Reviewerly/exHarmony huggingface dataset:
Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy

When pretrained language models (LMs) are applied to discriminative tasks such as multiple-choice questions, they place probability mass on vocabulary tokens that aren’t among the given answer choices. Spreading probability mass across multiple surface forms with identical meaning (such as “bath” and “bathtub”) is thought to cause an underestimation of a model’s true performance, referred to as the “surface form competition” (SFC) hypothesis. This has motivated the introduction of various probability normalization methods. However, many core questions remain unanswered. How do we measure SFC? Are there direct ways of reducing it, and does doing so improve task performance? We propose a mathematical formalism for SFC which allows us to quantify and bound its impact for the first time. We identify a simple method for reducing it—namely, increasing probability mass on the given answer choices by a) including them in the prompt and b) using in-context learning with even just one example. We show this method eliminates the impact of SFC in the majority of instances. Our experiments on three diverse datasets and six LMs reveal several additional surprising findings. For example, both normalization and prompting methods for reducing SFC can be ineffective or even detrimental to task performance for some LMs. We conclude with practical insights for effectively prompting LMs for multiple-choice tasks.
Background Summarization of Event Timelines

Generating concise summaries of news events is a challenging natural language processing task. While journalists often curate timelines to highlight key sub-events, newcomers to a news event face challenges in catching up on its historical context. In this paper, we address this need by introducing the task of background news summarization, which complements each timeline update with a background summary of relevant preceding events. We construct a dataset by merging existing timeline datasets and asking human annotators to write a background summary for each timestep of each news event. We establish strong baseline performance using state-of-the-art summarization systems and propose a query-focused variant to generate background summaries. To evaluate background summary quality, we present a question-answering-based evaluation metric, Background Utility Score (BUS), which measures the percentage of questions about a current event timestep that a background summary answers. Our experiments show the effectiveness of instruction fine-tuned systems such as Flan-T5, in addition to strong zero-shot performance using GPT-3.5.
Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models

Pretrained machine learning models are known to perpetuate and even amplify existing biases in data, which can result in unfair outcomes that ultimately impact user experience. Therefore, it is crucial to understand the mechanisms behind those prejudicial biases to ensure that model performance does not result in discriminatory behaviour toward certain groups or populations. In this work, we define gender bias as our case study. We quantify bias amplification in pretraining and after fine-tuning on three families of vision-and-language models. We investigate the connection, if any, between the two learning stages, and evaluate how bias amplification reflects on model performance. Overall, we find that bias amplification in pretraining and after fine-tuning are independent. We then examine the effect of continued pretraining on gender-neutral data, finding that this reduces group disparities, i.e., promotes fairness, on VQAv2 and retrieval tasks without significantly compromising task performance.
CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset

The CoNLL-03 corpus is arguably the most well-known and utilized benchmark dataset for named entity recognition (NER). However, prior works found significant numbers of annotation errors, incompleteness, and inconsistencies in the data. This poses challenges to objectively comparing NER approaches and analyzing their errors, as current state-of-the-art models achieve F1-scores that are comparable to or even exceed the estimated noise level in CoNLL-03. To address this issue, we present a comprehensive relabeling effort assisted by automatic consistency checking that corrects 7.0% of all labels in the English CoNLL-03. Our effort adds a layer of entity linking annotation both for better explainability of NER labels and as additional safeguard of annotation quality. Our experimental evaluation finds not only that state-of-the-art approaches reach significantly higher F1-scores (97.1%) on our data, but crucially that the share of correct predictions falsely counted as errors due to annotation noise drops from 47% to 6%. This indicates that our resource is well suited to analyze the remaining errors made by state-of-the-art models, and that the theoretical upper bound even on high resource, coarse-grained NER is not yet reached. To facilitate such analysis, we make CleanCoNLL publicly available to the research community.
Are Embedded Potatoes Still Vegetables? On the Limitations of WordNet Embeddings for Lexical Semantics

Knowledge Base Embedding (KBE) models have been widely used to encode structured information from knowledge bases, including WordNet. However, the existing literature has predominantly focused on link prediction as the evaluation task, often neglecting exploration of the models’ semantic capabilities. In this paper, we investigate the potential disconnect between the performance of KBE models of WordNet on link prediction and their ability to encode semantic information, highlighting the limitations of current evaluation protocols. Our findings reveal that some top-performing KBE models on the WN18RR benchmark exhibit subpar results on two semantic tasks and two downstream tasks. These results demonstrate the inadequacy of link prediction benchmarks for evaluating the semantic capabilities of KBE models, suggesting the need for a more targeted assessment approach.
PSST! Prosodic Speech Segmentation with Transformers

We develop and probe a model for detecting the boundaries of prosodic chunks in untranscribed conversational English speech. The model is obtained by fine-tuning a Transformer-based speech-to-text (STT) model to integrate the identification of Intonation Unit (IU) boundaries with the STT task. The model shows robust performance, both on held-out data and on out-of-distribution data representing different dialects and transcription protocols. By evaluating the model on degraded speech data, and comparing it with alternatives, we establish that it relies heavily on lexico-syntactic information inferred from audio, and not solely on acoustic information typically understood to cue prosodic structure. We release our model as both a transcription tool and a baseline for further improvements in prosodic segmentation.
Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing

In NLP, incremental processors produce output in instalments, based on incoming prefixes of the linguistic input. Some tokens trigger revisions, causing edits to the output hypothesis, but little is known about why models revise when they revise. A policy that detects the time steps where revisions should happen can improve efficiency. Still, retrieving a suitable signal to train a revision policy is an open problem, since it is not naturally available in datasets. In this work, we investigate the appropriateness of regressions and skips in human reading eye-tracking data as signals to inform revision policies in incremental sequence labelling. Using generalised mixed-effects models, we find that the probability of regressions and skips by humans can potentially serve as useful predictors for revisions in BiLSTMs and Transformer models, with consistent results for various languages.
Moving Object Activator in Background Subtraction Algorithm for Automatic Passenger Counter System in Public Transportation

Buses are the most used transportation by people in Indonesia when traveling between cities. However, to improve passenger comfort, a tool is required to determine the number of passengers on the bus. This research presents an automated system to count the number of passengers based on a background subtraction algorithm and moving object activator. This research aims to provide the number of passengers based on video images taken from the entranceway. The system is built with a camera, Raspberry Pi, and LCD. The APC system starts the counting process by removing the video background image from the captured object image. The entry and exit direction of the object is determined using the concept of moving object activator. The experiments were applied in several scenarios to determine the robustness of the system. The best APC performance was achieved when the system is positioned perpendicularly above the entranceway at a height of 230 cm and a light intensity of 800-1000 lux. Meanwhile, the moving object activator is effective in supporting the system's performance to determine the passenger's direction. In this scenario, the results stated that the accuracy of APC system performance reached 93.8%.
Strategies for Cultivating 'Double Innovation' Capabilities in High School E-Commerce Major Talent

In the rapidly evolving landscape of the e-commerce economy, an increasing number of students are opting to pursue in-depth studies in the field of e-commerce. E-commerce has become a prevailing trend in innovation and development across various industries, necessitating higher education institutions to align with contemporary trends. In the process of cultivating e-commerce professionals, there is a need for specialized adjustments in training and teaching methodologies, with a particular emphasis on nurturing students' innovative spirit, hands-on capabilities, and fostering their 'double innovation' abilities. This article primarily addresses these aspects, offering a platform for exploration and analysis. The paper delves into the current demand for e-commerce professionals, proposes related models and strategies for cultivating 'double innovation' talents, and conducts an in-depth examination of the issues in professional talent development.
On a modification of Visser’s formal logic and its connection with Solovay’s modal logic

We present a new logic called SPL, embedded into Solovay’s provability logic S using a translation that embeds Visser’s formal logic FPL into G¨odel-L¨ob’s provability GL. SPL is formulated in the form of sequent and natural deduction calculi, a relational semantics is proposed.
Applications of Marine Geographic Information Systems (GIS) in Ocean Surveying

This paper explores the key applications of Geographic Information Systems (GIS) in the field of marine surveying. GIS, as a powerful geospatial analysis tool, has played a significant role in marine surveying and ocean resource management. This paper begins by introducing the basic concepts of GIS and the importance of marine surveying. It then delves into four key application areas of GIS in marine surveying: marine map production, seafloor topography analysis, marine environmental monitoring, and ocean resource management. Each application area covers specific use cases and advantages of GIS technology. Finally, the paper summarizes the significant role of GIS in marine surveying and looks ahead to future developments.
Cyber Security Challanges for Online Trading

bstract: Today Security is a fundamental component in the computing and networking technology. The first and foremost thing of every network designing, planning, building, and operating a network is the importance of a strong security policy. Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern. The internet structure itself allowed for many security threats to occur. Online trading facilitates as a financial platform form for buying and selling shares, bidding, product sale or purchase using computers. The vital online trading between B2B, B2C and C2C transaction data may be under siege due to spywares, malwares, Hijackers and intruders. Often trading secrets and portfolios are in risk with network attackers who tracks unauthorizedly the personalized data of business clients. Cyber security is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It's also known as information technology security or electronic information security. The term applies in a variety of contexts, from business to mobile computing, and can be divided into a few common categories.
Shope House

Abstract: A Shop House System for Web Applications in the Automotive Industry: Enabling Seamless Collaboration between Buyers and Automotive Companies In the rapidly evolving landscape of the Automotive industry, the integration of web applications has become instrumental in facilitating efficient communication and transactions between buyers and Automotive companies. This research paper explores the design, development, and implementation of a robust Shop House system tailored for a web application in the Automotive sector. The proposed Automotive system serves as a pivotal tool for both buyers and Automotive entities, fostering seamless interactions, streamlined processes, and an enhanced user experience
Modeling of the Hodgkin–Huxley neural oscillators dynamics using an artificial neural network

The purpose of this study — to represent a detailed description of the procedure for creating and training a neural network mapping on the example of the dynamics modeling of a neural oscillator of the Hodgkin–Huxley type; to show that the neural network mappings trained for single oscillators can be used as elements of a coupled system that simulate the behavior of coupled oscillators. Methods. Numerical method is used for solving stiff systems of ordinary differential equations. Also a procedure for training neural networks based on the method of back propagation of error is employed together with the Adam optimization algorithm, that is a modified version of the gradient descent supplied with an automatic step adjustment. Results. It is shown that the neural network mappings built according to the described procedure are able to reproduce the dynamics of single neural oscillators. Moreover, without additional training, these mappings can be used as elements of a coupled system for the dynamics modeling of coupled neural oscillator systems. Conclusion. The described neural network mapping can be considered as a new universal framework for complex dynamics modeling. In contrast to models based on series expansion (power, trigonometric), neural network mapping does not require truncating of the series. Consequently, it allows modeling processes with arbitrary order of nonlinearity, hence there are reasons to believe that in some aspects it will be more effective. The approach developed in this paper based on the neural network mapping can be considered as a sort of an alternative to the traditional numerical methods of modeling of dynamics. What makes this approach topical is the current rapid development of technologies for creating fast computing equipment that supports neural network training and operation.
Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models

In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.
Optimalisasi Literasi Informasi Siswa SMP Negeri 5 Lamongan melalui Kegiatan Workshop Pengarang Cilik

Information literacy is essential for students to select, criticize, evaluate, and use information so they can be careful. This service activity aims to increase student information literacy and implement information literacy to produce a written work and be published in book form so that it can be disseminated to motivate other students. Community service activities were held at SMP Negeri 5 Lamongan, East Java. The target group is students who have high motivation for writing. Service activities consist of (1) the preparatory stage, which consists of observation, interviews, and identification of needs, (2) the implementation stage, which consists of observation, interviews, and identification of needs, (2) the implementation stage, which consists of writing seminars, monitoring, and collecting the products, (3) the monitoring stage; and (4) evaluation stage. The results of the service show that students' information literacy increases, and students can produce works that have been recorded and can be disseminated. It is hoped that this activity will be carried out consistently so that it can help students improve their information literacy skills to become great writers in the future.
Transformasi Pembelajaran di Era Digital: Penerapan Media Pembelajaran Interaktif untuk Meningkatkan Keterampilan Guru SMP di Kabupaten Takalar

This community service activities aims to evaluate the impact of instructional media training on participants' preferences and understanding, with a focus on the use of Canva as an alternative to PowerPoint. The pre-test indicated that the majority of participants had little or no experience using Canva before the training, while PowerPoint was a commonly used tool. After the training, there was a significant increase in participants' preferences and understanding of Canva, with 51.7% preferring it. Although 41.4% of participants found Canva easier to use, some still maintained a preference for PowerPoint (31%). Supportive factors for the success of these changes included well-organized and professional training, clear content, interactive teaching methods, guidance, and hands-on practice. However, old habits and initial discomfort with new platforms served as inhibiting factors. The outreach team is optimistic that with continued support and more practice, participants will become more comfortable and proficient in using interactive learning media. In conclusion, these changes in preferences and understanding are expected to become more consistent and supportive of increased use of interactive learning media in the learning context.
Peningkatan Peran Guru Dalam Memenuhi Kompetensi Siswa Untuk Menghadapi Ujian Kompetensi Keahlian Tahun 2023

In this age full of technology, humans find it easy to run their daily lives with the help of technology, especially web-based applications. A good application can accommodate the needs of its users. The process of making applications requires several skills, including the skills to analyse needs, make designs and apply these to programming languages to produce an application. Vocational high school students, especially software engineering majors, must have these skills to answer these challenges. For students to be able to create applications, teachers who will teach should already have qualified skills to teach to their students so that young developers who can work in the field of information technology are born. The steps can be taken through workshops/training on the use and application of programming languages used in building an application. This activity was carried out at SMK N 3 Pariaman, located on Jl. Siti Manggopoh, Manggung, North Pariaman, Pariaman City. The methods used are lecture, practice, question and answer, discussion and practice using case studies of Expertise Skills Exam questions. The target is that teachers use web-based programming languages, including HTML, CSS, Javascript, and PHP, using MYSQL as a data storage medium.
Pengenalan Game Edukasi untuk Meningkatkan Perbendaharaan Kosa Kata Benda dalam Bahasa Inggris

English is one of the subjects in the school curriculum in Indonesia. One of the English learning materials in class 3 of East Sumba 1 State Madrasah Ibtidaiyah (MIN) is the introduction to objects in English. The current learning process combined with educational game learning media shows an increase in the ability of 28 children to vocabulary nouns in English by 14.54% from the pre-test average result of 78 to 90 on the post-test results. The results of community service show that guidance and empowerment in learning English using educational games have a positive impact and get a good response from students. Empowerment has an important role in encouraging student motivation and increasing student achievement. It is hoped that this community service activity can be sustainable and become one of the collaborations between institutions so that these institutions help form an intelligent, wise and prosperous society.
Features of the Formation of Gold Placers in the Mountain Area of the Southern Urals (on the Example of Suleymenovskaya Multilayer Placer)

Analysis of the formation conditions of Quaternary gold placers in the mountain area of the Southern Urals on the example of the Suleymenskaya multilayer placer shows that the most productive are the Eopleistocene and Middle Neopleistocene placer cycles associated with the final phase of the continuously intermittent neoorogenic rise of the territory. At the same time, the productivity of placers increases with the increase in the scale of fluvial processing of sediments of the Eopleistocene paleo-cleft and the transition of placers to the Middle Neopleistocene. The source of gold of the Suleymenskaya placer is the zone of gold ore mineralization in the northern part of the Tungatarovskiy fault. Prospects for expanding the raw material base of the territory are associated with the productive sediments of buried Eopleistocene paleo-cleft, coinciding with the zones of goldsulfide mineralization.
Debiasing Multimodal Models via Causal Information Minimization

Most existing debiasing methods for multimodal models, including causal intervention and inference methods, utilize approximate heuristics to represent the biases, such as shallow features from early stages of training or unimodal features for multimodal tasks like VQA, etc., which may not be accurate. In this paper, we study bias arising from confounders in a causal graph for multimodal data, and examine a novel approach that leverages causally-motivated information minimization to learn the confounder representations. Robust predictive features contain diverse information that helps a model generalize to out-of-distribution data. Hence, minimizing the information content of features obtained from a pretrained biased model helps learn the simplest predictive features that capture the underlying data distribution. We treat these features as confounder representations and use them via methods motivated by causal theory to remove bias from models. We find that the learned confounder representations indeed capture dataset biases and the proposed debiasing methods improve out-of-distribution (OOD) performance on multiple multimodal datasets without sacrificing in-distribution performance. Additionally, we introduce a novel metric to quantify the sufficiency of spurious features in models’ predictions that further demonstrates the effectiveness of our proposed methods.