Reviewerly/exHarmony huggingface dataset:
Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages

We present the results of the WASSA 2023 Shared-Task 2: Emotion Classification on codemixed text messages (Roman Urdu + English), which included two tracks for emotion classification: multi-label and multi-class.The participants were provided with a dataset of codemixed SMS messages in English and Roman Urdu labeled with 12 emotions for both tracks.A total of 5 teams (19 team members) participated in the shared task.We summarized the methods, resources, and tools used by the participating teams.We also made the data freely available for further improvements to the task.
STUDI RETROSPEKTIF PADA INISIATIF LITERASI DIGITAL DI PROVINSI KALIMANTAN UTARA

Digital literacy as part of information literacy is important in the development of the world of information and technology today, and also as a capital to participate in digital society. The European Union Commission has provided a three-stage initiative in assessing the extent to which digital literacy has progressed in society, which are: Stage 1. Access to technology; Stage 2. Basic ICT (Information and Communication Technology) skills and; Stage 3. Increase public participation in the information society. By using a retrospective study, this paper has attempted to see how digital literacy initiatives are in North Kalimantan Province, based on the steps recommended by the European Union Commission. The data used in this paper comes from the Central Statistics Agency both nationally and from North Kalimantan itself, as well as from official national news information and from the Province of North Kalimantan. Some of the data placed on the Stage 1 initiative has shown that the percentage of access to the internet by the people of North Kalimantan is above 95%. Furthermore, in the Stage 2 initiative, it was found that the ability to use computers (desktop PCs, laptops, notebooks, tablets) was still low, but the percentage value was not too far from the national average for computer use. This has also been balanced with the number of people in North Kalimantan who have used mobile phones. Then in the Stage 3 initiative, some local government support was found to encourage community involvement in the use of ICT, one of which was by launching public service applications, as well as being involved in virtual events, which indirectly familiarized the community with technology. The North Kalimantan government has also supported content creators also has increased the competence of news creators. Some suggestions have also been written at the end of this paper to improve digital literacy in North Kalimantan Province in the future.
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost

Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages. To address issues of cross-linguistic access to such models and reduce energy consumption for sustainability during large-scale model training, this study proposes an effective and energy-efficient framework called GreenPLM that uses bilingual lexicons to directly ``translate'' pre-trained language models of one language into another at almost no additional cost. We validate this approach in 18 languages' BERT models and show that this framework is comparable to, if not better than, other heuristics with high training costs. In addition, given lightweight continued pre-training on limited data where available, this framework outperforms the original monolingual language models in six out of seven tested languages with up to 200x less pre-training efforts. Aiming at the Leave No One Behind Principle (LNOB), our approach manages to reduce inequalities between languages and energy consumption greatly. We make our codes and models publicly available at https://github.com/qcznlp/GreenPLMs.
Timestamp-Supervised Action Segmentation from the Perspective of Clustering

Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frames in each video to train the segmentation model. However, these methods suffer from incorrect pseudo-labels, especially for the semantically unclear frames in the transition region between two consecutive actions, which we call ambiguous intervals. To address this issue, we propose a novel framework from the perspective of clustering, which includes the following two parts. First, pseudo-label ensembling generates incomplete but high-quality pseudo-label sequences, where the frames in ambiguous intervals have no pseudo-labels. Second, iterative clustering iteratively propagates the pseudo-labels to the ambiguous intervals by clustering, and thus updates the pseudo-label sequences to train the model. We further introduce a clustering loss, which encourages the features of frames within the same action segment more compact. Extensive experiments show the effectiveness of our method.
HOI-aware Adaptive Network for Weakly-supervised Action Segmentation

In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed network to predict the action of each frame with the neighboring frames. However, this would result in ambiguity when estimating similar actions, such as pouring juice and pouring coffee. To address this, we aim to exploit temporally global but spatially local human-object interactions (HOI) as video-level prior knowledge for action segmentation. The long-term HOI sequence provides crucial contextual information to distinguish ambiguous actions, where our network dynamically adapts to the given HOI sequence at test time. More specifically, we first design a video HOI encoder that extracts, selects, and integrates the most representative HOI throughout the video. Then, we propose a two-branch HyperNetwork to learn an adaptive temporal encoder, which automatically adjusts the parameters based on the HOI information of various videos on the fly. Extensive experiments on two widely-used datasets including Breakfast and 50Salads demonstrate the effectiveness of our method under different evaluation metrics.
RuleMatch: Matching Abstract Rules for Semi-supervised Learning of Human Standard Intelligence Tests

Raven's Progressive Matrices (RPM), one of the standard intelligence tests in human psychology, has recently emerged as a powerful tool for studying abstract visual reasoning (AVR) abilities in machines. Although existing computational models for RPM problems achieve good performance, they require a large number of labeled training examples for supervised learning. In contrast, humans can efficiently solve unlabeled RPM problems after learning from only a few example questions. Here, we develop a semi-supervised learning (SSL) method, called RuleMatch, to train deep models with a small number of labeled RPM questions along with other unlabeled questions. Moreover, instead of using pixel-level augmentation in object perception tasks, we exploit the nature of RPM problems and augment the data at the level of abstract rules. Specifically, we disrupt the possible rules contained among context images in an RPM question and force the two augmented variants of the same unlabeled sample to obey the same abstract rule and predict a common pseudo label for training. Extensive experiments show that the proposed RuleMatch achieves state-of-the-art performance on two popular RAVEN datasets. Our work makes an important stride in aligning abstract analogical visual reasoning abilities in machines and humans. Our Code is at https://github.com/ZjjConan/AVR-RuleMatch.
Towards Incremental NER Data Augmentation via Syntactic-aware Insertion Transformer

Named entity recognition (NER) aims to locate and classify named entities in natural language texts. Most existing high-performance NER models employ a supervised paradigm, which requires a large quantity of high-quality annotated data during training. In order to help NER models perform well in few-shot scenarios, data augmentation approaches attempt to build extra data by means of random editing or by using end-to-end generation with PLMs. However, these methods focus on only the fluency of generated sentences, ignoring the syntactic correlation between the new and raw sentences. Such uncorrelation also brings low diversity and inconsistent labeling of synthetic samples. To fill this gap, we present SAINT (Syntactic-Aware InsertioN Transformer), a hard-constraint controlled text generation model that incorporates syntactic information. The proposed method operates by inserting new tokens between existing entities in a parallel manner. During insertion procedure, new tokens will be added taking both semantic and syntactic factors into account. Hence the resulting sentence can retain the syntactic correctness with respect to the raw data. Experimental results on two benchmark datasets, i.e., Ontonotes and Wikiann, demonstrate the comparable performance of SAINT over the state-of-the-art baselines.
The Effectiveness of Problem-Based Learning in Acquisition of Knowledge Using Online Learning

The adoption and use of creative teaching techniques, such as problem-based learning using online learning have relied upon improvement in order to improve educational outcomes. Regardless of the fact that problem-based learning is facilitated by online learning and has many clear advantages, it is still developing or at a very early level in many developing countries. Problem-based learning is a student-centered method where students study a subject by using online learning to collaborate in groups to solve an open-ended challenge. The research design was a cross-sectional field survey, was used in quantitative research approaches using PLS analysis. Additionally, the study's target population is all the class teacher students at private Universities. The author reviews the study on the investigation of influencing factors for problem-based learning strategy with online learning in Jordan to improve the education system as well. The findings demonstrated a positive influence between key factors and intention to use and this result enhance acquisition of Knowledge using online learning for Class Teacher students.
Audition

In this chapter, we discuss the second major sense in VR audition. Sound is important in nature and the real world in general. The lack of sounds in virtual worlds could be natural but could also make the virtual world poor and unnatural. This chapter explains how to produce natural sound in virtual worlds. Typical equipment for producing 3D sounds and recent development in immersive sound are explained.
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation

The task of dynamic scene graph generation (DynSGG) aims to generate scene graphs for given videos, which involves modeling the spatial-temporal information in the video. However, due to the long-tailed distribution of samples in the dataset, previous DynSGG models fail to predict the tail predicates. We argue that this phenomenon is due to previous methods that only pay attention to the local spatial-temporal information and neglect the consistency of multiple frames. To solve this problem, we propose a novel DynSGG model based on multi-task learning, DynSGG-MTL, which introduces the local interaction information and global human-action interaction information. The interaction between objects and frame features makes the model more fully understand the visual context of the single image. Long-temporal human actions supervise the model to generate multiple scene graphs that conform to the global constraints and avoid the model being unable to learn the tail predicates. Extensive experiments on Action Genome dataset demonstrate the efficacy of our proposed framework, which not only improves the dynamic scene graph generation but also alleviates the long-tail problem.
Using Machine Learning To Identify Software Weaknesses From Software Requirement Specifications

Secure software engineering is crucial but can be time-consuming; therefore, methods that could expedite the identification of software weaknesses without reducing the process efficacy would benefit the software engineering industry and thus benefit modern life. This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. The research uses the CWE repository and PROMISE exp dataset for training. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested, with SVM and neural network producing reliable results. The research is unique contribution lies in the mapping technique and algorithm selection. It serves as a valuable reference for the secure software engineering community seeking to expedite the development lifecycle without compromising efficacy. Future work involves testing more algorithms, optimizing existing ones, and improving the training sets accuracy.
Effect of Diacritics on Machine Translation Performance: A Case Study of Yemeni Literature

Many Arabic texts are written without diacritics. However, in some contexts this raises the high level of homography and in turn presents difficulties for machine translation programs. Homographs are words which are spelled identically but have different meanings and are mostly pronounced differently. To avoid the problem of homography, words require to be diacriticized. Thus, the main objective of the study is to assess the ability of machine translation (henceforth MT) in rendering diacritical words from Arabic into English with special reference to translating Yemeni literature into English. This study will also compare the translations of three MT programs, namely, (Reverso, Systran Translate and Free Translation Online) to find out which program is close to the original meaning of the source language texts. Further, the study aims to identify some causes that stand behind errors of translating diacriticized words that result from the mentioned programs. To achieve these aims, descriptive, analytical and comparative methods were followed by the researcher. Thus, the three common and modern MT programs, Reverso, Systran and Free Translation Online were selected to translate some diacriticized words. Then, some excerpts with their contexts were taken from the two Yemeni works, The Hostage (Ar-rahinah) (???????) by the Yemeni famous writer Zayd Muttee Dammaj and the Yemeni book Yemeni Wealth from Popular Proverbs ?????? ??????? ?? ??????? ??????? by the Yemeni writer Muhammad Al-Adimi. The chosen samples were inserted into these MT programs to be electronically translated and then analyzed and discussed qualitatively and quantitatively. The study has concluded that MT encountered the problem of diacritics in Arabic texts; as a result most of the time MT programs failed in recognizing diacritics on letters. Thus, most of the programs' translation results were incorrect and did not concord with the original meaning. It can also be concluded that Free Translation Online program produced the least errors of the three programs and Systran mistranslated all the diacriticized excerpts. These errors can be attributed to absence of programs which contain the diacritic system of Arabic.
Autonomous workload distribution for container-based micro services environments

Microservice architecture represents a cloud application design approach that transfers the intricacies from the conventional monolithic applications to the infrastructure. It involves breaking down the application into small, containerized microservices, each responsible for a specific functional requirement. These microservices can be independently deployed, scaled, and tested through automated orchestration systems. Our paper introduces an autonomous system for distributing workloads among containerized microservices within the cloud like a swarm, designed specifically for microservices operating within OpenVZ containers. This system has the potential to enhance performance compared to existing centralized container orchestration systems.
FAST COMPUTATION OF DIRECT EXPONENTIATION TO SPEED UP IMPLEMENTATION OF DYNAMIC BLOCK CIPHERS

MDS (maximum distance separable) matrices are ones that come from MDS codes that have been studied for a long time in error correcting code theory and have many applications in block ciphers. To improve the security of block ciphers, dynamic block ciphers can be created. Using MDS matrix transformations is a method used to make block ciphers dynamic. Direct exponentiation is a transformation that can be used to generate dynamic MDS matrices to create a dynamic diffusion layer of the block ciphers. However, for cryptographic algorithms that use an MDS matrix as a component of them, the implementation of matrix multiplication is quite expensive, especially when the matrix has a large size. In this paper, the mathematical basis for quick calculation of direct exponentiation of an MDS matrix will be presented. On that basis, it is to suggest how to apply that fast calculation to dynamic algorithms using the direct exponentiation. This result is very meaningful in software implementation for MDS matrices, especially when implementing dynamic block ciphers to increase execution speed.
Towards AI Forensics: Did the Artificial Intelligence System Do It?

Artificial intelligence (AI) makes decisions impacting our daily lives in an increasingly autonomous manner. Their actions might cause accidents, harm, or, more generally, violate regulations. Determining whether an AI caused a specific event and, if so, what triggered the AI's action, are key forensic questions. We provide a conceptualization of the problems and strategies for forensic investigation. We focus on AI that is potentially ``malicious by design'' and grey box analysis. Our evaluation using convolutional neural networks illustrates challenges and ideas for identifying malicious AI.
Comparative Analysis and Evaluation between Traditional and Online Learning in Engineering Education, Focused on Introduction to Engineering

Due to the COVID-19 Pandemic, face-to-face interactions are prohibited by the government, pushing various institutions to shift from the traditional to the online style of teaching.However, the question is whether online learning is as effective as the traditional learning mode.This research explores the learning experience and current issues within engineering education.It compares the learning experience between traditional learning and the enriched virtual mode of learning in Introduction to Engineering.The researchers conducted the study at the University of Santo Tomas, where they identified two groups of students.The control group consists of students from the traditional learning method, and the experimental group consists of students from the enriched virtual mode.The researchers used the Mann-Whitney-U test to see if there is a significant difference between groups under the factors chosen by the researchers that affect the learning experience.The main findings of the research state that there is no significant difference between the control and experimental groups in learning effectiveness, student performance, and student confidence.Researchers found a significant difference between the two groups in the two dependent variables, student satisfaction and faculty interaction.The study concluded that online learning is as significant as traditional learning methods and is even superior to the traditional method in some factors used in the study.Lastly, the researchers recognize that online learning can be adapted as the learning tool used in delivering Introduction to Engineering in the Faculty of Engineering at the University of Santo Tomas.
Approximating Human-Like Few-shot Learning with GPT-based Compression

In this work, we conceptualize the learning process as information compression. We seek to equip generative pre-trained models with human-like learning capabilities that enable data compression during inference. We present a novel approach that utilizes the Generative Pre-trained Transformer (GPT) to approximate Kolmogorov complexity, with the aim of estimating the optimal Information Distance for few-shot learning. We first propose using GPT as a prior for lossless text compression, achieving a noteworthy compression ratio. Experiment with LLAMA2-7B backbone achieves a compression ratio of 15.5 on enwik9. We justify the pre-training objective of GPT models by demonstrating its equivalence to the compression length, and, consequently, its ability to approximate the information distance for texts. Leveraging the approximated information distance, our method allows the direct application of GPT models in quantitative text similarity measurements. Experiment results show that our method overall achieves superior performance compared to embedding and prompt baselines on challenging NLP tasks, including semantic similarity, zero and one-shot text classification, and zero-shot text ranking.
Quality of teaching perceived by students of an Ecuadorian institution of higher education

This research determines the student perception of the quality of teaching in an Ecuadorian institution of higher education. An instrument consisting of 22 items, adapted from Alvarado-Lagunas et al (2015), was used, which evaluates the perception that students have of the multiservices offered by the University in the teaching process, through variables associated with four dimensions: physical component, teaching staff, teaching ability and integral development. The instrument was applied to a sample of 175 students. The results indicate a good level of perceived quality of teaching in the institution; and the factors that strengthen and weaken the system. It highlights the importance that students give to the physical facilities, equipment and staff that the institution has, the ability of teachers to provide education, among others. In addition, the results of this study suggest a significant relationship between the perceived quality and the degree of satisfaction with the teaching offered by the university.
The application of multiple regression model in interactive English classroom teaching

Abstract To verify the effect of the interactive teaching model in English teaching, the author proposes the application of the multiple regression model in the interactive teaching of English classrooms. Authors, by collecting data on each relevant factor, a multiple regression system with regression simulation computing capability was designed and implemented, and the model was solved and analyzed based on Matlab software. Experimental results show that: According to the multiple regression model, the average grade is 1.3732, which shows that the interactive teaching effect is obvious. Conclusion: The application of the multiple regression model in English classroom interactive teaching is of great significance to the reform of college English teaching.