Reviewerly/exHarmony huggingface dataset:
Image Super-Resolution Reconstruction Using Generative Adversarial Networks Based on Wide-Channel Activation

In recent years, residual learning has shown excellent performance on convolutional neural network (CNN)-based single-image super-resolution (SISR) tasks. However, CNN-based SISR approaches have focused mainly on the design of deep architectures, and the rectified linear units (ReLUs) used in these networks hinder shallow-to-deep information transfer. As a result, these methods are unable to utilize some shallow information, and improving model performance is difficult. To solve the above issues, this paper proposes an image SR reconstruction method based on a generative adversarial network with a residual dense architecture. First, before ReLU activation, the number of feature channels is expanded by a factor of 6~9 using a 1 × 1 convolutional layer, which improves the utilization of shallow information. Next, the original discriminator is replaced with a relativistic average discriminator, thereby improving the authenticity of the discriminative network. Finally, preactivation features are used to improve the perceptual loss, thus providing stronger monitoring for brightness consistency and texture restoration. Experimental results show that the proposed algorithm improves the utilization of shallow information in a deep network. Structural similarity (SSIM) index evaluations show that the overall utilization of shallow information is increased by 105.52%. In addition, the average runtime is 0.42 sec/frame, nearly 3.6 times faster than those of traditional methods. Moreover, the recovered images have an average natural image quality evaluator value of 3.4 and high perceptual quality, showing that the proposed method is suitable for image reconstruction applications in fields such as agriculture and medicine.
The High Performance of a Task Scheduling Algorithm Using Reference Queues for Cloud- Computing Data Centers

As the network technology continues to grow at a high rate of speed, the traditional network topology is improved with novel distributed topologies such as the Cloud computing network. A cloud computing environment consists of a huge number of processors and memories, high-speed networks, and various application services to provide a lot of services over the Internet for users. However, many services need to search for suitable service nodes, and the workload of each node can be unbalanced. Based on the reason above, the Reference Queue based Cloud Service Architecture (RQCSA) and Fitness Service Queue Selection Mechanism (FSQSM) are proposed to handle more tasks, lower the makespan and queue waiting time, and improve efficiency. Moreover, the tasks can be distributed more evenly to avoid overloading cluster managers and lower the efficiency of the system.
Frame-by-frame Wi-Fi attack detection algorithm with scalable and modular machine-learning design

The popularity of Wi-Fi networks coupled with the intrinsic vulnerability of wireless interfaces has promoted the investigation and proposal of traffic analysis and anomaly detection algorithms targeted to that application. We propose a scalable and modular algorithm architecture to set up a lightweight classifier, able to detect malicious frames with high reliability, allowing a simple implementation and suitable for real-time operations. We compare two design alternatives, based on either an optimized neuro-fuzzy classifier or a k-Nearest Neighbor classifier wrapped into a genetic optimization procedure. Both designs exploit a dissimilarity measure able to handle both numerical and non-numerical features. Scalability and modularity are obtained by considering an array of binary classifiers tuned to identify one specific attack against any other type of traffic. We exploit the Aegean Wi-Fi Intrusion Detection (AWID) dataset to assess the accuracy of the proposed algorithm, finding up to twelve out of the fourteen attack classes of the dataset can be identified with high reliability based just on the inspection of a single frame, provided the right features are observed.
Using Deep Learning to Solve Google reCAPTCHA v2’s Image Challenges

The most popular CAPTCHA service in use today is Google reCAPTCHA v2, whose main offering is an image-based CAPTCHA challenge. This paper looks into the security measures used in reCAPTCHA v2's image challenges and proposes a deep learning-based solution that can be used to automatically solve them. The proposed method is tested with both a custom object- detection deep learning model as well as Google's own Cloud Vision API, in conjunction with human mimicking mouse movements to bypass the challenges. The paper also suggests some potential defense measures to increase overall security and other additional attack directions for reCAPTCHA v2.
New bounds for linear codes of covering radius 3 and 2-saturating sets in projective spaces

The length function l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r, R) is the smallest length of a q-ary linear code of covering radius R and codimension (redundancy) r. In this paper, we obtained new upper bounds on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r, 3), r = 3t+1 ≥ 4,andr = 3t+2 ≥ 5, t ≥ 1.For r = 4, 5 we use the one-to-one correspondence between [n, n-r] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R codes and (R-1)-saturating sets (e.g. complete arcs) in the projective space PG(r-1, q). Then, with the help of lift-constructions increasing r, we obtain new upper bounds on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (3t + 1, 3), l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (3t + 2, 3). In particular, we show that p l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r, 3) <; 2.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> √ ln q · q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(r-3)/3</sup> , r = 3t + 1 ≥ 4, t ≥ 1, q ≥ 6553; p l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r, 3) <; 2.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> √ ln q · q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(r-3)/3</sup> , r = 3t + 2 ≥ 5, t ≥ 1, q ≤ 839. Also, in PG(3, q) we consider an iterative step-by-step construction of complete arcs and prove that uncovered points are evenly placed on the space. A natural conjecture on an estimate of the number of new covered points in every step is done. Under this conjecture, the following bounds for values of q, not limited from above, are proposed: l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (r, 3) <; 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> √ ln q · q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(r-3)/3</sup> , r = 3t + 1 ≥ 4, t ≥ 1.
A Summarization of Collision Avoidance Techniques for Autonomous Navigation of UAV

Unmanned Aerial Vehicle (UAV) has received wide interest in recent years because of their promising potentials for several applications that include border surveillance, intrusion, detection, emergent item delivery, wildlife monitoring, rescue operations etc. The growing number of entrances of drones into airspace has emerged a significant interest in the collision and obstacle avoidance in autonomous operation. One of major challenges for safer deployment of UAVs is collision avoidance in order to make UAVs autonomous and self-sustainable. In today’s technological era, it is possible for UAVs to avoid obstacles and prevent collisions while navigation by implementing cutting edge computer vision algorithms and visual tracking systems. But it is challenging due to the limited payload in UAV that only permits vision based sensors such as camera, inertial measurement unit such for control and obstacle detection. This paper aims to summarize existing solutions of collision avoidance strategies used in the navigation of UAV. Collision avoidance strategies based on geometry, optimization, sense and avoid, vision-based and force field based methods are investigated in this study.
Mobile Application for Storage and Retrieval of e-learning videos Using Hadoop

With the rapid advancement of low-cost mobile phones and fast internet access, students/learners are using smart phones for the academic purposes. Mobile learning (m-learning) has been recognized as a competent method of seeking knowledge. As the quantity of users and lecture video contents is increasing, the deployment of m-learning becomes dubious because of limited memory space in mobile phones. The learning materials like ppt., docs, occupy less space but lecture videos consume a lot of memory in a mobile device. Therefore, there should be some measure where students can store as well as access lecture videos. Hadoop has been evolved as a promising technology to overcome the problems in m-learning. It provides reliable, customized environments for end-users. We present a new mobile application for a mobile learning system, which is integrated with Hadoop, for storage and retrieval of lecture videos from mobile phone to the Hadoop Distributed File System (HDFS). Registered user of the application can perform the functions like upload video, search a video file and download a video file. We have calculated computational time to upload, search and download various size of video files (maximum size is 1.3 GB). The proposed application can be utilized where there is a high demand of exhaustive teaching and learning in higher education.
Online instruction. A system for educational inclusion in the university setting (La enseñanza en línea. Un sistema para la inclusión educativa en el entorno universitario)

The goal of this article is to analyse online instruction’s potential to foster the development of inclusive educational systems within higher education. To do so, it explores knowledge of virtual instruction environments and establishes three strands fostered by virtual learning systems which promote the inclusion of all higher education students. These three strands are made up of: (1) pedagogical models that enhance the learning process, such as Just-in-Time Instruction, Peer Instruction and Flipped Teaching; (2) eliminating barriers thanks to the flexibility of materials, times and spaces; (3) the development of student autonomy, stressing the value of the teacher’s work and the student’s activity, as well as the technological resources existing within online instruction. As a model of the implementation of this study, the proposal of the virtual campus of the International University of La Rioja will be presented, a university whose instruction takes place entirely online.
Skin Detection based Pornography Filtering using Adaptive Back Propagation Neural Network

As the internet becomes faster and cheaper, its misuses like pornographic production and consumption has also been increased. Pornography is considered a sensitive issue to discuss openly in our society and this is a neglected one too. Psychological research says that Pornographic and nude images create a negative impact on the viewer's mind. And also watching pornography is a kind of addiction too. At the first stage, such people create distance from their loved ones which leads them to depression and on extreme stages they could be involved in many types of criminal activities. In this article, the Skin Detection based Pornographic Filtering using Adaptive Back Propagation Neural Network (SD-PFT-ABPNN) Technique is presented. The Simulation results of Proposed SD-PFT-ABPNN techniques shown desirable results regarding MMSE and regression as compared to conventional skin detection-based Porn Filtering Techniques using Global Image Enhancement (PFTGIE), Porn Filtering Techniques Without using Global Image Enhancement (PFTWGIE) techniques. When the results were compared, it was seen that the BR algorithm has the highest accuracy rate with 99.70%.
Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order node proximity/adjacency matrix via computationally intensive matrix factorization techniques. These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. Moreover, most of the existing graph embedding techniques operate effectively in one specific metric space only (e.g., the one produced with cosine similarity), do not preserve higher-order structural features of the input graph and cannot automatically determine a meaningful number of dimensions for the embedding space. Typically, the produced embeddings are not easily interpretable, which complicates further analyses and limits their applicability. To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. Compared to a dozen state-of-the-art graph embedding algorithms, DAOR yields competitive results on both node classification (which benefits form high-order proximity) and link prediction (which relies on low-order proximity mostly). Unlike existing techniques, however, DAOR does not require any parameter tuning and improves the embeddings generation speed by several orders of magnitude. Our approach has hence the ambition to greatly simplify and speed up data analysis tasks involving graph representation learning.
Reinforcement Learning based Lane Change Decision-Making with Imaginary Sampling

This paper investigates the autonomous driving lane change problem with reinforcement learning methods. A two-stage control method is proposed which includes a decision-making module computing the high-level lane change action and a lateral control module outputting the low-level steering angle. Due to the decoupling system framework, the proposed controller can flexibly cancel the previous improper lane change command. To improve the data efficiency in the lateral control module, the Gaussian process is employed to modeling the local time-interval system models. These models are used to generate imaginary samples and facilitate the reinforcement learning training process. The lateral control experiments show that the imaginary samples can improve the data efficiency and speed up the training process. Finally, the two-lane scene and three-lane scene experiments validate the effectiveness of the proposed two-stage lane change method.
Uncertainty-Aware Personalized Readability Assessments for Second Language Learners

Assessing whether an ungraded second language learner can read a given text quickly is important for further instructing and supporting the learner, particularly when evaluating numerous ungraded learners from diverse backgrounds. Second language acquisition (SLA) studies have tackled such assessment tasks wherein only a single short vocabulary test result is available to assess a learner; such studies have shown that the text-coverage, i.e., the percentage of words the learner knows in the text, is the key assessment measure. Currently, count-based percentages are used, in which each word in the given text is classified as being known or unknown to the learner, and the words classified as known are then simply counted. When each word is classified, we can also obtain an uncertainty value as to how likely each word is known to the learner. Although such values can be informative for a readability assessment, how to leverage these values to guarantee their use as an assessment measure that is comparable to that of the previous values remains unclear. We propose a novel framework that allows assessment methods to be uncertainty-aware while guaranteeing comparability to the text-coverage threshold. Such methods involve a computationally complex problem, for which we also propose a practical algorithm. In addition, we propose a neural-network based classifier from which we can obtain better uncertainty values. For evaluation, we created a crowdsourcing-based dataset in which a learner takes both vocabulary and readability tests. The best method under our framework outperformed conventional methods.
Simplified Space Based Neural Architecture Search

In this paper, we propose Simplified Space based Neural Architecture Search (SSNAS), an efficient approach for automatic architecture search. Inspired by the popular convolutional neural networks which have strong capability for feature extraction with small convolutional kernels, we design a simplified search space of convolutional operations with small kernel and construct a large model based on it. Furthermore, we use an Long Short-Term Memory (LSTM) to sample child model from the large model in a way of selective activation. The probability distribution of selective activation is obtained by training the LSTM with reinforcement learning for maximizing the expected reward of selected child model on the validation set. Moreover, the trained weights are saved in a large model which concludes all child models. Each weight of new sampled child model will be restored when being selected by another one again instead of training from scratch so that SSNAS can greatly reduce the expensive computation than traditional approaches for automatic model design. Extensive experiments on CIFAR-10, ImageNet and VOC show that the proposed approach excels in discovering transferable architectures with high performance.
Automatic Detection of Toxic South African Tweets Using Support Vector Machines with N-Gram Features

Toxic South African corpus is not available to detect toxic tweets such as offensive, hate, bullying and violent tweets. But there are some offensive and hate speech corpora, mostly in English, which have been used to detect toxic tweets. This paper focuses on automatic detection of toxic South African tweets using a reliable English corpus. The review of text classification models has shown that Support Vector Machines have very often outperformed other classic machine learning algorithms, while word and character n-gram features have performed well with varying prediction performances in different contexts. This paper therefore evaluated the performance of different parameter settings of Support Vector Machines and n-gram features for detection of toxic South African tweets, with a view to hybridize the best among the classifiers. Different combinations of word and character n- gram features were used for the classification. The results show that the Support Vector Machine classifier with set of unigram and bigram as well as set of character n-gram with length sizes from 3 to 7 perform best. By combining the classifiers, the accuracy and F-measure improve from the initial highest Accuracy and F-Measure scores of 0.9085 and 0.94, respectively to 0.9095 and 0.95. The comparison of our results with the performance of previous work on the English corpus shows that our model is reliable.
Classification of Stenography using Convolutional Neural Networks and Canny Edge Detection Algorithm

As technology continues to evolve, some traditional practices tend to fade. Stenography is a practice of writing dictations in terms of speed devised by Isaac Pitman. The said practice had been around the early 19th century to address the slow phased transcription process in court trials. However, such a method can diminish as people tend to rely moreover upon recently developed advanced technologies. This study aims to use Convolutional Neural Networks and image processing techniques to preserve stenography by allowing machines to recognize and use the said writing practice in modern times. This research used 2000 common court stenography words and phrases as the core dataset for training, validation, and testing. Application of canny edge detection provides the detection of edges to provide a better classification of stenographic writings. Results generated show that through Convolutional Neural Networks, stenography writings can be recognized even by the machines. Canny Edge Detection highly supported the improvement of results by allowing the model to focus more on the necessary features for better classification accuracy. The researchers also determined future enhancements and works that can contribute in the given research.
Capturing the Persistence of Facial Expression Features for Deepfake Video Detection

The security of the Deepfake video has become the focus of social concern. This kind of fake video not only infringes copyright and privacy but also poses potential risks to politics, journalism, social trust, and other aspects. Unfortunately, fighting against Deepfake video is still in its early stage and practical solutions are required. Currently, biological signal based and learning-based are two major ways in detecting Deepfake video. We explore that facial expression between two adjacent frames appears significant differences in generative adversarial network (GAN)-synthesized fake video, while in a real video the facial expression looks naturally and transforms in a smooth way across frames. In this paper, we employ optical flow to capture the obvious differences of facial expressions between adjacent frames in a video and incorporate the temporal characteristics of consecutive frames into a convolutional neural network (CNN) model to distinguish the Deepfake video. In our experiments, we evaluate the effectiveness of our approach on a publicly fake video dataset, FaceForensics++. Experimental results show that our proposed approach achieves an accuracy higher than 98.1% and the AUC score reaches more than 0.9981.
Evolving Multiobjective Cancer Subtype Diagnosis From Cancer Gene Expression Data

Detection and diagnosis of cancer are especially essential for early prevention and effective treatments. Many studies have been proposed to tackle the subtype diagnosis problems with those data, which often suffer from low diagnostic ability and bad generalization. This article studies a multiobjective PSO-based hybrid algorithm (MOPSOHA) to optimize four objectives including the number of features, the accuracy, and two entropy-based measures: the relevance and the redundancy simultaneously, diagnosing the cancer data with high classification power and robustness. First, we propose a novel binary encoding strategy to choose informative gene subsets to optimize those objective functions. Second, a mutation operator is designed to enhance the exploration capability of the swarm. Finally, a local search method based on the "best/1" mutation operator of differential evolutionary algorithm (DE) is employed to exploit the neighborhood area with sparse high-quality solutions since the base vector always approaches to some good promising areas. In order to demonstrate the effectiveness of MOPSOHA, it is tested on 41 cancer datasets including thirty-five cancer gene expression datasets and six independent disease datasets. Compared MOPSOHA with other state-of-the-art algorithms, the performance of MOPSOHA is superior to other algorithms in most of the benchmark datasets.