Reviewerly/exHarmony huggingface dataset:
PRAGMATIC CYBER ANALYSIS IN THE MIDDLE EAST CONFLICT NEWS TEXT VIEWS THEO VAN LEEUWEN

The cause of this research discussed is the many problems of interest to readers of middle eastern news on several YouTube channels. His research aims to describe the text of the Middle East conflict news study Wacana Theo Van Leeuwen Discourse. Descriptive methods are used as a type of research. The data obtained is a sentence in the main news text of the Middle East conflict on YouTube. The researcher himself performs the instrument. In the data collection technique using the recording technique, one of them is orthographic sorting power. The results of his research are the first, in writing the comments of the audience, writing words by bringing up parties who have the authority to be involved in the news. Second, not all of Leeuwen's theories are contained in news texts. Third, Leeuwen's approach is described in the news text of the Middle East conflict based on the speech conveyed.
Integration of convolutional neural network and extreme gradient boosting for breast cancer detection

With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBoost as a classifier was used. After acquiring accuracy results through transfer learning, this reasearch connects the final layer to the XGBoost classifier. Furthermore, the interface design for the evaluation process was established using the Python programming language and the Django platform. The results: i) the stages of CNN-XGBoost integration on histopathology images for breast cancer detection were discovered. ii) Achieved a higher level of accuracy as a result of the CNN-XGBoost integration for breast cancer detection. In conclusion, breast cancer detection was revealed through the integration of CNN-XGBoost through histopathological images. The combination of CNN and XGBoost can enhance the accuracy of breast cancer detection.
A Lite Romanian BERT: ALR-BERT

Large-scale pre-trained language representation and its promising performance in various downstream applications have become an area of interest in the field of natural language processing (NLP). There has been huge interest in further increasing the model’s size in order to outperform the best previously obtained performances. However, at some point, increasing the model’s parameters may lead to reaching its saturation point due to the limited capacity of GPU/TPU. In addition to this, such models are mostly available in English or a shared multilingual structure. Hence, in this paper, we propose a lite BERT trained on a large corpus solely in the Romanian language, which we called “A Lite Romanian BERT (ALR-BERT)”. Based on comprehensive empirical results, ALR-BERT produces models that scale far better than the original Romanian BERT. Alongside presenting the performance on downstream tasks, we detail the analysis of the training process and its parameters. We also intend to distribute our code and model as an open source together with the downstream task.
College Website Using MERN Stack

Abstract: College Website is that the Growing significance of internet sites for various organizations is well known. Today, a well-planned digital strategy plays a significant role in an institution’s marketing strategies — especially considering younger generations’ growing dependence on technology. Put simply, a well-designed website will facilitate you’re engaging, current students, connect with prospective ones, and inform parents all without delay. It encompasses promoting school events, displaying course offerings, and showcasing campus life, etc. during this paper, we discuss the models submitted by our team, login, Admin panel, Instructor, Fees payment, and About us page. For these modules, we’ve used technical stacks React.js, Bootstrap (Front-end), Node.js, Express.js, and MongoDB for Database. Keywords: Full-Stack Development, College Website, Web Design, Web Development, Reactjs, Nodejs, Express, MongoDB.
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task

This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition.Our system's key contributions are as follows: 1) For multilingual NER tasks, we offer an unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource code-mixed NER task, one can easily enhance his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we propose a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information.Finally, our system achieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9, respectively, during the testing phase.
Modified Playfair cryptosystem for improved data security

Playfair is the earliest known classical block cipher which is capable of taking two characters as a unit in the process of encryption and decryption. However, the cipher is suffering from vulnerability to many cryptanalysis attacks due to a lack of confusion and diffusion properties, an inability to handle numbers and special characters in the process of encryption and decryption, and a host of other deficiencies. Although several modifications and improvements had been done by different researchers, the emphasis has been on the modification of the key matrix to accommodate more characters to increase the keyspace. No attention has been given to increment in the size of the block that the Playfair cipher can handle at a time. In this paper, a modified Playfair (MPF) cryptosystem that is capable of handling different block sizes with high diffusion and confusion properties is developed. cryptanalysis of the developed cryptosystem was carried out and the results show that the MPF cryptosystem is resistant to Known plaintext attack, chosen-plaintext attack, chosen ciphertext attack, frequency analysis attack, autocorrelation attack, differential cryptanalysis attacks, entropy attacks, brute force attack, and can handle variable block sizes.
DETERMINATION OF EMISSION FACTOR FROM LOGGING OPERATIONS IN ULU JELAI FOREST RESERVE, PAHANG USING THE INTEGRATION OF UAV AND HIGH-RESOLUTION IMAGERIES

Logging activity is one of the lead drivers of carbon emission from tropical forest, and this increases greenhouse gases which cause global warming.In this study, we assessed the emission factors that contributed to total carbon emission from selective logging activities, including the overall impact that was associated with selective logging in several compartments of Ulu Jelai Forest Reserve (UJFR), Lipis, Pahang, Malaysia.Data from different remote sensing platforms such as digital aerial photographs generated from unmanned aerial vehicle and satellite images (Planetscope and Worldview) were used to extract forest attributes associated with the logging process.Based on the findings, the major sources of emission were found to be from the construction of logging infrastructure, followed by timber extraction and incidental damage.The estimated emission factor was 1.305 Mg C m -3 when the logging area was logged.The value of total carbon emission for the selected compartments when the selective logging was over was 7050.54Mg C, with an average of 84.95 Mg C per ha -1 .This study explicates the relevant value of estimated carbon emission in the selectively logged tropical forest and we expect our approach to be utilised in determining the emission factor of other tropical forest units.
A HYBRID SECURITY FRAMEWORK FOR MEDICAL DATA IN IOT APPLICATIONS

When sensitive data stored and transmit over the internet while information is no longer protected based on physical boundaries, security is considered as the significant issue.The secure communication between different entities is ensured by an effective, essential, and efficient component known as cryptography, which transmit unintelligible data and allow the authorized recipient to access the data.A hybrid public key cryptography system is presented in this paper based on the Diffie Helman key exchange algorithm and Elliptic curve cryptography.Elliptic curves are used to generate the private keys at the user end.These private keys are used to compute the public keys which can then be shared between multiple parties forming a group.The proposed modified key generation process helps reducing the key size which helps in effectively exchanging the keys over internet.The proposed algorithm achieves this without compromising the cryptographic efficiency.Advance Encryption Standard (AES) is used for encryption of message based on the shared secret key.The shared secret key is hashed using SHA256 algorithm to provide an additional layer of security.The proposed hybrid model is compared with conventional Elliptic Curve Diffie Helman (ECDH) key exchange based cryptographic algorithm in terms of key length, run time, throughput and avalanche effect.The proposed method produced better results in terms of key length and run time while maintaining good throughput and avalanche effect.
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction

When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag. From the viewpoint of causal inference, such bag contextual prior works as a confounder and may result in model robustness and interpretability issues. Focusing on this problem, we propose a novel interventional multi-instance learning (IMIL) framework to achieve deconfounded instance-level prediction. Unlike traditional likelihood-based strategies, we design an Expectation-Maximization (EM) algorithm based on causal intervention, providing a robust instance selection in the training phase and suppressing the bias caused by the bag contextual prior. Experiments on pathological image analysis demonstrate that our IMIL method substantially reduces false positives and outperforms state-of-the-art MIL methods.
Junior high school student perception of online learning in pandemic Covid-19: Gender, social media ownership, and internet access duration perspective

This study aims to explore students’ perceptions of the implementation of online learning during the pandemic in terms of gender perspective, ownership of social media, and duration of internet access. This research was conducted by a survey involving 87 students (P = 51% and L = 49%) in junior high school. Data were analyzed using Logit Value of Item (LVI) and Wright map combined with Logit Value of Person (LVP). Students’ perceptions were reviewed based on gender, social media ownership, and duration of daily internet access. The results showed that, in general, students have a positive perception of online learning. Based on gender, male and female students have almost the same positive perception of online learning. In terms of ownership of social media accounts, students with one account or two social media accounts have a perception of online learning above the average logit person. Most students who access the internet for more than 5 hours have a perception above the average logit person. Students whose internet access duration is 3-4 hours have a perception above the average logit person. So, gender, social media ownership, and internet access duration of 3-4 hours have positive perceptions of online learning.
Development and Validation of an EFL Speaking Self-Efficacy Scale in the Self-Regulated Learning Context

Drawing on self-regulated learning theory and social cognitive theory, the study reported in this paper describes the development and psychometric evaluation of the Self-Efficacy for EFL Speaking Scale (SEESS), an instrument for assessing students' perceived capability to speak English as a foreign language in the learning-to-speak process.Data were collected from 316 university EFL students in China.Exploratory and confirmatory factor analyses resulted in determination of three factors (i.e., performance self-efficacy, self-regulatory efficacy, and linguistic self-efficacy) with a strong psychometric basis.A second-order confirmatory factor analysis further provided empirical evidence to consider EFL speaking self-efficacy as a unitary construct with three correlated but distinct selfefficacy factors.In addition, the significant correlations between these EFL speaking self-efficacy factors and students' EFL speaking learning strategies further confirmed the concurrent validity of the SEESS.Pedagogical implications of this study for classroom teaching and learning of EFL speaking were discussed.
Using 3D Reconstruction to create Pervasive Augmented Reality Experiences: A comparison

This paper presents a prototype for configuration and visualization of Pervasive Augmented Reality (AR) experiences using two versions: desktop and mobile. It makes use of <tex>$3\mathrm{D}$</tex> scans from physical environments to provide a reconstructed digital representation of such spaces to the desktop version and enable positional tracking for the mobile. While the desktop presents a non-immersive setting, the mobile provides continuous AR in the physical environment. Both versions can be used to place virtual content and ultimately configure an AR experience. The authoring capabilities of the proposed solution were compared by conducting a user study focused on evaluating their usability.
Within or Between? Comparing Experimental Designs for Virtual Embodiment Studies

When designing virtual embodiment studies, one of the key choices is the nature of the experimental factors, either between-subjects or within-subjects. However, it is well known that each design has ad-vantages and disadvantages in terms of statistical power, sample size requirements and confounding factors. This paper reports a within-subjects experiment with 92 participants comparing self-reported embodiment scores under a visuomotor task with two conditions: synchronous motions and asynchronous motions with a latency of 300 ms. With the gathered data, using a Monte-Carlo method, we created numerous simulations of within- and between-subjects experiments by selecting subsets of the data. In particular, we explored the impact of the number of participants on the replicability of the results from the 92 within-subjects experiment. For the between-subjects simulations, only the first condition for each user was considered to create the simulations. The results showed that while the replicability of the results increased as the number of participants increased for the within-subjects simulations, no matter the number of participants, between-subjects simulations were not able to replicate the initial results. We discuss the potential reasons that could have led to this surprising result and potential methodological practices to mitigate them.
LITERASI NUMERASI DALAM PEMBELAJARAN TEMATIK SISWA KELAS 3 SD/MI

The purpose of the study was to describe numeration literates in integrated thematic study in a bandung class of 3 mi al-rasyidiyah. The approach in this research is a descriptive culinary. This research conducted a data collection with an interview technique for a 3rd grade teacher - rasyidiyah, test sheets and documentation. Test sheets are composed of several numerical literations about weight-unit matter. The conclusion based on the discussion was the ability of 3rd graders in mi al-rasyidiyah the city of bandung in dealing with numeration literacy was said to be good enough, but more care is required when understanding the content of a story text in the problem and the student's ability when doing solutions on numeration literacy needs to be enhanced again
Few-Shot Object Detection Method Based on Knowledge Reasoning

Human beings have the ability to quickly recognize novel concepts with the help of scene semantics. This kind of ability is meaningful and full of challenge for the field of machine learning. At present, object recognition methods based on deep learning have achieved excellent results with the use of large-scale labeled data. However, the data scarcity of novel objects significantly affects the performance of these recognition methods. In this work, we investigated utilizing knowledge reasoning with visual information in the training of a novel object detector. We trained a detector to project the image representations of objects into an embedding space. Knowledge subgraphs were extracted to describe the semantic relation of the specified visual scenes. The spatial relationship, function relationship, and the attribute description were defined to realize the reasoning of novel classes. The designed few-shot detector, named KR-FSD, is robust and stable to the variation of shots of novel objects, and it also has advantages when detecting objects in a complex environment due to the flexible extensibility of KGs. Experiments on VOC and COCO datasets showed that the performance of the detector was increased significantly when the novel class was strongly associated with some of the base classes, due to the better knowledge propagation between the novel class and the related groups of classes.
CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation

Traversability prediction is a fundamental perception capability for autonomous navigation.The diversity of data in different domains imposes significant gaps to the prediction performance of the perception model.In this work, we make efforts to reduce the gaps by proposing a novel coarse-to-fine unsupervised domain adaptation (UDA) model -CALI.Our aim is to transfer the perception model with high data efficiency, eliminate the prohibitively expensive data labeling, and improve the generalization capability during the adaptation from easyto-obtain source domains to various challenging target domains.We prove that a combination of a coarse alignment and a fine alignment can be beneficial to each other and further design a first-coarse-then-fine alignment process.This proposed work bridges theoretical analyses and algorithm designs, leading to an efficient UDA model with easy and stable training.We show the advantages of our proposed model over multiple baselines in several challenging domain adaptation setups.To further validate the effectiveness of our model, we then combine our perception model with a visual planner to build a navigation system and show the high reliability of our model in complex natural environments where no labeled data is available.The robot navigation demonstration can be seen in this video: https://www.youtube.com/watch?v=Nqsegaq x-o.
OTExtSum: Extractive Text Summarisation with Optimal Transport

Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary.While learning-based methods have achieved promising results, they have several limitations, such as dependence on expensive training and lack of interpretability.Therefore, in this paper, we propose a novel non-learning-based method by for the first time formulating text summarisation as an Optimal Transport (OT) problem, namely Optimal Transport Extractive Summariser (OTExtSum).Optimal sentence extraction is conceptualised as obtaining an optimal summary that minimises the transportation cost to a given document regarding their semantic distributions.Such a cost is defined by the Wasserstein distance and used to measure the summary's semantic coverage of the original document.Comprehensive experiments on four challenging and widely used datasets -MultiNews, PubMed, BillSum, and CNN/DM demonstrate that our proposed method outperforms the state-of-theart non-learning-based methods and several recent learning-based methods in terms of the ROUGE metric. 1
Lossy compression of matrices by black box optimisation of mixed integer nonlinear programming

In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques.
Reversible data hiding in encrypted images with high payload

Reversible data hiding in encrypted images (RDH-EI) can be used as an efficient technique to directly embed additional data in the encrypted domain without violating privacy. In this study, a multi-MSB (most significant bit) prediction-based RDH-EI scheme is proposed, which is completely reversible and has high embedding payload. An improved gradient adjustment predictor (GAP) is used for pixel prediction. A binary location map is employed to store the location of the prediction errors. Subsequently, pre-processing is performed on accurately predicted pixels in the original image to store the prediction difference. The pre-processed image is encrypted using a stream cipher, and then the data hider can substitute multi-MSB values of each available pixel in encrypted image with secret message bits to obtain the marked encrypted image. On the recipient side, the secret message can be extracted without any error and the original image can be reconstructed losslessly. Experimental results show that not only the embedding payload is improved but also the hidden data can be extracted accurately, and the cover image can be recovered perfectly.
Table-based Fact Verification with Self-adaptive Mixture of Experts

The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem.It inherently requires informative reasoning over natural language together with different numerical and logical reasoning on tables (e.g., count, superlative, comparative).Considering that, we exploit mixture-of-experts and present in this paper a new method: Self-adaptive Mixtureof-Experts Network (SaMoE).Specifically, we have developed a mixture-of-experts neural network to recognize and execute different types of reasoning-the network is composed of multiple experts, each handling a specific part of the semantics for reasoning, whereas a management module is applied to decide the contribution of each expert network to the verification result.A self-adaptive method is developed to teach the management module combining results of different experts more efficiently without external knowledge.The experimental results illustrate that our framework achieves 85.1% accuracy on the benchmark dataset TAB-FACT, comparable with the previous state-ofthe-art models.We hope our framework can serve as a new baseline for table-based verification.Our code is available at https: //github.com/THUMLP/SaMoE.