Reviewerly/exHarmony huggingface dataset:
Learner Perceptions of Taboo Topics in English Language Teaching

The ubiquitous access to digital media content leads to a multifarious dissemination of topics learners encounter in their everyday lives. These often include socio-culturally tabooed topics such as racism, sexuality, and violence, which thus call for a need to rethink the integration of such topics into English language teaching (ELT). Drawing on data from an online survey through which we investigated learners’ perspectives on integrating taboos and challenging topics into ELT (n = 841), this contribution discusses its central outcomes. Focusing on the perspectives of 13–19-year-olds living in Germany, we explored which taboo topics adolescents are interested in and confronted within their daily lives. Furthermore, our interest was to draw conclusions about adolescents’ experiences related to dealing with such topics in the classroom and in English lessons. The results indicate adolescents’ awareness of taboo topics in their everyday lives, and the data show an interest among learners in addressing taboo topics in school as well as in English language classes. Participants expressed interest in topics such as racism and environmentally destructive lifestyles, but they were reluctant towards topics that concern their personal lives. Overall, they assessed taboo topics as interesting and important. Further research could focus on pedagogic approaches that integrate taboo topics into ELT.
Smart Contracts Auto-generation for Supply Chain Contexts

The introduction of blockchain technology into Supply Chain management has opened the possibility of faster and more secure transactions of commodities and services. As for every blockchain, Smart Contracts are the tool for controlling the transactions in blockchain-based supply chains. In this paper, we introduce a method for automating the implementation of natural language contracts into Smart Contracts in the Supply Chain context. The basic idea here is to extract information from a natural language contract using two Natural Language Processing (NLP) techniques, the Named Entity Recognition (NER) and Relation Extraction (RE), and then use this extracted information to automatically create a corresponding Smart Contract. This is an ongoing project, and we implemented the first phase of NLP, i.e., NER. The main issue we are facing here is the limited availability of annotated contract datasets. To tackle this challenge, we created an annotated legal contract dataset dedicated to the NER task. The dataset is analyzed with the deep learning method (BiLSTM) and transformer-based method (BERT). As per the generation of smart contracts, our approach consists of identifying meaningful entities and the relations between them and then representing them as business logic that can be directly incorporated into computer code as blockchain smart contracts.
Estimating Software Functional Size via Machine Learning

Measuring software functional size via standard Function Points Analysis (FPA) requires the availability of fully specified requirements and specific competencies. Most of the time, the need to measure software functional size occurs well in advance with respect to these ideal conditions, under the lack of complete information or skilled experts. To work around the constraints of the official measurement process, several estimation methods for FPA have been proposed and are commonly used. Among these, the International Function Points User Group (IFPUG) has adopted the “High-level FPA” method (also known as the NESMA method). This method avoids weighting each data and transaction function by using fixed weights instead. Applying High-level FPA, or similar estimation methods, is faster and easier than carrying out the official measurement process but inevitably yields an approximation in the measures. In this article, we contribute to the problem of estimating software functional size measures by using machine learning. To the best of our knowledge, machine learning methods were never applied to the early estimation of software functional size. Our goal is to understand whether machine learning techniques yield estimates of FPA measures that are more accurate than those obtained with High-level FPA or similar methods. An empirical study on a large dataset of functional size predictors was carried out to train and test three of the most popular and robust machine learning methods, namely Random Forests, Support Vector Regression , and Neural Networks. A systematic experimental phase, with cycles of dataset filtering and splitting, parameter tuning, and model training and validation, is presented. The estimation accuracy of the obtained models was then evaluated and compared to that of fixed-weight models (e.g., High-level FPA) and linear regression models, also using a second dataset as the test set. We found that Support Vector Regression yields quite accurate estimation models. However, the obtained level of accuracy does not appear significantly better with respect to High-level FPA or to models built via ordinary least squares regression. Noticeably, fairly good accuracy levels were obtained by models that do not even require discerning among different types of transactions and data.
Network Traffic Classification Based on LSTM+CNN and Attention Mechanism

Nowadays, the Internet is characterized by diversification, large scale, and high public dependence. The static and rigid traditional network is increasingly showing its limitations. In order to better meet the public's requirements for an efficient and secure Internet, the intelligent collaborative identification network shows its strong vitality. But at the same time, the new network also puts forward higher requirements for the classification of network traffic. To this end, based on deep learning theory, this paper constructs a traffic classification model that combines CNN and LSTM. To solve the problem of traffic data imbalance and temporal correlation, the model introduces Squeeze & Excitation and Data Augmentation, and finally, three sets of experiments are used to verify that the CNN + LSTM model has better performance than the ordinary single model, and it has excellent performance when dealing with classification problems with different granularities and different traffic representation.
Network Link Status Classification Method Based on Graph Autoencoder

In recent years, with the rapid development of the Internet, the amount of data in the network increases exponentially, and people have higher requirements on the quality of network links, which poses new challenges to network management and analysis. Network link status classification can be used to predict network link status categories by using the current network topology information and feature information, facilitating network management and analysis. However, most of the existing models consider to predict the time sequence information of the link status without considering the topology information and node attributes of the network. Therefore, we design a network link status classification model based on graph autoencoder for computer network scenarios. The attention mechanism is introduced into the encoder, and then the node vector is spliced into edge vector, which is then input into the multi-layer perceptron for classification. Finally, the feasibility and validity of the link status classification model are verified on two datasets.
TraCGAN: An Efficient Traffic Classification Framework Based on Semi-supervised Learning with Deep Conventional Generative Adversarial Network

Traffic classification is an engineering technology widely used in network design, service differentiation, accounting, cyber security, etc. Internet technology developed rapidly in recent years. Meanwhile, web applications become varied, and the scale of web service traffic increases dramatically. Hence it remains an open problem to classify different service traffic. To deal with this issue, we propose TraCGAN, an improved deep conventional generative adversarial network (DCGAN) model based on a semi-supervised learning method. We adopted and adjusted the internal network structure of DCGAN so that TraCGAN could work as a semi-supervised model. A softmax layer is added, and the input and output are redesigned to classify characteristic representations of network traffic. TraCGAN can recognize multiclass traffic accurately on traffic classification tasks, including traffic produced by the generator. Our experimental results show it reaches state-of-the-art performance compared with the baseline 2D-CNN.
Network Traffic Identification Method Based on Temporal and Multi-scale Spatial Fusion

Network traffic identification is the basic module required to realize any traffic management operation. From network traffic partition and traffic processing (such as alarm, traffic shaping, etc.) to general security operations (such as firewall, traffic filtering, anomaly detection, etc.), network traffic identification is an indispensable part. Traditional port-based and payload-based network traffic identification approaches become less accurate as network traffic encryption technology advances and user privacy concerns are raised. However, the method based on flow statistical features requires a lot of additional calculation in feature extraction, and also requires domain experts to carry out feature engineering, which results in large algorithm cost and poor generalization. Recent studies have introduced DL-based methods, but most current DL-based methods use neural network models to extract network traffic features with problems such as a single feature scale and insufficient information extraction. This paper presents a method based on spatiotemporal fusion about network traffic identification. The model uses ResNet to obtain the spatial features of original traffic at first, then a module for multi-scale feature extraction is added to allow for the multi-scale pooling operation to obtain spatial feature information at various scales, overcoming the single performance limitation of existing DL-based network traffic identification methods. Then, the LSTM is used to further learn network traffic’s temporal features, and finally the identification results of the network traffic are obtained based on the extracted spatiotemporal features. Public datasets used in the experiments demonstrate the proposed method’s good performance and certain degree of effectiveness.
DIGITAL LITERACY IN THEMATIC LEARNING OF ELEMENTARY SCHOOL STUDENTS WITH THE ASSISTANCE OF FLIPBOOK MEDIA

This study aims to analyze digital literacy in thematic learning of elementary school students with the help of Flipbook media. To obtain data about digital literacy of elementary school students using questionnaires, observation sheets and in-depth interviews. Questionnaires were given to 23 grade VI elementary school students at SDN Trawas 1 who had operated flipbook media. For the validity and validity of the data conducted intensive interviews with students. Data analysis by reducing data in questionnaire tables, observation sheets and interviews. Then present it in narrative form to draw conclusions. The results of this study indicate that students' digital literacy in Thematic learning with the help of flipbook media is in the high category. Therefore thematic learning assisted by flipbook media needs to be developed and implemented so that elementary school students' digital literacy increases.
An Analysis of Student's Difficulties in Listening Comprehension on Listener Factors

This research aimed to describe listening comprehension difficulties on listener factors of second semester-students in the English Education program Universitas Tanjupura academic year 2021/2022. This research used descriptive research. There are 50 samples of second-semester students in the English Education program at Universitas Tanjungpura. The data are obtained using a survey technique conducted by using a questionnaire. The questionnaire's obtained data were analyzed using descriptive analysis with computer software for statistical analysis, SPSS, in the form of percentages. The result showed that most students still encountered fatigue and distraction during listening. The others were concerned about recognizing and understanding the spoken text, had problems interpreting the meaning of new vocabularies, had difficulties understanding pronunciation and unfamiliar accents, and the complex grammar structure that affected the student's listening performance. Since listening is considered a passive skill, it needs to be understood as one of the primary skills in learning the English language Keywords: Listening Comprehension, Listening Comprehension Difficulties, Listener Factors
Relativity and Electrodynamics

Many important laws of physics are of the nature of dynamical laws, that is, laws which involve spatial variables and dependence on time. Since Maxwell’s equations involve derivatives with respect to space and time, they are clearly dynamical laws. When stating a dynamical law of physics, one has to specify the frame of reference with respect to which the spatial coordinates and time are to be measured.
Mathnawī Poems By ‘Abd Al‑Hamīd Momand: Stylometric Results

The article aims to solve the authorship problem of the texts of three Afghan mathnawi poems by ‘Abd al‑Hamid Momand (d. ca. 1144/1732): Shah‑u darwish, Nayrang‑i ʻishq and Shir‘at al‑islam. Using the methods of machine and rolling classification via the R‑Stylo program, the texts of the poems were checked and compared against the corpus of texts by both the alleged author (Hamid) and other poets who created their works around the same time and / or in the style similar to Hamid's. As a result of textual analysis using computer stylometry, we conclude that the two love and mystery poems Shah‑u darwish and Nayrang‑i ʻishq belong to ‘Abd al‑Hamid; however, Hamid's authorship regarding the poem Shir‘at al‑islam is apparently questioned.
Public Opinion Analytics with Social Media

In the past two decades, the measurement and analysis of social media data have grown in importance. Citizens are increasingly likely to use social media platforms to access political information and participate in civic and political protests or movements and express their political opinions. These proclivities have corresponded with surging interest amongst political analysts and academics to forecast political events based on how citizens behave and what they discuss on social media. This article first examines computational approaches for public opinion mining from social media data. Secondly, it engages with the theoretical and methodological issues that researchers face. Finally, it identifies directions for future research.
Real Time Image Based Gesture Recognition Model for Dynamic Applications using Transfer Learning

The most effective way to communicate with people of various ages, who speak different languages, and who are deaf and dumb or visually or audibly disabled is through gestures. The transfer learning method is used in this paper to propose a real-time image-based gesture detection model and its dynamic applications. The goal of this project is to show how human-computer interaction concepts can be put into action to make a fully functional model, which can be further implemented in IoT such as in a self-driving car or in a computer game where one can play the game using hand gestures without using any additional hardware. The model basically uses a hand posture estimation algorithm to track the movement.
SLOGAN

The trend of progressive technology scaling makes the computing system more susceptible to soft errors. The most critical issue that soft error incurs is silent data corruption (SDC) since SDC occurs silently without any warnings to users. Estimating SDC probability of a program is the first and essential step towards designing protection mechanism. Prior work suffers from prediction inaccuracy since the proposed heuristic-based models fail to describe the semantic of fault propagation. We propose a novel approach SLOGAN which transfers the prediction of SDC probability into a graph regression task. A program is represented in the form of dynamic dependence graph. To capture the rich semantic of fault propagation, we apply structured graph attention network, which includes node-level, graph-level and layer-level self-attention. With the learned attention coefficients from node-level, graph-level, and layer-level self-attention, the importance of edges, nodes, and layers to the fault propagation can be fully considered. We generate the graph embedding by weighted aggregation of the embeddings of nodes and compute the SDC probability by the regression model. The experiment shows that SLOGAN achieves higher SDC accuracy than state-of-the-art methods with a low time cost.
A Night Image Enhancement Algorithm Based on MDIFE-Net Curve Estimation

In order to solve the low-quality problems such as low brightness, poor contrast, noise interference and color imbalance in night images, a night image enhancement algorithm based on MDIFE-Net curve estimation is presented. This algorithm mainly consists of three parts: Firstly, we design an illumination estimation curve (IEC), which adjusts the pixel level of the low illumination image domain through a non-linear fitting function, maps to the enhanced image domain, and effectively eliminates the effect of illumination loss; Secondly, the DCE-Net is improved, replacing the original Relu activation function with a smoother Mish activation function, so that the parameters can be better updated; Finally, illumination estimation loss function, which combines image attributes with fidelity, is designed to drive the no-reference image enhancement, which preserves more image details while enhancing the night image. The experimental results show that our method can not only effectively improve the image contrast, but also make the details of the target more prominent, improve the visual quality of the image, and make the image achieve a better visual effect. Compared with four existing low illumination image enhancement algorithms, the NIQE and STD evaluation index values are better than other representative algorithms, verify the feasibility and validity of the algorithm, and verify the rationality and necessity of each component design through ablation experiments.
Message from the Winner

We are very honored to receive the prestigious JDR Award for the Most Cited Paper 2022. The winning paper, “Real-Time Tsunami Prediction System Using DONET,” discusses a system that uses data from the Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET), which was installed in the rupture areas of the 1944 Tonankai and 1946 Nankai earthquakes in order to instantly generate and visualize tsunami prediction information. The forecast information from this system consists of tsunami arrival time, maximum tsunami height, tsunami inundation area, and inundation depth distribution. The system, which visualizes and distributes forecast information for areas where users need it as a supplement to the tsunami information provided nationwide by the Japan Meteorological Agency, has been introduced in Wakayama Prefecture, Mie Prefecture, Chiba Prefecture, and the city of Owase, as well as by Chubu Electric Power Co., Inc. After DONET was installed in the Tonankai rupture area, we were considering the possibility of using DONET data regionally, and we developed the system as results of discussions with Wakayama Prefecture and Chubu Electric Power Co., Inc. Under the concept that the users themselves would be operating the system, we intended to make the system as simple and easy to understand as possible, and to reduce costs by minimizing the number of hardware units. We also considered making the system flexible and scalable, recognizing that each user has a different way of how to use the tsunami forecast information. We were able to receive this award thanks to the cooperation of the people who were involved in many discussions with us during the process of establishing the concept. We will not become complacent going forward, but will continue to improve our system in ways that reflect the opinions of the users. We would like to thank everyone who has cited this paper with their interests.
Efficiently mining maximal l-reachability co-location patterns from spatial data sets

A co-location pattern is a set of spatial features that are strongly correlated in space. However, some of these patterns could be neglected if the prevalence metrics are based solely on the clique (or star) relationship. Hence, the l-reachability co-location pattern is proposed by introducing the l-reachability clique where the members of each instance pair can be reachable to each other in a given step length l. Because the average size of l-reachability co-location patterns tends to be longer, maximal l-reachability co-location pattern mining is researched in this paper. First, some sparsification strategies are introduced to shorten star neighborhood lists of instances in an updated graph called the l-reachability neighbor relationship graph, and then, they are grouped by their corresponding patterns. Second, candidate maximal l-reachability co-location patterns are iteratively detected in a size-independent way on bi-graphs that contain group keys and their intersection sets. Third, the prevalence of each candidate maximal l-reachability co-location pattern is checked in a binary search way with a natural l-reachability clique called the ⌊l/2⌋-reachability neighborhood list. Finally, the effectiveness and efficiency of our model and algorithms are analyzed by extensive comparison experiments on synthetic and real-world spatial data sets.
Report on the 12th Temporal Web Analytics Workshop (TempWeb 2022) at WWW 2022

TempWeb focuses on investigating infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Web scale. Emphasis is given to data analysis along the time dimension for web data that has been collected over extended time periods. A major challenge in this regard is the sheer size of the data it exposes and the ability to make sense of it in a useful and meaningful manner for its users. It is worth noting that this trend of using big data to make inferences is not specific to Web content analytics, so work presented here might be useful in other areas, too. TempWeb has been created for this purpose and is the ideal venue to discuss all its facets. Date: 25 May, 2022. Website: http://temporalweb.net/.