Reviewerly/exHarmony huggingface dataset:
WhiteNet: Phishing Website Detection by Visual Whitelists

Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites - in particular against unseen phishing pages. This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN). VisualPhishNet learns profiles for websites in order to detect phishing websites by a similarity metric that can generalize to pages with new visual appearances. We furthermore present VisualPhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner. We show that our method outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks.
Fusing Stereopsis & Corner Sparse Optical Flow for Real-time Obstacle Avoidance of Unmanned Aerial Vehicle

The autonomous and maneuverable flight for an Unmanned Aerial Vehicle (UAV) in a complicated environment is an urgent problem to be addressed. This paper presents a real-time obstacle avoidance solution to autonomous flight for UAV systems. A binocular stereo matching model and an enhanced Pyramidal Lucas Kanade Optical Flow (PLKOF) algorithm were built for fast obstacle detection. The stereopsis fused with corner sparse optical flow is presented to an obstacle avoidance algorithm. The proposed obstacle avoidance solution is efficiently running on the DJI M100 equipped with a multiple sensor system Guidance to detect obstacle features and produce efficient motions at a frequency of 5 Hz in a multi-task condition. The solution is evaluated through rigorous comparative experiments and the results demonstrate its effectiveness obviously.
Domain Driven Design

Domain Driven Design offers a solid, systematic, and comprehensive approach to software design and development. It provides a set of tools and techniques which helps break down business complexity while keeping the core business model as the centerpiece of the approach.
Remote sensing of land covers over a test area

Summary Remote sensing methods as a part of geophysics have wide applications in different areas of science and practice. In the presented work the team of PhD students and Post-docs aim to collect spectral data for filling in thematic database. The spectral data are result of performed laboratory and terrain spectrometric measurements of the presented in a test area land covers (minerals, rocks, etc.). The result of the project work is collection of remote sensing data and additional information for filling in thematic spectral database in remote sensing monitoring of land covers in Novi Iskar region, Sofia, Bulgaria. Therefore the results will increase efficiency of research investigations in remote sensing monitoring of land covers, synergy between different fields of science and practice, and shared information between researchers.
Quantum Algorithm for Appointment Scheduling

Suppose a practical scene that when two or more parties want to schedule an appointment, they need to share their calendars with each other in order to make it possible. According to the present result the whole communication... | Find, read and cite all the research you need on Tech Science Press
Visual Servo Control Based on HSV

In view of the reliability and accuracy of robot operation in practical application, visual technology is needed to control the robot servo. Therefore, a scheme based on HSV color space is proposed to realize the servo control of the robot. The method mainly analyzes the acquired image in real time, preprocesses the image, converts it into HSV color space and recognizes it to guide the robot to operate on the target. The effectiveness of the proposed method is proved by experiments.
Stochastic Optimal Control Using Gaussian Process Regression over Probability Distributions

In this paper, we address optimal control of nonlinear stochastic systems under motion and measurement uncertainty with finite control input and measurement spaces. Such problems can be formalized as partially-observable Markov decision processes where the goal is to find policies via dynamic programming that map the information available to the controller to control inputs while optimizing a performance criterion. However, they suffer from intractability in scenarios with continuous state spaces and partial observability which makes approximations necessary. Point-based value iteration methods are a class of global approximate methods that regress the value function given the values at a set of reference points. In this paper, we present a novel point-based value iteration approach for continuous state spaces that uses Gaussian processes defined over probability distribution for the regression. The main advantages of the proposed approach is that it is nonparametric and therefore approximation quality can be adjusted by choosing the number and the position of reference points in the space of probability distributions. In addition, it provides a notion of approximation quality in terms of variance.
Chatbot for Career Guidance Using AI

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Triangular Type-2 Fuzzy Sets Based Weighted Regularized Extreme Learning Algorithm

In this paper, triangular type-2 fuzzy sets are implemented to enhance the generalization ability of the extreme learning machine (ELM). Two algorithms named the triangular weighted regularized extreme learning machine (TriWR-ELM) and triangular type-2 weighted regularized extreme learning machine (TriT2WR-ELM) are proposed. The introduction of triangular type-2 fuzzy sets and extended t-norms renders the proposed extreme learning machines more stable than weighted and regularized ELMs, and only slightly increases the computation time owing to approximate type-reduction set of the triangular type-2 fuzzy set. Experiments are conducted on the Sinc function and thermal power plant’s flue gas denitrification efficiency prediction problem to test the efficiency of TriWR-ELM and TriT2WR-ELM. Results show that the proposed two algorithms are more robust to larger outliers than conventional algorithms.
Cyberpsychological Approach to the Analysis of Multisensory Integration

The paper relates to the branch of cyberpsychology associated with risk factors during immersion in a virtual environment. Specialists in the development and operation of virtual reality systems know that immersion into this environment may be accompanied by symptoms similar to the “motion sickness” of transport vehicle passengers (ships, aircraft, cars). In the paper, these conditions are referred to as a cybersickness (or, cyberdisease). The three leading theories, proposed as an explanation of the causes of cybersickness, are discussed: the theory of sensory conflict, the theory of postural instability (the inability to maintain equilibrium), and the evolutionary (aka toxin) theory. A frequent occurrence of symptoms of cybersickness is a conflict between visual signals and signals from the vestibular system. It is shown that such conflicts can be stimulated in the framework of a specially organized experiment (e.g., the illusion of out-of-body experience) using virtual reality systems. When competing signals (visual, auditory, kinesthetic, tactile, etc.) reach the brain, the data gained with the use of virtual reality systems give a chance to hypothetically determine the localization of the specific area in the brain that ensures the integration of multisensory stimuli.
Review Paper on Data Integrity for Cloud

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Educational Implications of Art Education from Kant's Concept of Imagination

본 연구는 칸트 미학에서 제시된 상상력 개념을 성질적 측면에서 고찰하여 미술을 교육적 측면에서 탐색해 보는데 목적이 있다. 상상력은 경험하지 못한 일이나 사물을 우리의 경험과 지식으로 연결시킴으로서 새로움을 창조할 수 있는 역량으로 중요성이 커지고 있으며, 상상력을 강조해 온 예술 분야의 특성상 미술교육에서는 상상력을 중요한 요소로 다루고 있다. 우리의 교육에서 상상력에 대한 연구는 취미 판단이나 예술작품의 창조라는 기능적 관점에서 바라보았으며, 상상력을 어떻게 활용할 것인가에 대한 문제 등 예술적 관점에서 다루어져 왔다고 할 수 있다. 그러나 칸트의 상상력 개념에서 취미 판단이 숭고의 판단으로 확대되어 가고, 예술작품의 창작이 미적 이념의 표현으로 확대되어 가는 논의 전개에서 자율성과 확장성이라는 상상력의 성질적 측면을 발견할 수 있었다. 이러한 발견은 상상력을 개인적 기질이 아닌 인간의 역량으로 바라볼 수 있는 시각을 제공하여 상상력의 교육가능성을 확인할 수 있었을 뿐 아니라, 미술을 예술적 측면이 아닌 교육적 측면으로 바라볼 수 있는 시각적 토대와 다른 교과와 차별된 미술 교육의 특성들이 지니는 교육적 가치를 고려하는 데 있어 커다란 시사점을 제공하고 있다는 것을 확인 할 수 있었다.The purpose of this study is to examine the importance of imagination presented in Kant s aesthetics in art education. Imagination can connect our experience and direction with what we have not experienced before. This suggests that it is a capability to create newness. In the future, the importance of imagination will be increased. Imagination emphasized in the art field have been an important factor in art education. In our education, research on imagination has been considered a functional element as hobby judgment or creation of artwork. Therefore, question on how to apply imagination in art education have been discussed. interestingly, imagination of Kant strongly shown that hobby judgment lead to judgment of sublime, and the creation of works of art is expanded to the of aesthetic ideology. in line with this, we discover that Autonomy and scalability which were known as factor shown in Imagination. Therefore, when looking at imagination as a human capacity rather than a personal temperament, the educational potential of imagination can be advanced.
Behavior and Biometrics Based Masquerade Detection Mobile Application

Mobile phone has become an important asset when it comes to information security since it has become a virtual safe. However, to protect the information inside the mobile, the manufacturers use the technologies as password protection, face recognition or fingerprint protection. Nevertheless, it is clear that these security methods can be bypassed. That is when the urge of a post-authentication is coming to the surface. In order to protect the phone from an unauthorized or illegitimate user this method is proposed as a solution. The aim of the proposed solution is to detect the illegitimate user by monitoring the behavior of the user by four main parameters. They are: 1) Keystroke dynamics with a customized keyboard; 2) location detection; 3) voice recognition; 4) Application usage. In the initial state machine learning is used to train this mobile application with the authentic user’s behavior and they are stored in a central database. After the initial training period the application is monitoring the usage and comparing it with the already saved data of the user. Another unique feature of this is the prevention mechanism it executes when an illegitimate user is detected. Furthermore, this application is proposed as an inbuilt application in order to avoid the deletion of app or uninstallation of the app by the intruder. With this Application which is introduced as “AuthDNA” will help you to protect the sensitive information of your mobile device in a case of theft and bypassing of initial authentication.
Sistem Pendukung Keputusan Pemilihan Sepeda Motor Bebek Dengan Metode Simple Additive Weighting

Abstract - Motorcycle is a transportation with the most fans. Various types of motorcycle is used more, one of the motorcycle cub/moped. The underbone is one that is a favorite of the riders, especially men's riders. Some motor manufacturers such as Yamaha and Honda produces underbone, with a range of offerings and offered advantages from each manufacturer. It turned out to be one more attention from consumers, they started trouble choosing which underbone that best fits their criteria. In this case, the criteria taken into consideration, including the price, the use of fuel efficient, design body, and the dimensions of the motor frame. This needs to of a Decision Support System that could helped give any information and recommendations on underbone in accordance with criteria consumers. In the development of a decision support system there is one of the models that are commonly used, namely Fuzzy MADM (Multiple Attribute Decision Making). In this research will use one method problem solving of the model , that is simple additive weighting. This method was chosen because it can determine the best alternative of some alternatives so that in accordance with the criteria referred to. In this research will be carried out the calculation of the weighted alternative that will produce the best alternative, namely the underbone elected that could be a recommendation.&#x0D; Keywords: Motorcycle Duck, Decision Support System, FMADM, SAW
The use of WhatsApp group discussion to improve students’ writing achievement

The aim of this study was to find out whether the use of WhatsApp group could improve students’ ability in English writing achievement. The population of the study covered all of the second semester students of English study program in STKIP Muhammadiyah Pagaralam. Fourty students were selected purposively from all the population. The researcher used Quasi-experimental as the research methodology. To collect the data, written test was used to measure the students’ achievement in English writing. The data were analyzed by using paired sample t-test and independent sample t-test. The result of data analysis showed that the students obtained better improvement in English writing ability after being treated by using WhatsApp group discussion. Based on the result of independent t-test, the t value obtained was 5.298 and the significance level was p <.001. Futhermore, it can be concluded that there was a significant difference between the students who were taught through WhatsApp group discussion and those who were not. The students were interested in using WhatsApp group as media of learning especially in English writing. During the discussion in the WhatsApp group, they could get input such as new knowledge, better grammar, and improved vocabulary. WhatsApp group is a good way to be used as a teaching media especially in increasing students’ writing achievement.
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets

Autonomous driving requires various computer vision algorithms, such as object detection and tracking.Precisely-labeled datasets (i.e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects. However, it is very time-consuming and hence expensive to generate precise labels for image sequences at scale. In this paper, we propose DeepBbox, an algorithm that corrects loose object labels into right bounding boxes to reduce human annotation efforts. We use Cityscapes dataset to show annotation efficiency and accuracy improvement using DeepBbox. Experimental results show that, with DeepBbox,we can increase the number of object edges that are labeled automatically (within 1\% error) by 50% to reduce manual annotation time.
Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

With the rapid development of stereo image applications, there is an increasing demand to develop a versatile tool to evaluate the perceived quality of stereo images. Therefore, in this study, a blind stereo image quality evaluation (SIQE) algorithm based on convolutional network and saliency weighting is proposed. The main network framework used by the algorithm is the quality map generation network, which is used to train the distortion image dataset and quality map label to obtain an optimal network framework. Finally, the left view, right view, and cyclopean view of the stereo image are used as inputs to the network frame, respectively, and then weighted fusion for the final stereo image quality score. The experimental results reveal that the proposed SIQE algorithm can improve the accuracy of the image quality prediction and prediction score to a certain extent and has good generalization ability.
Object Classification for Robotic Platforms

Computer vision has been revolutionised in recent years by increased research in convolutional neural networks (CNNs); however, many challenges remain to be addressed in order to ensure fast and accurate image processing when applying these techniques to robotics. These challenges consist of handling extreme changes in scale, illumination, noise, and viewing angles of a moving object. The project main contribution is to provide insight on how to properly train a convolutional neural network (CNN), a specific type of DNN, for object tracking in the context of industrial robotics. The proposed solution aims to use a combination of documented approaches to replicate a pick-and-place task with an industrial robot using computer vision feeding a YOLOv3 CNN. Experimental tests, designed to investigate the requirements of training the CNN in this context, were performed using a variety of objects that differed in shape and size in a controlled environment. The general focus was to detect the objects based on their shape; as a result, a suitable and secure grasp could be selected by the robot. The findings in this article reflect the challenges of training the CNN through brute force. It also highlights the different methods of annotating images and the ensuing results obtained after training the neural network.
An Optical Implementation of Quantum Bit Commitment Using Infinite-Dimensional Systems

Unconditionally secure quantum bit commitment (QBC) was widely believed to be impossible for more than two decades. But recently, based on an anomalous behavior found in quantum steering, we proposed a QBC protocol which can be unconditionally secure in principle. The protocol requires the use of infinite-dimensional systems, therefore it may seem less feasible in practice. Here we propose a quantum optical method based on Mach-Zehnder interferometer, which gives a very good approximation to such infinite-dimensional systems. Thus, it enables a proof-of-principle experimental implementation of our protocol, which can also serve as a practically secure QBC scheme. Other multi-party cryptographic protocols such as quantum coin tossing can be built upon it too. Our approach also reveals a relationship between infinity and non-locality, which may have an impact on the research of fundamental theories.
!MDP Playground: Meta-Features in Reinforcement Learning.

Reinforcement Learning (RL) algorithms usually assume their environment to be a Markov Decision Process (MDP). Additionally, they do not try to identify specific features of environments which could help them perform better. Here, we present a few key meta-features of environments: delayed rewards, specific reward sequences, sparsity of rewards, and stochasticity of environments, which may violate the MDP assumptions and adapting to which should help RL agents perform better. While it is very time consuming to run RL algorithms on standard benchmarks, we define a parameterised collection of fast-to-run toy benchmarks in OpenAI Gym by varying these meta-features. Despite their toy nature and low compute requirements, we show that these benchmarks present substantial difficulties to current RL algorithms. Furthermore, since we can generate environments with a desired value for each of the meta-features, we have fine-grained control over the environments' difficulty and also have the ground truth available for evaluating algorithms. We believe that devising algorithms that can detect such meta-features of environments and adapt to them will be key to creating robust RL algorithms that work in a variety of different real-world problems.
Diversified Arbitrary Style Transfer via Deep Feature Perturbation

Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style). Although there have been some efforts to improve the diversity of style transfer by introducing an alternative diversity loss, they have restricted generalization, limited diversity and poor scalability. In this paper, we tackle these limitations and propose a simple yet effective method for diversified arbitrary style transfer. The key idea of our method is an operation called deep feature perturbation (DFP), which uses an orthogonal random noise matrix to perturb the deep image feature maps while keeping the original style information unchanged. Our DFP operation can be easily integrated into many existing WCT (whitening and coloring transform)-based methods, and empower them to generate diverse results for arbitrary styles. Experimental results demonstrate that this learning-free and universal method can greatly increase the diversity while maintaining the quality of stylization.
Audio-Visual TV Broadcast Signal Segmentation

Research in the field of audio-visual broadcast programs transcription and indexing has been solved for more than 20 years. Great progress has been made mainly in the area of broadcast transcription from audio signal. In the last 10 years, this research has become more intense, mainly due to the use of deep or convolutional neural networks and because of large IT companies (Google, Microsoft, IBM, Amazon) that can rely on a large number of custom embedded multimedia databases. Very important part of system for audio-visual broadcast signal transcription is subsystem for signal segmentation. Signal segmentation is usually solved separately for audio and visual signal. In this paper, a methodology for audio-visual broadcast signal segmentation is presented and described. The result from audio signal segmentation is used for improving of visual signal segmentation.