Reviewerly/exHarmony huggingface dataset:
Implemetasi Jaringan Saraf Tiruan Untuk Mendeteksi Serangan DDoS Pada Forensik Jaringan

Network attacks that are often carried out including using Distributed Denial of Service (DDoS) have caused significant financial losses and require very large recovery costs to reach double. Activities that damage, interfere with, steal data, and anything that harms the system owner of a computer network is illegal and can be legally sanctioned in court. Network forensics mechanism to find criminals in order to be ensnared by law. Investigators usually use network monitoring systems such as Intrusion Detection System (IDS) for forensics purposes. The use of IDS allows the detection of errors or changes in traffic and new types of attacks because attacks are carried out using syn packages, where the syn protocol is considered legal because it is needed in the authentication process of communication between devices in the Internet network. Signature-based detection and notification systems are also not strong enough to be used as evidence in the trial. An analysis mechanism is needed to test the accuracy of DDoS attacks that have been detected by the intrusion detection system. Testing the accuracy of DDoS attacks can be done using the neural network classification method using statistical calculations. Based on the results of the analysis and testing carried out found an accuracy value of 95.23%. These results can be used to support and strengthen the evidence of findings in the trial. Keywords: DDoS, IDS, network forensics, JST
Near-Duplicate Document Detection Using Semantic-Based Similarity Measure: A Novel Approach

Detection of near-duplicate pages, especiallyRoul, Rajendra Kumar based on their semantic content is a relevant concern in information retrieval. It is needed to avoid redundancy in the search resultsSahoo, Jajati Keshari against a query as well as facilitate the ranking of the documents in the order of their semantic similarities. Although much work has been done in near-duplicate page detection based on content similarity (as evident in existing literature), the realm of semantic similarity provides a relatively unexplored pool of opportunities. In this paper, a novel technique is proposed to detect whether two documents belonging to a corpus have near-duplicate semantic content or not and a heuristic method is introduced to rank the documents based on their semantic similarity scores. This objective is achieved by examining the proposed technique for computing semantic-based similarity between two documents and applying an averaging mechanism to associate a similarity score to each document in the corpus. The empirical results on DUC datasets witness the effectiveness of the proposed approach.
Epigraphic Database Heidelberg – Data Reuse Options

The “Epigraphic Database Heidelberg” (EDH) is one of the longest running database projects in digital Latin epigraphy with a start as early as 1986. In 1997 the website of the “Epigraphic Database Heidelberg” has gone online: All inscriptions, images, bibliographic and geographic records can be searched and browsed online. In the last years a growing number of researchers asked for direct access to EDH data: Consequently an “Open Data Repository” has been added to the EDH website (https://edh-www.adw.uni-heidelberg.de/data) and improved continuously since then. Options for accessing data include simple CSV downloads, an API returning JSON responses, a SPARQL endpoint, an IIIF API and access via a Distributed Text Services API. This article describes the data in the EDH repository, the various ways of how users can reuse this data for their own research and when to choose which option.
The Influence of God Class and Long Method in the Occurrence of Bugs in Two Open Source Software Projects: An Exploratory Study (S)

Context: Code smells are associated with poor design and programming style that often degrades code quality and hampers code comprehensibility and maintainability.Goal: In this paper, we investigated to which extent classes affected by the God Class and Long Method code smells were more susceptible to the occurrence of software bugs.Method: We conducted an exploratory study targeting two well known open source software projects, Apache Tomcat and Eclipse JDT Core Component.We applied correlation analysis in order to evaluate to which extent Long Method and God Class were related to the occurrence of bugs.Results: We have found a significant correlation of Long Method and Commits and, on the other hand, a poor correlation of God Class and Commits in the two analyzed projects.Therefore, we expected that the higher the number of occurrences of Long Method, the higher the chances of more commits in a class that contains this method, which could result in the increase of occurrence of bugs.Conclusion: Based on the results, we confirmed what other studies pointed out, regarding classes affected by Long Method being more bug-prone than others.In practice, we found evidence, from analyzed data, that the occurrence of Long Method implies more effort in maintenance tasks.
A Fast Hybrid Model for Large–Scale Zero–Shot Image Recognition Based on Knowledge Graphs

Zero-shot learning aims to recognize unseen categories by learning an embedding space between data samples and semantic representations. For the large-scale datasets with thousands of categories, embedding vectors of category labels are often used for semantic representation since it is difficult to define the semantic attributes of categories manually. Facing the problem of underutilization of prior knowledge during the construction of embedding vectors, this paper first constructs a novel knowledge graph as the supplement to the basic WordNet graph, and then proposes a fast hybrid model ARGCN-DKG, which means Attention based Residual Graph Convolutional Network on Different types of Knowledge Graphs. By introducing residual mechanism and attention mechanism, and integrating different knowledge graphs, the accuracy of knowledge transfer between different categories can be improved. Our model only use 2-layer GCN, the pretrained image features and category semantic features, so the training process could be done in minitues on single GPU, which could be one of the fastest training models for large-scale image recognition. Experiment results demonstrate that ARGCN-DKG model could get better results for large-scale datasets than the state-of-the-art model.
Effectiveness of the Indonesian literacy school program in improving the quality of basic education for marginal communities in the Indonesian border area

Abstract This study aims to compile basic data, develop and implement the Indonesian School of Literacy program in Improving the Quality of Basic Education for Marginal Communities. The program aims to establish and improve school performance through a mentoring process for school principals and teachers. In addition, this program also involves parents in the learning process of students while at home. Parents are involved through parenting training held at school. The Indonesian Literacy School is a program that seeks to realize a community-based school model that concentrates on improving the quality of instructional systems and the development of school culture, with a distinctive literacy culture. This program focuses on improving school governance by referring to indicators of improving school performance based on the Uswah Method Tools. The stages in the development of this program are: program planning, program implementation, and the end of the program. This research was conducted in 2018 to make program design and development of program tools. After that, an initial assessment of the area and school was carried out for program intervention. Program interventions are carried out in the Indonesian verandah, namely: Kepulaun Meranti (Riau Province) and Nunukan (North Kalimantan). The results showed that there was an improvement in school governance through improving the quality of instructional systems and the development of school culture, with a distinctive literacy culture.
An Improvement in Fall Detection System by Voting Strategy

The number of elderly people is increasing but the regenerative ability of those people is decreasing every day. Falls and the consequences of falls impact highly on their health. The fall detection systems are developed for years. This paper proposes the improvement in the fall detection system by the voting strategy. The three Kinect cameras provide the depth image to the fall detection systems. Each system performs (1) Frame Smoothing by Median Filtering, (2) Background Subtraction by Mixture of Gaussian Model and Binary Thresholding, and (3) Event Recognition to predict the fall or non-fall events by Convolutional Neural Network. The voting strategy results in the majority event from the three detections. The results show that the voting strategy increases the accuracy, precision, and fall detection rate to 96.48%, 90.20%, and 96.65% respectively.
Smart Measurements and Analysis for Software Quality Enhancement

Requests to improve the quality of software are increasing due to the competition in software industry and the complexity of software development integrating multiple technology domains (e.g., IoT, Big Data, Cloud, Artificial Intelligence, Security Technologies). Measurements collection and analysis is key activity to assess software quality during its development live-cycle. To optimize this activity, our main idea is to periodically select relevant measures to be executed (among a set of possible measures) and automatize their analysis by using a dedicated tool. The proposed solution is integrated in a whole PaaS platform called MEASURE. The tools supporting this activity are Software Metric Suggester tool that recommends metrics of interest according several software development constraints and based on artificial intelligence and MINT tool that correlates collected measurements and provides near real-time recommendations to software development stakeholders (i.e. DevOps team, project manager, human resources manager etc.) to improve the quality of the development process. To illustrate the efficiency of both tools, we created different scenarios on which both approaches are applied. Results show that both tools are complementary and can be used to improve the software development process and thus the final software quality.
Single stroke aerial robot light painting

This paper investigates trajectory generation alternatives for creating single-stroke light paintings with a small quadrotor robot. We propose to reduce the cost of a minimum snap piecewise polynomial quadrotor trajectory passing through a set of waypoints by displacing those waypoints towards or away from the camera while preserving their projected position. It is in regions of high curvature, where waypoints are close together, that we make modifications to reduce snap, and we evaluate two different strategies: one that uses a full range of depths to increase the distance between close waypoints, and another that tries to keep the final set of waypoints as close to the original plane as possible. Using a variety of one-stroke animal illustrations as targets, we evaluate and compare the cost of different optimized trajectories, and discuss the qualitative and quantitative quality of flights captured in long exposure photographs.
A Modified Image Processing Method for Deblurring Based on GAN Networks

In computer vision literature, it is really a challenging issue about removing the images blur resulted from camera shake. As the existing image deblurring methods do not apply to the image degraded by partial motion blur, and the existing partial blur detection approaches only used low -level blur features to measure the blurry degree of an image, the blur regions extracted via these methods usually have misclassification. To solve the imaging deblurring problem, we propose an image deblurring method based on Generative Adversarial Network (GAN) architecture using dual path connection. In comparison with the traditional image deblurring algorithm, this model can avoid the dependence on apriori-knowledge of blurred image. Experimental results show that the proposed method significantly outperforms other state-of-art algorithms on image deblurring.
Online Course Learning Outcome evaluation Method Based on Big Data Analysis

In order to solve the low credibility and poor timeliness of the online course learning effect evaluation, a method based on big data for online course learning results evaluation is proposed, which can better solve the problems existing in traditional methods. A statistical average analysis model for big data of online course learning outcomes is constructed for learning outcome big data analysis based on the sample regression analysis method; a decision objective function of online course learning outcome evaluation is established. The experimental results show that when the method proposed in this paper is adopted to evaluate online course learning outcomes, the method has relatively high confidence and overall timeliness, and the time taken to evaluate the effectiveness of online courses is 28 s and 29 s less than that of the other two methods, so it can provide accurate and reliable evaluation results.
Image blind detection based on LBP residue classes and color regions

Forgery detection is essential to verify the integrity and authenticity of images. Existing block-based detection techniques detect forgery in the same image, most of which use similar frameworks while differ in the feature extraction schemes. These methods have high accuracy in detecting the forged regions, but the computational load is heavy when facing exhaustive search problems. This paper describes a forgery detection method based on local binary pattern residue classes and color regions. An image is divided into overlapped blocks. Local binary pattern residue classes are computed for each block. The plane formed by a dimensional and b dimensional from Lab color space is divided into 16 regions. Similar blocks are searched in the overlapped blocks with the same local binary pattern residue class and color region, then they are grouped into several suspicious regions. Finally, we analyze the multi-region relation of these suspicious regions and their areas to locate the tampered regions. The small hole is filled through the morphologic operation. The results of experiments demonstrated that our method has good performance in that it improved detection accuracy and reduced execution time under various challenging conditions. As the proposed method reduces the search range for similar blocks, it has a higher speed than exhaustive search and has comparable detection results at the same time.
P-tree oriented association rule mining of multiple data sources

A prominent research area in data mining field is (ARM). As distributed databases emerged, need to mine different patterns across them become necessary and hence distributed ARM algorithms were desired. But these algorithms increased communication complexity and overhead. This paper proposes a new algorithm P-tree oriented distributed association rule mining (PDAM) for mining association rules from distributed databases. This algorithm enables a quicker computation of support counts of item sets. P-tree, a special kind of data structure is used in the algorithm which holds the transactional data. These tree data structures do effective storage of data by employing lossless compression techniques. Message exchange optimisation is proposed in this paper. Both the database scans as well as message exchanges are reduced by the proposed method. It would also reduce the size of average transactions, data sets and message exchanges.
Image Semantic Segmentation With Edge and Feature Level Attenuators

Image segmentation is one of the popular techniques for vision application, i.e., retrieving object information from an image. Segmentation not only provides the class and location of an object, but the associated contour as well. Recent advances of the segmentation deploy the encoder and decoder architectures with skip connection, and also utilize the edge information to obtain the best contour. This work focuses on maintaining the information flow from skip connection, and also identifies suited feature from the bottom layer. The feature selector can be also deployed in edge information for yielding segmentation contour. Designed filters are also employed to analyze the best feature for the reconstruction purpose. Experiment results show that the proposed scheme can improvise the performance of the simple ENet to achieve a superior IoU from 51.3% to 57.9%.
The University of Helsinki Submissions to the WMT19 Similar Language Translation Task

This paper describes the University of Helsinki Language Technology group’s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech <-> Polish and Spanish <-> Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding.
THE EFFECTIVENESS OF USING TED TALKS VIDEO IN IMPROVING STUDENTS PUBLIC SPEAKINGS SKILLS IN SENIOR HIGH SCHOOL

This study tried to analyse the effectiveness of using TED talks video in improving students public speaking skill and also the students’ perception about using TED talks video in improving students’ public speaking skills. Participant of this study were thirty four students. A mixed method was used to collect quantitative and qualitative data. To achieve the aims of this study, pre-test and post-test were administered as the quantitative data collection, and close-ended questionnaires as the qualitative data collection. From the data taken, the result showed that the students’ perception about using TED talks video in improving students’ public speaking skills is that the students response positively towards how the writer taught them. In addition, the data showed that the writer used a variety of instructional methods to reach the teaching objectively and the strategies were tried to find based on the students need in many ways. Therefore, it was concluded that the use of TED Talks is very useful to improve students’ public speaking skill. Key words: Effectiveness, Ted Talks Video, Public Speaking Skills
PENGARUH PENGGUNAAN APLIKASI VIRTUAL REALITY TERHADAP HASIL BELAJAR SISWA PADA MATERI SISTEM TATA SURYA

This study aims to determine the effect of using virtual reality applications on student learning outcomes and improvement. This research was conducted using experimental research methods with the experimental design used by Pretest-Posttest Control Group Design. The results of the study conducted at  MTs Ulul Albab Banjar Agung 7th grade are average score N-gain from an average score of student learning outcomes in the experimental class is 0,7 (high category) and based on N-gain score obtained an increase in the average score of student learning outcomes in the experimental class is 64%. In the control class, a score of N-gain was obtained the average student learning outcomes were equal to 0,50(medium category) with an increase in the average score of 35%. Based on the results of the Independent Sample T-test, the value of Asymp. Sig. (2-Tailed) is less than 0.05 is  0,000, it can be stated there is a significant influence on the use of virtual reality applications on learning outcomes.
Visual vocabulary tree-based partial-duplicate image retrieval for coverless image steganography

The traditional image steganographic approaches embed the secret message into covers by modifying their contents. Therefore, the modification traces left in the cover will cause some damages to the cover, especially embedding more messages in the cover. More importantly, the modification traces make successful steganalysis possible. In this paper, visual vocabulary tree-based partial-duplicate image retrieval for coverless image steganography is proposed to embed the secret messages without any modification. The main idea of our method is to retrieve a set of duplicates of a given secret image as stego-images from a natural image database. The images in the database will be divided into a number of image patches, and then indexed by the features extracted from the image patches. We search for the duplicates of the secret image in the image database to obtain the stego-images. Each of these stego-images shares one similar image patch with the secret image. When receiver obtains those stego-images, our method can recover the secret image approximately by using the designed protocols. Experimental results show that our method not only resists the existing steganalysis tools, but also has high capacity.
Hierarchical Actor-Critic with Hindsight for Mobile Robot with Continuous State Space

Hierarchies are used in reinforcement learning to increase learning speed in sparse reward tasks. In this kind of tasks, the main problem is elapsed time, required for the initial policy to reach the goal during the first steps. Hierarchies can split a problem into a set of subproblems that could be reached in less time. In order to implement this idea, Hierarchical Reinforcement Learning (HRL) algorithms need to be able to learn the multiple levels within a hierarchy in parallel, so these smaller subproblems could be solved at the same time. Most famous existing HRL algorithms that can learn multi-level hierarchies are not able to efficiently learn levels of policies simultaneously, especially in continuous space and action space environment. To address this problem, we had analyzed the newest existing framework, Hierarchical Actor-Critic with Hindsight (HAC), test it in the simulated mobile robot environment and determine the optimal configuration of parameters and ways to encode information about the environment states.
A new revocable reputation evaluation system based on blockchain

Reputation evaluation system, as the publisher and analyser of evaluation, is an important influencing factor for users and sellers in online business. Traditional reputation evaluation system always requires a third party to achieve the operation of analysis and publishment. However, a third party often exposes the identity of the users, and leaks the information. As far as we know, all existing revocable reputation evaluations are based on the third-party model. In this paper, we present a new reputation evaluation system based on blockchain. Compared with traditional reputation evaluation systems, our system removes the third party, it allows users to modify their own evaluation information. Moreover, the user's privacy also can be protected. The experiment performance demonstrates that the overhead of the system is acceptable, the system is feasible and efficient.