Reviewerly/exHarmony huggingface dataset:
Multi-layer fusion network for blind stereoscopic 3D visual quality prediction

Stereoscopic 3D (S3D) visual quality prediction (VQP) is used to predict human perception of visual quality for S3D images accurately and automatically. Unlike that of 2D VQP, the quality prediction of S3D images is more difficult owing to complex binocular vision mechanisms. In this study, inspired by the binocular fusion and competition of the binocular visual system (BVS), we designed a blind deep visual quality predictor for S3D images. The proposed predictor is a multi-layer fusion network that fuses different levels of features. The left- and right-view sub-networks use the same structure and parameters. The weights and qualities for the left- and right-view patches of S3D images can be predicted. Furthermore, training patches with more saliency information can improve the accuracy of prediction results, which also make the predictor more robust. The LIVE 3D Phase I and II datasets were used to evaluate the proposed predictor. The results demonstrate that the performance of the proposed predictor surpasses most existing predictors on both asymmetrically and symmetrically distorted S3D images.
Towards the optical character recognition of DSLs

OCR engines aim to identify and extract text strings fromdocuments or images. While current efforts focus mostly inmainstream languages, there is little support for program-ming or domain-specific languages (DSLs). In this paper, wepresent our vision about the current state of OCR recognitionfor DSLs and its challenges. We discuss some strategies toimprove the OCR quality applied to DSL textual expressionsby leveraging DSL specifications and domain data. To bettersupport our ideas we present the preliminary results of anempirical study and outline a research roadmap.
Automatic Gamma Correction Based on Root-Mean-Square-Error Maximization

Gamma correction is a well-known method used often for image enhancement, mainly to improve the luminance levels of dark and bright images. However, the enhancement level is highly affected by the gamma parameter used. In this paper, we propose a new iterative algorithm for automating the selection of the gamma value γ by maximizing the mean error between the pixel values. The experimental results (based on qualitative and quantitative assessments) prove the robustness of the proposed algorithm, particularly, when compared to some of the state-of-the-art methods.
Modeling Smart Cloud Computing Resource Allocation in E-Learning

Resource allocation is one of the main factors that affect the performance of cloud computing and its applications. E-learning as one of cloud computing applications helps busy schedule learners with great facilities related to time and place, that's why e-learning had great attention over the last years. This paper introduced a smart resource allocation algorithm for e-learning systems in cloud computing environment, called Modeling Smart Cloud Computing Resource Allocation in E-Learning MS2CRAE that uses the ABC strategy in its work; ABC belongs to the swarm intelligent family. The proposed algorithm assigned resources depending on the calculated probability value for each resource.
A Realistic Model for Rescue Operations after an Earthquake

When a natural disaster occurs, emergency responders need information and real-time imagery in order to make better decisions and save time. Unmanned aerial vehicles (UAVs) used by emergency services, such as police officers or firefighters, can rapidly provide situational awareness over a large area, reducing the time and number of researchers required to locate and rescue people, thus reducing the costs and risks of search and rescue missions. This work considers the problem of completely overfly an area just affected by an earthquake with the objective of opportunely direct rescue teams. It provides a complete model that tries to keep into account all the main real-life issues in two different realistic scenarios.
A Comparison of Data-Driven and Traditional Approaches to Employee Performance Assessment

Employee performance monitoring is not new. Traditional systems consider an average of peer and direct supervisor reviews across various subjective criteria in yearly performance appraisals. This leads to problems such as recency and social biases that devalue the credibility of the review process. Modern, data-driven systems have made it easier to automatically track employee data regarding various common performance metrics in useful ways. Statistical measures are derived from these metrics that are collated over a defined period. These measures are used to determine if and when any appropriate action needs to be taken. However, the statistics fail to account for common characteristics of the employee population. Instead of comparing individual performance against rigid idealistic norms, this paper presents an automated and objective approach to determine the collective population norm and calculate abnormal deviations from it. Employees who deviate from the norm are automatically flagged and subject to further review from management. Ideally, a combination of calculated metrics using this approach can be used to accurately reflect real-world situations and highlight anomalies in the employee work force. We compare the results of the proposed statistical approach with traditional human review approaches and evaluate their correlation.
An Accelerated Prototype with Movidius Neural Compute Stick for Real-Time Object Detection

Object detection and recognition in realtime is the key task in many computer vision applications such as security surveillance, medical diagnosis, automated vehicle systems, etc. Now-a-days many deep learning techniques, especially convolutional neural networks (CNN) is widely used for real-time image detection and classification. The development of CNN models boosts the accuracy of object detection. However, the complex and data-intensive processing slows down the performance while implemented on hardware. This paper presents a low-powered, portable prototype on Xilinx PYNQ Z2 board with Movidius neural compute stick (NCS) that accelerates the object detection in real-time. Also, the proposed prototype utilized You Only Look Once (YOLO) approach for object detection. Frames per second (FPS), computation time and the probability of object recognition are the parameters considered to evaluate the performance of the proposed prototype and outperform the existing models.
Towards Light‐Weight Portrait Matting via Parameter Sharing

Abstract Traditional portrait matting methods typically consist of a trimap estimation network and a matting network. Here, we propose a new light‐weight portrait matting approach, termed parameter‐sharing portrait matting (PSPM). Different from conventional portrait matting models where the encoder and decoder networks in two tasks are often separately designed, here a single encoder is employed for the two tasks in PSPM, while each task still has its task‐specific decoder. Thus, the role of the encoder is to extract semantic features and two decoders function as a bridge between low‐resolution feature maps generated by the encoder and high‐resolution feature maps for pixel‐wise classification/regression. In particular, three variants capable of implementing the parameter‐sharing portrait matting network are proposed and investigated, respectively. As demonstrated in our experiments, model capacity and computation costs can be reduced significantly, by up to and , respectively, with PSPM, whereas the matting accuracy only slightly deteriorates. In addition, qualitative and quantitative evaluations show that sharing the encoder is an effective way to achieve portrait matting with limited computational budgets, indicating a promising direction for applications of real‐time portrait matting on mobile devices.
Control a Drone Using Hand Movement in ROS Based on Single Shot Detector Approach

These days, drones become one of the most popular robots and have lots of application in delivering, transportation, construction, agriculture, data recording. Nevertheless, the increment in using drones, the rate of world aviation accidents increased too, and most of them were because of hardness to keep the drone in wanted location, and also superficial acknowledgement about multi rotors structures. In this paper, a new method is introduced for controlling a drone rather than controlling the drone by classical approach via radio control. In this new method, a deep learning algorithm and machine vision applied to detect an object and use its position to order the drone. In the performed experimental study, which was done in Human and Robot Interaction Laboratory, a human hand was used as an object (to simplify the problem) which its movement is made equivalent to the command of a radio controller. In order to accurate the result of hand detection the so-called Single Shot Detector is used which has been trained by using the Ego Hand dataset. The latter leads to a precise result under different conditions like viewpoint variation, background clutter, illumination and so on. Furthermore, a stereo camera vision set up used instead of using Kinect or leap motion devices to evaluate the depth of hand which enable to control the drone in a three-dimensional coordinate system. Also, the proposed algorithm are implemented in the Robot Operation System(ROS) and Gazebo were applied to get these positions and simulate the drone. The drone, which is based on px4 as a flight controller-in the Gazebo environment- receives its position from Mavros node which connected the drone to the ROS and could move continuously along x, y, and z axes. According to the results, it can be inferred that the proposed method was much easier and had more functionality instead of previous methods reported in the literature.
Object Detection in Remote Sensing Images with Mask R-CNN

Abstract With the wide applications of remote sensing technology in engineering, the demands of efficient object detection algorithms for remote sensing images have also been significantly increased in recent years. Traditional detection methods have the shortcomings of low accuracy and poor robustness, which is difficult to be applied to remote sensing images with complex background and varieties of objects. Recently, the deep convolutional neural networks have already shown great advances in object detection and outperformed many traditional methods. In this work, we study the performance of a region proposal-based method, Mask R-CNN, for detecting airplane and ship in remote sensing images. Specifically, we add an FPN module to improve the accuracy of small objects detection, and a mask branch is used to describe the shape of objects. In addition, we used a series of data augmentation strategies during training for meeting the CNN’s requirement of training samples diversity. The experimental results show that our model has superior performance in object detection of remote sensing images.
Semantic segmentation guided face inpainting based on SN-PatchGAN

As a specific application of image inpainting, face inpainting based on generative adversarial network (GAN) has made great process in recent years. However, there are still many problems in the current face inpainting methods, such as asymmetric eyes, unsuitable size of nose and artificial expression. Considering the obvious structural feature of human face, this paper proposes a face image restoration method based on semantic segmentation guidance. In the base of the repair network Spectral-Normalized PatchGAN (SN-PatchGAN), the semantic segmentation network is used to guide the repair process, which can make the inpainting face image to be more realistic. Moreover, an asymmetry loss is designed to reduce the eye asymmetry. Experiments on public dataset show that our approach outperform existing methods quantitatively and qualitatively.
A Rubric for the Detection of Students in Crisis

Abstract For some students, standardized tests serve as a conduit to disclose sensitive issues of harm or distress that may otherwise go unreported. By detecting this writing, known as crisis papers , testing programs have a unique opportunity to assist in mitigating the risk of harm to these students. The use of machine learning to automatically detect such writing is necessary in the context of online tests and automated scoring. To achieve a detection system that is accurate, humans must first consistently label the data that are used to train the model. This paper argues that the existing guidelines are not sufficient for this task and proposes a three‐level rubric to guide the collection of the training data. In showcasing the fundamental machine learning procedures for creating an automatic text classification system, the following evidence emerges as support of the operational use of this rubric. First, hand‐scorers largely agree with one another in assigning labels to text according to the rubric. Additionally, when this labeled data are used to train a baseline classifier, the model exhibits promising performance. Recommendations are made for improving the hand‐scoring training process, with the ultimate goal of quickly and accurately assisting students in crisis.
Fast Encoding of AG Codes Over C<sub>ab</sub>Curves

We investigate algorithms for encoding of one-point algebraic geometry (AG) codes over certain plane curves called Cab curves, as well as algorithms for inverting the encoding map, which we call “unencoding”. Some Cab curves have many points or are even maximal, e.g. the Hermitian curve. Our encoding resp. unencoding algorithms have complexity Õ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/2</sup> ) resp. Õ(qn) for AG codes over any C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ab</sub> curve satisfying very mild assumptions, where n is the code length and q the base field size, and Õ ignores constants and logarithmic factors in the estimate. For codes over curves whose evaluation points lie on a grid-like structure, for example the Hermitian curve and norm-trace curves, we show that our algorithms have quasi-linear time complexity Õ(n) for both operations. For infinite families of curves whose number of points is a constant factor away from the Hasse-Weil bound, our encoding and unencoding algorithms have complexities Õ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5/4</sup> ) and Õ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/2</sup> ) respectively.
FCOS Based Seatbelt Detection System Using Thermal Imaging for Monitoring Traffic Rule Violations

This work is related to building a Seatbelt Detection system based on Fully Connected One Shot (FCOS). It is one of the most recent Deep Learning approaches primitively built using single shot detection proposal. Unlike the double stage region-based object detection schemes this technique do not follow semantic segmentation, it does not undergo loss of the object information such as disappearance of the gradients and it does not require pre-defined anchors. This technique comprises strong feature extractors and reinforce multi scale object detection and it is very quick in the multithreaded GPU environments. Since our fundamental research is concentrated on object classification related to ADAS applications, as a first step we choose to detect the drivers and co-passengers of a four-wheeler vehicle not wearing seatbelts from thermal dataset. Therefore, we used thermal images and videos possessed from thermal cameras 5m to 20m away from the vehicles as our dataset in building the model and testing. The FCOS extracts the features of an object using its efficient per-pixel fashion. Finally, the performance analysis of these model in terms of mean Average Precision (mAP) indicates that the modelling using FCOS performs in a promising way and it can be used in automatic seatbelt detection systems.
A Numerical Study of Bravyi-Bacon-Shor and Subsystem Hypergraph Product Codes

We provide a numerical investigation of two families of subsystem quantum codes that are related to hypergraph product codes by gauge-fixing. The first family consists of the Bravyi-Bacon-Shor (BBS) codes which have optimal code parameters for subsystem quantum codes local in 2-dimensions. The second family consists of the constant rate “generalized Shor” codes of Bacon and Cassicino [1], which we re-brand as subsystem hypergraph product (SHP) codes. We show that any hypergraph product code can be obtained by entangling the gauge qubits of two SHP codes. To evaluate the performance of these codes, we simulate both small and large examples. For circuit noise, a [21], [4], [3] BBS code and a [49], [16], [3] SHP code have pseudthresholds of 2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and 8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , respectively. Simulations for phenomenological noise show that large BBS and SHP codes start to outperform surface codes with similar encoding rate at physical error rates 1 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> and 4 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , respectively.
Application Research of Docker Based on Mesos Application Container Cluster

With the increasing maturity of docker container virtualization technology, the technology based on Docker application container cluster is gradually moving towards a production environment. How to realize efficient resource utilization and provide a highly available supporting environment for complex distributed applications deployed in Docker application container cluster has become a new challenge for enterprise IT resource management. To meet the above needs, the industry has introduced a number of open source Docker application container cluster management tools, represented by Kubernetes and Mesos, which have received extensive attention. Based on the above background, this paper uses MVC development mode and Mosos application container cluster management tool to simulate the system verification and login process in terms of cluster architecture, system design, implementation and testing, and makes a comprehensive description of resource scheduling and application container cluster based on docker. It is mainly used to verify the communication of Docker container in application cluster and the influence of system resources on service response time. At present, the system has been tested in both function and performance. The test results show that the system is feasible. The application container built based on Mesos can ensure normal communication between Docker containers, and the impact speed of the program can be improved to a certain extent by modifying resource scheduling parameters, which provides strong support for further study and use.
MEE : An Automatic Metric for Evaluation Using Embeddings for Machine Translation

We propose MEE, an approach for automatic Machine Translation (MT) evaluation which leverages the similarity between embeddings of words in candidate and reference sentences to assess translation quality. Unigrams are matched based on their surface forms, root forms and meanings which aids to capture lexical, morphological and semantic equivalence. We perform experiments for MT from English to four Indian Languages (Telugu, Marathi, Bengali and Hindi) on a robust dataset comprising simple and complex sentences with good and bad translations. Further, it is observed that the proposed metric correlates better with human judgements than the existing widely used metrics.
Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics

A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users' behavior is commonly investigated in adaptive systems, which is considering the individual preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.
Lipschitz Constrained Neural Networks for Robust Object Detection at Sea

Abstract Autonomous ships rely on sensory data to perceive objects of interest in their environment. Deep Learning based object detection in the image domain commonly used to solve this issue. The robustness of such approaches in non-ideal conditions is, however, still to be proven. In this work state of the art methods are applied on the RetinaNet architecture attempting to create a more robust object detection network given noisy input data. The GroupSort activation function and Spectral Normalization is used and the results are compared to the standard RetinaNet network. Our findings show that these modifications perform better and ensure robustness under moderate noise levels, than the standard RetinaNet network.
RETRACTED: Image recognition of martial arts movements based on FPGA and image processing

The work is to perceive the intricate karate activity groupings consequently. It is to give a proportion of the nature of the activity performed. This is because there is a problem that requires essentially model. This work can be removed from the two motion capture hardware and consumer derived from the input sequence. The solution for the skeleton's movement proposed a depth-sensing system. The speed of the body's action because there is a karate sport classification.On the other hand, because the practice with even skilled martial arts for years, behavior patterns are repetitive. Similarly, there is no interest in the commercial market for solutions used in today's computer entertainment and tutoring system. The future of collecting analysis of elements present in the correct posture, accelerated by a punch and kick, the different aspects of performance, such as synchronizing the limb's movement and evaluation. This work aims to provide a survey to evaluate the quality of the whole body movement technology. It proposes a data set and its plan to implement the first reference file of the work's future. Generally speaking, FPGA is programmable with a group of programmable interconnect resources through digital circuits or programmable logic blocks surrounded by input/output blocks. Any system silicon chip is put together. The processor and the FPGA are essentially indeed parallel.