Reviewerly/exHarmony huggingface dataset:
Inter-X: Towards Versatile Human-Human Interaction Analysis

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions, lack of hand gestures and fine-grained textual descriptions. To better perceive and generate human-human interactions, we propose Inter-X, a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns, together with detailed hand gestures. The dataset includes ~11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions, semantic interaction categories, interaction order, and the relationship and personality of the subjects. Based on the elaborate annotations, we propose a unified benchmark composed of 4 categories of downstream tasks from both the perceptual and generative directions. Extensive experiments and comprehensive analysis show that Inter-X serves as a testbed for promoting the development of versatile human-human interaction analysis. Our dataset and benchmark will be publicly available for research purposes.
Entanglement topography of large-scale quantum networks

Large-scale quantum networks, necessary for distributed quantum information processing, are posited to have quantum entangled systems between distant network nodes. The extent and quality of distributed entanglement in a quantum network, that is its functionality, depends on its topology, edge-parameter distributions and the distribution protocol. We uncover the parametric entanglement topography and introduce the notion of typical and maximal viable regions for entanglement-enabled tasks in a general model of large-scale quantum networks. We show that such a topographical analysis, in terms of viability regions, reveals important functional information about quantum networks, provides experimental targets for the edge parameters and can guide efficient quantum network design. Applied to a photonic quantum network, such a topographical analysis shows that in a network with radius $10^3$ kms and 1500 nodes, arbitrary pairs of nodes can establish quantum secure keys at a rate of $R_{sec}=1$ kHz using $1$ MHz entanglement generation sources on the edges and as few as 3 entanglement swappings at intermediate nodes along network paths.
A Logically Consistent Chain-of-Thought Approach for Stance Detection

Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets. Incorporating background knowledge to enhance transferability between seen and unseen targets constitutes the primary approach of ZSSD. However, these methods often struggle with a knowledge-task disconnect and lack logical consistency in their predictions. To address these issues, we introduce a novel approach named Logically Consistent Chain-of-Thought (LC-CoT) for ZSSD, which improves stance detection by ensuring relevant and logically sound knowledge extraction. LC-CoT employs a three-step process. Initially, it assesses whether supplementary external knowledge is necessary. Subsequently, it uses API calls to retrieve this knowledge, which can be processed by a separate LLM. Finally, a manual exemplar guides the LLM to infer stance categories, using an if-then logical structure to maintain relevance and logical coherence. This structured approach to eliciting background knowledge enhances the model's capability, outperforming traditional supervised methods without relying on labeled data.
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids

Quantum visual computing is advancing rapidly. This paper presents a new formulation for stereo matching with nonlinear regularizers and spatial pyramids on quantum annealers as a maximum a posteriori inference problem that minimizes the energy of a Markov Random Field. Our approach is hybrid (i.e., quantum-classical) and is compatible with modern D-Wave quantum annealers, i.e., it includes a quadratic unconstrained binary optimization (QUBO) objective. Previous quantum annealing techniques for stereo matching are limited to using linear regularizers, and thus, they do not exploit the fundamental advantages of the quantum computing paradigm in solving combinatorial optimization problems. In contrast, our method utilizes the full potential of quantum annealing for stereo matching, as nonlinear regularizers create optimization problems which are NP-hard. On the Middlebury benchmark, we achieve an improved root mean squared accuracy over the previous state of the art in quantum stereo matching of 2% and 22.5% when using different solvers.
Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation

NIR-to-RGB spectral domain translation is a challenging task due to the mapping ambiguities, and existing methods show limited learning capacities. To address these challenges, we propose to colorize NIR images via a multi-scale progressive feature embedding network (MPFNet), with the guidance of grayscale image colorization. Specifically, we first introduce a domain translation module that translates NIR source images into the grayscale target domain. By incorporating a progressive training strategy, the statistical and semantic knowledge from both task domains are efficiently aligned with a series of pixel- and feature-level consistency constraints. Besides, a multi-scale progressive feature embedding network is designed to improve learning capabilities. Experiments show that our MPFNet outperforms state-of-the-art counterparts by 2.55 dB in the NIR-to-RGB spectral domain translation task in terms of PSNR.
LLM-SAP: Large Language Model Situational Awareness Based Planning

This work pioneers evaluating emergent planning capabilities based on situational awareness in large language models. We contribute (i) novel benchmarks and metrics for standardized assessment; (ii) a unique dataset to spur progress; and (iii) demonstrations that prompting and multi-agent schemes significantly enhance planning performance in context-sensitive planning tasks. Positioning this within a situated agent and automated planning research, we highlight inherent reliability challenges--efficiently mapping world states to actions without environmental guidance remains open despite simulated domain advances. Although out-of-scope, limitations around validation methodology and data availability indicate exciting directions, including fine-tuning on expanded planning corpora and optimizations for triggering fast latent planning. By conclusively demonstrating current methods' promise and limitations via rigorous comparison, we catalyze investigating reliable goal-directed reasoning for situated agents.
fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations

In this study, we propose two novel input processing paradigms for novel view synthesis (NVS) methods based on layered scene representations that significantly improve their runtime without compromising quality. Our approach identifies and mitigates the two most time-consuming aspects of traditional pipelines: building and processing the so-called plane sweep volume (PSV), which is a high-dimensional tensor of planar re-projections of the input camera views. In particular, we propose processing this tensor in parallel groups for improved compute efficiency as well as super-sampling adjacent input planes to generate denser, and hence more accurate scene representation. The proposed enhancements offer significant flexibility, allowing for a balance between performance and speed, thus making substantial steps toward real-time applications. Furthermore, they are very general in the sense that any PSV-based method can make use of them, including methods that employ multiplane images, multisphere images, and layered depth images. In a comprehensive set of experiments, we demonstrate that our proposed paradigms enable the design of an NVS method that achieves state-of-the-art on public benchmarks while being up to $50x$ faster than existing state-of-the-art methods. It also beats the current forerunner in terms of speed by over $3x$, while achieving significantly better rendering quality.
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task. The causal feature attribution helps describe how much contribution the value of this feature can make to the risk prediction result. After the causal feature distillation, a deep neural network is applied to produce trustworthy prediction results with causal interpretability and high precision/recall. We evaluate the performance of our TDCFD method on several synthetic and real datasets, and the results demonstrate its superiority over the state-of-the-art methods regarding precision, recall, interpretability, and causality.
JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report

Document retrieval in many languages has been largely relying on multi-lingual models, and leveraging the vast wealth of English training data. In Japanese, the best performing deep-learning based retrieval approaches rely on multilingual dense embeddings. In this work, we introduce (1) a hard-negative augmented version of the Japanese MMARCO dataset and (2) JaColBERT, a document retrieval model built on the ColBERT model architecture, specifically for Japanese. JaColBERT vastly outperform all previous monolingual retrieval approaches and competes with the best multilingual methods, despite unfavourable evaluation settings (out-of-domain vs. in-domain for the multilingual models). JaColBERT reaches an average Recall@10 of 0.813, noticeably ahead of the previous monolingual best-performing model (0.716) and only slightly behind multilingual-e5-base (0.820), though more noticeably behind multilingual-e5-large (0.856). These results are achieved using only a limited, entirely Japanese, training set, more than two orders of magnitudes smaller than multilingual embedding models. We believe these results show great promise to support retrieval-enhanced application pipelines in a wide variety of domains.
CodeScholar: Growing Idiomatic Code Examples

Programmers often search for usage examples for API methods. A tool that could generate realistic, idiomatic, and contextual usage examples for one or more APIs would be immensely beneficial to developers. Such a tool would relieve the need for a deep understanding of the API landscape, augment existing documentation, and help discover interactions among APIs. We present CodeScholar, a tool that generates idiomatic code examples demonstrating the common usage of API methods. It includes a novel neural-guided search technique over graphs that grows the query APIs into idiomatic code examples. Our user study demonstrates that in 70% of cases, developers prefer CodeScholar generated examples over state-of-the-art large language models (LLM) like GPT3.5. We quantitatively evaluate 60 single and 25 multi-API queries from 6 popular Python libraries and show that across-the-board CodeScholar generates more realistic, diverse, and concise examples. In addition, we show that CodeScholar not only helps developers but also LLM-powered programming assistants generate correct code in a program synthesis setting.
THE EFFECTS OF SRSD WITH MNEMONIC TRAP ON EFL UNIVERSITY STUDENTS’ READING COMPREHENSION SKILL

This paper examines the impact of a Self-Regulated Strategy Development using the mnemonic TRAP on the teaching of English reading comprehension skills. The primary goal of the research is to figure out whether: 1) SRSD with mnemonic TRAP is insightful in enhancing students' reading comprehension skills, and 2) the effects size of SRSD with mnemonic TRAP on students' reading comprehension skills. The research utilizes a quasi-experimental design using a pretest and posttest control group design. As of now, the research has been conducted on fifth semester college students in Sumenep, East Java, Indonesia. This research involves 70 students, with 35 assigned to the experimental group obtaining SRSD with the mnemonic TRAP and 35 assigned to the control group obtaining the RAP method - common strategy. There are significance differences in reading comprehension between students taught using SRSD with the mnemonic TRAP and those taught using the RAP strategy, confirming the results of a paired sample t-test. Students' reading comprehension improves significantly after utilizing the SRSD with the mnemonic TRAP, with an 80% contribution.
Enhancing Preschool English Vocabulary Through Multimedia Tools: Insights from a Mixed-Methods Study

Purpose – The primary objective of this research was to evaluate the effectiveness of multimedia tools in augmenting English vocabulary enrichment in preschool environments. This study was driven by the need to explore innovative approaches in early childhood language education and assess these tools' impact in enhancing learning experiences. Design/methods/approach – Adopting a mixed-methods approach, the study, conducted in two North Aceh preschools, evaluated multimedia integration in teaching and its impact on student outcomes, motivation, and engagement. It combined observations, document analysis, interviews, and tests, employing qualitative (educator interviews) and quantitative (vocabulary pre-and post-tests) data collection methods. Findings – The findings significantly improved students' vocabulary acquisition and active participation. Using multimedia tools in teaching and incredibly interactive PowerPoint presentations led to enhanced engagement and effectiveness in learning. Students showed marked improvements in self-confidence and language skills. The study also observed that multimedia integration fostered collaborative learning and increased technological proficiency among students. Research implications/limitations – These findings imply a potential paradigm shift in early childhood language education, suggesting incorporating multimedia tools for more engaging and compelling learning experiences. The study's limitations include its focus on specific preschool environments and the potential challenge of replicating the approach in resource-limited settings. Further research is needed to assess the approach's applicability in diverse educational contexts and its long-term impacts. Originality/value – This study offers novel insights into the application of multimedia tools in early childhood language education. It highlights how innovative teaching methods can significantly improve vocabulary skills and educational outcomes. These findings suggest impactful changes in teaching practices and pave the way for future research and potential shifts in educational policies. Paper Type Research paper
Learning System for the Empowerment of High School Girls Students

Currently, in the world there is a gender gap between men and women, this is manifested in gender-based violence and social injustice. An alternative to reduce this gap is the empowerment of women and girls through learning content that gives them leadership and independence, knowing their rights, and preventing acts of violence. There are few technological tools that support female empowerment, these are generally aimed at adult women. This study proposes an IT model for the empowerment of girls in high school education, and an EmpowerMe system, which implements the proposed model, using Moodle, digital library, chatbot, webinar and a game-based learning platform. EmpowerMe was validated through a case study and expert judgment. The results show that there was a learning improvement of 4 points. In addition, the usability, content, follow-up and satisfaction survey show an average rating of 4.4 out of 5.
PersianLLaMA: Towards Building First Persian Large Language Model

Abstract Despite the widespread use of the Persian language by millions globally, limited efforts have been made in natural language processing for this language. The use of large language models as effective tools in various natural language processing tasks typically requires extensive textual data and robust hardware resources. Consequently, the scarcity of Persian textual data and the unavailability of powerful hardware resources have hindered the development of large language models for Persian. This paper introduces the first large Persian language model, named PersianLLaMA, trained on a collection of Persian texts and datasets. This foundational model comes in two versions, with 7 and 13 billion parameters, trained on formal and colloquial Persian texts using two different approaches. PersianLLaMA has been evaluated for natural language generation tasks based on the latest evaluation methods, namely using larger language models, and for natural language understanding tasks based on automated machine metrics. The results indicate that PersianLLaMA significantly outperforms its competitors in both understanding and generating Persian text. PersianLLaMA marks an important step in the development of Persian natural language processing and can be a valuable resource for the Persian-speaking community. This large language model can be used for various natural language processing tasks, especially text generation like chatbots, question-answering, machine translation, and text summarization.
School Information System Innovation by Eduversal through Edunav Implementation in Indonesia: A Case Study at SMP-SMA Kesatuan Bangsa Bilingual Boarding School Yogyakarta

Technological innovation is a science which will continue to develop over time, therefore knowledge, skills, and understanding are needed in life, especially in educational institutions. Many researchers have conducted studies on educational system innovations because the implementation of information systems in educational institutions has made a positive contribution to education management. The existence of an educational information system provides convenience in communicating, exchanging information quickly and accurately, as well as an increase in educational services. This research is based on the implementation of the Edunav Information System as an information system innovation which is applied to educational institutions. Edunav is an application for student progress reports in learning in the form of e-reports. The form of the reported activity from the learning process includes attendance, homework, quizzes, unit tests and so on. This study aims to uncover the factors which drive the Edunav Information System to be implemented and the benefits derived from it at the SMP-SMA Kesatuan Bangsa Bilingual Boarding School Yogyakarta. The method used in this research is a qualitative research method with a case study approach. The data of this research are obtained from in-depth interviews, observation, and literature study. In-depth interviews were conducted with several informants by means of a snowball. The results of this study show that there are several factors which encourage the implementation of the Edunav Information System at the Kesatuan Bangsa School, which are 1) complex problems occur in the field as there is no integrated system; 2) manual data processing which is recorded in books; 3) the existence of school initiatives to make changes from conventional to digital schools by increasing the use of information technology; 4) increasing competitiveness among schools is significant, especially for private schools. The perceived benefits of the Edunav Information System are 1) as an information system for schools, parents, and students; 2) as a media to support education; 3) as a means of control for schools, parents and students.
The Digital Revolution in Higher Education: Transforming Teaching and Learning

Digital transformation in higher education has greatly disrupted conventional teaching and learning models. This review traces the evolution of educational technology over the past few decades. It analyzes how tools such as learning management systems, data analytics, online learning, and artificial intelligence impact pedagogy, student experience, educator roles, and institutional learning. The great potential of technology to enhance learning is also discussed, along with the ongoing challenges of justice, ethics, and human relations in its application. Current and emerging technologies are examined, with their implications for key stakeholders. This study uses a multi-faceted methodology to investigate digital transformation in higher education: Historical Analysis, Technology Review Case Studies, Stakeholder Surveys, and Ethical and Social Implications. Predictive modeling and trend analysis are performed to project higher education's future digital transformation trajectory. This includes analysis of new technologies, potential disruptions, and the evolving needs of the education sector. The review brings together insights from academic literature, industry publications, and the digital transformation of higher education over the past few decades, which has been widespread and measurable. Early adopters of technologies such as analytics, AI, and immersive media will be best positioned to improve the student experience in the future. However, carefully applying and considering students' needs and equality is essential to avoid marginalized groups.
Privacy-Preserving Task Offloading in Vehicular Edge Computing

With the development of mobile edge computing (MEC), MEC-based task offloading of vehicle edge computing (VEC) has become popular due to its low delay. However, when vehicle users (VUs) offload data to the edge servers (ESs), user privacy may be leaked. In addition, communication among ESs can also threaten the privacy of VUs. Therefore, a five-objective optimization model including time delay of VLCU, time delay of ES, energy consumption, load balancing, and privacy protection degree is constructed. To solve the above model, an improved CMA-ES algorithm based on triple analysis decision variable grouping strategy (TADG-CMA-ES) is proposed. To improve the dimension reduction performance of the algorithm, the variables in divided subgroups are further grouped according to the convergence relevance degrees (CRD) of the variables.