Reviewerly/exHarmony huggingface dataset:
The More Things Change, the More They Stay the Same: Integrity of Modern JavaScript

The modern web is a collection of remote resources that are identified by their location and composed of interleaving networks of trust. Supply chain attacks compromise the users of a target domain by leveraging its often large set of trusted third parties who provide resources such as JavaScript. The ubiquity of JavaScript, paired with its ability to execute arbitrary code on client machines, makes this particular web resource an ideal vector for supply chain attacks. Currently, there exists no robust method for users browsing the web to verify that the script content they receive from a third party is the expected content.
Multi-Aspect Heterogeneous Graph Augmentation

Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous graphs remains under-explored. Considering that heterogeneous graphs contain different types of nodes and links, ignoring the type information and directly applying the data augmentation methods of homogeneous graphs to heterogeneous graphs will lead to suboptimal results. In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. Specifically, MAHGA consists of two core augmentation strategies: structure-level augmentation and metapath-level augmentation. Structure-level augmentation pays attention to network schema aspect and designs a relation-aware conditional variational auto-encoder that can generate synthetic features of neighbors to augment the nodes and the node types with scarce links. Metapath-level augmentation concentrates on metapath aspect, which constructs metapath reachable graphs for different metapaths and estimates the graphons of them. By sampling and mixing up based on the graphons, MAHGA yields intra-metapath and inter-metapath augmentation. Finally, we conduct extensive experiments on multiple benchmarks to validate the effectiveness of MAHGA. Experimental results demonstrate that our method improves the performances across a set of heterogeneous graph learning models and datasets.
Few-shot News Recommendation via Cross-lingual Transfer

The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these solutions is limited by the amount of records available from warm users to use. Thus, building a recommendation system based on few interaction records from a few users still remains a challenging problem for unpopular or early-stage recommendation platforms. This paper focuses on solving the few-shot recommendation problem for news recommendation based on two observations. First, news at different platforms (even in different languages) may share similar topics. Second, the user preference over these topics is transferable across different platforms. Therefore, we propose to solve the few-shot news recommendation problem by transferring the user-news preference from a many-shot source domain to a few-shot target domain. To bridge two domains that are even in different languages and without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. A user encoder is constructed on top of the aligned news encoding and transfers the user preference from the source to target domain. Experimental results on two real-world news recommendation datasets show the superior performance of our proposed method on addressing few-shot news recommendation, comparing to the baselines. The source code can be found at https://github.com/taichengguo/Few-shot-NewsRec.
Breaking Filter Bubble: A Reinforcement Learning Framework of Controllable Recommender System

In the information-overloaded era of the Web, recommender systems that provide personalized content filtering are now the mainstream portal for users to access Web information. Recommender systems deploy machine learning models to learn users’ preferences from collected historical data, leading to more centralized recommendation results due to the feedback loop. As a result, it will harm the ranking of content outside the narrowed scope and limit the options seen by users. In this work, we first conduct data analysis from a graph view to observe that the users’ feedback is restricted to limited items, verifying the phenomenon of centralized recommendation. We further develop a general simulation framework to derive the procedure of the recommender system, including data collection, model learning, and item exposure, which forms a loop. To address the filter bubble issue under the feedback loop, we then propose a general and easy-to-use reinforcement learning-based method, which can adaptively select few but effective connections between nodes from different communities as the exposure list. We conduct extensive experiments in the simulation framework based on large-scale real-world datasets. The results demonstrate that our proposed reinforcement learning-based control method can serve as an effective solution to alleviate the filter bubble and the separated communities induced by it. We believe the proposed framework of controllable recommendation in this work can inspire not only the researchers of recommender systems, but also a broader community concerned with artificial intelligence algorithms’ impact on humanity, especially for those vulnerable populations on the Web.
Peramalan curah hujan di Provinsi Aceh menggunakan metode Box-Jenkins

Floods are one of the natural disasters that frequently occur in Indonesia, including in Aceh Province. Floods primarily occur when rainfall is intense, mainly in the rainy season. Flood disaster management in Aceh Province is still not optimal, so efforts are required to minimize the negative effects of the flood disaster. One of the efforts that can be done is to predict the rainfall that may occur in the future. This study aims to predict rainfall in Aceh Province in 2022 and 2023. The data employed are monthly rainfall data in Aceh Province from 2014 to 2021. The forecasting method employed to predict rainfall is the Autoregressive Moving Average (ARMA) model. Forecasting results using the ARMA (8,6) model show that rainfall in Aceh Province in 2022 and 2023 are predicted to tend to be intense in March, April, May, September, October and November. The Aceh government together with the district/city governments need to prepare anticipatory steps, for example by cleaning water flows like ditches and rivers and socializing about the dangers of flooding and its causes, especially for people living in watersheds. These actions are expected to minimize the potential for flooding in Aceh Province in the future, especially when rainfall is predicted to be intense.&#x0D; Keywords:Aceh Province, box-jenkins, flood, forecasting, rainfallMSC2020:62M10
Update on the Department of Energy Leadership Computing Facilities

Did you ever say to yourself “Boy I wished I had a bigger computer.” Do you have a modeling problem that could benefit from “really” high performance computing but do not know how to get it? Are you trying to create big models but find that cloud computing is the wrong answer but that a large high-performance computer might be the right answer? The Department of Energy (DOE) Leadership Computing Facilities (LCF) are a set of the highest performance publicly accessible computing facilities in the US. ALCF current has Frontier (#1), Summit (#5), and Perlmutter (#8) among the top 10 in the Top500 HPC list. A two exascale floating point computer, Aurora at Argonne National Laboratory, is set to come online as part of LCF in the next year. This presentation will update the previous introductory presentation on the DOE LCF and explain how researchers and industry can gain access and training to use these incredible facilities.
Gesture recognition of continuous wavelet transform and deep convolution attention network

To solve the problem of missing data features using a deep convolutional neural network (DCNN), this paper proposes an improved gesture recognition method. The method first extracts the time-frequency spectrogram of surface electromyography (sEMG) using the continuous wavelet transform. Then, the Spatial Attention Module (SAM) is introduced to construct the DCNN-SAM model. The residual module is embedded to improve the feature representation of relevant regions, and reduces the problem of missing features. Finally, experiments with 10 different gestures are done for verification. The results validate that the recognition accuracy of the improved method is 96.1%. Compared with the DCNN, the accuracy is improved by about 6 percentage points.
To Quantify an Image Relevance Relative to a Target 3D Object

Given a 3D object, our purpose is to find, among numerous 2D images within databases, which ones represent this object the best. Selected images should be both informative and offer a relevant view of the object, i.e.a pose that presents the essential characteristic information about the 3D object. To estimate the quality of the view, we propose to rely on repeatable, second order features, extracted with a curvilinear saliency detector, in order to both compute the pose within the image and to build a relevance score, independent of colors and textures. Based on this score, and given a set of images containing the same object, we are able to rank images from the one that best showcases the object to the worst one. Neural networks dedicated to detection and classification are able to recognise the object with a confidence score. So, we also develop an automatic approach based on a confidence score extracted from Convolutional Neural Networks. For evaluating and comparing the deterministic and the learning based methods, we use an objective image ranking based on gradual simulated degradations. We also provide visual qualitative results on a real dataset. The results demonstrate the efficiency of the approaches, the robustness of the deterministic method and help understanding the behavior of the methods based on confidence score.
SCIENTIFIC AND PROJECT ACTIVITY OF THE DEPARTMENT OF PUBLISHING STUDIES OF THE INSTITUTE OF JOURNALISM AT BORYS GRINCHENKO KYIV UNIVERSITY (ALL-UKRAINIAN AND INTERNATIONAL PROJECTS, INTERNSHIPS)

The article examines the history of development of scientific and design areas in the activities of the Department of Publishing of the Institute of Journalism at Borys Grinchenko Kyiv University during 2016–2021. The participation of teachers and students of the department in international academic mobility programmes, as well as in international research projects is analysed. Information on the development and submission of grant applications is collected an structured, in particular with regard to the winning applications. It is examined the history of scientific publications of the teachers at the department, in particular those which were published in publications indexed in the scientometric databases “Scopus” and “Web of Science”. Data on the participation of teachers and students at the department in Ukrainian and international scientific and practical conferences are summarised. Further prospects of scientific and project activity of the department are outlined.
IDENTIFIKASI PANJANG LANGKAH DAN FREKUENSI LANGKAH SEBAGAI FAKTOR PENENTU KECEPATAN LARI SPRINT 100 M

This study aims to identify the variability of stride length and stride frequency of junior-level 100 m sprinters with different performance levels and to verify the kinematics parameters of the phases of sprinting. Data were collected from a sample of 9 junior sprinter athletes determined by purposeful sampling. The analysis results show that the average number of steps for 100 m is 52.8. For the variable stride length, the average is 1.88 m. The step frequency variable shows an average of 4.77 steps/second (max step frequency). During the acceleration phase, the average stride frequency is 4.48 steps/second. The score in the last phase, i.e., the sprinters must maintain their velocity until the end of the race from a distance of 60 – 100 m, shows an average rate of 4.07 steps/second. The average sprinting time of the sprinters is 11.74 seconds, and the average speed of the sprinters is 8.54 ms-1, with a top speed of 8.95 ms-1 and the lowest speed of 7.99 ms-1. Based on the analysis of kinematic parameters, the different phase structures. The results show that stride length significantly contributes to the 100 m sprint performance than stride frequency, which is no longer considered the most critical determinant of performance at junior-level athletes.
Cyber Attack Surface Management System

Defenders struggle to keep up with the pace of digital transformation in the face of an expanding modern enterprise attack surface and more sophisticated adversaries. A conceptual framework for relating attack surface management (ASM) to vulnerability management and cyber threat intelligence (CTI) improves cyber defense. The framework explains how ASM improves cyber resiliency in proactively detecting and responding to weaknesses that adversaries could exploit to cause unacceptable harm. Defenders should prioritize ASM aligning with the business continuity and enterprise risk management functions. A CTI-driven ASM conceptual framework (CTI-ASM) helps defenders achieve decision clarity on how best to prioritize preventing the most impactful exploitations based on adversaries’ capabilities, opportunities, and intent. Security researchers have applied decision analysis methodology to solve various security challenges generally. Applying decision analysis methodology to CTI-ASM may improve the quality of its implementation and support higher quality CTI. Potentially helpful decision analysis tools and concepts include relevance diagrams, possibility and probability trees, sensitivity analysis, corporate risk attitudes, weighing imperfect information, and accounting for cognitive biases.
Effectiveness of Comic-Based Reading Materials in Enhancing the Reading Comprehension of Grade 6 Pupils

This quasi-experimental study was conducted to determine the effectiveness of Comic-Based Reading Materials tin enhancing the reading comprehension of Grade 6 pupils in Sampiruhan Elementary School. The researcher crafted these Comic-Based reading materials, which are reading texts and/or literatures adapted from the DepEd R4A Pivot Modules in English transformed into comics-type reading materials. These served as the instrument of the study. The result of the study was analyzed using the outcome of the pupils’ pretest and posttest.&#x0D; Two group of pupils served as the participants of the study. Each group was composed of thirty (30) pupils each chosen using a match-pairing from the result of their pretest. The participants’ baseline on reading comprehension were measure using a standardized test from the Phil-Iri. The comic-based reading materials crafted by the researcher was implemented to the experimental group while the comparison group utilized the common materials from the DepEd R4A Pivot Modules provided by the Department of Education. Independent and Paired Sample T-test were used to analyze the data and answer the inferential questions from the study.&#x0D; After the implementation of the instrument, the researcher found out that the experimental group performed better on their posttest from their pretest. In addition, the experimental group also outscored the comparison group during their posttest. Between the scores of the two groups a significant difference was obviously observed. This revealed the effectiveness of the comic-based reading materials to the reading comprehension of grade 6 pupils. Hence, the hypothesis stating that there is no significant difference between the posttest of the two groups was rejected.&#x0D;
Dal precettore a Duolingo: un’interpretazione linguistico-educativa e alcune considerazioni sul suo impiego e la sua efficacia Il corso di norvegese

Among the most successful language self-teaching apps, Duolingo stands out due to its ease of access and simplicity of use. In this paper, after a general description of the app, an interpretation will be attempted in terms of language learning, with particular reference to the approaches, methodologies and techniques it seems to be based upon, which skills it aims to develop and with what likely results. Its pros and cons will be highlighted, taking into specific examination the Norwegian language course. In conclusion, the use of the app as a support for a structured language course will be outlined.
Implementation of Project CBISp-EDP (Communicate, Build and Intensify Stakeholders’ Participation- Exemplified and Deepened Partnership) and External Stakeholders’ Participation

The study mainly focused on assessing the implementation of Project CBISp-EDP and external stakeholders’ participation in Calamba Bayside Integrated School. The study utilized a descriptive correlational research design. The respondents of the study were the 80 teachers and 99 parents of CBIS. Teacher-respondents were identified using a simple random sampling technique, while purposive sampling was used to identify the parent-respondents. Results of the study revealed that all teachers have the same level of observation in implementing Project CBISp-EDP. It also showed that external stakeholders participated in most school-initiated activities. Moreover, it also disclosed that the implementation of Project CBISp-EDP has a significant relationship with external stakeholders’ participation. It is recommended that the school consider the crafted action plan to strengthen stakeholders’ participation further.&#x0D; &#x0D;
Statistical modelling of precipitation data in Canadian Prairies with a dynamic mixture structure

The present paper aims to model statistical structure of heavy-tailed precipitation data. In particular for agricultural purposes in Canadian Prairies, this is essential in many aspects such as assessing and managing risks resulting from the occurrence of unexpected precipitation events. Daily (or weekly) precipitation time series often contain many zeros (on dry days) and also exhibit important characteristics such as heavy-tailedness and volatility clustering. These features make it challenging to develop an effective model from both theoretical and practical viewpoints. In this paper, we propose a dynamic mixture model constructed based on a generalized Gaussian crack distribution with a GARCH specification to take into account the full range of precipitation measurements with a sufficient flexibility to fit both thin- and heavy-tailed data, and stochastic volatility. The method of maximum likelihood estimation with the profile log-likelihood algorithm is illustrated with some simulation studies. The model fitting results on a historical dataset from twelve stations in Canadian Prairies show the applicability of the proposed model.
Student Attitude Towards English Language Learning in an Urban Senior High School in Batu: A Survey

This study aimed to investigate urban students’ attitudes toward English learning. This study uses a quantitative method by giving an online questionnaire using Likert scales. The questionnaire focused on two aspects of attitude: BALLI by Hortwitz (1988) and Attitude towards English Language Learning by Sudirman and Huzairin (2017). 42 students from Islamic Senior High School in Batu, East Java, Indonesia were the participants of this study. The results of this study found that the students generally had a positive attitude toward English language learning. However, a unique pattern shows a duality where the students are anxious to speak English, but they also consider making mistakes as a part of learning. The students also felt that English is important to foster their interest in the language and encourage them to learn more.&#x0D; Keywords: Attitudes, Urban school, English language Learning
Disentanglement of Latent Factors of Real and Fake Appearance for Deepfake Face Manipulation Detection

A deepfake video is a video in which generative models are used to alter the facial features to make the subject appear to be a different person. There are various ways to utilize such content, including those that are positive such as entertainment. However, it is also very easy to exploit deepfake videos for harmful use, including for spreading fake news or creating unwanted content. Thus there have been numerous attempts to detect whether a video has been manipulated using deepfake technology so as to prevent further harm. Previous approaches for achieving this purpose have attempted to detect discrepancies in the video frames through the use of techniques such as exploiting the temporal consistency between each frame with convolutional neural networks. Though this has produced adequate results, its accuracy is insufficient for real-world use. In this paper, we propose a novel method of using a convolutional neural network based autoencoder to detect whether a video is pristine or deepfake. Our method successfully disentangles latent factors of real and fake appearance to increase the classification accuracy while maintaining a relatively low time complexity, enhancing real-world applicability. Results from extensive experimentation show significant improvement from state-of-the-art-methods by upwards of 18.51%.
Combining Multi-vision Embedding in Contextual Attention for Vietnamese Visual Question Answering

Visual question answering (VQA) is a challenging vision-and-language task that has recently gained high interest from many researchers. Existing methods adopt the strategy to extract object regional features, which effectively captures the local context of the image, but on the other hand, causes the loss of global context. Motivated by the strength of Vision Transformer, we propose to extract and combine both global and local contextual visual features for to be a Multi-vision Embedding for better image understanding. We also propose a Multimodal Contextual Attention method to adaptively learn the attentional embeddings of different contexts from image and question that are guided by each other. While most VQA models are optimized for the English dataset, few works are accomplished for the Vietnamese language. Hence, in this paper, we perform extensive experiments on a Vietnamese VQA dataset to evaluate and demonstrate the effectiveness of our approach. Our proposed model achieves an accuracy of 60.76% on the test set and outperforms former baseline methods in the Vietnamese language.
Análisis de la competencia transversal de educación financiera durante la formación del estudiante en ciencias de la educación de la UJAT

The pandemic caused by COVID-19 modified all the traditionally operative schemes applicable to the various human groups, in turn, exacerbated social problems that range from economic precariousness, unemployment, maladjustment, rejection of educational approaches in virtuality, even the high competitiveness in labor markets, which have revealed the notable digital and economic gaps that various theorists had previously pointed out. Undoubtedly, the economic component has been decisive for the level of participation and presence in virtuality by university students, as analyzed with a sample of 100 students of Education Sciences of the UJAT, since it is through this one that accesses technological devices and internet connections, so necessary for the virtual presence. Therefore, the research work called "analysis of the transversal competence of financial education during the student's training in educational sciences at UJAT", allowed to know the main factors of influence on financial education and the impact on training. integral of students of Sciences of the Education of the UJAT. Under a quantitative design, with a descriptive study and the survey as an instrument.