Reviewerly/exHarmony huggingface dataset:
Cross-Site Scripting Recognition Using LSTM Model

Cross-site Scripting (XSS) is one of the most prominent types of web application assaults. It is one of the most serious hazards to online applications and one of the top vulnerabilities, according to the Open Web Application Security Project (OWASP). As a result, detecting and countering this attack is crucial. As a result, we’ve demonstrated how to leverage the Long Short-Term Memory (LSTM) model to detect the XSS attack script in this paper. First, we acquired several scripts for the dataset from the XSS vulnerability archives, and then we preprocessed the data by generalizing it, tokenizing it, and changing text to the sequence. The processed data then is trained and tested using the LSTM model, a Recurrent Neural Network. With a precision rate of 99.57% and an f1 score of 99.78%, the proposed approach can yield a substantial outcome.
A Distantly-Supervised Relation Extraction Method Based on Selective Gate and Noise Correction

Entity relation extraction, as a core task of information extraction, aims to predict the relation of entity pairs identified by text, and its research results are applied to various fields. To address the problem that current distantly supervised relation extraction (DSRE) methods based on large-scale corpus annotation generate a large amount of noisy data, a DSRE method that incorporates selective gate and noise correction framework is proposed. The selective gate is used to reasonably select the sentence features in the sentence bag, while the noise correction is used to correct the labels of small classes of samples that are misclassified into large classes during the model training process, to reduce the negative impact of noisy data on relation extraction. The results on the English datasets clearly demonstrate that our proposed method outperforms other baseline models. Moreover, the experimental results on the Chinese dataset indicate that our method surpasses other models, providing further evidence that our proposed method is both robust and effective.
Air transport implications in tourist destinations

Most of the travel destinations rely almost entirely on air services for their visitor traffic. Air transport facilitates integration into the economy and provides vital connectivity on a: • National, • Regional, • International scale between countries, • Promoting tourism growth, Create employment opportunities in tourist 11 UNWTO, eLibrary, https://www.e-unwto.org/doi/abs destinations. Over time, the rate of growth will gradually slow down, from 3.8% in 2011 to 2.5% in 2030, but on top of growing base numbers. The total economic impact of travel and tourism can be assessed by measuring current and capital expenditures in each of the fields including those by: • consumers, • businesses, • government. The adequate system of Air transport is an essentialm requirement for the successful development of tourism to many destinations. The emergence of LCC,s contributed to the increase in tourist arrivals. There is a powerful synergy between the development of International Air transport and International tourism. Air transport contributes to consumer welfare and travel and tourism provide substantial socialbenefits, including: • Increasing understanding of different cultures and nationalities which facilitatesmcloser international integration,m • Improving living standards by widening choice Air transport contributes to sustainable development and makes a major contribution to sustainable development by supporting andpromoting international tourism. Tourism helps reduce poverty by generating economic growth, providing employment opportunities, increasing tax collection, and by fostering the development and conservation of protected areas and the environment
Simulation Training to Develop the Skills of Sanitary Evacuation of Emergency Victims

During the exercises of the search and rescue team of the Main Directorate of the Ministry of Emergency Situations of Russia for the Khabarovsk Territory, according to the international methodology for response and rescue operations INSARAG, simulation training was carried out during the sanitary evacuation of victims of emergencies with the development of practical skills in implementing SOPs and under control using checklists. During the training, using standard equipment, we used a previously tested free ECG simulator program — Six Second ECG and simple mannequins.
Economics Teaching Materials Short Story Text For Class XI Students of SMA Negeri 1 Palimanan, Cirebon District

This study aims to design short story text economics teaching materials and how the results of the implementation of short story text economics teaching materials for class XI students of SMA Negeri 1 Palimanan. This research method is to use qualitative research with analytic descriptive method. The research location was carried out at SMA Negeri 1 Palimanan, Cirebon Regency. The results showed that based on the implementation of short story text teaching materials for class XI students of SMA Negeri 1 Palimanan, namely the results of the student response questionnaire showed a satisfactory response, meaning that students could well accept short story teaching materials, on average students were quite good at understanding the contents of the material. teaching materials and can help teaching materials in teaching and learning activities in class, especially in short story material. Students hope that teaching materials that are made according to the needs of students like this can continue to be developed to support learning materials. Students feel interested and easily understand the material because it is neatly arranged and the front cover illustrations of the books are adjusted to the colors that make students interested in reading them, the use of color in teaching materials is very important so that students when reading material or assignments do not get bored and want to work on the material. teach it. The language used in textbooks is in accordance with the readability level of high school students and in terms of illustrations and pictures in teaching materials it really helps students' interest in reading. The teaching materials presented make it easier for students to understand the material because it is adapted to the curriculum so that it facilitates learning activities, especially short story text material.
Pengembangan Media Pembelajaran Kartu UNO Pada Mata Pelajaran Ekonomi Di Kelas XI IPS SMA Negeri 8 Kota Jambi

This research aims to determine the feasibility of the UNO card learning media used in the economicslearning process in class XI IPS SMA Negeri 8 Jambi City using the UNO card media based on the assessmentof material experts, media experts and student responses. This type of research is research and development,while the techniques in this research use design analysis, implementation development and evaluation. Theresult of the research show that overall the field test participants assessment of learning media is in the verydecent category. Thus, the UNO card learning media in economics subjects is worthy of being develop as alearning media that is proven to increase students enthusiasm for learning.
Evolution of the US’ Strategic Ambiguity in the Cross-Strait Relations

Purpose: This paper analyzes how the US policy on Taiwan is gradually evolving from strategic ambiguity to strategic clarity. First, I would like to analyze China's Taiwan policy in terms of the core interests of the Communist Party of China. Second, I would like to specify why the US policy on Taiwan is evolving from strategic ambiguity to strategic clarity. Third, in the event of the Taiwan contingency, the security threat to the Korean Peninsula is identified and policy alternatives are proposed.&#x0D; Method: This paper delves into the US policy on Taiwan with the Balance of Interest theory of Randall Schweller. A neoclassical realist suggests that national foreign policy is indirect and complex and must be reinterpreted through a parameter unit of domestic factors. According to Neo-Classic realists, the parameters that affect a country's foreign policy are such as domestic interest groups and the political leader’s ideology and identity.&#x0D; Results: The main parameters of the US policy shift towards Taiwan from the perspectives of Neo-classical Realism are as follows. First, it is Biden’s identity of value diplomacy which prioritizes democracy and human rights. Second, Xi Jinping's military threats to annex Taiwan, and the pursuit of a gray zone strategy are driving factors for the US Policy shift from strategic ambiguity to strategic clarity. Third, the US policy shift to Taiwan was initiated by curbing China's rise through the control of the Indo-Pacific maritime control and global supply chain.&#x0D; Conclusion: The contingency in the Taiwan Strait is closely linked to security on the Korean Peninsula due to the US’ strategic flexibility. Currently, tensions between the US and China are rising in the Indo-Pacific region as the US military support for Taiwan's self-defense. The ROK should be wary of being involved in the unintended Taiwan crisis. To build a free and prosperous Indo-Pacific, strategic communication between the US and China should be strengthened while respecting the “one China” principle.
SIGN LANGUAGE DETECTION USING ACTION RECOGNITION WITH PYTHON

Sign language detection plays a crucial role in bridging the communication gap between individuals with hearing impairments and the rest of the population. This study presents a novel approach for sign language detection using action recognition and LSTM deep learning models implemented in Python. The proposed system aims to accurately recognize and interpret sign language gestures in real-time. The research focuses on leveraging the power of deep learning techniques, specifically the Long Short-Term Memory (LSTM) architecture, to effectively capture temporal dependencies and classify sign language gestures. A comprehensive dataset comprising a wide range of sign language gestures is collected and preprocessed to train and evaluate the LSTM model. The pipeline for sign language detection consists of several stages, including video acquisition, preprocessing, feature extraction, and model training. In the pre-processing stage, the acquired video data is segmented into individual frames, and various image processing techniques are applied to enhance the quality and remove noise. Next, robust features are extracted from the preprocessed frames using techniques like optical flow or deep learning-based feature extraction methods. The LSTM model is then trained on the extracted features to learn the temporal dynamics of sign language gestures. Transfer learning is also explored to leverage pre-trained models on large-scale action recognition datasets. The trained model is evaluated using a comprehensive set of metrics, including accuracy, precision, recall, and F1-score, to assess its performance. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing sign language gestures. The LSTM-based deep learning model achieves a high accuracy rate, showcasing its capability to handle the temporal nature of sign language. The system exhibits real-time performance, enabling it to be deployed in various applications, such as real-time interpretation tools or assistive devices for individuals with hearing impairments
A Mathematical model for fall detection predication in elderly people

The falling risk of elderly people has become a significant issue. The use of a single sensor to detect the falling was found ineffective. Hence, methods such as video detection and the use of more sensors were investigated, and falling prediction based on Human Body Kinematics (HBK) and motion was studied. The model consisted of two algorithms: a prediction algorithm to predict the occurrence of a fall from Daily Activity Living (DAL) and a decision-making algorithm to classify the DAL (fall or no fall). The model was analyzed using three Inertial Measurement Unit (IMU) sensors with three Degrees of Freedom (DOF) that were assumed to be set on the thoracic, hip, and knee joints. The model used quaternions to represent the orientation of the three joints. To determine the occurrence of a fall, the joint angles for the thoracic, hip, and knee were calculated, and the world frame was used as a reference and a T-pose skeleton for coordinate calculation. The proposed model was evaluated using a ready-made dataset called Inertial Measurement Unit dataset (IMU); which contains real-time human motion obtained from IMU sensors. The evaluation was done using MATLAB simulation. The outcomes of the evaluation show that the proposed model is efficient and promising.
Adaptive Ship Detection From Optical to SAR Images

Recent advances in Synthetic Aperture Radar (SAR) ship detection have witnessed remarkable success by using large-scale annotated datasets. However, the annotation of SAR images requires strong domain-specific expertise, significantly hindering the prompt adoption of modern object detectors in this regime. Compared to SAR data, the optical data in geoscience are considerably easier to label. Motivated by this, we investigate a new and challenging problem – adaptive ship detection – with the goal of enhancing ship detection performance on SAR images by leveraging knowledge transferred from optical images. Considering the large distributional discrepancy between the source (optical) and target (SAR) domains, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OmniAdapt</i> , a novel framework that progressively narrows the distance between the two types of images at the pixel, feature, and classifier levels. Specifically, OmniAdapt consists of three main modules, Target-like Generation Module (TLGM), Multi-feature Alignment Module (MFAM), and Common Specific Decomposition Module (CSDM). TLGM minimizes the visual disparity by infusing the target domain style into the source domain. MFAM aligns local- and global-level feature representations in an adversarial manner. Finally, CSDM decomposes the classifier into two independent components, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the domain-common component and the domain-specific component, and promotes the recognition ability of the former via regularization learning. Experimental results demonstrate the effectiveness of the proposed method.
<i>Reading Broadly to Open Your Mind</i> Improving Open Relation Extraction With Search Documents Under Self-Supervisions

Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional dependency on external assumptions. However, these works can only obtain information signals from limited existing knowledge bases or datasets. In this work, we propose a self-supervised framework named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Web-SelfORE</monospace> , which exploits self-supervised signals by requiring a large pretrained language model to extensively read real-world relevant documents from the web, and obtain contextualized relational features by mixing contextualized representations of entities from different documents. We perform adaptive clustering on contextualized relational features and bootstrap the self-supervised signals by improving contextualized features in relation classification. We additionally compare the effectiveness of self-supervisions brought by different document sources, and introduce relevance and redundancy evaluation metrics to obtain higher-quality self-supervisions. Experimental results on four public datasets show the effectiveness and robustness of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Web-SelfORE</monospace> on open-domain relation extraction task when comparing with competitive baselines.
Long-Term Adaptive VCG Auction Mechanism for Sustainable Federated Learning With Periodical Client Shifting

Federated Learning (FL) system needs to incentivize clients since they may be reluctant to participate in the resource consuming process. Existing incentive mechanisms fail to construct a sustainable environment for the long-term development of FL system: 1) They seldom focus on system economic properties (e.g., social welfare, individual rationality, and incentive compatibility) to guarantee client attraction. 2) Current online auction modeling methods divide the whole continual process into multiple independent rounds and solve them one-by-one, which breaks the correlation between each round. Besides, the inherent characteristics of FL system (model-agnostic and privacy-sensitive) also prevent it from the optimal strategy by precise mathematical analysis. 3) Current system modelings ignore the practical problem of periodical client shifting, which cannot adaptively update its strategy to handle system dynamics. To overcome the above challenges, this paper proposes a long-term adaptive Vickrey-Clarke-Groves (VCG) auction mechanism for FL system, which incorporate a multi-branch deep reinforcement learning (DRL) algorithm. First, VCG auction is the only one that can simultaneously guarantee all crucial economic properties. Second, we extend the economic properties to long-term forms and apply the experience-driven DRL algorithm to directly obtain long-term optimal strategy, without any prior system knowledge. Third, we reconstruct a multi-branch DRL network to accommodate periodical client shifting by adaptive decision head switching for different time periods. Finally, we theoretically prove he extended economic properties (i.e., IC) and conduct extensive experiments on several real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL system increases by 36% with a 37% reduction in payment. Besides, the multi-branch network can adaptively handle periodical client shifting on the timeline.
Perceptual Light Field Image Coding with CTU Level Bit Allocation

Light field imaging simultaneously records the position and direction information of light in scene, as one of the important techniques for digital media. The amount of light field image (LFI) data is huge, it needs to be effectively compressed. In this paper, a perceptual LFI coding method with coding tree unit (CTU) level bit allocation strategy is proposed. To remove angular redundancy, a hybrid coding framework with joint deep learning reconstruction networks is constructed. At the encoder side, only four corner sub-aperture images (SAIs) are compressed with new CTU level bit allocation, a complete SAIs array is reconstructed by a LFI angular super-resolution network at the decoder side. To remove perceptual redundancy, we design a CTU level bit allocation strategy with the assumption of perceptual consistency, considering the characteristics of the human visual system in the bit allocation process. Experimental results show that for the proposed method with the designed CTU level bit allocation strategy, an average BD-BR savings of 13.676% in Y-PPSNR metric and 2.045% in VSI metric can be achieved. Compared with the high efficiency video coding (HEVC) intra coding model, the proposed method can achieve an average BD-BR savings of over 90%.
Image Forgery Detection System Using Convolution Neural Networks

In today’s digital era, the power of images to convey our emotions and messages has reached new heights. Detecting close-to-identical images requires matching altered images to their original counterparts, a crucial task in recognizing manipulated visuals. Significant efforts have been devoted to developing pictorial applications that rely on efficient image similarity measurements and labeling Digital images can be easily edited and manipulated due to the advanced capabilities of image processing software. Consequently, this presents the challenge of accurately matching slightly altered images with their authentic sources, a task commonly known as “close-to-copy image discovery.” This research paper delves into various image-matching algorithms that have been studied for this purpose. One prevalent image manipulation technique is image recoloring, which can alter image tones or subjects, making it difficult for the human eye to detect the changes. However, there is a lack of specialized methods designed to detect such manipulations. In response to this gap, our paper proposes an end-to-end system designed to detect recolored images among genuine ones. Our approach involves taking the original image and two auxiliary inputs based on brightness consistency and inter-channel correlation. The system then outputs the likelihood of the image being recolored. The core of our proposed method leverages a convolutional neural network (CNN) architecture, comprising three feature abstraction blocks and a feature fusion segment. To train the CNN, we assembled a dataset of recolored images and their corresponding ground truth, generated using various recoloring techniques. Through extensive experiments on a diverse set of recolored images, our proposed network demonstrates excellent generalization and robustness, making it highly effective for detecting image recoloring.
Frequency-Constrained Substring Complexity

We introduce the notion of frequency-constrained substring complexity. For any finite string, it counts the distinct substrings of the string per length and frequency class. For a string x of length n and a partition of [n] in $$\tau $$ intervals, $$\mathcal {I}=I_1,\ldots ,I_\tau $$ , the frequency-constrained substring complexity of x is the function $$f_{x,\mathcal {I}}(i,j)$$ that maps i, j to the number of distinct substrings of length i of x occurring at least $$\alpha _j$$ and at most $$\beta _j$$ times in x, where $$I_j=[\alpha _j,\beta _j]$$ . We extend this notion as follows. For a string x, a dictionary $$\mathcal {D}$$ of d strings (documents), and a partition of [d] in $$\tau $$ intervals $$I_1,\ldots ,I_\tau $$ , we define a 2D array $$S=S[1\mathinner {.\,.}|x|,1\mathinner {.\,.}\tau ]$$ as follows: S[i, j] is the number of distinct substrings of length i of x occurring in at least $$\alpha _j$$ and at most $$\beta _j$$ documents, where $$I_j=[\alpha _j,\beta _j]$$ . Array S can thus be seen as the distribution of the substring complexity of x into $$\tau $$ document frequency classes. We show that after a linear-time preprocessing of $$\mathcal {D}$$ , for any x and any partition of [d] in $$\tau $$ intervals given online, array S can be computed in near-optimal $$\mathcal {O}(|x| \tau \log \log d)$$ time.
EFL students’ language production characteristics in an asynchronous online learning

This research aims to gain information and general description of students’ language production in writing email in an asynchronous task based online language classroom. It covers how a certain amount of writing time pressure on within task planning stage could enhance students’ language.This research applied a qualitative approach using case study research design. It was conducted in a vocational high school in Sumedang including the X grade students which were divided into two different groups A and B with different pressured online planning, namely 60 minutes and 45 minutes. The data obtained 36 emails. They were analyzed and observed using Ellis and Barkhuizen (2005). It resulted that the 45 minutes group performed better on fluency and accuracy, while longer time limit benefited language complexity. That means the students in 45 minutes group paid more attention on meaning and form while the 60 minutes group gave students chance for restructuring as the result of taking risk.&#x0D; Key words: online task based writing and language production&#x0D; &#x0D;