Reviewerly/exHarmony huggingface dataset:
De-anonymizability of social network

Social network de-anonymization, which refers to re-identifying users by mapping their anonymized network to a correlated network, is an important problem that has received intensive study in network science. However, it remains less understood how network structural features intrinsically affect whether or not the network can be successfully de-anonymized. To find the answer, this paper offers the first general study on the relation between de-anonymizability and network symmetry. To this end, we propose to capture the symmetry of a graph by the concept of graph bijective homomorphism. By defining the matching probability matrix, we are able to characterize the de-anonymizability, i.e., the expected number of correctly matched nodes. Specifically, we show that for a graph pair with arbitrary topology, the de-anonymizability is equal to the maximal diagonal sum of the matching probability matrix generated from homomorphisms. Due to the prohibitive cost of enumerating all possible homomorphisms, we further obtain an upper bound of such de-anonymizability by counting the orbits of each of the two graphs, which significantly reduces the computational cost. Such a general result allows us to theoretically obtain the de-anonymizability of any networks with more specific topology structure. For example, for any classic Erdős-Rènyi graph with designated n and p, we can represent its de-anonymizability numerically by calculating the local symmetric structure that it contains. Extensive experiments are performed to validated our findings.
A UAV Autonomous Maneuver Decision-Making Algorithm for Route Guidance

In order to improve the autonomy of UAV for route guidance, an UAV autonomous maneuver decision- making algorithm is proposed. The optimal UAV maneuver policy is developed in an interactive environment. Firstly, the UAV maneuver decision-making model based on MDPs was established, where the flight state space, the flight action space and the flight assessment function are designed. Then, we proposed the algorithm based on Double Deep Q-Learning with Prioritized Experience Replay. Finally, according to the simulation results, the efficiency and autonomy of algorithm we proposed is proved.
Video Description with GAN

Video description is to convert rich information of video data into text information, Which has been attracting broad research attention in the Artificial Intelligence Community. Deep learning has given computers a strong understanding of one-dimensional picture data and two-dimensional video data. However, In real application scenarios, it still faces the problem of insufficient robustness. For example, the generated text information is unreasonable, the scene information and semantic information rich in video data cannot be extracted effectively. GAN (Generative Adversarial Nets) is a model that generates data using countermeasures, in recent years, which is widely used in text generation, dialogue system, image synthesis, etc. However, there has not been much effort on exploring GAN for Video description. In this paper, we design a new discriminant network on the basis of the traditional text description. In addition, we add the long-short Time memory networks into the model to minimize the loss of information in the encoding or decoding process, so as to generate more reasonable sentences. Experimental results demonstrate that our proposed model exceeds most video description methods in public datasets.
DeepFake video production and SIFT-based analysis

Nowadays advantages in face-based modification using DeepFake algorithms made it possible to replace a face of one person with a face of another person. Thus, it is possible to make not only copy-move modifications, but to implement artificial intelligence and deep learning for replacing face movements from one person to another. Still images can be converted into video sequences. Consequently, the contemporaries, historical figures or even animated characters can be lively presented. Deepfakes are becoming more and more successful and it is difficult to detect them in some cases. In this paper we explain the video sequences we produced (e.g. using X2Face method, and First Order Motion Model for Image Animation) and perform deepfake video analysis using SIFT (Scale Invariant Feature Transform) based approach. The experiments show the simplicity in video forgery production, as well as the possible role of SIFT keypoints detection in differentiation between the deeply forged and original video content.
Random Forest Model for Intrusion Detection in Crowd-Sourced Reviews

Online reviews will always leverage an incredible effect on the present business and trade. Dynamic acquisition of online items occupies the most part that relies upon reviews given by the clients. Instead, smart people or meetings are created to track item feedback for their own purposes. This paper presents a semi-directed and managed text mining models that has the ability to identify the fake online reviews as well as analysing the competence of both dataset procedures that contain lodging reviews. The outcome of this research deploys the intrusion of detected online sources by using the random forest method of machine learning.
An NSGA-III-Based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures

Parameter Sweep Experiments (PSEs) are commonplace to perform computer modelling and simulation at large in the context of industrial, engineering and scientific applications. PSEs require numerous computational resources since they involve the execution of many CPU-intensive tasks. Distributed computing environments such as Clouds might help to fulfill these demands, and consequently the need of Cloud autoscaling strategies for the efficient management of PSEs arise. The Multi-objective Intelligent Autoscaler (MIA) is proposed to address this problem, which is based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III), while aiming to minimize makespan and cost. MIA is assessed utilizing the CloudSim simulator with three study cases coming from real-world PSEs and current characteristics of Amazon EC2. Experiments show that MIA significantly outperforms the only PSE autoscaler (MOEA autoscaler) previously reported in the literature, to solve different instances of the problem.
Lightweight Image Super-resolution with Local Attention Enhancement

In recent years, methods based on convolutional neural network (CNN) have been the mainstream in single image super-resolution (SISR). Although these methods have achieved excellent performance, the massive amount of model parameters and heavy computation limit their application. On the other hand, channel attention (CA) mechanism, which can enhance network performance, has also been widely used in SR task recently. However, the channel attention mechanism is introduced from high-level vision tasks to the SR task. The original design of this mechanism doesn't consider the specificity of the SR task. To address these issues, we propose a lightweight expansion and distillation residual network (EDRN) for image super-resolution. Specifically, through the diverse use of different feature channels and different convolution kernel sizes, our network can effectively reduce the amount of parameters while achieving superior performance. To further explore the potential of channel-wise attention in the SR task, we develop a novel plug-and-play local channel attention enhancement strategy (LCAES) to make the network better use the characteristics of local features of the image. Furthermore, comprehensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art SR algorithms in terms of visual quality, reconstruction accuracy, and parameter amount.
Exploring Mobile Learning - Engaging Students where they are

This paper describes an investigation into the use of mobile platforms to enhance student learning. Given the tremendous capacity of mobile devices, apps and games for capturing the attention of their users, how can educators deploy these to enhance student engagement in their courses?
Classification Accuracy Comparison for Imbalanced Datasets with Its Balanced Counterparts Obtained by Different Sampling Techniques

Machine learning (ML) is accurate and reliable in solving supervised problems such as classification, when the training is performed appropriately for the predefined classes. In real world scenario, during the dataset creation, class imbalance may arise, where one of the classes has huge number of instances while the other class has very less in numbers. In other words, the class distribution is not equal. Such scenarios results in anomalous prediction result. Handling of imbalanced dataset is therefore required to make correct prediction considering all the class scenarios in an equal ratio. The paper mentions various external and internal techniques to balance dataset found in literature survey along with experimental analysis of four different datasets from various domains- medical, mining, security, finance. The experiments are done using Python. External balancing techniques are used to balance the datasets- two oversampling SMOTE and ADASYN techniques and two undersampling Random Undersampling and Near Miss techniques. These datasets are used for binary classification task. Three machine learning classification algorithms such as logistic regression, random forest and decision tree are applied to imbalanced and balanced datasets to compare and contrast the performances. Comparisons with both balanced and unbalanced are reported. It has been found that undersample technique loses many important datapoints and thereby predicts with low accuracy. For all the datasets it is observed that oversampling technique ADASYN makes some decent prediction with appropriate balance.
A Two-Stage Evolutionary Fuzzy Clustering Framework for Noisy Image Segmentation

This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization stage, the fuzzy clustering on pixels in inhomogeneous regions is converted into a multi-objective problem, which can preserve image details while restraining noise. The multi-objective problem is decomposed into several sub-problems by the Tchebycheff approach. A trade-off can be obtained by optimizing these sub-problems simultaneously. In the fuzzy clustering segmentation stage, fuzzy clustering with the trade-off between preserving image details and restraining noise is performed on the whole observed image. To deal with this fuzzy clustering problem, an adaptive evolutionary fuzzy clustering algorithm with spatial information is proposed. Experiment results on synthetic and real images illustrate the effectiveness of the proposed framework for noisy image segmentation.
Oversampling for Detection of Malicious JavaScript in Realistic Environment

The threats of cyber-attacks caused by malicious JavaScript are becoming more and more dangerous recently. In order to be able to detect new malicious JavaScript with low cost, methods with machine learning techniques have been proposed and gave positive results. These methods focus on achieving a light-weight filtering model that can quickly and precisely filter out malicious data for dynamic analysis. A method constructs a language model to represent the data in vector form from the source code, the represented data will then be used for machine learning. This method has high testing score with the balanced dataset, but the experiment with an imbalanced dataset has not been done. To construct an efficient language model, and to deal with the data imbalance problem, we focus on oversampling techniques and Natural Language Processing techniques. In our research, our method is the first to use oversampling and machine learning to detect malicious JavaScript. We use Natural Language Processing techniques for extracting unique words from source code and converting them into vectors The experimental result shows that our method can detect new malicious JavaScript more accurately and efficiently. Our model can quickly filter out malicious data for dynamic analysis. The best F-measure achieves 0.78.
Study on Impact of RNN, CNN and HAN in Text Classification

Text classification is one of the major research areas in the field of Natural Language Processing. This is the task of determining the category of the text based on its content. This paper analyzes how the semantic and contextual information of the text data is encoded with different deep learning models such as Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM), Convolutional Neural Network (CNN) and Hierarchical Attention Network (HAN). Each of the three models exhibits specific characteristic while representing the contextual information like RNN keeps the sequential structure of the text data, CNN captures the n-gram feature of the document, and HAN maintains the hierarchical structure of the document by considering the critical words and sentences in a document. These models were implemented on the data set, BBC News text and the results are analyzed on the basis of the various parameters like precision, recall, accuracy, F1-Score, confusion matrix and loss.
Classification of Criminal News Over Time Using Bidirectional LSTM

With the rapid expansion of digital newspapers, readers have an overwhelming amount of news available daily. However, it is difficult to keep track of the news that is only of interest to the reader. Because of this, this research discusses the use of deep learning for the classification of news, especially crime related, published by Mexican digital newspapers as well as an analysis of the predictions obtained through the proposed model. According to the experimental results, the proposed system achieves 98.87% of accuracy.
Color Image Segmentation Using Superpixel-Based Fast FCM

A large number of improved variants of the Fuzzy c-means were used to segment the gray scale and color image. Nevertheless, some of them take time to yield the best and optimal results and for two reasons cannot produce adequate segmentation results for color images. This work proposes a simple FCM clustering algorithm based on the Super pixel Algorithm (SFFCM), which is considerably faster and more robust for image segmentation applications which are based purely on the color parameter. In this research work, to attain an accurate contour super pixel image for improved local spatial neighborhoods, an efficient algorithm referred as multi-scale morphological gradient reconstruction (MMGR) operation is originally described. The significance of the proposed method lies in the relationships between the pixels and how they are utilized in the flow of the application. Compared to the conventional adjacent fixed-size and shape frame, the super pixel image provides improved adaptive and exact amount of irregular local spatial communities which help to enhance color image segmentation. Coming to the next step, the original color image is essentially based on the obtained super pixel picture, and the number of pixels in each super pixel region easily decides its histogram. Ultimately, FCM is implemented to obtain final segmentation results that increase the histogram parameter on the super pixel picture. The performance of the proposed method is computed using Matlab computing language and also a statistical measure is carried out based on different parameters and their respective graphical representations.
An Improved LSSVM Fault Diagnosis Classification Method Based on Cross Genetic Particle Swarm

It is difficult to select the parameters of least squares support vector machine (LSSVM) when studying the classification algorithm, A particle swarm optimization algorithm based on crisscross inheritance method is proposed to find the optimal parameters of LSSVM. Further, the wavelet packet is adopted to process the bearing signal and extract time-frequency domain features, which are used as the input of the LSSVM. The classification model is established and applied to identify the fault of bearing. Classification result shows the classification accuracy is improved, and the LSSVM is optimized.
Edge Filtering through Recursive Application using Canny Edge Detector algorithm on small sub-blocks in an Image

Edges are important features of an object in an image. With the help of edge detection, the amount of data present in the image can be reduced significantly while maintaining the structure of the object. However, factors like varying illumination, random noise present in nature and defect of sensor material all contribute to noise in an image, which leads to the detection of false edges. The performance of many computer vision algorithms relies on the performance of the feature detection algorithm. In this paper, an algorithm is proposed to apply the Canny algorithm of Edge detection on small sub-blocks of images rather than the entire image. The performance analysis of the proposed algorithm is carried out and their output is compared with a Cannyedge detection algorithm.
Implementation on benchmark of SC2LE environment with advantage actor – critic method

Deep reinforcement learning has already surpassed human performance in many video games, which is mainly achieved with reinforcement learning algorithms based on the actor-critic framework. With the release of PySC2 reinforcement learning environment by Google DeepMind and Blizzard Entertainment, deep reinforcement learning algorithms have attracted the attention of many AI developers on StarCraft II games. In this paper, we used the advantage actor - critic algorithm to achieve the training of agents on seven mini-maps released by DeepMind, we obtain the experimental results and compare them with DeepMind's experimental benchmark. The experimental results show that the agent trained based on the advantage actor-critic algorithm has excellent performance on SC2LE environment.
Exploring Augmented Reality as Craft Material

Craft making is associated with tradition, cultural preservation, and skilled hand-making techniques. While there are examples of digital craft making analyses in the literature, Augmented Reality (AR) applied to craft making practice has not been explored, yet applying AR to craft making practices could bring insight into methods of combining virtual and physical materials. This paper investigates how AR is considered by craft makers. We find that narrative is essentially physically located in craft objects, and while virtual elements may describe and annotate an artefact, it is not considered part of the craft artefact’s narrative.
Barriers and Drivers to Adoption of Cloud Infrastructure Services: A Security Perspective

This study investigates the barriers and drivers to cloud infrastructure adoption as perceived by Information Technology Professionals. If the barriers to adoption of cloud computing technologies exist in small then the identification of these barriers will help organizations implement cloud technologies into their operating environments. This study will focus on the adoption of Infrastructure-as-a-Service technologies and not Software-as-a-Service technologies. Much of the literature on this topic revolves around security and the perception that it is less secure. While there are security issues to be addressed, many of the barriers can be overcome through an understanding of the cloud technology. A survey was conducted to measure the perception and readiness of Information Technology professionals and business decision makers. The results of the survey show that the major barriers to adoption are not always security related and that some benefits may also be found. Perceived security is found to be a significant driver for adoption of IaaS.
On the Benefits of Color Information for Feature Matching in Outdoor Environments

The detection and description of features is one basic technique for many visual robot navigation systems in both indoor and outdoor environments. Matched features from two or more images are used to solve navigation problems, e.g., by establishing spatial relationships between different poses in which the robot captured the images. Feature detection and description is particularly challenging in outdoor environments, and widely used grayscale methods lead to high numbers of outliers. In this paper, we analyze the use of color information for keypoint detection and description. We consider grayscale and color-based detectors and descriptors, as well as combinations of them, and evaluate their matching performance. We demonstrate that the use of color information for feature detection and description markedly increases the matching performance.
Hand-Drawn Object Detection for Scoring Wartegg Zeichen Test

The Wartegg Zeichen Test (WZT) is a method of personality evaluation developed by the psychologist Ehrig Wartegg. Three new scoring categories for the WZT consist of Evocative Character, Form Quality, and Affective Quality. In this paper, we present the object detection model in scoring the Affective Quality of WZT. Our works consist of two main parts: 1) using the YOLO (You Only Look Once) model to detect the hand-drawn square boxes from the WZT form, and 2) using YOLO to detect the object in the hand-drawn square box. In the experiments, YOLOv3 achieved 88.94% of mAP for hand-drawn square box detection and 46.90% of mAP for hand-drawn object detection.
Motion Context Network for Weakly Supervised Object Detection in Videos

In weakly supervised object detection, most existing approaches are proposed for images. Without box-level annotations, these methods cannot accurately locate objects. Considering an object may show different motion from its surrounding objects or background, we leverage motion information to improve the detection accuracy. However, the motion pattern of an object is complex. Different parts of an object may have different motion patterns, which poses challenges in exploring motion information for object localization. Directly using motion information may degrade the localization performance. To overcome these issues, we propose a Motion Context Network (MC-Net) in this letter. Our method generates motion context features by exploiting neighborhood motion correlation information on moving regions. These motion context features are then incorporated with image information to improve the detection accuracy. Furthermore, we propose a temporal aggregation module, which aggregates features across frames to enhance the feature representation at the current frame. Experiments are carried out on ImageNet VID, which shows that our MC-Net significantly improves the performance of the image based baseline method (37.4% mAP v.s. 29.8% mAP).
Design of wide field of view pinhole lens for VR/AR image quality test

With the widespread use of AR/VR head-mounted displays, there is an increasing demand for pinhole cameras to test their image quality. However, it has been a challenge to design a pinhole camera with a wide field of view (FOV), high image performance and low distortion while maintaining compact and lightweight. In this paper, a pinhole camera optical system is designed. Based on a 1 in. sensor, the pinhole lens has a FOV of 100&deg; and an entrance pupil diameter of 4mm. In our final design, the distortion is limited within 2.5% and the modulation transfer function values across the entire field are above 0.3 at 161 line pairs/mm (lps/mm). The spherical aberration, color aberration and field curvature are corrected. The design result shows that the optical system of the pinhole camera is compact and meets the requirements of the VR/AR image quality test system.