Reviewerly/exHarmony huggingface dataset:
A 3D Marker Recognition Method for AR Game Development

Many game development frameworks have become supporting Augmented Reality (AR) games using cameras and depth sensors embedded in smartphones. In most cases, they detect horizontal and vertical planes such as tabletops or walls in a room at first, and put virtual game characters and props on the faces. 2D markers are currently widely used for making virtual items over real-world gadgets. For sharing the playing game data between multiple devices, data sharing of the game world is also supported. Such the face detection function, 2D marker recognition, and the world sharing mechanism are the main features of the AR game support. However, in the recent situation that 3D printers are rapidly becoming popular all over the world, 3D markers might be important technology for AR game development. In this paper, we introduce a simple yet powerful method for 3D marker recognition that can be flexibly used in AR games.
Research on Subway Pedestrian Detection Algorithm Based on Big Data Cleaning Technology

The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images’ quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model’s performance.
Annotation Difficulties in Natural Language Inference

State-of-the-art models have obtained high accuracy on mainstream Natural Language Inference (NLI) datasets. However, recent research has suggested that the task is far from solved. Current models struggle to generalize and fail to consider the inherent human disagreements in tasks such as NLI. In this work, we conduct an experiment based on a small subset of the NLI corpora such as SNLI and SICK. It reveals that some inference cases are inherently harder to annotate than others, although good-quality guidelines can reduce this difficulty to some extent. We propose adding a Difficulty Score to NLI datasets, to capture the human difficulty level of agreement.
Public Key Encryption with Flexible Pattern Matching

Many interesting applications of pattern matching (e.g. deep-packet inspection or medical data analysis) target very sensitive data. In particular, spotting illegal behaviour in internet traffic conflicts with legitimate privacy requirements, which usually forces users (e.g. children, employees) to blindly trust an entity that fully decrypts their traffic in the name of security.The compromise between traffic analysis and privacy can be achieved through searchable encryption. However, as the traffic data is a stream and as the patterns to search are bound to evolve over time (e.g. new virus signatures), these applications require a kind of searchable encryption that provides more flexibility than the classical schemes. We indeed need to be able to search for patterns of variable sizes in an arbitrary long stream that has potentially been encrypted prior to pattern identification. To stress these specificities, we call such a scheme a stream encryption supporting pattern matching.Recent papers use bilinear groups to provide public key constructions supporting these features [, ]. These solutions are lighter than more generic ones (e.g. fully homomorphic encryption) while retaining the adequate expressivity to support pattern matching without harming privacy more than needed. However, all existing solutions in this family have weaknesses with respect to efficiency and security that need to be addressed. Regarding efficiency, their public key has a size linear in the size of the alphabet, which can be quite large, in particular for applications that naturally process data as bytestrings. Regarding security, they all rely on a very strong computational assumption that is both interactive and specially tailored for this kind of scheme.In this paper, we tackle these problems by providing two new constructions using bilinear groups to support pattern matching on encrypted streams. Our first construction shares the same strong assumption but dramatically reduces the size of the public key by removing the dependency on the size of the alphabet, while nearly halving the size of the ciphertext. On a typical application with large patterns, our public key is two order of magnitude smaller than the one of previous schemes, which demonstrates the practicality of our approach. Our second construction manages to retain most of the good features of the first one while exclusively relying on a simple (static) variant of \(\mathsf {DDH}\), which solves the security problem of previous works.
A Mobile Application About Earthquake to be Used Before and After a Disaster

Earthquake is one of the biggest natural disasters in the world. Earthquakes cause both loss of life and property. It is one of the big problems in Turkey. The aim of the project is to provide information about earthquakes that may occur in our country and to raise awareness before an earthquake. In the project, there is a section containing the emergency numbers that people can use at the time of the earthquake, a section showing what should be in the earthquake bag to be used after the earthquake, in order of importance, and a general purpose comfortable section that helps to locate the casualty by making a sound during the earthquake. Therefore, a mobile application with many earthquake-related information and functions is presented. In the study, the reason for making a mobile project is that people will be able to access the application much more easily.
Geochemistry and mineral chemistry of pelitic gneiss of Ikare area, southwestern Nigeria

This study conducts Electron Probe Microanalysis (EPMA), X-ray Fluorescence (XRF) and Laser Ablation Inductively Coupled Plasma Spectrometry (LA-ICP-MS) analysis on the pelitic gneiss of the Basement complex of Nigeria. Phase equilibrium modeling was undertaken in the MnO–Na2O–CaO–K2O–FeO–MgO–Al2O3–SiO2–H2O–TiO2–O2 (MnNCKFMASHTO) chemical system using THERMOCALC. The rock comprises of sillimanite + biotite + garnet + K-feldspar + quartz + ilmenite + plagioclase ± cordierite. Garnet has inclusions of sillimanite, biotite, and quartz. There is a very close association between biotite and sillimanite. Fibrolitic and prismatic sillimanite are both present. Fibrolitic sillimanite surrounding biotite could have possibly formed through melt-residue back reactions involving crystallization of melt. Garnet has almandine higher than 60 % and pyrope less than 33 %. Plagioclase feldspar has anorthite content between 29 and 39 %, comprising of both oligoclase and andesine. Microtextures such as cordierite corona round garnet, symplectite and kinked biotite were observed. The textural association indicates that cordierite was formed at the expense of garnet and sillimanite; garnet + sillimanite + plagioclase => cordierite + melt/fluid. Metamorphic peak conditions of 770–840 °C and 5.5–7 kbar were obtained for the mineral assemblage garnet-biotite-sillimanite-K-feldspar-quartz indicating medium-pressure granulite facies metamorphism.
ARinVR: Bringing Mobile AR into VR

Augmented Reality (AR) and Virtual Reality (VR) techniques are widely used for people to experience and interact with 3D digital content. However, the user has to choose either AR or VR separately with different devices for different interaction modalities. This work presents ARinVR, a novel concept that seamlessly and synchronously integrates handheld mobile AR and head-worn VR to allow users to see an augmented scenario on a phone screen and in a VR space at the same time. We developed a prototype system that enabled users to operate a smartphone and observe mobile AR scenarios in VR environment as naturally as in the real world. The system extends the AR space and occludes the physical surroundings, enabling users to stay focused on task performance and enhancing spatial perception.
Evaluation of one-class algorithms for anomaly detection in home networks

We have seen significant growth in adopting new protocols and intelligent devices for the residential environment in recent years. These devices provide convenience, security, and energy efficiency to users. For instance, security cameras can detect unauthorized movement, and smoke sensors can detect potential fire accidents. However, many examples have shown that such devices may generate new cyber threats causing concerns about the security of sensitive data present in the home environment. In this article, we explore the application of machine learning to identify anomalous activities that can take place in a smart-home environment. We evaluated the performance of twelve one-class algorithms in three test cases; the best models showed an AUC higher than 94%, which indicates the usefulness of adopting unsupervised learning algorithms in identifying anomalous traffic in home networks.
Covert Communication Scheme Based on Bitcoin Transaction Mechanism

Due to the unique characteristics of blockchain, such as decentralization, anonymity, high credibility, and nontampering, blockchain technologies have become an integral part of public data platforms and public infrastructure. The communication between the stakeholders of a given blockchain can be used as a carrier for covert communication under cover of legal transactions, which has become a promising research direction of blockchain technology. Due to the special mechanism of blockchain, some traditional blockchain covert communication schemes are not mature enough. They suffer from various drawbacks, such as weak concealment of secret information, cumbersome identification and screening of special transactions, poor availability, and low comprehensive performance. Therefore, this paper designs a scheme of covert communication in the Bitcoin blockchain, which takes normal transactions as a mask and leverages the Bitcoin transaction mechanism to embed secret information in the public key hash field. Specifically, we propose a novel key update mechanism combined with the hash algorithm to construct a covert channel. It ensures security and can update the channel to prevent the related problems caused by address reuse. We are taking advantage of the feature of Bitcoin that cannot be double-spent to solve the problem of burning bitcoin when paying bitcoin to a fake public key hash. In our scheme, both parties to the communication are anonymous, and the attacker cannot detect the covert data or track the transaction and address. Our proposed scheme was tested in real Bitcoin blockchain network, and the experimental results were analyzed to verify its security, availability, and efficiency.
STRASS Dehazing: Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples

In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles.The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples.Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.The final dehazed image is yielded after iterations of the high-pass filter.STRASS can be run directly without any machine learning.Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts.Image dehazing can be applied in the field of printing and packaging, our method is of great significance for image pre-processing before printing.
ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

A reliable and precise tool for medical machine learning is in demand.The diagnosis datasets are mostly unbalanced.To propose an accurate prediction tool for medical data we need an accurate machine-learning algorithm for unbalanced data classification.In binary class unbalanced medical dataset, accurate prediction of the minority class is important.Traditional classifiers designed to improve accuracy by giving more weight to the majority class.Existing techniques gives good results by accurately classifying the majority class.Despite the fact that they misclassify the minority cases, the total accuracy value does not reflect this.When the misclassification cost of minority class is high, research should focus on reducing the total misclassification cost.This paper presents a new cost-sensitive classification algorithm that classifies unbalanced data accurately without compromising the accuracy of the minority class.Our proposed minority-sensitive decision tree algorithm employs new splitting criteria called MSplit to ensure accurate prediction of the minority class.The proposed splitting criteria MSplit derived from the exclusive causes of the minority class.For our experiment, we mainly focused on the breast cancer dataset by considering its importance in women's health.Our proposed model shows good results as compared to the recent studies of breast cancer detection.It shows 0.074 misclassification cost that is the least among the other comparison methods.Our model improves the performance for other unbalanced medical datasets as well.
Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network

In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method used to learn the characteristics of sample data. It is a multilayer network, which can learn the information from the bottom to the top of the image through the multilayer network, so as to extract the characteristics of the sample, and then perform identification and classification. The purpose of deep learning is to make the machine have the same analytical and learning capabilities as the human brain. The ability of deep learning in data processing (including images) is unmatched by other methods, and its achievements in recent years have left other methods behind. This article comprehensively reviews the application research progress of deep convolutional neural networks in ancient Chinese pattern restoration and mainly focuses on the research based on deep convolutional neural networks. The main tasks are as follows: (1) a detailed and comprehensive introduction to the basic knowledge of deep convolutional neural and a summary of related algorithms along the three directions of text preprocessing, learning, and neural networks are provided. This article focuses on the related mechanism of traditional pattern repair based on deep convolutional neural network and analyzes the key structure and principle. (2) Research on image restoration models based on deep convolutional networks and adversarial neural networks is carried out. The model is mainly composed of four parts, namely, information masking, feature extraction, generating network, and discriminant network. The main functions of each part are independent and interdependent. (3) The method based on the deep convolutional neural network and the other two methods are tested on the same part of the Qinghai traditional embroidery image data set. From the final evaluation index of the experiment, the method in this paper has better evaluation index than the traditional image restoration method based on samples and the image restoration method based on deep learning. In addition, from the actual image restoration effect, the method in this paper has a better image restoration effect than the other two methods, and the restoration results produced are more in line with the habit of human observation with the naked eye.
Digital Competence Of University Teachers: Self-Perception Of Skills In Online-Environment

The accelerated introduction of digital technologies into the higher education system against the backdrop of the COVID-19 epidemic is raising the issue of digital competence of higher education teachers.The skills of using digital tools for communicating with students, transferring, and monitoring knowledge are gaining special weight in the list of professional competencies of a teacher.The article presents some results of a study focused on studying the consequences of digitalization for the socio-professional status of a teacher.The method of focus group discussion was used with teachers of social, humanitarian, and natural disciplines from Russian universities with at least three years of professional experience.In total, 5 focus groups were held with 7 teachers in each.The purpose of the study is to show how, through selfperception and self-assessment of their own digital competence, higher education teachers develop an attitude towards the digital transformation of education.The adaptation of university teachers to digital technologies in the educational environment, teachers' assessment of their own digital competence and the effectiveness of online communication with students; the study of socio-cultural problems generated by the digitalization of education, including the emergence of new forms of alienation and educational inequalities are among the tasks.The conclusion is made about the differentiation of teachers into three categories depending on the mastery of digital competencies -innovators, active and passive.Each category is characterized in terms of the emotional, cognitive, and behavioral aspects of attitudes towards digitalization of higher education.
Detection and Localization of Anamoly in Videos Using Fruit Fly Optimization-Based Self Organized Maps

Detection of anomalies in crowded videos has become an eminent field of research in the community of computer vision. Variation in scene normalcy obtained by training labeled and unlabelled data is identified as Anomaly by diverse traditional approaches. There is no hardcore isolation among anomalous and non-anomalous events; it can mislead the learning process. This paper plans to develop an efficient model for anomaly detection in crowd videos. The video frames are generated for accomplishing that, and feature extraction is adopted. The feature extraction methods like Histogram of Oriented Gradients (HOG) and Local Gradient Pattern (LGP) are used. Further, the meta-heuristic training-based Self Organized Map (SOM) is used for detection and localization. The training of SOM is enhanced by the Fruit Fly Optimization Algorithm (FOA). Moreover, the flow of objects and their directions are determined for localizing the anomaly objects in the detected videos. Finally, comparing the state-of-the-art techniques shows that the proposed model outperforms most competing models on the standard video surveillance dataset.
Stream Classification Algorithm Based on Decision Tree

Due to the rise of many fields such as e-commerce platforms, a large number of stream data has emerged. The incomplete labeling problem and concept drift problem of these data pose a huge challenge to the existing stream data classification methods. In this respect, a dynamic stream data classification algorithm is proposed for the stream data. For the incomplete labeling problem, this method introduces randomization and iterative strategy based on the very fast decision tree VFDT algorithm to design an iterative integration algorithm, and the algorithm uses the previous model classification result as the next model input and implements the voting mechanism for new data classification. At the same time, the window mechanism is used to store data and calculate the data distribution characteristics in the window, then, combined with the calculated result and the predicted amount of data to adjust the size of the sliding window. Experiments show the superiority of the algorithm in classification accuracy. The aim of the study is to compare different algorithms to evaluate whether classification model adapts to the current data environment.
An Efficient Distributed SPARQL Query Processing Scheme Considering Communication Costs in Spark Environments

Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the grouped subqueries calculate the cost of all possible query execution paths to select an efficient query execution path. The efficient query execution path is selected through the algorithm considering the data parsing cost of all possible query execution paths, amount of data communication, and queue time per node. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.
Hand Gesture Recognition using Auto Encoder with Bi-direction Long Short Term Memory

Hand Gestures provide a means for a series of interactive communications with human beings who are deaf and dumb.Deep learning based models have been revolutionized for achieving human level performance through computer vision that automates, classifies and detects presence of objects in an image.Various challenges were faced through this process such as variation in the hand size, color, illumination, skin tone, view point, complex natural backgrounds etc, because of the various conditions present on the images.The present research overcomes the problem of high dimensional data and improved the classification accuracy in gesture pattern recognition for the incomplete data.Then, multilevel segmentation method and morphological model are used to segment the gesture regions and furthermore, Auto Encoder (AE) algorithm is applied to reduce the dimension of the data.Additionally, Bi-Long Short Term Memory (LSTM) classifier is applied to classify the alphabets, numbers, and video symbols.In the experimental phase, the improved AE-BiLSTM model achieved effective performance in recognition of hand gesture in terms of accuracy.Compared to the existing CNN and PCA based uni-modal feature-level fusion model, the proposed AE-BiLSTM model showed a maximum value of 99.85 % in terms of accuracy for ISL dataset and 99.75% for NUS dataset.
Intuitionistic fuzzy evaluation of artificial neural network model

In this paper a method that evaluates a trained artificial neural network is presented. The learning type of an artificial neural network is supervised learning which requires labeled input training vectors. Labeled medical data is provided to train the network, where the labels can either be 1 if the person is alive, or 0 if the person has deceased. The data is divided into training and validation vectors. The validation input vectors are used to evaluate the model and the results are summarized by using intuitionistic fuzzy values.