Reviewerly/exHarmony huggingface dataset:
Big Data Measures of Environmental Concern

Environmental concern is a subjective state of society and researchers have typically relied on survey data to measure it. However, survey-based methods only capture a snapshot of it at the time and place the surveys were conducted. To overcome these problems, we develop an observable indicator that allows us to study environmental concern over time and across territories based on big data. The indicator composes of keyword groups that fit with the environmental concern measures revealed by a large-scale survey. We find that keywords associated with climate change, water pollution and waste management are the strongest predictors of environmental concern. To the best of our knowledge, our paper is the first to use online search data to capture subjective environmental concern.
Utilization of Canva Application in Social Knowledge Lessons

Background. At present, the use of this Canva application in Social Science learning can be used as a means of learning media on the use of technology as an easy learning medium.&#x0D; Purpose. This article is an activity to provide insight to students at Primary School 23 Rambatan in using the canva application as a social studies learning media..&#x0D; Method. This article uses descriptive qualitative research methods, th e steps of which are by going directly to the field and looking for natural data sources, so that the data obtained is accurate and meets the desired results.&#x0D; Results. The result of this study is that the canva application is very suitable for use as a learning media for students of Primary School 23 Rambatan because the media is attractive and able to increase students' enthusiasm for learning.&#x0D; Conclusion. Creating designs with Canva In providing teaching materials for students, for example, such as designs in the form of education in order to attract the attention of students to be interested in seeing the content created.
Rectangle Attacks on Reduced Versions of the FBC Block Cipher

The FBC block cipher is an award-winning algorithm of the recent Cryptographic Algorithm Design Competition in China, which has three versions: FBC128-128 with a 128-bit block size and a 128-bit key size, FBC128-256 with a 128-bit block size and a 256-bit key size, and FBC256 with a 256-bit block size and a 256-bit key size. The best previously published cryptanalysis results on FBC are an impossible differential attack on 13-round FBC128-128 and a boomerang attack on 13-round FBC128-256. In this paper, we exploit a 12-round rectangle distinguisher with probability $$2^{-234}$$ of FBC128 and a 16-round rectangle distinguisher with probability $$2^{-448}$$ of FBC256, and observe that preliminary satisfying ciphertext quartets can be efficiently filtered out by sorting plaintext pairs according to some nibble positions at the ciphertext side during key-recovery phase, and finally we mount rectangle attacks on 14-round FBC128-128, 15-round FBC128-256 and 19-round FBC256 to recover their respective user key. Our attacks break more rounds than any previously published attacks on FBC.
Middle-Products of Skew Polynomials and Learning with Errors

We extend the middle product to skew polynomials, which we use to define a skew middle-product Learning with Errors (LWE) variant. We also define a skew polynomial LWE problem, which we connect to Cyclic LWE (CLWE), a variant of LWE in cyclic division algebras. We then reduce a family of skew polynomial LWE problems to skew middle-product LWE, for a family which includes the structures found in CLWE. Finally, we give an encryption scheme and demonstrate its IND-CPA security, assuming the hardness of skew middle-product LWE.
An Upper-Bound on the Decoding Failure Probability of the LRPC Decoder

Low Rank Parity Check (LRPC) codes form a class of rank-metric error-correcting codes that was purposely introduced to design public-key encryption schemes. An LRPC code is defined from a parity check matrix whose entries belong to a relatively low dimensional vector subspace of a large finite field. This particular algebraic feature can then be exploited to correct with high probability rank errors when the parameters are appropriately chosen. In this paper, we present theoretical upper-bounds on the probability that the LRPC decoding algorithm fails.
Practical and Efficient FHE-Based MPC

We present a reactive MPC protocol built from FHE which is robust in the presence of active adversaries. In addition the protocol enables reduced bandwidth via means of transciphering, and also enables more expressive/efficient programs via means of a $$\textsf{Declassify}$$ operation. All sub-components of the protocol can be efficiently realised using existing technology. We prove our protocol secure in the UC framework.
ZK-for-Z2K: MPC-in-the-Head Zero-Knowledge Proofs for $$\mathbb {Z}_{2^k}$$

In this work, we extend the MPC-in-the-Head framework, used in recent efficient zero-knowledge protocols, to work over the ring $$\mathbb {Z}_{2^k}$$ , which is the primary operating domain for modern CPUs. The proposed schemes are compatible with any threshold linear secret sharing scheme and draw inspiration from MPC protocols adapted for ring operations. Additionally, we explore various batching methodologies, leveraging Shamir’s secret sharing schemes and Galois ring extensions, and show the applicability of our approach in RAM program verification. Finally, we analyse different options for instantiating the resulting ZK scheme over rings and compare their communication costs.
LayerCFL: an efficient federated learning with layer-wised clustering

Abstract Federated Learning (FL) suffers from the Non-IID problem in practice, which poses a challenge for efficient and accurate model training. To address this challenge, prior research has introduced clustered FL (CFL), which involves clustering clients and training them separately. Despite its potential benefits, CFL can be computationally and communicationally expensive when the data distribution is unknown beforehand. This is because CFL involves the entire neural networks of involved clients in computing the clusters during training, which can become increasingly time-consuming with large-sized models. To tackle this issue, this paper proposes an efficient CFL approach called LayerCFL that employs a Layer-wised clustering technique. In LayerCFL, clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental methods. Our experimental results demonstrate the effectiveness of LayerCFL in mitigating the impact of Non-IID data, improving the accuracy of clustering, and enhancing computational efficiency.
EVALUASI KETERGUNAAN WEBSITE PERPUSTAKAAN UNIVERSITAS KRISTEN SATYA WACANA DENGAN MENGGUNAKAN METODE SYSTEM USABILITY SCALE

This research was conducted with the aim of evaluating the usability of the Satya Wacana Christian University (UKSW) Library website using the System Usability Scale (SUS) method. This research also aims to identify areas that need to be improved in the UKSW Library website. Respondents in this study were UKSW students who had used the library website. This study used the SUS questionnaire consisting of 10 questions with a 5-point Likert scale. The results of the SUS calculation show that the average value of the usability of the UKSW Library website is 59.514, which is in the medium or "OK" category. Further analysis shows that areas that need to be improved are clarity of information, ease of navigation, and visual appearance. This research is expected to provide input for the UKSW Library in developing the website in order to increase usability and meet the needs of its users. The conclusion of this research is that the UKSW Library website still has some areas that need to be improved to increase usability.
Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks

Bayesian networks are a type of probabilistic graphical model widely used to characterize various real-world problem scenarios due to their ability to model probabilistic dependence between variables. In classical bayesian networks, performing exact as well as approximate inferences are NP-Hard. Quantum circuit developed to represent bayesian network can be used to perform inference, but it is prone to quantum noise and strictly limited by the maximum number of shots that can be performed on the quantum hardware. In this paper, we propose a technique to implement hybrid quantum approximate inference methods and subsequently analyze their performance on the quantum circuit representation of the bayesian network. The approach involves computing priors and conditionals from the quantum circuit generated samples to perform likelihood-weighted sampling and MCMC-based sampling. The intuition behind this approach is that a quantum circuit can compromise on the structural dependency of the bayesian network due to the decoherence errors and also has difficulty in encoding the probability of rare events, which can be overcome with the help of hybrid quantum inference techniques. The error analysis on different types of queries has been carried out to compare the sampling approaches for three Bayesian Networks. All the experiments performed in this paper utilize the SV1 quantum simulator provided by the Amazon Braket environment.
Explaining Finetuned Transformers on Hate Speech Predictions Using Layerwise Relevance Propagation

Explainability of model predictions has become imperative for architectures that involve fine-tuning of a pretrained transformer encoder for a downstream task such as hate speech detection. In this work, we compare the explainability capabilities of three post-hoc methods on the HateXplain benchmark with different encoders. Our research is the first work to evaluate the effectiveness of Layerwise Relevance Propagation (LRP) as a post-hoc method for fine-tuned transformer architectures used in hate speech detection. The analysis revealed that LRP tends to perform less effectively than the other two methods across various explainability metrics. A random rationale generator was found to be providing a better interpretation than the LRP method. Upon further investigation, it was discovered that the LRP method assigns higher relevance scores to the initial tokens of the input text because fine-tuned encoders tend to concentrate the text information in the embeddings corresponding to early tokens of the text. Therefore, our findings demonstrate that LRP relevance values at the input of fine-tuning layers are not a good representative of the rationales behind the predicted score.
GEC-DCL: Grammatical Error Correction Model with Dynamic Context Learning for Paragraphs and Scholarly Papers

Over the past decade, there has been noteworthy progress in the field of Automatic Grammatical Error Correction (GEC). Despite this growth, current GEC models possess limitations as they primarily concentrate on single sentences, neglecting the significance of contextual understanding in error correction. While a few models have begun to factor in context alongside target sentences, they frequently depend on inflexible boundaries, which leads to the omission of vital information necessary for rectifying certain errors. To address this issue, we introduce the Dynamic Context Learner (DCL) model, which identifies optimal breakpoints within paragraphs or documents to retain maximum context. Our method surpasses those employing fixed sequence lengths or assuming a limited number of preceding sentences as context. Through extensive evaluation on the CoNLL-2014, BEA-Dev, and FCE-Test datasets, we substantiate the efficacy of our approach, achieving substantial F $$_{0.5}$$ score enhancements: 77% increase, 19.61% boost, and 10.49% rise respectively, compared to state-of-the-art models. Furthermore, we contrast our model’s performance with LLaMA’s GEC capabilities. We extend our investigation to scientific writing encompassing various context lengths and validate our technique on the GEC S2ORC dataset, yielding cutting-edge results in scholarly publications.
Revolutionizing High School Physics Education: A Novel Dataset

Despite the growing capabilities of Large Language Models (LLMs) in various domains, their proficiency in addressing domain-specific high-school physics questions remains an unexplored area. In this study, we present a pioneering data set curated from NCERT exemplar solutions strategically designed to facilitate the use of LLMs to solve school physics questions. Originally comprising 766 questions accompanied by LaTeX representations, the dataset underwent a sophisticated augmentation process that expanded its scope to an impressive 7,983 questions. The augmentation employed innovative techniques which effectively broaden the dataset’s coverage. The dataset, prioritizing text-based questions, is formatted as JSON objects detailing instructions, inputs, and outputs. Post evaluation, we noted significant scores: METEOR at 0.282 and BERTScore F1 at 0.833, indicating a close alignment between generated and reference texts.
SciPhyRAG - Retrieval Augmentation to Improve LLMs on Physics Q &amp;A

Large Language Models (LLMs) have showcased their value across diverse domains, yet their efficacy in computationally intensive tasks remains limited in accuracy. This paper introduces a comprehensive methodology to construct a resilient dataset focused on High School Physics, leveraging retrieval augmentation. Subsequent finetuning of a Large Language Model through instructional calibration is proposed to elevate outcome precision and depth. The central aspiration is reinforcing LLM efficiency in educational contexts, facilitating more precise, well-contextualized, and informative results. By bridging the gap between LLM capabilities and the demands of complex educational tasks, this approach seeks to empower educators and students alike, offering enhanced support and enriched learning experiences. Compared to Vicuna-7b, the finetuned retrieval augmented model SciPhy-RAG exhibits a 16.67% increase in BERTScore and 35.2% increase on ROUGE-2 scores. This approach has the potential to be used to reshape Physics Q &A by LLMs and has a lasting impact on their use for Physics education. Furthermore, the data sets released can be a reference point for future research and educational domain tasks such as Automatic Evaluation and Question Generation.
KG-CTG: Citation Generation Through Knowledge Graph-Guided Large Language Models

Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document. In CTG, the generated text draws upon contextual cues from both the source document and the cited paper, ensuring accurate and relevant citation information is provided. Previous work in the field of citation generation is mainly based on the text summarization of documents. Following this, this paper presents a framework, and a comparative study to demonstrate the use of Large Language Models (LLMs) for the task of citation generation. Also, we have shown the improvement in the results of citation generation by incorporating the knowledge graph relations of the papers in the prompt for the LLM to better learn the relationship between the papers. To assess how well our model is performing, we have used a subset of standard S2ORC dataset, which only consists of computer science academic research papers in the English Language. Vicuna performs best for this task with 14.15 Meteor, 12.88 Rouge-1, 1.52 Rouge-2, and 10.94 Rouge-L. Also, Alpaca performs best, and improves the performance by 36.98% in Rouge-1, and 33.14% in Meteor by including knowledge graphs.
A Comparative Study of Physical and Virtual Reality Prototyping of a Migrating Agent Interface

Prototyping methods are commonly employed iteratively throughout the design and product development, typically ranging from early low-fidelity to later high-fidelity prototypes. We present a case study focusing on prototyping a receptionist agent migrating between three platforms (a monitor on the wall, a mobile phone, and a physical robot). More specifically, we compare virtual reality (VR) and physical (real-world) prototyping methods. The two methods are compared in terms of fidelity and usability. The breadth of features, the degree of functionality, and the interactivity were similar. However, the aesthetic refinement differed. The VR prototyping method also had much higher prerequisites in terms of equipment and skills, and the learning curve for the designer was steep. Both methods were equally efficient in user testing, but the VR method revealed more usability issues in the efficiency category, while the physical space method revealed more issues in the effectiveness category.
From Self to Avatar as Likeness: Legal Barriers and Opportunities

Abstract. When we think of modern - hybrid - society, it consists of more entities than just human beings. Besides the purely biological beings we see semi-autonomous (humanized) technology or technologized beings up to entities, to which also a hybrid personality can be attributed under certain conditions. All of these entities have a different perceptual exterior. Just as individuals in the real world adopt an exterior to express their personality externally, there is also the possibility in the virtual world to personalize one's appearance by designing avatars. Our contribution deals with this integration of personality into the virtual world and the associated challenges, opportunities and limitations from a legal perspective.
Investigating the Effects of Dialogue Summarization on Intervention in Human-System Collaborative Dialogue

Dialogue systems are widely utilized in chatbots and call centers. However, it is often difficult for such systems to deliver fully autonomous dialogue. For users to have a better dialogue experience, a framework for human-system collaborative dialogue is proposed in which a human operator takes over the dialogue when needed, engaging in conversation with the user instead of the system (we call this process intervention). Operators join the dialogue in the middle; therefore, it is believed that dialogue summarization can be helpful for interventions. However, it is currently unclear whether dialogue summarization is actually useful. Therefore, in this study, we aim to investigate the usefulness of dialogue summaries for interventions through a field experiment conducted at an actual facility combining an aquarium and a zoo. The results of the field experiment revealed that dialogue summaries were more useful for intervention than dialogue history. Furthermore, we found no differences in the word categories included in the operator utterances during interventions irrespective of whether the dialogue history or dialogue format summary was presented to the operators, suggesting that dialogue format summary has content similar to that of dialogue history but improves the usefulness in intervention.
Human-like “agents” or “tools”?: Exploring the implicature-of-quantity in HAI

A quantitative implicature task was used to investigate whether humans believe that robots can make human-like inferences. As quantifier terms, such as “little” and “much,” do not specify an exact amount, the actual quantity must be inferred from various pieces of information. The participants (N = 24) had to encounter either 100% perfectly-controllable-robots or randomly-controllable-robots. The participants then determined the exact amount of energy to charge to each vehicle depending on the initial-state of the vehicle and the agent’s request. The results indicated that the participants’ estimates of the amounts for randomly-controlled-robots were similar to those for human agents. However, participants with experience in perfectly-controlled-robots estimated a lower amount when the request was labeled as “little,” indicating their estimation was based on literal interpretation. People seemed to predict that perfectly-controlled-robots would not use human-like inference, implying that perfectly-controlled-robots can be assumed to be just “tools” not human-like “agents.”
Investigating the Intervention in Parallel Conversations

In recent years, a framework of parallel conversations has been proposed to facilitate efficient conversations through cooperation between humans and dialogue systems. This approach aims to enable simultaneous conversations with multiple users by enabling the system to handle basic conversation and human operators to intervene when problems arise in the system’s conversation. Previous studies on parallel conversations have primarily focused on delegating simple exchanges such as greetings and acknowledgments to the system, with humans taking over for more complex interactions like providing guidance. Recent advancements in large language models may change this situation, enabling dialogue systems to engage in more advanced interactions. In this study, to examine which interventions will be made when large language models are utilized, we placed six dialogue robots based on large language models in an actual facility and conducted a field experiment involving parallel conversations for about a month. Our analysis of the collected data on dialogues and interventions showed that the most frequent interventions were made for supporting interactions when the system failed to react to the user utterances, indicating the limitations of using large language models alone and clarifying our next steps for facilitating smoother parallel conversations.
First International Workshop on Reciprocal Knowledge Elicitation for Human-Agent Collaboration

research-article Share on First International Workshop on Reciprocal Knowledge Elicitation for Human-Agent Collaboration Authors: Joost Vennekens Department of Computer Science, De Nayer Campus, KU Leuven, Belgium Department of Computer Science, De Nayer Campus, KU Leuven, Belgium 0000-0002-0791-0176View Profile , Marjolein Deryck FlandersMake@KULeuven, Belgium FlandersMake@KULeuven, Belgium 0000-0003-1054-6165View Profile , Christian Fleiner Department of Computer Science, De Nayer Campus, KU Leuven, Belgium Department of Computer Science, De Nayer Campus, KU Leuven, Belgium 0000-0003-4280-670XView Profile Authors Info & Claims HAI '23: Proceedings of the 11th International Conference on Human-Agent InteractionDecember 2023Pages 493–495https://doi.org/10.1145/3623809.3623977Published:04 December 2023Publication History 0citation8DownloadsMetricsTotal Citations0Total Downloads8Last 12 Months8Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access