Reviewerly/exHarmony huggingface dataset:
Is Encrypted ClientHello a Challenge for Traffic Classification?

Although the widely-used Transport Layer Security (TLS) protocol hides application data, an unencrypted part of the TLS handshake, specifically the server name indication (SNI), is a backdoor for encrypted traffic classification frameworks. The recently developed Encrypted ClientHello (ECH) amendment to the TLS protocol aims to protect the privacy-sensitive content of the ClientHello message, including SNI. Conversely, ECH can be a game-changer in the early detection of encrypted traffic. The paper shows that the performance of the state-of-the-art traffic classification algorithms degrades significantly with the introduction of the ECH. Hence, novel approaches to real-time traffic classification are required. The paper develops two novel traffic classification algorithms to address this challenge. The first one uses unencrypted bytes of the TLS Hello messages as independent features of the Random Forest algorithm. It is extremely lightweight and suits throughput-focused traffic classification. It is faster than state-of-the-art algorithms by three times and achieves higher classification quality. The second algorithm augments the approach of the first one by focusing on the particular metadata of the handshake. This way, it efficiently extracts data from the exchange and achieves the highest classification quality in all the considered scenarios. It has a three times lower error rate than state-of-the-art algorithms and provides a reliable classification of ECH traffic.
Enhancing Vocabulary Performance Through Mobile Assisted Language Learning at a Rural School in Indonesia

Living in this era of globalization and in the 4.0 industrial revolution, we cannot get away from technology because it has integrated into our lives and has penetrated the educational system. There were studies done on the use of mobile-assisted language learning (MALL) to improve students’ vocabulary achievement in other parts of the world and also in some urban schools in Indonesia, but this study seeks to find whether the students’ vocabulary improved through the use of MALL at rural school in Bandung- Indonesia. This study was carried out to find the enhancement of using MALL in vocabulary teaching to 79 grade 8 students in Bandung. This study was designed for quantitative and experimental research. The students were separated into two groups, experimental and control groups. A day before coming to class, 15-20 words to learn the following day was sent to the experimental group through a short messages system (SMS) for them to read, find the synonyms and meanings of those words before coming to the class. The result of the study showed that students in the experimental group performed better than the control group. It also proves that technology makes a significant difference in the learning of vocabulary in school.
Kendala dalam Belajar Bahasa Inggris dan Cara Mengatasinya

Language plays a crucial role in life, one of the roles of language is to communicate ideas to others. Language skills are the basic element for everyone to express themselves in the form of feelings, desires, opinions and needs of each individual. The more languages ​​a person knows, the easier it will to communicate, one of them is learning English. Understanding of English must be strengthened to rising generation especially students, due to globalization which requires society to speak English. For Indonesian, the use of English is still an obstacle due to a lack of understanding of the meaning of the language itself, students must be able to write and pronounce English words with different spellings. This complexity makes students or children less interested in learning English. The development of tourism in Bali is growing swiftly, this can be proven by the increasing number of visits by local and foreign tourists. To expand interaction, then must be able to master foreign languages, one of them is English. It is realized that the ability to speak English is crucial in the field of tourism, so it is necessary to prepare provisions for children to be able to communicate using English. Based on this, the Master of Linguistics of Warmadewa University through its Community Service activities held training in the form of teaching English about obstacles in learning English and how to overcome them. This study was designed to determine the obstacles to learning English for children and to increase students' interest in learning English. This research used a case study research type. Technique of collecting data in groups, namely asking about the problems experienced when learning English. The results of this study are that community service activities run well and structured in order to find solutions to the main problems, that is knowing the obstacles in learning English and how to overcome them.&#x0D;
Personalized Federated Learning with Contextualized Generalization

The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models. However, the sole global model may easily transfer deviated context knowledge to some local models when multiple latent contexts exist across the local datasets. In this paper, we propose a novel concept called contextualized generalization (CG) to provide each client with fine-grained context knowledge that can better fit the local data distributions and facilitate faster model convergence, based on which we properly design a framework of PFL, dubbed CGPFL. We conduct detailed theoretical analysis, in which the convergence guarantee is presented and a speedup of order 1/2 w.r.t the number of contexts over most existing methods is granted. To quantitatively study the generalization-personalization trade-off, we introduce the generalization error measure and prove that the proposed CGPFL can achieve a better trade-off than existing solutions. Moreover, our theoretical analysis further inspires a heuristic algorithm to find a near-optimal trade-off in CGPFL. Experimental results on multiple real-world datasets show that our approach surpasses the state-of-the-art methods on test accuracy by a significant margin.
Penalized Proximal Policy Optimization for Safe Reinforcement Learning

Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still struggle for efficient policy updates with hard constraint satisfaction. In this paper, we propose Penalized Proximal Policy Optimization (P3O), which solves the cumbersome constrained policy iteration via a single minimization of an equivalent unconstrained problem. Specifically, P3O utilizes a simple yet effective penalty approach to eliminate cost constraints and removes the trust-region constraint by the clipped surrogate objective. We theoretically prove the exactness of the penalized method with a finite penalty factor and provide a worst-case analysis for approximate error when evaluated on sample trajectories. Moreover, we extend P3O to more challenging multi-constraint and multi-agent scenarios which are less studied in previous work. Extensive experiments show that P3O outperforms state-of-the-art algorithms with respect to both reward improvement and constraint satisfaction on a set of constrained locomotive tasks.
CounterGeDi: A Controllable Approach to Generate Polite, Detoxified and Emotional Counterspeech

Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation model, they might not include the appropriate properties required to counter a particular hate speech instance. In this paper, we propose CounterGeDi - an ensemble of generative discriminators (GeDi) to guide the generation of a DialoGPT model toward more polite, detoxified, and emotionally laden counterspeech. We generate counterspeech using three datasets and observe significant improvement across different attribute scores. The politeness and detoxification scores increased by around 15% and 6% respectively, while the emotion in the counterspeech increased by at least 10% across all the datasets. We also experiment with triple-attribute control and observe significant improvement over single attribute results when combining complementing attributes, e.g., politeness, joyfulness and detoxification. In all these experiments, the relevancy of the generated text does not deteriorate due to the application of these controls.
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation

Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the higher-order collaborative signal such as relational structure information among users, recipes and food items. In this paper, we formalize the problem of recipe recommendation with graphs to incorporate the collaborative signal into recipe recommendation through graph modeling. In particular, we first present URI-Graph, a new and large-scale user-recipe-ingredient graph. We then propose RecipeRec, a novel heterogeneous graph learning model for recipe recommendation. The proposed model can capture recipe content and collaborative signal through a heterogeneous graph neural network with hierarchical attention and an ingredient set transformer. We also introduce a graph contrastive augmentation strategy to extract informative graph knowledge in a self-supervised manner. Finally, we design a joint objective function of recommendation and contrastive learning to optimize the model. Extensive experiments demonstrate that RecipeRec outperforms state-of-the-art methods for recipe recommendation. Dataset and codes are available at https://github.com/meettyj/RecipeRec.
Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs

In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking design elements (e.g., texts and embellishments) as model inputs, which inevitably leaves hint of the ground truth. We study the misalignment between the training inputs (with hint masks) and test inputs (without masks), and design a novel domain alignment module (DAM) to narrow this gap. For training, we built a large-scale layout dataset which consists of 60,548 advertising posters with annotated layout information. To evaluate the generated layouts, we propose three novel metrics according to aesthetic intuitions. Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions. The data and code will be available at https://github.com/minzhouGithub/CGL-GAN.
Causes of Effects: Learning Individual Responses from Population Data

The problem of individualization is crucial in almost every field of science. Identifying causes of specific observed events is likewise essential for accurate decision making as well as explanation. However, such tasks invoke counterfactual relationships, and are therefore indeterminable from population data. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated; it cannot be estimated from experimental data, even when conditioned on fine-grained features, because we cannot test both possibilities for an individual. Tian and Pearl provided bounds on this and other probabilities of causation using a combination of experimental and observational data. Those bounds, though tight, can be narrowed significantly when structural information is available in the form of a causal model. This added information may provide the power to solve central problems, such as explainable AI, legal responsibility, and personalized medicine, all of which demand counterfactual logic. This paper derives, analyzes, and characterizes these new bounds, and illustrates some of their practical applications.
The Realization of Politeness Strategies in EFL Teacher-Students Classroom Interaction

Politeness is one of the prominent issues in pragmatics. It becomes a major issue in education due to the implementation of curriculum 2013 which emphasized on character education. This study aimed at analyzing the realization of politeness strategies and sociological factors influencing the choice of politeness strategies in EFL Teacher-students classroom interaction at SMP Semesta Bilingual School. This study used qualitative research in the form of classroom discourse analysis. The participants were an EFL teacher and 30 EFL students in two EFL classrooms. The research instruments were made based on Brown and Levinson (1987) framework of politeness strategies. The findings showed that bald on record, positive politeness, negative politeness, and off records were realized in the classroom interaction. The teacher dominantly used positive politeness to show solidarity and to maintain a close relationship with the students, bald on records to give a clear and unambiguous instruction, negative politeness to minimize the coercion to the students, and off record to give hints. In addition, the sociological factors, namely distance, power, and degree of imposition influence the choice of politeness strategies. As the conclusion, politeness is important in maintaining relationship and creating a comfortable environment in EFL classroom.
Contextual Graph Attention Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to predict the sentiment polarities towards the target aspects given in sentences. To address the issues of insufficient semantic information extraction and high computational complexity of attention mechanisms in existing aspect-level sentiment classification models based on deep learning, a contextual graph attention network (CGAT) is proposed. The proposed model adopts two graph attention networks to aggregate syntactic structure information into target aspects and employs a contextual attention network to extract semantic information in sentence-aspect sequences, aiming to generate aspect-sensitive text features. In addition, a syntactic attention mechanism based on syntactic relative distance is proposed, and the Gaussian function is cleverly introduced as a syntactic weight function, which can reduce computational complexities and effectively highlight the words related to aspects in syntax. Experiments on three public sentiment datasets show that the proposed model can make better use of semantic information and syntactic structure information to improve the accuracy of sentiment classification.
iCorrVision-2D: An integrated python-based open-source Digital Image Correlation software for in-plane measurements (Part 1)

<h2>Abstract</h2> The main purpose of this work is to present a new fully-customizable out-of-the-box open-source 2D Digital Image Correlation (2D-DIC) software, so-called <i>iCorrVision-2D</i>. It is implemented in Python, including image acquisition (grabber), numerical correlation and post-processing modules. The proposed software has an intuitive graphical user interface to support selecting all main correlation parameters, calibration and region of interest. The <i>iCorrVision-2D</i> software stands out over other open-source projects due to the great number of functionalities and the control of all important inputs, such as correlation domain, approach (spatial and incremental) and matching criterion, displacement filtering, interpolation techniques, strain window and reconstruction shape functions. Results demonstrate that the <i>iCorrVision-2D</i> software is robust and can be used to measure full-field displacements and strains with satisfactory accuracy and precision. For out-of-plane measurements, <i>iCorrVision-3D</i> will be presented in Part 2 (<i>iCorrVision-3D</i>, SoftwareX, 2022).
Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology

Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases to deep learning, negatively influencing transparency, reproducibility, and classification performance. In particular, biases are well known to cause poor generalization. Proposed tools from explainable artificial intelligence (XAI), bias detection, and bias discovery suffer from technical challenges, complexity, unintuitive usage, inherent biases, or a semantic gap. A promising XAI method, not studied in the context of digital histopathology is automated concept-based explanation (ACE). It automatically extracts visual concepts from image data. Our objective is to evaluate ACE’s technical validity following design science principals and to compare it to Guided Gradient-weighted Class Activation Mapping (Grad-CAM), a conventional pixel-wise explanation method. To that extent, we created and studied five convolutional neural networks (CNNs) in four different skin cancer settings. Our results demonstrate that ACE is a valid tool for gaining insights into the decision process of histopathological CNNs that can go beyond explanations from the control method. ACE validly visualized a class sampling ratio bias, measurement bias, sampling bias, and class-correlated bias. Furthermore, the complementary use with Guided Grad-CAM offers several benefits. Finally, we propose practical solutions for several technical challenges. In contradiction to results from the literature, we noticed lower intuitiveness in some dermatopathology scenarios as compared to concept-based explanations on real-world images.
Prediction of Employment Index for College Students by Deep Neural Network

With the acceleration of popularization of higher education in China and the intensification of employment difficulties for college graduates, the employment field has gradually widened, the number of entrepreneurs has gradually increased, and the regional differences are obvious. The employment difficulty of college graduates has aroused wide-spread concern in the society. Therefore, the convolution neural network (CNN) is used to establish a prediction and evaluation model for the employment development trend of college graduates in this paper. The feasibility and practicability are proved by a case, which is of great significance for the government and colleges to provide decision-making support and suggestions to solve the problem of difficult employment.
Searching for the Dimensions of Today’s Political Incivility

Incivility in public discourse is a central concern for scholars and citizens alike. However, the definitions of the concept offered by scholars are dissimilar and difficult to compare. This research, which adopts an approach based on citizens’ perceptions of incivility, addresses this issue by empirically testing a three-dimensional model of political incivility. The findings of a survey of 797 students to track differences in perceptions of specific types of uncivil speech show that political incivility consists of a three-dimensional concept, that is, incivility as “impoliteness,” as “individual delegitimization,” and as “institutional delegitimization.” Future studies can use this model to corroborate these results and verify whether this composition of the concept holds up in other contexts.
Cyber Threat Landscape and Readiness Challenge of Pakistan

Today, vital social infrastructures — electricity, finance, water, transportation, health and food — are increasingly dependent on the Information and Communication Technology (ICT) networks for functioning, distribution and interconnectedness. This dependence results in both opportunities and vulnerabilities, which can be exploited by a variety of actors ranging from individuals to organisations and governments. This indicates that information revolution, experienced by the contemporary world, is both a boon and bane. To a large extent, it is a bane because the ICT has an ‘enabling function’ for disruption, crime and state-level aggression. The ICT dependence may become more prone to vulnerabilities in the times of social unrest, political tensions and other appalling events. At present, Pakistan experiences a fast growing application of the ICT in different sectors but seriously lacks in cyber readiness. In addition, the country confronts a hostile security environment internally as well as externally. These factors expose it to various cyber threats. Drawing on the securitisation theory, this paper attempts to examine the cyber threat landscape of Pakistan and focuses on the cyber threats that the country faces across the spectrum — hacking, serious and organised cybercrime, cyberterrorism and cyberwarfare. This is followed by an evaluation of Pakistan’s cyber readiness profile in the light of the five-pillar criteria laid down by the UN specialised agency the International Telecommunication Union (ITU), namely legal, technical, organisational, capacity building and international cooperation.&#x0D;
Tabular Data: Deep Learning is Not All You Need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.
Graph Signal Processing: Dualizing GSP Sampling in the Vertex and Spectral Domains

Vertex based and spectral based GSP sampling has been studied recently. The literature recognizes that methods in one domain do not have a counterpart in the other domain. This paper shows that in fact one can develop a unified graph signal sampling theory with analogous interpretations in both domains just like sampling in traditional DSP. To achieve it, we introduce a spectral shift <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> acting in the spectral domain rather than shift <inline-formula><tex-math notation="LaTeX">$A$</tex-math></inline-formula> that acts in the vertex domain. This leads to a GSP theory that starts from the spectral domain, for example, linear shift invariant (LSI) filtering in the spectral domain is with polynomial filters <inline-formula><tex-math notation="LaTeX">$P(M)$</tex-math></inline-formula>. We then develop GSP vertex and spectral domain dual versions for each of the four standard sampling steps of subsampling, decimation, upsampling, and interpolation. We show how GSP sampling reduces to DSP sampling when the graph is the directed time cycle graph. Simple examples illustrate the impact of choices that are available in GSP sampling.