG) is to learn knowledge from multiple training domain, and build a domain-agnostic model that could be used to an unseen domain. In this paper, take advantage of aggregating data method from all source and latent domains as a novel, we propose episodic training for domain generalization, aim to improve the performance during the trained model used for prediction in the unseen domain. To address this goal, we first designed an episodic training procedure that train a domain-generalized model without using domain labels. Firstly, we divide samples into latent domains via clustering, and design an episodic training procedure. Then, trains the model via adversarial learning in a way that exposes it into domain shift which decompose the model into feature extractor and classifier components, and train each component on the episodic domain. We utilize domain-invariant feature for clustering. Experiments show that our proposed method not only successfully achieves un-labeled domain generalization but also the training procedure improve the performance compared conventional DG methods.
Smart library automation using face recognition

Abstract The Library Automation System is gaining more importance as the number of its users is increasing. Automation of libraries reduces the duplication of work, saves time, and brings accuracy and speed. It also increases efficiency in library services and boosts the quality of work. One such effective system is the Smart Library Automation System using Face Recognition. The Proposed system uses face recognition for entering and getting the details of an end user. Through this application, the book gets issued to the end-user by identifying the user with the help of face recognition and identifying the book using barcode capture. In our system, we are using dlib face recognition, which is 99.38% accurate. We have provided the validations on the user registration form so that we get the right information. The registration process captures the face of the user and book issue, and the book return process checks the details of the user. End-users of the application will be librarians, students, and teachers. Our project aims at making the tasks of the library easy and to make the book issuing and returning process more interactive and effortless for the user. Also, our system is responsible for preserving the details of book issue and return and paperless report generation.
Localization of Deep Video Inpainting Based on Spatiotemporal Convolution and Refinement Network

Deep learning-based video inpainting can fill the missing or undesired regions with spatial-temporal consistent contents without obvious visually distortion. Although the original purpose of deep inpainting is to repair flawed videos, it can also be adopted for malicious purposes, e.g., removal of specific objects. Therefore, automatically locating the inpainted regions is a challenging task in video forensics. This paper proposes a new forensic refinement framework to localize the deep inpainted regions by considering the spatial-temporal viewpoint. Firstly, we design a spatiotemporal convolution to suppress redundancy for highlighting deep inpainting traces. Then, a detection module is constructed with four concatenated ResNet blocks, and two upsampling layers to achieve a rough location map. Finally, a modified U-net based refinement module is developed for the pixel-wise localization map. Deep inpaiting video datasets created by the state-of-the-art deep inpainting method, have been evaluated, and extensive experimental results clearly demonstrate the efficacy of the proposed approach.
Joint 2D Object Detection and 3D Reconstruction via Adversarial Fusion Mesh R-CNN

Joint 2D object detection and 3D reconstruction is an essential computer vision task to get more accurate detection and representation model of the target object. We proposed a novel joint 2D object detection and 3D reconstruction model that enhances the ability of the 2D object detection and the 3D reconstruction, called Adversarial Fusion Mesh Region Convolutional Neural Networks (AFM R-CNN). Our proposed model introduces the Deep Convolutional Generative Adversarial Network (DCGAN) to generate adversarial images and input the real and adversarial images into the object detection module GA-RPN to determine the position and anchor box of the target object. Next, to make better use of the two-dimensional information of the image, the voxel conversion and Fusion model Pix2Vox is introduced to fuse the two types of image features and generate coarse voxels. Afterwards, to differentiate the voxel information more efficiently, we use the Principal Neighborhood Aggregation network (PNA) model in 3D model refinement. The contrast experimental results on the open domain dataset (Pix3D) with baseline models demonstrate the effectiveness of AFM R- CNN in joint 2D object detection and 3D reconstruction task.
An application specific dynamic behaviour model using function-call sequence and memory access-graph for execution integrity verification

Computing systems demand stringent security checks to guard themselves against powerful attack vectors. Deployment of the Control Flow Integrity(CFI) checks limit the control flow of a program to a set of valid destinations, derived from the static analysis of the code. CFI checks guarantee the integrity of the executed code, but, they do not guarantee the integrity of execution, as sophisticated attack vectors create attacks that comply with the normal control flow of the program. This paper proposes an application specific, dynamic, execution integrity verification scheme, to detect static and dynamic integrity breaches. The system builds a behaviour model consisting of function call sequences and memory access graphs, to verify the semantic behaviour exhibited by the application. Dynamic variation of the function behaviour is further recorded as Access Graph Variance Vector (AGVV). Temporal sequencing of function calls, together with the memory access graph comparison, yields a two-level integrity verification system. Apart from detecting the deviations from normal behaviour, our proposed method limits the abnormal behaviour from being propagated. Experimental evaluation of the proposed method using applications from MiBench benchmark suite against static integrity breaches shows cent percent verification accuracy with less than 5% false positive rate. The system also identifies the dynamic integrity violation caused by ROP based attacks from the RIPE benchmark suite, indicating an accuracy of 94.44% against dynamic integrity breaches.
Video Object Detection based on Non-local Prior of Spatiotemporal Context

The appearances of objects in video sequence affected by complex background, motion blur and partial occlusion, which make the object detection in video sequence a hard work. Due to these problems, traditional image object detection methods cannot perform well in video sequence image. A fast and effective video object detection method is necessary to improve the detection efficiency. In this paper, we propose a non-local prior based spatiotemporal attention model based for video object detection. Unlike existing attention models, the proposed model can make full use of the spatiotemporal contextual information extracted from video sequence images. We apply our models in common object detection framework and evaluate it on Overhead Contact System (OCS) driving recorder dataset and OTB50 dataset. The proposed model achieves a greater increase in mAP value which proves our model can gains good performance in various complex video sequences.
Accelerometer Based Electric Wheelchair

A wheelchair is a tool to facilitating patients moving from one place to another; it is used in a patient who suffered decreased muscle strength or people with motor system disabilities so that they experience a disruption in balance. The kind of wheelchair that is in demand is the electric wheelchairs. It is preferable because patients can use it independently. This study is on the electrical wheelchair utilizing the output signal from the ADXL335 Accelerometer sensor connected to microcontroller Arduino. When the patient's head moves to one side (nodding, looking up or tilting left and right) the Accelerometer ADXL335 sensor will make a reading process which is then processed in the microcontroller Arduino, then the Arduino will provide voltage to the H-Bridge Relay Driver to drive the DC motor so that the wheelchair can move forward, backward or left and right. An ultrasound safety system is located behind the patient. The order execution test is carried out 25 times and obtained accuracy of 88% advance, right turn 84%, left turn 88%, reverse 92%. In accordance with the accuracy values are obtained that the electric wheelchair based on accelerometer can function properly.
Object Detection for P&amp;ID Images using various Deep Learning Techniques

Piping and Instrumentation Diagrams are detailed diagrams that show the piping and process equipment together with the instrumentation and control devices used in the process industry. These are updated during the plant life cycle to depict the latest changes and modifications done in the plant. These diagrams are detailed and are usually in the form of PDF, they are difficult to modify, and inferring details from these diagrams requires a deep understanding of plant process and engineering. To reduce the difficulty in finding and retrieving information from the diagrams, the proposal of automating the task of detection of the symbols and the texts embedded is explored in this paper. This paper compares various Deep Learning models on P&I Diagrams by using object detection for symbols using the Transfer Learning technique. Initially, the Connectionist Text Proposal Network algorithm is used for text detection in the images, then for the geometrically shaped objects, OpenCV library is used. The models trained by Transfer Learning are exported and the results are compared by performing symbol detection. This paper is an overall study of various deep learning methods implemented on P&I Diagrams.