Reviewerly/exHarmony huggingface dataset:
Cross Channel Scripting Attacks (XCS) in Web Applications

XCS is one of the most common Web application vulnerabilities, which is also known as Cross Channel Scripting. It is a variant of Cross-Site Scripting (XSS); in this attack, inoculation of a malicious vector is achieved via networking protocols and embedded devices that have Web interfaces like cameras, photo frames, routers, etc. These devices permit the Web administrator to perform various activities from the browser to the server. XCS attacks are performed by injecting the malicious content into the embedded devices having the Web interface and this malicious code is exploited in the client browser. Further, the malicious content can be injected into the device through network protocols like File Transfer Protocol and Network File System. In this article, the analysis of scripting defending approaches at the client-side and server-side have been discussed and exploited using penetration testing tools. In the literature, XCS vulnerability detection and mitigation are major extents covered by most of the studies. We have also conferred various state-of-the-art XCS techniques with their strengths, weaknesses, and identified the research gaps.
The Post-Chornobyl Library: Ukrainian Postmodernism of the 1990s. By Tamara Hundorova. Sergiy Yakovenko, trans. Brighton, Mass.: Academic Studies Press, 2019. Dist. Harvard Ukrainian Research Institute. xvi, 338 pp. Notes. Bibliography. Index. $42.00, paper.

The Post-Chornobyl Library: Ukrainian Postmodernism of the 1990s. By Tamara Hundorova. Sergiy Yakovenko, trans. Brighton, Mass.: Academic Studies Press, 2019. Dist. Harvard Ukrainian Research Institute. xvi, 338 pp. Notes. Bibliography. Index. $42.00, paper. - Volume 80 Issue 2
A Text Correction and Recognition for Intelligent Railway Drawing Detection

In current train control system, there are many drawings that need to be identified manually. This approach leads to many problems, such as misidentification and low efficiency. It is difficult to recognize these drawings automatically because the text on them often has a series of changes, like rotation, tilt, font changes and close to lines. To solve this problem, we divide the task into two parts: text recognition and graphical symbol recognition. This article focuses on text recognition. We use aerial detection technology to classify and detect graphical symbol and text, followed by choosing BILSTM to conducting sequence modeling and using convolutional recurrent neural network (CRNN) iterative training to focus on single word rotation, including tilting, noise-addition, and blurring post-processing. So that the training model can cope with complex scenario and improve the recognition rate of text on drawings. Finally, RESNET is applied to CRNN feature extraction network, and CRNN outputs the recognition and detection results in sequence according to the detection sequence, achieving entry-level detection, and the text recognition rate reaches 98.36%
Workshop Proposal: Making the World Cleaner: Data Computation and Communication in a Design Landscape

To gain maximum benefit from maker projects it is important to facilitate young people to fully engage with the outputs. In a multi week project we have been exploring ways to combine making and design with instruction on data interpretation, data visualization and science communication. Young people aged 12/13 have disseminated findings from an environmental project to children in primary schools. In this workshop we seek to bring together others doing, or aiming to do, similar work with the hope to bring together a community of interested parties and to share good practice on the integration of building and instruction. This workshop will focus on exploring how maker projects can be used to raise environmental awareness for both the participants and for those who are informed about such projects.
Retrieval Augmentation for Deep Neural Networks

Deep neural networks have achieved state-of-the-art results in various vision and/or language tasks. Despite the use of large training datasets, most models are trained by iterating over single input-output pairs, discarding the remaining examples for the current prediction. In this work, we actively exploit the training data, using the information from nearest training examples to aid the prediction both during training and testing. Specifically, our approach uses the target of the most similar training example to initialize the memory state of an LSTM model, or to guide attention mechanisms. We apply this approach to image captioning and sentiment analysis, respectively through image and text retrieval. Results confirm the effectiveness of the proposed approach for the two tasks, on the widely used Flickr8 and IMDB datasets. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> htttp://github.com/RitaRamo/retrieval-augmentation-nn.
Organizati on of dista nce learning using the platf orm "Russian electronic school"

The article discusses aspects of the organization of distance learning using educational platforms, the capabilities of Google Tables and distance communication systems. The experience of organizing distance learning also in the mode of full-time education is given. The transition to a distance learning format was the only way to continue training and effective feedback with students in a pandemic, but, as practice has shown, the skills of organizing training in a distance format have been closely integrated into the educational process, especially for an open school. The use of a Russian educational platform with ready-made tools for teaching, monitoring and analyzing the data obtained made it possible in the shortest possible time to restructure the learning process with minimal labor costs for both school teachers and students and their parents.
Towards Unifying the Explainability Evaluation Methods for NLP

Neural models make decisions based on the features learned from the training data, each prediction being mostly influenced by one or more similar training data points. While extractive approaches of explainability for Deep Learning text classifiers focus on highlighting the most important parts of the input text, they do not take into consideration the training set. Furthermore, the interpretability methods proposed by the research community vary not only in methodology, but also in adopting different metrics for evaluating the explanations, which makes it difficult for users to compare and choose an explainable method appropriate for their use case. We propose a novel deep learning interpretability approach and a method-agnostic evaluation metric for measuring the added value of a feature-based explanation for natural language processing applications. The model architecture selects the phrase that influences a given prediction the most out of a set of key phrases extracted in an unsupervised manner from the training set. The evaluation approach enables one to compare natural language explanations based on the quantification of the faithfulness to the model, metric that we refer to as the contribution metric.
PQRST (Preview, Question, Read, State and Test) Strategy in Analyzing Students’ Reading Comprehension of Narrative Text Strategi

The application of strategy in the learning process can be crucial to support the students’ competence. This study is descriptive - survey research data was obtained from the questionnaire. This study aims to determine the use of the PQRST strategy to improve the reading ability of class XI BHS students at SMAN 1 Porong. The participants in this study were 31 students. In this questionnaire, the researcher gave 10 questions related to learning English, especially in reading and learning after being given the strategy. Based on the questionnaire, it was found that student's like to be taught using the PQRST strategy. It can be seen from the questionnaire question, more student's choose Yes than No and only some students choose sometimes. This is reinforced from the results of the questionnaire that the application of the PQRST strategy is able to help students improve their reading skills and students understand the content of the text from reading.
Processing bone fracture image before and after surgery using GPU

A fracture is the solution of continuity of bone tissue in any bone of the body occurs as a result of excessive stress that exceeds bone resistance, ie is the consequence of a single or multiple overload and occurs in milliseconds. The development of magnetic resonance imaging and computerized tomography have made it possible to know and evaluate the different pathologies of the human being more accurately. Edge detection is a fundamental tool in image medical processing, particularly in the areas of feature detection, which aim at identifying points in a digital image at which the image has discontinuities. In order to improve the computing speed, was used parallel computing which support NVIDIA GPU. This work presents an improved methodology for processing bone fracture images before and after surgery using segmentation and graphic accelerator cards to help the medical specialist in the analysis and evaluation of the images.
Densely End Face Detection Network for Counting Bundled Steel Bars Based on YoloV5

Steel bars are important materials in infrastructure construction. It is necessary to know the number of steel bars that are bundled in production, storing, and trading, while manually counting bundled steel bars is time-consuming and error-prone. A densely end face detection network for counting bundled steel bars is developed. Cross stage partial connection is introduced to reduce the duplicate gradient information of PeleeNet. An attention module is added to enhance the feature extraction ability of the network. Features at different levels of the backbone network are adaptively spatial fused and used as inputs of the network for end face detection of bundled steel bars. The F1 score of the densely end face detection network for counting bundled steel bars is 98.84%, while the model size is 0.52M, and the inference time is 0.022 s.
Doodle Recognition Using Ensemble Learning

The system uses machine learning techniques to identify the sketches drawn by people and recognize them correctly. The recognition system tries to consider nuances of size, shape, and orientation which might occur due to the individuality of human beings. Doodle and its recognition have recently drawn a lot of attention from the scientific community from using a sketch for search engines, psychological analysis, and many more, and it has initiated work in this domain. Here, the system has taken into consideration 30 different classes of objects that can be recognized. The method used here is ensemble learning, using an ensemble of CNN as well as LSTM algorithms to identify and categorize the doodle correctly.
Virtual Negotiation Training "Beat the Bot"

The VR application "Beat the Bot" has successfully combined VR and AI in a negotiation-based dialogue scenario. The purpose is to achieve real success with users, enabling access into future, modern negotiation-based training. The aim of this VR application is to experience a negotiation situation in the form of a pitch and to learn to apply an ideally optimal negotiation style in a highly competitive sales negotiation for capital goods. The user slips into the role of the seller and uses natural language to negotiate with two virtual, AI-controlled agents acting in the role of two professional buyers. The VR application motivates the repetition and consolidation of the learning content by incorporating playful elements and creating a serious game environment [1].
Recognizing Vector Graphics without Rasterization.

In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be scaled up or down into any resolution without aliasing or information loss, due to the analytic representation of the primitives in the document. Furthermore, vector graphics are able to give extra structural information on how low-level elements group together to form high level shapes or structures. These merits of graphic vectors have not been fully leveraged in existing methods. To explore this data format, we target on the fundamental recognition tasks: object localization and classification. We propose an efficient CNN-free pipeline that does not render the graphic into pixels (i.e. rasterization), and takes textual document of the vector graphics as input, called YOLaT (You Only Look at Text). YOLaT builds multi-graphs to model the structural and spatial information in vector graphics, and a dual-stream graph neural network is proposed to detect objects from the graph. Our experiments show that by directly operating on vector graphics, YOLaT out-performs raster-graphic based object detection baselines in terms of both average precision and efficiency.
Tingkat Risiko Bencana Banjir di Kota Bandar Lampung dan Upaya Pengurangannya Berbasis Penataan Ruang

In the past ten years (2010-2019), Bandar Lampung has experienced 26 disasters, and 14 of them were floods. The flood that occurred in the city of Bandar Lampung is a serious problem, because in recent years the city of Bandar Lampung has always been hit by floods during the rainy season. Therefore, the purpose of this study is to identify the level of risk of flooding in the city of Bandar Lampung and to recommend spatial-based reduction efforts. This study uses a combination analysis approach between qualitative and quantitative analysis. The level of flood risk identified in this study is calculated based on Regulation of the Head of BNPB Number 2 of 2012 concerning General Guidelines for Disaster Risk Assessment. From the research results, it is known that Bandar Lampung City has three classes of flood risk, namely low, medium and high risk. The area classified as low risk is 11,460.96 ha or about 62.37% of the total area of Bandar Lampung City. Meanwhile, the area classified as high risk is 3,781.12 ha or about 20.58% of the total area of Bandar Lampung City. The main factors affecting the risk level of floods are hazard, vulnerability and capacity. To reduce the risk of floods in Bandar Lampung, effective efforts are needed through spatial planning, spatial use and spatial use control.
A Shadow Detection Method Based on SLICO Superpixel Segmentation

This paper proposes a shadow detection algorithm based on SLICO superpixel segmentation to address the issues of single-image shadow detection. Firstly, SLICO superpixels is used to segment the shadow image to generate superpixel blocks to detect the shadow contour. Then proposes a fusion-characteristics-based SVM classifiers, the superpixel blocks are classified and merged to detect the shadow areas. Finally, the proposed algorithm is compared with Otsu threshold method and traditional SVM detection method. The experiment results verified the effectiveness of the proposed algorithm. The detection performances comparison of SSIM and PSNR indicates that the proposed algorithm obtains relative higher performances than the reference algorithms.
Analysis of the Application Prospect of Computer Information Technology in the Internet

research-article Analysis of the Application Prospect of Computer Information Technology in the Internet Share on Author: Geng Li Wuhan University of Technology, China Wuhan University of Technology, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2158–2162https://doi.org/10.1145/3482632.3484119Online:22 November 2021Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Understanding security vulnerabilities in student code: A case study in a non-security course

Secure coding education is quite important for students to acquire the skills to quickly adapt to the evolving threats towards the software they are expected to create once they graduate. Educators are also more aware of this situation and incorporate teaching security in their respective fields. An effective application of this is only possible by cultivating the teaching and learning perspectives. Understanding the security awareness and practice of students is useful as an initial step to create a baseline for teaching methods and content. In this paper, we first survey to investigate how students approach security and what could motivate them to learn and apply security practices. Then, we analyze the source code for 6 semesters of coding assignments for 2 tasks using a source code vulnerability analysis tool. In our analysis, we report the types of vulnerabilities and various aspects between them while incorporating the effect of student grades. We then explore the lexical and structural features of security in student code using data analysis and machine learning. For the lexical analysis, we build a classifier to extract informative features and for the structural analysis, we utilize Syntax Trees to represent code and perform clustering in terms of structural features where clusters themselves yield different vulnerability levels.
Explanation of Several Problems with Complementary Code

In order to improve the teaching effect of the course of Principles of Computer Composition, this article has conducted a more in-depth discussion on the teaching of complements.Complementary code is a difficult point in computer science.In the process of teaching, students feel that there are various difficulties in truly understanding this concept.Through the explanation of various forms of complements, including integers, decimals, and deformed complements, this article strives to make this concept concrete and visualized, so as to provide some help to the teaching of this concept in the teaching process.The teaching effect shows that the methods discussed in the article are helpful to improve the teaching effect.
Automatic Arabic Speech Recognition- A Comparative Study

The cutting-edge studies on Automatic Speech Recognition approach have reported exceptional accuracy rates that are even comparable to human transcribers – posing a question if machine has reached human performance.
Technology

This chapter presents undergraduate students in teacher education programs with practical solutions for integrating digital tools in the discovery, design, organization, and distribution of their research projects. It highlights digital skills, concepts, and capabilities that can enhance any undergraduate research projects, across a range of academic disciplines representative of the pool of students in teacher education programs. Alongside these strategies, the author challenges students to embrace and promote digital literacy, in addition to adopting best practices in the application of online resources.
Deep Learning Based TTS-STT Model with Transliteration for Indic Languages

Abstract: This paper introduces a new text-to-speech presentation from end-to-end (E2E-TTS) using toolkit called ESPnet-TTS, which is an open source extension. ESPnet speech processing tools kit. Various models come under ESPnet TTS TacoTron 2, Transformer TTS, and Fast Speech. This also provides recipes recommended by the Kaldi speech recognition tool kit (ASR). Recipes based on the composition combined with the ESPnet ASR recipe, which provides high performance. This toolkit also provides pre-trained models and samples of all recipes for users to use as a base .It works on TTS-STT and translation features for various indicator languages, with a strong focus on English, Marathi and Hindi. This paper also shows that neural sequence-to-sequence models find the state of the art or near the effects of the art state on existing databases. We also analyze some of the key design challenges that contribute to the development of a multilingual business translation system, which includes processing bilingual business data sets and evaluating multiple translation methods. The test result can be obtained using tokens and these test results show that our models can achieve modern performance compared to the latest LJ Speech tool kit data. Terms of Reference — Open source, end-to-end, text-to-speech
Peer review declaration

All papers published in this volume of IOP Conference Series: Earth and Environmental Science have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind • Conference submission management system: We conduct paper submission through open conference system (http://conference.unhas.ac.id/ocs/index.php/bctb/2021/login) and the manuscript from author has submitted before the conference through the conference website (https://bctb.unhas.ac.id/) • Number of submissions received: Total received abstract = 251 titles • Number of submissions sent for review: 231 titles for abstract • Number of submissions accepted: 138 titles of fullpaper from 231 titles of fullpaper submitted • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 59.7 % • Average number of reviews per paper: 7- 8 papers per reviewer • Total number of reviewers involved: 18 reviewers • Any additional info on review process: There are some steps on review process 1. Abstract review and acceptance At the begin, the abstract is submitted by the online system which is available on the conference page. The submitted abstracts are then collected by the committee for review one by one regarding the format, language and suitability of the conference scope. The abstract review was carried out by an internal reviewer. If there are any discrepancies, the abstract will be sent back to participants via email. 2. Submission of Full Paper Secondly, uploading of full paper may only be done by participants who attend and present their research activities during the conference. This upload and revision activity was carried out on July 25 - September 15 2021 3. Revision of Full Paper At the revision stage, participants will be requested to make revisions based on the input given by the reviewer, this input is generally in the form of format, language and continuity of each discussion. 4. Peer review of Full Paper At the Fourth stage, full paper previously submitted through system were reviewed following a single blind peer review process. The papers were sent to reviewer that consisted of 18 reviewers according to topic of the papers and reviewer’s competency. Review was conducted on content, language, and format of the paper based on IOP Conference Series. At this stage, Reviewer submit a decision for the acceptance of the papers for publication in IOP Conference Series. 5. Revision of Reviewed Full Paper As the end, the submitted papers were then subjected to proofread stage and check for similarity using Turnitin program. Subsequently, papers were prepared for submission to IOP Conference Series. • Contact person: Mukrimin Mukrimin Department of Forestry, Faculty of Forestry, Universitas Hasanuddin, Makassar, Indonesia mukrimin@unhas.ac.id