Reviewerly/exHarmony huggingface dataset:
A Theoretical Perspective of Machine Learning with Computational Resource Concerns

Conventional theoretical machine learning studies generally assume explicitly or implicitly that there are enough or even infinitely supplied computational resources. In real practice, however, computational resources are usually limited, and the performance of machine learning depends not only on how many data have been received, but also on how many data can be handled with the computational resources available. Note that most current ``intelligent supercomputing'' facilities work like exclusive operating systems, where a fixed amount of resources are allocated to a machine learning task without adaptive scheduling strategies considering important factors such as learning performance demands and learning process status. In this article, we introduce the notion of machine learning throughput, define Computational Resource Efficient Learning (CoRE-Learning) and present a theoretical framework that takes into account the influence of computational resources in learning theory. This framework can be naturally applied to stream learning where the incoming data streams can be potentially endless with overwhelming size and it is impractical to assume that all received data can be handled in time. It may also provide a theoretical perspective for the design of intelligent supercomputing operating systems.
Enhancing Pashto Text Classification using Language Processing Techniques for Single And Multi-Label Analysis

Text classification has become a crucial task in various fields, leading to a significant amount of research on developing automated text classification systems for national and international languages. However, there is a growing need for automated text classification systems that can handle local languages. This study aims to establish an automated classification system for Pashto text. To achieve this goal, we constructed a dataset of Pashto documents and applied various models, including statistical and neural machine learning models such as DistilBERT-base-multilingual-cased, Multilayer Perceptron, Support Vector Machine, K Nearest Neighbor, decision tree, Gaussian na\"ive Bayes, multinomial na\"ive Bayes, random forest, and logistic regression, to identify the most effective approach. We also evaluated two different feature extraction methods, bag of words and Term Frequency Inverse Document Frequency. The study achieved an average testing accuracy rate of 94% using the MLP classification algorithm and TFIDF feature extraction method in single-label multiclass classification. Similarly, MLP+TFIDF yielded the best results, with an F1-measure of 0.81. Furthermore, the use of pre-trained language representation models, such as DistilBERT, showed promising results for Pashto text classification; however, the study highlights the importance of developing a specific tokenizer for a particular language to achieve reasonable results.
Open Information Extraction via Chunks

Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments. We argue that SaC has better quantitative and qualitative properties for OIE than sentence as token sequence, and evaluate four choices of chunks (i.e., CoNLL chunks, simple phrases, NP chunks, and spans from SpanOIE) against gold OIE tuples. Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC. Chunk-OIE achieves state-of-the-art results on multiple OIE datasets, showing that SaC benefits OIE task.
Guided Image Synthesis via Initial Image Editing in Diffusion Model

Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images. While previous research has primarily focused on improving the control of image generation through adjusting the denoising process, we propose a novel direction of manipulating the initial noise to control the generated image. Through experiments on stable diffusion, we show that blocks of pixels in the initial latent images have a preference for generating specific content, and that modifying these blocks can significantly influence the generated image. In particular, we show that modifying a part of the initial image affects the corresponding region of the generated image while leaving other regions unaffected, which is useful for repainting tasks. Furthermore, we find that the generation preferences of pixel blocks are primarily determined by their values, rather than their position. By moving pixel blocks with a tendency to generate user-desired content to user-specified regions, our approach achieves state-of-the-art performance in layout-to-image generation. Our results highlight the flexibility and power of initial image manipulation in controlling the generated image.
Specification and verification of a linear-time logic for graph transformation

We present a first-order linear-time temporal logic for reasoning about the evolution of directed graphs. Its semantics is based on the counterpart paradigm, thus allowing our logic to represent the creation, duplication, merging, and deletion of elements of a graph as well as how its topology changes over time. We then introduce a positive normal forms presentation, thus simplifying the actual process of verification. We provide the syntax and semantics of our logics with a computer-assisted formalisation using the proof assistant Agda, and we round up the paper by highlighting the crucial aspects of our formalisation and the practical use of quantified temporal logics in a constructive proof assistant.
WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models

This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both achieve high performance as measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and first, respectively, of all submissions to the shared task. Expert human scrutiny indicates that notes generated via the ICL-based approach with GPT-4 are preferred about as often as human-written notes, making it a promising path toward automated note generation from doctor-patient conversations.
Visualization in the Era of Artificial Intelligence: Experiments for Creating Structural Visualizations by Prompting Large Language Models

Large Language Models (LLMs) have revolutionized natural language processing by generating human-like text and images from textual input. However, their potential to generate complex 2D/3D visualizations has been largely unexplored. We report initial experiments showing that LLMs can generate 2D/3D visualizations that may be used for legal visualization. Further research is needed for complex 2D visualizations and 3D scenes. LLMs can become a powerful tool for many industries and applications, generating complex visualizations with minimal training.
On Belief Propagation Decoding of Quantum Codes with Quaternary Reliability Statistics

In this paper, we investigate the use of quaternary reliability statistics for ordered statistics decoding (OSD) of quantum codes. OSD can be used to improve the performance of belief propagation (BP) decoding when it fails to correct the error syndrome. We propose an approach that leverages quaternary reliability information and the hard-decision history output by BP to perform reliability sorting for OSD. This approach improves upon previous methods that separately treat X and Z errors, by preserving the X/Z correlations during the sorting step. Our simulations show that the refined BP with scalar messages and the proposed OSD outperforms previous BP-OSD combinations. We achieve thresholds of roughly 17.5%--17.7% for toric, surface, and XZZX codes, and 15.42% for hexagonal planar color codes.
Towards Feminist Intersectional XAI: From Explainability to Response-Ability

This paper follows calls for critical approaches to computing and conceptualisations of intersectional, feminist, decolonial HCI and AI design and asks what a feminist intersectional perspective in HCXAI research and design might look like. Sketching out initial research directions and implications for explainable AI design, it suggests that explainability from a feminist perspective would include the fostering of response-ability - the capacity to critically evaluate and respond to AI systems - and would centre marginalised perspectives.
RARES: Runtime Attack Resilient Embedded System Design Using Verified Proof-of-Execution

Modern society is getting accustomed to the Internet of Things (IoT) and Cyber-Physical Systems (CPS) for a variety of applications that involves security-critical user data and information transfers. In the lower end of the spectrum, these devices are resource-constrained with no attack protection. They become a soft target for malicious code modification attacks that steals and misuses device data in malicious activities. The resilient system requires continuous detection, prevention, and/or recovery and correct code execution (including in degraded mode). By end large, existing security primitives (e.g., secure-boot, Remote Attestation RA, Control Flow Attestation (CFA) and Data Flow Attestation (DFA)) focuses on detection and prevention, leaving the proof of code execution and recovery unanswered. To this end, the proposed work presents lightweight RARES -- Runtime Attack Resilient Embedded System design using verified Proof-of-Execution. It presents first custom hardware control register (Ctrl_register) based runtime memory modification attacks classification and detection technique. It further demonstrates the Proof Of Concept (POC) implementation of use-case-specific attacks prevention and onboard recovery techniques. The prototype implementation on Artix 7 Field Programmable Gate Array (FPGA) and state-of-the-art comparison demonstrates very low (2.3%) resource overhead and efficacy of the proposed solution.
Phase Neural Operator for Multi-Station Picking of Seismic Arrivals

Seismic wave arrival time measurements form the basis for numerous downstream applications. State-of-the-art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic data by examining the whole network jointly. Here, we introduce a general-purpose network-wide phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our model, called PhaseNO, leverages the spatio-temporal contextual information to pick phases simultaneously for any seismic network geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking more phase arrivals, while also greatly improving measurement accuracy. Following similar trends being seen across the domains of artificial intelligence, our approach provides but a glimpse of the potential gains from fully-utilizing the massive seismic datasets being collected worldwide.
A Survey on Out-of-Distribution Detection in NLP

Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past years. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches. First, we provide a formal definition of OOD detection and discuss several related fields. We then categorize recent algorithms into three classes according to the data they used: (1) OOD data available, (2) OOD data unavailable + in-distribution (ID) label available, and (3) OOD data unavailable + ID label unavailable. Third, we introduce datasets, applications, and metrics. Finally, we summarize existing work and present potential future research topics.
Decentralized diffusion-based learning under non-parametric limited prior knowledge

We study the problem of diffusion-based network learning of a nonlinear phenomenon, $m$, from local agents' measurements collected in a noisy environment. For a decentralized network and information spreading merely between directly neighboring nodes, we propose a non-parametric learning algorithm, that avoids raw data exchange and requires only mild \textit{a priori} knowledge about $m$. Non-asymptotic estimation error bounds are derived for the proposed method. Its potential applications are illustrated through simulation experiments.
Deep Fake Image Classification Using VGG-19 Model

Fake images, also known as "deepfakes," are a growing concern in today's digital age.These images are often created with the intent of benefiting one party and can be difficult to distinguish from real images.They are often disseminated through digital media and newspapers, and can spread misinformation or propaganda, which can have serious consequences if not detected and addressed.To effectively detect image falsification in many image data, an architectural model that can process several pixels in the image is required, as well as a method that is effective and adjustable with training data for practical use in daily life.In this paper to detecting fake images usingVGG19 is a convolutional neural network (CNN) architecture that has been successful in a variety of image classification tasks.The proposed VGG19 is better model compared existing models it provides 96% accuracy.
Sociopragmatic Analysis of Filipino Celebrities’ Posts and Fans’ Comments

The phenomenon of hate speech occurring in one's nation and the relationship between social media celebrities and their audience is very interesting to draw. Social media has changed how people interact, comprehend, and respond to online discourse. This paper looks at the topics posted by celebrities as sources of information through the lens of the dimensions of online persona and hostile fan comments through impoliteness theory and Crystal's theory of language and technology, respectively. The data were collected from the leading social media platforms in the Philippines, namely, Facebook, Instagram, and Twitter. The study shows that toxic online discourse was pervasive during the entire election period. Most celebrities use the public dimension (41%) and social media fans frequently on graphology (88%), bald-on record (33%), and sarcasm or mock politeness strategy (33%). The study also found two primary trigger mechanisms: the surrounding context of the social media celebrity's post and the social media fans' behavior in their use of language.
Pembelajaran Bahasa Inggris Dasar Tema Makanan Bergizi Menggunakan Augmented Reality Berbasis Android

Knowledge is one of the human links to perfection. The human tendency to crave makes him always move forward to become better from time to time. One of the enthusiastic actions of humans is illustrated by the development of technology and information that makes work easier. Learning at SDN Durman 049 is still conventional learning that is less interesting or less effective when in the current state of online learning. Ar food application was created to facilitate and be an alternative in learning at SDN Durman 049 especially for grade IV. AR food is an androd based Augmented ReaIity technology app that contains food in English. Android operating system devices are the most widely used devices today. AR food is proven to help learn English for grade IV in online conditions like this, in addition to adding alternatives in learning AR food increases the effectiveness of online learning. AR food displays 3D English food by scanning the marker using the camera and a 3D object of English food will appear above the marker.
A Proposal for Standardization of the Division of Works among Three Occupational Types in Schools and the Establishment of Institutional Mechanisms

Objectives This study aims to propose policy measures for standardizing the division of work and establishing institutional mechanisms in order to suggest alternatives regarding the division of work among teachers, administrative staffs, and non-fixed term workers (teaching support workers, administrative support workers, etc.) in schools.&#x0D; Methods To the purpose, we used prior literature researches, office of education’s data, Korean education statistics service data, online 263 questionnaires on the division of duties of three occupations in schools, and FGI for the relevant expert groups (16 people).&#x0D; Results As a result, specific tasks with insufficient the division of works turned out to be low-income families support work, CCTV related work, safety work, recruitment work for fixed-term workers etc. If these works are listed, they are not consistently in charge of a specific occupation in the school's work list, and even they are not listed in many cases. Therefore there is a great need to standardize the division of works.&#x0D; Conclusions We have proposed several points of policy measures as follows. First, the problem of division of works needs to be dealt with in conjunction with the reduction of the total amount of work of school workers. Secondly, standardization of the division of works among three occupational types should be achieved above a certain level. Thirdly, a certain degree of autonomy should be required at the school level. Lastly, existing trade unions in each occupational types should participate in consultative bodies. At this time, in order to reduce the total amount of work, it is necessary to conduct a fact-finding survey on work in schools and eliminate unnecessary work that has no need to be carried out.
Correction to: Unravelling interlanguage facts via explainable machine learning

In the originally published version of this manuscript, in the following paragraph, four instances of letters of the phonetic alphabet were erroneously given without a preceding space, as follows:'Indeed, since Spanish does not exhibit the phoneme/ô/, Spanish learners of English approximate the voiced interdental fricative to the dental sound/d/, which, conversely, belongs in the Spanish phonological system.Interestingly, voice prevails over manner and place of articulation.In fact, Spanish does make use of the phoneme /h/, which is the unvoiced counterpart of/ô/.Yet, learners instinctively approximate the latter to/d/.These errors have now been corrected.
EXPLORING LINEARITY AND TYPES OF SELF-CORRECTIONS IN THE TRANSLATION OF STUDENT TRANSLATORS

Considering the importance of correction (post-drafting phase) in translation process, this study aims at finding the linearity and types of self-corrections done by student translators in completing their translation tasks. Three students majoring translation studies who, in this study, were called student translators, were selected as the participants using a purposive sampling method. They were selected based on their relatively homogeneous profile following their linguistic competence including Test of bahasa Indonesia proficiency (UKBI), Test of English proficiency (TOEFL), and Test of typing speed by using TQ (TypingQueen) typing test. In the process of data collection, they were asked to translate two texts from English into bahasa Indonesia. The translation process was recorded using Translog and screen recording (Camtasia Studio 8). In doing their tasks, they were allowed to use online dictionaries and resources. The results of this study show that eight types of self-corrections were done by the student translators, including deletion, word substitution, spelling, return, addition, meaning, capitalization, and grammar, among which word substitution was most frequently used. This study also found types of word deletion that include deleting (i) unnecessary words, (ii) unnecessarily added words, (iii) incomplete words, (iv) repeated words, (v) miscollocation, and (vi) redundancy.
A Data-Centric Approach for Portuguese Speech Recognition: Language Model And Its Implications

Recent advances in Automatic Speech Recognition have made it possible to achieve a quality never seen before in the literature, both for languages with abundant data, such as English, which has a large number of studies and for the Portuguese language, which has a more limited amount of resources and studies. The most recent advances address speech recognition problems with Transformers based models, which have the capability to perform the speech recognition task directly from the raw signal, without the need for manual feature extraction. Some studies have already shown that it is possible to further improve the quality of the transcription of these models using language models within the decoding stage, however, the real impact of such language models is still not clear, especially for the Brazilian Portuguese scenario. Also, it is known that the quality of the data used for training the models is of paramount importance, however, there are few works in the literature addressing this issue. This work explores the impact of language models applied to Portuguese speech recognition both in terms of data quality and computational performance, with a data-centric approach. We propose an approach to measure similarity between datasets and, thus, assist in decision-making during training. The approach indicates paths for the advancement of the state-of-the-art aiming at Portuguese speech recognition, showing that it is possible to reduce the size of the language model by 80% and still achieve error rates around 7.17% for the Common Voice dataset. The source code is available at https://github.com/joaoalvarenga/language-model-evaluation.
A sociolinguistic study of neologisms with the ‘K-NOUN’ form in Korean news broadcasts between 1997 and 2021

This study aims to describe the use of neologisms using the ‘K-NOUN’ form, such as ‘K-pop,’ over 25 years (1997~2021) in recent Korean society. In order to trace whether there has been any change in the use of K-NOUNs, examples were collected from the news transcripts of a Korean television broadcaster (MBC). Subsequently, the referents of the K-NOUNs were categorized into nine types according to the nine root nodes for nouns in lexical semantic networks (Korlex and WordNet). The analysis shows that only limited types of K-NOUNs were used to refer to some concrete entities initially; however, the types of referents have become diverse. Consequently, the analysis shows that 129 kinds of K-NOUNs were used during the period. The increase in the kinds of K-NOUNs would appear to be caused by the onset of Hallyu, the Korean Wave.