Reviewerly/exHarmony huggingface dataset:
Calibration and 3D Reconstruction of Omni-directional Multi-view Image Measurement System

This article presents an omnidirectional multi-view image measurement system to provide multi-camera 3D reconstruction and multi-view image information. The system consists of four multi-view cameras, one of which is the fixed center camera that measures the geometric center point and provides a top-view image. The other three cameras provide side side-view images. They are 120 degrees apart in a sphere and are adjustable on arc rods. Any camera can be moved along arc measuring rods to a specified position to capture images within the sphere. Altering the angles among the cameras, the range of images captured by the cameras is changed accordingly. If the multi-view images of four cameras are captured at the same time, any two cameras are used to form a stereo camera pair. Using the principle of parallax matching between left and right images, stereo camera pairs can be formed for three-dimensional reconstruction. Finally, through the verification of calibration and 3D reconstruction examples, it is shown that the system can be successfully applied to omnidirectional multi-view image measurement and 3D reconstruction.
Video fingerprinting: Past, present, and future

The last decades have seen video production and consumption rise significantly: TV/cinematography, social networking, digital marketing, and video surveillance incrementally and cumulatively turned video content into the predilection type of data to be exchanged, stored, and processed. Belonging to video processing realm, video fingerprinting (also referred to as content-based copy detection or near duplicate detection ) regroups research efforts devoted to identifying duplicated and/or replicated versions of a given video sequence (query) in a reference video dataset. The present paper reports on a state-of-the-art study on the past and present of video fingerprinting, while attempting to identify trends for its development. First, the conceptual basis and evaluation frameworks are set. This way, the methodological approaches (situated at the cross-roads of image processing, machine learning, and neural networks) can be structured and discussed. Finally, fingerprinting is confronted to the challenges raised by the emerging video applications ( e.g. , unmanned vehicles or fake news) and to the constraints they set in terms of content traceability and computational complexity. The relationship with other technologies for content tracking ( e.g., DLT - Distributed Ledger Technologies) are also presented and discussed.
Attention transfer from human to neural networks for road object detection in winter

As an essential feature of autonomous road vehicles, obstacle detection must be executed on a real-time onboard platform with high accuracy. Cameras are still the most commonly used sensors in autonomous driving. Most detections using cameras are based on convolutional neural networks. In this regard, a recent teacher–student approach, called transfer learning, has been used to improve the neural network training process. This approach has only been used with a neural network acting as a teacher to the best of our knowledge. This paper proposes a novel way of improving training data based on attention transfer by getting the attention map from a human. The proposed method allows the dataset size reduction by 50%, which leads to up to a 60% decline in the training time. The experimental results indicate that the proposed method can enhance the F1-score of the network by up to 10% in winter conditions.
Sentiment analysis based on CNN - LSTM hotel reviews

Abstract In order to obtain the implied semantic information of hotel reviews for emotional analysis, the correlation between discontinuous words is ignored in the traditional convolutional neural network (CNN) emotional analysis. Therefore, a novel sentiment analysis method based on CNN - LSTM model is proposed. In this method, CNN is used to extract semantic features from hotel review texts, and LSTM is used to add sentence structure features to enhance deep semantic learning. This model improves the accuracy and F1 value on the CHN senticorp-HTOL-BA-6000 hotel review data set, and can better solve the task of text sentiment analysis and discover the emotional orientation of text information.
Detection of spammers disseminating obscene content on Twitter

Spammers distributing adult content are becoming an apparent and yet intrusive problem with the increasing prevalence of online social networks among users. For improving user experience and especially preventing exposure to users of lower age groups, these accounts need to be detected efficiently. In this work, a model is proposed in which a lexicon-based approach is used to label users with their values. This study is based on the fact that users behave according to the values they possess. The amalgamation of content-based features like values, the entropy of words, lexical diversity, and context-based word embeddings are found to be robust. Among several machine learning models, XGboost performs exceedingly well with accuracy (92.28 ± 1.28%) for all features. Feature importance and their discriminative power have also been shown. A comparative study is also done with one of the latest approaches and our approach is found to be more efficient.
Real-time Face mask Detection Using Deep Learning and MobileNet V2

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams.
Relative phase distribution and the precision of optical phase sensing in quantum metrology

One of the quantum metrology goals is to improve the precision in the measurement of a small optical phase introduced in one optical mode in an interferometer, i.e., phase sensing. In this paper, we obtain the relative phase distribution introduced by Luis and Sánchez-Soto (LSS) (Luis and Sánchez-Soto, 1996) for several two-mode pure quantum light states useful in quantum metrology. We show that, within the numerical precision of our calculations, the Fisher information obtained from the LSS relative phase distribution is equal to the quantum Fisher information for the considered states (the average difference for the tested states is smaller than 0.1%). Our results indicate that the LSS relative phase distribution can be used to predict the minimum uncertainty possible in the process of phase sensing in quantum metrology, since this uncertainty depends on the quantum Fisher information, at least for pure states.
Utilization of Eco Enzyme for Wastewater Treatment (Greywater) at Kuningan University

Greywater in Kuningan University comes from bathing, hand washing and washing utensils.The waste is discarded into the environment without going through the treatment stage so that it has the potential to pollute the environment.In this study, we used an environmentally friendly and cost-effective biological treatment method.This method uses eco-enzyme derived from a mixture of brown sugar, pineapple and water in a ratio of 1:3:10 which is fermented for 90 days.The purpose of this study is to determine the characteristics of greywater before and after being treated using eco-enzyme with a concentration of 10% for 5 and 15 days.Results obtained show that the eco enzyme is effective in aerobic conditions with a duration of 15 days after it's being confirmed in the Regulation of the Minister of Environment and Forestry of the Republic of Indonesia Number: P.68/Menlhk/Setjen/Kum.1/8/2016concerning domestic waste quality standards.Further research is needed to find a more appropriate dose of eco enzyme so that the BOD and COD content of greywater which discarded into the environment meets environmental standards.
Detecting cyberbullying in Spanish texts through deep learning techniques

Recent recollected data suggests that it is possible to automatically detect events that may negatively affect the most vulnerable parts of our society, by using any communication technology like social networks or messaging applications. This research consolidates and prepares a corpus with Spanish bullying expressions taken from Twitter in order to use them as an input to train a convolutional neuronal network through deep learning techniques. As a result of this training, a predictive model was created, which can identify Spanish cyberbullying expressions such as insults, racism, homophobic attacks, and so on.
An exploration of high performance computing cloud contract for SMEs: a systematic literature review

Purpose High performance computing (HPC) is used to solve complex calculations that personal computing devices are unable to handle. HPC offers the potential for small- and medium-size enterprises (SMEs) to engage in product innovation, service improvement and the optimization of resource allocation (Borstnar and Ilijas, 2019). However, the expensive infrastructure, maintenance costs and resource knowledge gaps that accompany the use of HPC can make it inaccessible to SMEs. By moving HPC to the cloud, SMEs can gain access to the infrastructure without the requirement of owning or maintaining it, but they will need to accept the terms and conditions of the cloud contract. This paper aims to improve how SMEs access HPC through the cloud by providing insights into the terms and conditions of HPC cloud contracts. Design/methodology/approach This paper adopts a systematic literature review by implementing a four-step approach. A comprehensive search was undertaken and results synthesized to enable this paper’s objectives to be met. Findings This paper proposes that SMEs could gain competitive advantage(s) by understanding their own needs and improving their contract negotiation abilities, service management skills and risk management abilities before accepting the terms and conditions of the cloud contract. Furthermore, a checklist, service-level agreement, easily ignored elements and risk areas are presented as guidance for SMEs when reviewing their HPC cloud contract(s). Originality/value While HPC cloud contracts are a niche research topic, it is one of the key factors influencing the ability of SMEs to access HPC through the cloud. It is, however, by no means a level playfield with SMEs at a distinct disadvantage because of not influencing the writing up of the HPC cloud contract. The added value of the paper is that it contributes to our overall understanding of the terms and conditions of HPC cloud contracts.
Analysis and Improvement of Lossless Compression Algorithms

The widespread application of information systems makes all walks of life face the challenge of massive data storage space and transmission capacity. How to efficiently store and transmit data has become an urgent problem to be solved. This paper firstly expounds the principles of the commonly used lossless compression Huffman algorithm and LZW algorithm through an example, and then the advantages and disadvantages of the two algorithms are compared, and corresponding improvement strategies are proposed for the shortcomings of the algorithm, which provides certain reference for the subsequent researchers of the compression algorithm.
High Performance Crypto for 5G Wireless on x86 Platform

The crypto, including ciphering and integrity protection, is a high cycle consumption job inside PDCP in 5G. When 5G RAN is deployed on Cloud and Edge based on general x86 platform, there are two common ways to implement the crypto functions, namely software lib based or hardware acceleration QAT based. The former occupies computing resource, but is quick for small packets, while the latter saves computing resource, but has longer processing latency. Both of them may meet the performance scaling issue, as the packet size varies, especially when eMBB slice and uRLLC slice are both enabled. This paper provides an efficient way to have both high computation performance and low processing latency for crypto functions with Intel Architecture technologies.
Implement contour following task of objects with unknown geometric models by using combination of two visual servoing techniques

In this paper, two visual servoing techniques are combined to implement the contour following task of objects with unknown geometric models and the contour plane can be anywhere in the workspace. The scaled Euclidean position and orientation of the contour plane are defined based on decomposing the homography matrix. The shortest path visual servoing (SPVS) technique is applied to control the robot end-effector or the camera to the position parallel to the contour plane. Then, the image-based visual servoing (IBVS) technique is applied to conduct the contour following task. The desired feature command points are extracted from the contour using image processing algorithms. Based on the feature command point, four virtual feature points are generated to form the interaction matrix used in IBVS. The effectiveness of the proposed approach is verified based on the simulation result by using a camera attached at the end-effector of the six-DOF PUMA 560 manipulator.
Volumetric Video Content Creation for Immersive XR Experiences

Volumetric video (VV) is an emergent digital media that enables novel forms of interaction and immersion within eXtended Reality (XR) applications. VV supports 3D representation of real-world scenes and objects to be visualized from any viewpoint or viewing direction; an interaction paradigm that is commonly seen in computer games. This allows for instance to bring real people into XR. Based on this innovative media format, it is possible to design new forms of immersive and interactive experiences that can be visualized via head-mounted displays (HMDs) in virtual reality (VR) or augmented reality (AR). This paper highlights technology for VV content creation developed by the V-SENSE lab and the startup company Volograms. It further showcases a variety of creative experiments applying VV for immersive storytelling in XR.
Retraction: Research on the Integration of ESP and Big Data in Modern College English Teaching (J. Phys.: Conf. Ser. 1852 022046)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022
Retraction: A Brief Analysis of the Application of English Language in British and American Literature Based on Artificial Intelligence (J. Phys.: Conf. Ser. 1915 022060)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022
Reinforced WAVSEP: a Benchmarking Platform for Web Application Vulnerability Scanners

Web applications do represent one of the main targets of cyber attacks. Detection systems such as Web Application Firewalls (WAFs) can help detect web application attacks and allow IT teams to respond rapidly. However, this approach is usually far from optimal, implying that a vulnerability has been exploited and that the attacker has already compromised the vulnerable application. It is, therefore, crucial to detect web application vulnerabilities before an attacker is able to exploit them. One of the most valuable techniques to detect input handling vulnerabilities relies on the use of Web Application Vulnerability Scanners (WAVS), i.e., tools that send malicious payloads against the web application to detect vulnerabilities. Indeed, these tools should be assessed to evaluate their effectiveness. This is usually achieved by testing them against benchmarking platforms, i.e., web applications with both vulnerable and non-vulnerable test cases. One of the most widely deployed benchmarking platforms is WAVSEP, a web application with a lot of relevant vulnerable test cases. Unfortunately, the application has not been updated since 2014 and thus has a few intrinsic limitations. In particular, it does not offer relevant vulnerability classes, nor does it include non-vulnerable test cases, which prove fundamental in evaluating whether or not a web scanner generates a high number of false positives. The scope of this work is to propose a reinforced WAVSEP platform that addresses the problems mentioned above. We integrated 72 new cases in the existing vulnerability classes and created two new vulnerability classes while adding 40 non-vulnerable test cases. Finally, we tested web scanners against both the original WAVSEP and the reinforced one. As the reinforced platform covers a wider set o vulnerability test cases, web scanners were able to detect fewer vulnerabilities by confirming the effectiveness of our platform.
Evaluating Co-Creative XR Resource Design and Development; Observations From the Field

There has been an increased interest in medical education and, specifically, in identifying appropriate methods to ensure efficiency in medical students’ learning process. The design and development of Immersive experiential technologies, including Virtual patients, chatbots, and Virtual, Augmented, or Mixed Reality (VR/AR/MR), through co-creative methods, find fertile ground to support medical educational material. The aim of the current paper was to evaluate the perceptions and gain insights on healthcare technologists’ and educators’ experience regarding a series of co-creation sessions conducted during the design and development of XR educational resources. Results of the qualitative research showed that co-creation can be an interactive and engaging method that involves individuals from multidisciplinary backgrounds for effectively designing XR educational resources. However, strict adherence to best practices, as described in this work, is required to avoid hindering the effectiveness of the process and the quality of the final resources.
The Method of Creel Positioning Based on Monocular Vision

Automatic replacement of bobbins on the frame is one of the core problems that must be solved in the textile industry. In the process of changing the bobbin, it is faced with problems such as the position offset of the bobbin, the loosening and deformation of the bobbin, which will lead to the failure of replacing the bobbin. Therefore, it is necessary to initialize the creel coordinates regularly, also considering the labor intensity and poor reliability of manual positioning. This paper proposes an automatic creel positioning method based on monocular vision. Firstly, the industrial camera was installed on the drum changing manipulator controlled by the truss system, and each yarn frame in the yarn area was inspected. Secondly, the end face image of the creel was collected at a reasonable distance, and the collected images were transmitted to the computer in real time through the Ethernet bus. Thirdly, the center coordinates (x, y) and radius r of the creel were marked by the improved Hough circle detection algorithm. Finally, the coordinate deviation of the creel was calculated and transmitted to the system controller to realize deviation correction. Before the test, the creel positioning markers were specially designed and the camera was calibrated. Thus, the influence of image complex background, creel end roughness, reflection and other factors can be reduced, and the image processing speed and positioning accuracy can be improved. The results show that the positioning effect of this method is fine when the distance between the center of the camera lens and the center of the end face of the creel is 170~190 mm. Moreover, when the distance is 190 mm, the positioning effect is the best, with an average error of only 0.51 mm. In addition, the deviation between the center coordinate and the radius of the end face of the marker is also very small, which is better than the requirements of bobbin yarn changing accuracy.
Parasitic reflection separation deflectometry based on harmonic analysis

• Considering the nonlinear response of screen-camera system, the parasitic reflection model is proposed. • Due to the greater frequency difference of harmonic, the harmonic analysis is used to untangle the parasitic reflection of thin element. • The double surface reflections can be separated for 5 mm thick transparent planar element. • The front surface error of transparent planar element is 80 nm in RMS. In the measurement of transparent planar element using deflectometry, the fringe patterns reflected off the double surfaces of transparent planar element are superimposed on the camera, causing the phenomenon of parasitic reflection. The parasitic reflection makes it difficult for traditional deflectometry to accurately obtain the front surface shape. Previous solutions suffer from inability to reliably measure the element with thickness less than 10 mm. In practice, modulated by the nonlinear response, the signal captured by the camera contains higher-order harmonics, whose frequency difference is larger than that of the fundamental. Therefore, a novel method based on harmonic analysis is proposed. This parasitic issue of thin transparent element can be eliminated by analyzing the harmonic component in power spectrum. The surface shape of element with thickness of 5 mm is measured in experiment. The measurement error is around 80 nm in RMS with a diameter of 52 mm.
Creating A New Color Space utilizing PSO and FCM to Perform Skin Detection by using Neural Network and ANFIS

Skin color detection is an essential required step in various applications related to computer vision. These applications will include face detection, finding pornographic images in movies and photos, finding ethnicity, age, diagnosis, and so on. Therefore, proposing a proper skin detection method can provide solution to several problems. In this study, first a new color space is created using FCM and PSO algorithms. Then, skin classification has been performed in the new color space utilizing linear and nonlinear modes. Additionally, it has been done in RGB and LAB color spaces by using ANFIS and neural network. Skin detection in RBG color space has been performed using Mahalanobis distance and Euclidean distance algorithms. In comparison, this method has 18.38% higher accuracy than the most accurate method on the same database. Additionally, this method has achieved 90.05% in equal error rate (1-EER) in testing COMPAQ dataset and 92.93% accuracy in testing Pratheepan dataset, which compared to the previous method on COMPAQ database, 1-EER has increased by %0.87.