Reviewerly/exHarmony huggingface dataset:
Measuring Social Network De-Anonymizability by Means of Morphism Property

Anonymization techniques have tranquilized current social network users in terms of privacy leakage, however, it does not radically prevent adversaries from de-anonymizing users, as they may map the users to an un-anonymized network. Till now, researchers share a common thread in such de-anonymization attack: unveiling conditions leading to successful de-anonymization under the chosen network model. However, it has not yet been well understand how the structural property in different network models intrinsically determines de-anonymizability. We address the above issue in this paper by making the two contributions: (i) We discover that the automorphic degree and homomorphic degree of social networks determine their de-anonymizability universally. The automorphic degree characterizes the distinguishability of the users in a network, while the homomorphic degree models the similarities of users between two networks. We conclude that a smaller automorphic degree and a larger homomorphic degree conduce to a higher de-anonymizability. Such model-independent phenomenon refreshes us with a latitudinal study as it generalizes the essential commonness of de-anonymization in different network models. (ii) We derive explicit parametric bounds of the de-anonymizability for three classic network models, showing that such bounds correspond well to our conclusion about morphism property. We then algorithmically and experimentally show that such theoretical results literally make sense to adversaries. Such longitudinal study, including welding theory, algorithm and validation, promises applicability of our results on morphism property in real cases.
Image Colorization Using CNNs

This paper presents a survey on the current technological processes that are used to colorize images with little or no human intervention, using image processing and machine learning techniques. We also analyze the more recent deep- learning-based approaches, specifically those using convolutional neural networks (CNNs), which are specially optimized mathematical models for image processing and manipulation. By diving deep into the mathematical and statistical theory behind the implementation of these models, we have managed to decipher the essence of the process of colorization. Here, in this paper we present a comparative study of different approach and then propose changes that may theoretically increase the accuracy and color-reproducibility of these models.
A Proposed Structure, Primarily Based Totally on Cloud Computing for Enhancing Electronic Commerce Applications

The cloud companies have caught the IT Company's attention, to give new approach of services concept. This is particularly seen in the companies of e-commerce. At present, E-commerce companies and associations, has a major environmental cost problem because of with the cloud computing advent, the aim of this paper is to introduce the suggested applications of ecommerce and their infrastructure based on various concepts and origins, cloud computing development trends on the basis of resource storage and e-commerce related problems. With the help of proper implementation the cost for the activities of ecommerce and problem solution at company level is insufficient to make proper activities regarding e-commerce due to unavailability of sources.
Effective Classification of Tweets Using Machine Learning

Twitter data analysis plays a vital role in disaster to identify the precious and non-precious tweets in the form of text and images. This real time information is required to take necessary action during the disaster times. In the existing method, the Stacked convolution method is used which is a combination of SVM classifier and KNN classifier as methods for classification of disaster tweets. The SVM classifier takes the six dimensional vector as input and finally gives the tweet classification requires huge computational time which leads to improper classification of the tweets and uncertainty in the classification. In this proposed work image classification and text classification has been performed using the improved GAN-BERT and GAN-UNET for identifying the precious and non-precious tweets classification based on text and image features. Immense experiments are carried out on Twitter dataset during various disasters. The results of the proposed models are shown superior than existing models.
On the Structure of Format Preserving Sets in the Diffusion Layer of Block Ciphers

In 2016, Chang et al. proposed a Format Preserving Encryption (FPE) scheme over a finite field and used an MDS matrix in the diffusion layer of the scheme for optimal diffusion. Later that year, Gupta et al. defined an algebraic structure named Format Preserving Set (FPS) is the diffusion layer of an FPE scheme. In 2018, Barua et al. showed that it is not possible to construct an FPS over a finite field in the diffusion layer of an FPE scheme if the cardinality of the set is not a power of prime. They extended the search of FPS over a finite commutative ring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {R}$ </tex-math></inline-formula> and showed that if an FPS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S \subseteq \mathcal {R}$ </tex-math></inline-formula> is closed under addition then it gets module structure over some subring of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {R}$ </tex-math></inline-formula> . Moreover, in this case, the only possible cardinalities of FPS are some power of the cardinalities of subrings when the module is free. The purpose of this article is twofold. Firstly, we show that it is possible to construct format preserving sets over a finite commutative ring which are not closed under addition. Secondly, we search for format preserving sets and MDS matrices over torsion modules. We provide examples of format preserving sets of cardinalities 26 and 52 over torsion modules and rings. These cardinalities are interesting because they correspond to the set of English alphabets, without and with capitalization. By considering a finite Abelian group as a torsion module over a PID, we show that a matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> with entries from the PID is MDS if and only if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is MDS under the projection map on the same Abelian group.
Mapping and Path Planning for the Differential Drive Wheeled Mobile Robot in Unknown Indoor Environments Using the Rapidly Exploring Random Tree Method

The probabilistic searching methods are popular in robot motion planning, especially when the robot moves in an unknown environment with many obstacles. Among those methods, the rapidly exploring random tree method (RRT) has proven effective and has been widely applied to complex multidirectional movements in unstructured environments. This paper conducts the design of the optimal motion trajectory for a differential drive wheeled mobile robot with two independent drive wheels. The workspace mapping of the robot is designed within a large space including many walls and obstacles in custom positions. The robot builds the map automatically with limit points by combining the local RRT and global RRT solutions. Through the experiment on Robot Operating System (ROS), the mapping achieved high performance while also not missing the map areas located at the border nodes. On that basis, optimal paths towards the goals are built according to different navigation cases. Therefore, this method can be used efficiently at boundary nodes, it is applicable to narrower and more complex environments than existing RRT algorithms.
Computer-assisted Ancient Documents Re-organization

A large number of uninterpreted ancient documents exist in Asia, which record a large amount of information about the politics, economy, and culture. Since the beginning of oracle bone inscription, characters have been developing and evolving, and the forms of ancient documents have also been changing. As a large amount of information is preserved in ancient documents, the interpretation and collation of ancient documents are of great importance. However, due to the age, many ancient documents are moth-eaten, stained, damaged, and other problems. And most ancient documents are written in a style that is no longer in use, leaving few experts able to interpret them. This paper from oracle bone inscription, ancient hand-written character, rubbings three diferent types of ancient documents, introduces the recognition technology of assisted ancient documents re-organization including image processing, deep learning, and data analysis and deep learning combined method.
Hybrid Based Model Face Shape Classification Using Ensemble Method for Hairstyle Recommender System

The face shape classification has a great impact on the fashion industry, and it helps to select the appropriate hairstyle, makeup style, and eyewear frames for an individual base on their face shape and looks. Computer-aid face shape classification system helps to enhance the beauty and attractiveness of the system, and furthermore it minimizes the cost and time spent by experts. In this paper, we have presented machine learning and engineering-based method to classify the face shape to recommend hairstyle. The method proposed combines facial landmarks, the geometrical, and the pre-train model features to classify facial shapes into four categories. The method can extract a wide range of characteristics and select the most effective attributes to be evaluated using the distinct method of machine learning. The overall accuracy of the proposed method is 86.5%. This is an innovative method of face shape classification, which shows good performance against the state-of-the-art.
Overlapped Data Processing Scheme for Accelerating Training and Validation in Machine Learning

For several years, machine learning (ML) technologies open up new opportunities which solve traditional problems based on a rich set of hardware resources.Unfortunately, ML technologies sometimes waste available hardware resources (e.g., CPU and GPU) because they spend a lot of time to wait for a previous step inside ML procedure.In this paper, we first study data flows of the ML procedure in detail to find avoidable performance bottlenecks.Then, we propose ol.data, the first software-based data processing scheme that aims to (1) overlap training and validation steps in one epoch or two adjacent epochs, and (2) perform validation steps in parallel, which helps to significantly improve not only the computation time but also the resource utilization.To confirm the positive effectiveness of ol.data, we implemented a convolution neural network (CNN) model with ol.data and compared it with the traditional approaches, Numpy (i.e., baseline) and tf.data on three different datasets.As a result, we confirmed that ol.data reduces the inference time by up to 41.8% and increases the utilization of CPU and GPU resources by up to 75.7% and 38.7%, respectively.
A Hybrid Security Framework for Medical Image Communication

Authentication of the digital image has much attention for the digital revolution. Digital image authentication can be verified with image watermarking and image encryption schemes. These schemes are widely used to protect images against forgery attacks, and they are useful for protecting copyright and rightful ownership. Depending on the desirable applications, several image encryption and watermarking schemes have been proposed to moderate this attention. This framework presents a new scheme that combines a Walsh Hadamard Transform (WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding (DRPE). First, on the sender side, the secret medical image is encrypted using DRPE. Then the encrypted image is watermarking based on WHT. The combination between watermarking and encryption increases the security and robustness of transmitting an image. The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Normalized cross-correlation (NC), and Feature Similarity Index (FSIM).
Analysis of Body-Gestures Elucidated Through Elicitation Study for Natural Locomotion in Virtual Reality

Proxy gestures are a powerful input interaction method for travel in virtual environments (VEs). Gestures provide an alternative mode of interaction for a design that is more natural and intuitive than the controller-based methods. A key challenge lies in understanding the patterns in the gestures and the relevant user behavior while performing the gestures in a specific scenario. In this paper, we classify and analyze the full-body travel gestures that were obtained as a result of our previous elicitation study. The travel gestures were performed in a seated position by 40 participants for 3 different VEs (VE1, VE2 and VE3) where the task of virtual travel was combined with placement task. We observed a total of 405 gestures and identified 24, 24, and 9 unique full-body gestures for the three different VEs respectively. We categorize them into upper body and lower body gestures. We further classified them based on hand usage and gesture form. The results indicate that static pose and path form was predominantly used in VE1 and VE3, static pose and static pose and path form was used in VE2. The findings also indicate that as the multitasking level increased in the VE, the usage of dynamic pose and dynamic pose and path form decreased. From the analysis, we obtain a better understanding of users’ thoughts and behavior while performing the gesture. Using our results future designers and developers can identify suitable gesture categories for task-related to virtual travel in a seated position for multitasking scenarios.
Automatic Recognition of Fuzzy Characters in the Transport Task Tables of Scanned Handwritten Student Papers

Students' handwritten work should be checked during distance learning. Students scan their work and submit it for review using the distance learning system or by email. The challenge is therefore to automate the checking of student work, requiring the recognition of handwritten papers. Recognition automation is especially important for checking large handwritten tables, such as transport tables and simplex tables used in logistics and linear programming problems. The current study presents an analysis of the process of recognising individual handwritten characters found in tables or formulas of scanned student work. The most important is the recognition of digital tabular data simplex-tables and tables used in solving logistical problems. It is necessary to recognize large-volume tables used in solving the transportation problem using the methods of North-western corner, least element, improvement of the transportation plan by Vogel method, improvement of the transportation plan by the method of potentials. A skeletal method of character recognition is considered. Recognized handwritten tables designed for automatic and semi-automatic verification. Methods of skeletonization of selected areas based on the wave approach are analyzed. Alternatively, the paper proposes a method in which skeleton graphs are constructed multiple times for fuzzy characters in the decision tree, using different filtering and skeletonisation options. The software implementation of the proposed method is also proposed.
Violent Target Detection Based on Improved YOLO Network

With the progress of the times, the Internet has deeply influenced everyone. The content of online information types is complex, with a large number of violence-related contents containing undesirable elements such as gore and violence, which make the online ecological environment seriously polluted and constantly pose a threat to the worlds online ecological security. Deep learning has been performing well in computer vision, and deep learning-based target detection algorithms have great potential for identifying violent elements. Target detection is an important theoretical basis for using high-level vision such as images and events. The aim of this paper is to investigate the application of deep learning techniques to the detection of violence-involved elements. By evaluating the neural network structure and spatial model, the application of the latest YOLOv5 algorithm is able to identify violence-involved elements more accurately and determine the class of elements, counting the mAP value of each class label. This is important for discovering and processing information about riot-related organisations and riot-related networks, grasping the dynamics of riot-related organisations, analysing and tracking the movements of riot-related organisations, and targeting them for combat.
Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.
Examining Bias in Sentiment Analysis Algorithms Interacting with Emojis with Skin Tone Modifiers

Emojis are commonplace in text, and skin-toned emojis have gained widespread adoption. Unfortunately, the level of bias or errors in Sentiment Analysis (SA) algorithms is unknown when working with online text containing emojis with skin tone modifiers. This study investigates SA algorithms to understand potential changes in polarity when the same sentence is presented with emojis of different skin tone modifiers. We found that many of the SA algorithms ignored the skin tone of the emoji in their calculations; however, for some algorithms, the method used to translate the emoji to text affected the SA polarity score. We aim to present a methodology to compare commonly used SA algorithms and lexicons and to highlight potential bias areas. This research shows that there may be discrepancies based on the SA methods and algorithms used to process text with skin tone emojis.
Bayesian Network Structure Learning Approach Based on Searching Local Structure of Strongly Connected Components

Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem. As an excellent search &#x0026; score based method, the K2 algorithm strongly depends on the input of global order of all nodes to ensure the result is a directed acyclic graph. And the K2 algorithm searches parents for each node from the nodes before it in the global order. Incorrect node order is likely to result in a wrong structure. In this paper, we propose a new method to avoid the global order by Local structure Searching for Strongly Connected Components with hill climbing method Twice. Firstly, we search the best parent nodes for each node from all the remaining nodes except itself, and form a global directed graph by concatenating the arcs between nodes and their parents. Then, the directed acyclic structure of all the strongly connected components are determined by hill climbing algorithm twice. Finally, we adopt local search method further to get the final result by taking the previous result as start point. The proposed algorithm is evaluated on some standard benchmark networks with sampled data. Experimental results show that our algorithm outperforms the four compared algorithms in terms of structural Hamming distance, Bayesian information criterion score and their average ranking.
Influence of Teachers’ Occupational Stress on Anxiety by Using Cross-Media Teaching Method

This paper is to study the conditions of teachers’ occupational stress and anxiety by using cross-media teaching method, and reveals the influence relationship between them. To this end, a questionnaire survey of 228 teachers using cross-media teaching method from 3 schools in Guangdong Province (China) was conducted. The conclusions are as follows: Teachers who use cross-media teaching method have high levels of occupational stress and anxiety, lack of leadership and administrative support, overloaded work, state anxiety and trait anxiety are all at a high level. Under general characteristics differences, gender does not constitute a factor causing occupational stress and anxiety of the teachers using cross-media teaching. With the increase in the use of cross-media teaching, teachers feel gradually increase of occupational stress and trait anxiety in more work tasks, and occupational stress and state anxiety shows ups and downs due to lack of school policy support. From the relationship between occupational stress and anxiety, occupational stress and sub-variables without leadership and administrative support, overloaded work, relationships with colleagues, and relationships with parents are all positively correlates with anxiety and have significant positive effects. Thereinto, whether the influence of occupational stress sub-variable on anxiety, or the state anxiety and trait anxiety of the anxiety sub-variables, overloaded work and lack of leadership and administrative support have always been the key factors that cause anxiety. Therefore, if the school or the relevant organization provides appropriate support and assistance to cross-media teaching, or appropriately reduce heavy tasks of teachers in cross-media teaching, so as to relieve occupational press and anxiety of the teachers, create good teaching quality, and promote the development of teaching technology.