Reviewerly/exHarmony huggingface dataset:
Poor Data Throughput Root Cause Analysis in Mobile Networks using Deep Neural Network

As the mobile data usage is increasing with the complexity of new services, ensuring proper quality of experience (QoE) becomes challenging. While traditional machine learning techniques have been intensively applied in mobile networks, new training models are also mushrooming and are to some extent explored to handle large amount of data and complexity of new services in mobile data networks. Lately, deep learning algorithms have gained popularity since they scale efficiently with large data input and make use of the computing power to train the models. This paper proposes a deep learning model based on a deep neural network (DNN) architecture to train and evaluate the root cause analysis (RCA) for poor throughput in mobile networks. The proposed approach considered both the radio and the core network performance indicators of a mobile data network as inputs to ensure end-to-end correlation. Furthermore, the local interpretable model-agnostic explanations (LIME) method was used to provide features importance both for the global system and for individual subscribers. The prediction accuracy on the unseen data was 98.9% with an area under the receiver operating characteristic (ROC) curve of 99.87% and an F1_score of 99.23%.
Detecting Malicious Campaigns in Obfuscated JavaScript with Scalable Behavioral Analysis

Modern security crawlers and firewall solutions have to analyze millions of websites on a daily basis, and significantly more JavaScript samples. At the same time, fast static approaches, such as file signatures and hash matching, often are not enough to detect advanced malicious campaigns, i.e., obfuscated, packed, or randomized scripts. As such, low-overhead yet efficient dynamic analysis is required. In the current paper we describe behavioral analysis after executing all the scripts on web pages, similarly to how real browsers do. Then, we apply light "behavioral signatures" to the collected dynamic indicators, such as global variables declared during runtime, popup messages shown to the user, established WebSocket connections. Using this scalable method for a month, we enhanced the coverage of a commercial URL filtering product by detecting 8,712 URLs with intrusive coin miners. We evaluated the impact of increased coverage through telemetry data and discovered that customers attempted to visit these abusive sites more than a million times. Moreover, we captured 4,633 additional distinct URLs that lead to scam, clickjacking, phishing, and other kinds of malicious JavaScript. Our findings provide insight into recent trends in unauthorized cryptographic coin-mining and show that various scam kits are currently active on the Web.
Moral values of professional activity in information society

The article identifies the moral values of professional activity in information society on the basis of the systematic approach. Informatization of society in the 21st century entered a new stage of its development. The new information environment aims at forming certain ideological and axiological priorities, according to which the value characteristics of information society change and reproduce. One of the most important value areas in information society is professional activity, whose moral values - mutual responsibility, loyalty to duty, tolerance, decency, and focus on cooperation - develop during personal professionalization. Subsequently, in the course of professional work, professional conscience, duty, responsibility, dignity, tact, etc. develop. Professionalism is the most important moral value of professional activity; however, it should not be limited to the sum of professional knowledge and skills. A true professional has a developed, based on value orientations, moral culture, deep understanding of his professional duty, the most scrupulous attitude to professional honor, a high degree of professional responsibility. The success of his activities, the integrity of his personality, and his creative self-expression in the chosen profession depend on his professional and moral principles - their unity and consistency. The task of forming the professional values of an employee is an essential component of personal professionalization in order to achieve an optimal combination of traditional and creative elements, of specific professional experience and public morality. From the ethical perspective, professionalism in the ability to ensure harmony of professional activity and its moral evaluation, readiness for worthy deeds in any professional situations.
Video Steganography using Convolutional Neural Network and Temporal Residual Method

Steganography involves data hiding in computer files.Steganographic coding, include a file including the document, image, program as well as guidelines, can be included in electronic communication in a transport layer.Because of their large size media files are suitable for the steganography transmission.This paper focuses on video steganography.Video steganography is nothing but hiding the complete secret video within the cover video.Firstly the residual of the secret video and cover video is obtained because hiding the residual video is much easier when compared to hiding original video.This model uses the deep convolutional neural network method.The model compares the particular model with other method.And all results show that this method is efficient.
Deep Convolutional Neural Network and Its Application in Image Recognition of Road Safety Projects

Road safety projects constitute an important part of road safety facilities.Assessing the safety of these projects is important for assessing the safety of roads.In recent years, road safety problems have caused enormous losses to the country and its people.Traditional inspection and maintenance of road safety projects mainly involve manual inspection and on-site maintenance; however, manual inspection is time-consuming and laborious, and it cannot be used to identify safety issues in large areas.This paper focuses on the application of the deep convolutional neural network algorithm, a deep learning algorithm, for the recognition of road safety projects.Comparative analysis of the experimental results shows that both the convolutional neural network models VGG16 and InceptionV3 can identify the pre-processed data sets of the road safety projects; however, the accuracy of the test set model InceptionV3 is higher than that of VGG16, reaching 93.3%.
Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.
Youtube as the Learning Media to Improve the Speaking Skill in Expression Orale Course

This research aims to improve the speaking skill for the students who take the oral expression course. The objects of this research are the students in the French department, especially who take the expression oral course 3. It is found that the previous class couldn’t improve the speaking skill of the students very well. Most of the students were not very interested in the learning process. From the questionnaire given, it is found that they were bored because nothing was interesting and exciting in the class. The media they used was only a book from the lecturer. So, the making learning media of expression Orale by using YouTube aims to help learners to understand French with the local cultural situation. Making learning media of speaking using YouTube based on local culture is very important to improve the learning abilities of French. This video will also be handy for French tourists who visit Indonesia, especially Medan because this video will present the real situation in Medan. The research method uses Research and Development which is carried out in the French Language Education Study Program. The results of this research show that the students are enthusiastic in the learning process, their ability in speaking is also improving, and the media supports the learning process very good.
A Generative Approach for Incomplete High Resolution Range Profile Sequence Recognition

The High-Resolution Range Profile (HRRP) sequence recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). In this paper, a novel HRRP sequence recognition method based on Restricted Boltzmann Machine (RBM) is proposed for the poor performance of the traditional methods on incomplete data and noise corrupted data. Two main procedures are included in our method, which are estimation and recognition. Firstly, infinite Restricted Boltzmann Machine, as a generative model, can estimate the missing frame HRRP signal by learning the features of several adjacent frames of the missing HRRP data. Additionally, the original incomplete sequence combined with the estimated HRRP constitutes a new complete sequence, which is utilized to complete the recognition task. Therefore, our method provides a novel idea for the identification of incomplete data and has strong robustness to noise. Experiment results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, indicating that our method is suitable for incomplete data and noise corrupted data.
Adaptive ANN-based method of constructing an interpolation formula for doubling the image size

The architecture of an artificial neural network that solves the problem of constructing interpolation formulas for doubling the size of images is proposed. The trained model receives a 4×4 matrix as an argument. The result is an interpolation formula represented as a weight vector for 4 points. A comparison of the main quality assessments of the proposed method with some well-known adaptive approaches is made. The results of the comparative analysis show that the proposed approach has a better interpolation quality than NEDI and DCCI methods.
TransFG: A Fine-Grained Model for Knowledge Graph Embedding

Although concepts and instances in a knowledge graph (KG) are distinguished, TransC embeds concepts, instances, and various relations into the same vector space, which leads to the following problems: (1) The same instance in different triples that model different relations between instances is represented as the same vector, resulting in improper representation of different properties possessed by this instance; (2) Multiple instances not belonging to one concept may be located in the sphere representing this concept, resulting in an inaccurate modeling of the instanceOf relations between these instances and the concept. Based on TransC, this paper proposes a fine-grained KG embedding model called TransFG. TransFG embeds concepts, instances, and relations into different vector spaces and projects the instance vectors from the instance space to the concept space and the relation spaces through dynamic mapping matrices. This causes the projected vectors of the same instance in different triples to have different representations and the projected vectors of multiple instances belonging to the same concept to be spatially close to each other; otherwise they are far away. Experiments on the YAGO39K and M-YAGO39K datasets show that on the triple classification task, TransFG outperforms TransC and other typical KG embedding models in terms of accuracy, precision, recall and F1-score in most cases, and on the link prediction task, TransFG outperforms these compared models in terms of MRR and Hits@N in most cases.
Conceptualizing a collaboration framework between Blockchain technology and the Internet of Things

The Internet of Things (IoT) system is a concept that binds together multiple deployments of heterogenous distributed and decentralized systems. In such systems it is very hard to keep track of user data, enforce complex privacy policies, ensure safe data storage and sharing, as well as employ an efficient mechanism for process and resource management. Blockchain is a technology that emerged in 2008 with Bitcoin, and has proven to be effective in solving these problems. IoT applications are distributed by nature. Hence, distributed ledger technology (DLT) such as blockchain has the potential to play a significant role in how devices communicate in IoT. This paper will address these issues by introducing the high-level concept of blockchain as a service or BCaaS - a blockchain-based framework for internal and external collaboration throughout the IoT ecosystem. It will also discuss current problems with IoT and blockchain technologies integration, as well as how to overcome them.
I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

Intelligent systems are created to automate decision making process that is similar to human intelligence. Incorporating intelligent component has achieved promising results in many applications, including in education. Intelligence modules in a tutoring system would bring the application and its capability closer to a human's ability to serve its human users and to solve problems. However, the majority of the online learning provided in the literature review especially in Malaysia, normally only provide the lecture notes, assignments and tests and rarely suggest or give feedbacks on what the students should study or do next in order to fully understand the subjects. Hence, the researchers propose an online learning environment called Intelligent Online Assessment and Revision (I-OnAR). It facilitates the learning process at multiple learning phases such as test creation, materials revision, feedback for improvement and performance analysis. These components are incorporated into the tutoring system to assist self-pace learning at anytime and anywhere. The intelligent agent uses a Rule-based Machine Learning method for the adaptive capabilities such as automated test creation and feedbacks for improvement. The system has been tested on a group of students and found to be useful to support learning process. The results have shown that 60% of the subjects’ performance have improved with the help of the system. The students were given feedbacks on the topic they did poorly as well as how to improve their performance. This proves that the Intelligent Online Assessment and revision (I-OnAR) can be a useful tool to help online students intelligently, systematically and efficiently. For the future works, the researchers would like to apply the usage of other techniques such as Fuzzy Logic to strengthen the analysis and decision of the current system.
Making Sense of a Changing Neighborhood: Art Students’ Experiences of Place Explored Through a Material-Discursive Analytical Lens

Sensory research approaches are often used to study the relationship between people and their living environment. The type of data collected in such research projects poses analytical challenges. How do we best make sense of a body of visual, auditory, tactile data? How do such data contribute to our knowing? In this paper, we propose and illustrate an analytical apparatus for studying the complex entanglement of discursive and material aspects of sensorial experiences related to place. Place-interactive methods such as sensory go-along interviews with art students and voice-giving procedures through the making of art works formed the basis for the analysis.
IR-NAS: Neural Architecture Search for Image Restoration

Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose HiNAS (Hierarchical NAS), an effort towards employing NAS to automatically design effective neural network architectures for image denoising. HiNAS adopts gradient based search strategies and employs operations with adaptive receptive field to build an flexible hierarchical search space. During the search stage, HiNAS shares cells across different feature levels to save memory and employ an early stopping strategy to avoid the collapse issue in NAS, and considerably accelerate the search speed. The proposed HiNAS is both memory and computation efficient, which takes only about 4.5 hours for searching using a single GPU. We evaluate the effectiveness of our proposed HiNAS on two different datasets, namely an additive white Gaussian noise dataset BSD500, and a realistic noise dataset SIM1800. Experimental results show that the architecture found by HiNAS has fewer parameters and enjoys a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. We also present analysis on the architectures found by NAS. HiNAS also shows good performance on experiments for image de-raining.
A mixed-methods measurement and evaluation methodology for mobile application usability studies

Low usability of mobile application is thought to diminish the perceived level of the quality by a user whose experiences substantially determines its market success or failure.However, while a single method of measurement employed to study usability may produce an unreliable or incomplete evaluation outcome, in this paper, contrarily we propose to take advantage of both qualitative and quantitative methods adequate to collect data, that would describe all usability attributes.In particular, in the scope of mobile application usability studies, this paper (i) depicts the main assumptions of elaborated M4MAUME methodology, (ii) describes the self-developed software tool (RVDA) for retrospective video data analysis, (iii) specifies the experimental setup, and (iv) discusses the preliminary results obtained from 20 experiments performed on four different groups of mobile applications.Eventually our findings lead us to the conclusion that mixing different methods have produced reliable and valuable outcomes which may be used to improve and manage usability in current and future projects, as well as to enhance existing software quality assurance (SQA) programs.
SEGMENTATION OF IMAGE PAIRS FOR 3D RECONSTRUCTION

Abstract. Image segmentation is an essential task in many computer vision applications such as object detection and recognition, object tracking, image classification, 3D reconstruction. Most of the current techniques utilise the colour or grayscale information of an image without considering the camera geometry. In this paper, a method is proposed to utilise the camera relative orientation of a pair of images to find a reliable object segmentation. The inputs to the method are a rectified image pair and a disparity map which could be computed from the rectified image pair, the disparity map is used to determine a set of local homographies between planar surfaces in the two images. The planar surfaces are corresponding to image segments despite the inconsistency of the RGB information. Homography based segmentation alone is not reliable due to possible noise in the disparity map and existence of non-planar objects in the scene. Therefore, an RGB technique is used as a complementary approach to enhance the segmentation result. Two colour-based segmentation techniques are used here, the first is the colour edge detector, and the second is Grabcut. Experimental results show the although the colour edge detector is a simpler algorithm than Grabcut, it does not include noisy data in the segmentation results. the This useful for 3D reconstruction, as it is preferable to exclude noisy areas like the sky and window glass. The outcome of the proposed segmentation algorithm is an object-based segmentation of the pair of images as well as a segmented disparity map.
Micro-Frequency Shifting Projection Technique for Inter-Reflection Removal

Inter-reflection removal is vital for complex-scene reconstruction. However, most methods assume that the tested surface is a diffuse, and are limited to removal of inter-reflection caused only by diffuse reflections. For all kinds of inter-reflections caused by diffuse and specular reflections, a micro-frequency shifting (MFS) projection technique is presented. Because the modulation variation with frequency in inter-reflection regions is larger than that of other regions, we use the MFS technique to detect inter-reflections, where patterns with specifically designed frequency-shifts and base frequencies are projected. Inter-reflections are detected through large variations in modulation, and removed using a regional-projection technique. Experimental results validate the effectiveness for diffuse and specular inter-reflection removal.
I see EK: A lightweight technique to reveal exploit kit family by overall URL patterns of infection chains

The prevalence and nonstop evolving technical sophistication of exploit kits (EKs) is one of the most challenging shifts in the modern cybercrime landscape.Over the last few years, malware infections via drive-by download attacks have been orchestrated with EK infrastructures.Malicious advertisements and compromised websites redirect victim browsers to web-based EK families that are assembled to exploit client-side vulnerabilities and finally deliver evil payloads.A key observation is that while the webpage contents have drastic differences between distinct intrusions executed through the same EK, the patterns in URL addresses stay similar.This is due to the fact that autogenerated URLs by EK platforms follow specific templates.This practice in use enables the development of an efficient system that is capable of classifying the responsible EK instances.This paper proposes novel URL features and a new technique to quickly categorize EK families with high accuracy using machine learning algorithms.Rather than analyzing each URL individually, the proposed overall URL patterns approach examines all URLs associated with an EK infection automatically.The method has been evaluated with a popular and publicly available dataset that contains 240 different real-world infection cases involving over 2250 URLs, the incidents being linked with the 4 major EK flavors that occurred throughout the year 2016.The system achieves up to 100% classification accuracy with the tested estimators.