Reviewerly/exHarmony huggingface dataset:
Narratron: Collaborative Writing and Shadow-playing of Children Stories with Large Language Models

Shadow puppetry or shadow play, allows bodily participation into the process of linguistic storytelling, while the potential of multi-modal interaction through shadow plays in existing large-language-model-based creative tools has not been fully discovered. We propose Narratron, a generative story-making tool that co-creates and co-performs children stories from shadow using Claude 2 model. To achieve Narratron, our system is designed to recognize hand gestural inputs as main character and to develop story plot in accordance with character change. Through our system, we seek to stimulate creativity in shadow play storytelling and to facilitate a multi-modal human-AI collaboration.
LayerShift: Reconfigurable Layer Expression Using Robotic Transparent Displays

In this paper, we propose a new robotic display equipped with a transparent display, whose position and posture can be controlled, and a design space that uses it. Robotic displays have been researched in which the display is mounted on a movable robotic arm to extend the expression through motion expression in addition to visual expression. In this paper, we propose combining a transparent display with a robotic display to extend the design space of the robotic display through the characteristics of transparent display. We implement a prototype and propose a design space for a single display and multiple cooperating displays.
ProactiveAgent: Personalized Context-Aware Reminder System

We introduce ProactiveAgent, a proactive application that harnesses the capabilities of large language models (LLMs) and personal agents to provide context-aware, personalized reminders and suggestions. By assimilating real-time environmental data, user histories, and verbal interactions, the system discerns user intent and offers tailored recommendations. The application captures visual activity and spoken interactions, integrating them into short and long-term memory storage for context-rich decision support. We propose scenarios where ProactiveAgent could be valuable: suggesting snack options depending on the time, offering culinary options based on dietary preferences, and even guiding users in their daily tasks. In envisioned use cases, ProactiveAgent could potentially track user attributes during their shopping experience, such as time spent on items and other cues, leading towards insightful product recommendations. Our work represents a potential advancement in the realm of personalized assistance, merging LLM strengths with personal agent technologies to enhance user decision-making in dynamic real-world scenarios.
ChainForge: An open-source visual programming environment for prompt engineering

Prompt engineering for large language models (LLMs) is a critical to effectively leverage their capabilities. However, due to the inherent stochastic and opaque nature of LLMs, prompt engineering is far from an exact science. Crafting prompts that elicit the desired responses still requires a lot of trial and error to gain a nuanced understanding of a model’s strengths and limitations for one’s specific task context and target application. To support users in sensemaking around the outputs of LLMs, we create ChainForge, an open-source visual programming environment for prompt engineering. ChainForge is publicly available, both on the web (https://chainforge.ai) and as a locally installable Python package hosted on PyPI. We detail some features of ChainForge and how we iterated the design in response to internal and external feedback.
Waste Genie: A Web-Based Educational Technology for Sustainable Waste Management

In this work, we proposed and designed a web-based educational technology to support sustainable living. It is called Waste Genie. Waste Genie integrates formal and informal learning features to help users learn about waste management. A field trial was conducted to evaluate its effectiveness, and the results indicated the platform was well-received by the users.
ChromaNails: Re-Programmable Multi-Colored High-Resolution On-Body Interfaces using Photochromic Nail Polish

We demonstrate ChromaNails, a physical nail reprogramming device that enables high-resolution multi-color textures on fingernails using photochromic nail polish for on-body interaction. Our ChromaNails reprogrammer uses a miniature RGB projector and a UV light source to project different wavelengths of light onto our photochromic nail polish. We create this nail polish by mixing cyan, magenta, and yellow (CMY) photochromic dye into a base substrate polish. This enables us to control the saturation and desaturation of the CMY particles inside our nail polish to various colors inside the CMY color space. Our integrated user interface enables laypeople to select their preferred color texture and adapts to various nail shapes. We demonstrate the usefulness of ChromaNails for on-body interaction through four application examples on reprogrammable fingernail QR codes, on-body calendars, security, and fashion.
SoK: Fault Injection Attacks on Cryptosystems

Fault injection attacks are a powerful technique that intentionally induces faults during cryptographic computations to leak secret information. This paper provides a survey of fault attack techniques on different cryptosystems. The fault attack consists of two main components: fault modeling, which examines methods for injecting faults in a target device, and fault analysis, which analyzes the resulting faulty outputs to deduce secrets in each cryptosystem. We first categorize various fault attack methods by fault model and fault analysis. We then give examples of the various fault attacks on symmetric key cryptosystems and public key cryptosystems. This paper aims to provide a background on fault attack research and directions for further study on securing real-world cryptosystems against fault injection attacks.
Triton: Software-Defined Threat Model for Secure Multi-Tenant ML Inference Accelerators

Secure machine-learning inference is essential with the advent of multi-tenancy in machine learning-as-a-service (MLaaS) accelerators. Model owners demand the confidentiality of both model weights and architecture, while end users want to protect their personal data. Moreover, ML models used in mission-critical applications like autonomous vehicles or disease classification need integrity protection. While hardware trusted execution environments (TEE) [4, 41] provide data confidentiality and integrity, they face two challenges in the adoption for ML inference. First, TEEs are susceptible to numerous side channels, arising from resource sharing in multi-tenant systems. Second, the performance overhead of these TEEs is often proportional to the secret data size, making them unattractive for data-intensive real-time inference.
DINAR: Enabling Distribution Agnostic Noise Injection in Machine Learning Hardware

Machine learning (ML) has seen a major rise in popularity on edge devices in recent years, ranging from IoT devices to self-driving cars. Security in a critical consideration on these platforms. State-of-the-art security-centric ML algorithms (e.g., differentially private ML, adversarial robustness) require noise sampled from Laplace or Gaussian distributions. Edge accelerators lack CPUs [15, 25, 36, 50] to add such noise. Existing hardware approaches to generate noise on-the-fly incur high overheads and leak side-channel information that can undermine security [34, 47]. To remedy this, we propose DINAR,1 lightweight hardware that enables noise addition from arbitrary distributions. For differentially private ML, DINAR enables noise addition while incurring 23 × lower area and 40 × lower energy compared to producing noise directly on-chip.
EntryBleed: A Universal KASLR Bypass against KPTI on Linux

For years, attackers have compromised systems by developing exploits that rely on known locations of kernel code and data segments. KASLR (Kernel Address Space Layout Randomization) is a key mitigation in modern operating systems which hampers these attacks through runtime randomization of the kernel image base address. KPTI (Kernel Page Table Isolation) is another defense mechanism, originally introduced to defend against the 2018 Meltdown attack by unmapping kernel addresses during user code execution. This security mechanism makes it harder for attackers to leak kernel address mappings through micro-architectural side channels. However, a few pages for system call and interrupt handling were exempted from isolation for the sake of user to kernel context transitions.
Automated Security Analysis for Real-World IoT Devices

Automatic security protocol analysis is a fruitful research topic that demonstrates the application of formal methods to security analysis. Several endeavors in the last decades successfully verified security properties of large-scale network protocols like TLS, sometimes unveiling unknown vulnerabilities.
Improving Adaptive Knowledge Graph Construction via Large Language Models with Multiple Views

Knowledge graph construction (KGC) aims to build the semantic network which expresses the relationship between named entities. Despite the success of prior studies, it is struggling to accommodate existing KGC models with evolving entity-relation knowledge schema. In this paper, we propose a schema-adaptive KGC method driven by the instruction-tuning large language models (LLM). We fine-tune a LLM with tailored KGC corpus, through which the generalization ability of LLMs are transfered for KGC with evolving schema. To alleviate the bias of a single LLM, we integrate the superiority of several expert models to derive credible results from multiple perspectives. We further boost KGC performances via an elaborately designed schema-constrained decoding strategy and a LLM-guided correction module. Experimental results validate the advantages of our proposed method. Besides, our method achieved the first place in the first task of CCKS-2023 Knowledge Graph Construction.
A Military Domain Knowledge-Based Question Answering Method Based on Large Language Model Enhancement

With the rise of big language model technology, the application of big models in the military field is increasingly being valued. How to combine big language models with knowledge graph technology to improve the effectiveness of knowledge Q&A is currently a key research direction for improving military knowledge services. Based on the big language model technology, this paper implements knowledge Q&A in the military field by using template learning and template matching methods. For the key steps of knowledge linking and template matching, this paper uses the knowledge linking and semantic matching technology enhanced by the big language model. Finally, experimental verification was conducted in the test set provided by the CCKS (China Conference on Knowledge Graph and Semantic Computing) conference, and F1 reached 0.869. In summary, this paper provides a new solution for natural language Q&A in the military field using a large language model. This method achieves high accuracy while reducing dependence on training corpus data.
Comparison of Writing Drama Text Skills Between Eleventh-Grade Science 1 Students and Eleventh-Grade Social Students at SMA Negeri 1 Bandar Sei Kijang

This paperdiscusses the use of technological media and the students’writing drama text skillsbetween Class XI IPA 1 and XI IPS at SMA N 1 Bandar Sei Kijang. The research method used is the descriptive qualitative method. The research instrument is an interview questionnaire guideline that will be administered to Indonesian teachers. The research was conducted at SMA N 1 Bandar Sei Kijang with 23 students of class XI IPA 1 and 21 students of class XI IPS 1. The data collection technique in the study was to use interview guidelines, which contain questions about students’ learning conditions, students’ learning achievements, and students’ learning outcomes by applying different media and methods. The results indicate that technology media with the question and answer method was more effective to use than print media with the lecture method. It was proven based on the interviews. The students who used technology media with the question and answer method obtained results that tended to be high with a value of 83.52. The use of technology in learning can ease students and teachers to carry out learning.
Move Structure Recognition in Scientific Papers with Saliency Attribution

Move analysis is a primary research topic in computational linguistics that relates to pragmatics. It plays a crucial role in analyzing the intent and coherence of the text. This paper introduces a innovative exploration of move analysis to scientific papers and presents a novel task - move structure recognition in scientific papers. Existing datasets are inadequate to support this task. Thus, we manually annotated a dataset called Scientific Abstract Moves Dataset (SAMD). The implicit mixture and counterfactual reasoning in the move structure’s content has led to poor performance in move recognition. This research examines the issue in depth and presents a new concept of move saliency attribution, which can illuminate the contribution of words to specific move structures. On this foundation, we design a new move recognition training mechanism, which fully consider the context information of the move to achieve promising performance on SAMD and NLPContributionGraph shared task dataset (NCG). This is the first attempt at interpretability of move recognition, giving us the possibility to understand how the model makes decisions and identify potential biases or errors in the model.
A Graph Learning Based Method for Inductive Knowledge Graph Relation Prediction

In recent years, with the continuous progress of knowledge graph technologies, lots of large-scale knowledge graph have been applied to a variety of downstream tasks, including question answering and information retrieval. However, even the largest knowledge graph at present is still incomplete. Specially, in an open environment, the knowledge graph itself is dynamic, and new knowledge graph are constantly being constructed. Therefore, it is necessary to complete the knowledge graph with relation prediction methods. However, since the transductive relation prediction method assumes that the entities in the testing samples have appeared in the training samples, it is impossible to perform relation prediction on unseen entities. This paper propose a new inductive knowledge graph completion method called EGraIL, in which the entity embeddings are initialized by the surrounding relation embeddings and then calculate the possibility of triples through conventional knowledge graph embedding score function. The model is trained on the triple classification and relation prediction task at the same time. Finally, the results are significantly better than baseline method GraIL, and achieve good rank in the Inductive Knowledge Graph Relation Prediction Task in CCKS2023.
Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts

Knowledge Base Question Answering (KBQA) is a significant task in natural language processing, aiming to retrieve answers from structured knowledge bases in response to natural language questions. NL2Cypher is crucial for accurately querying answers from knowledge bases, but there is limited research in this area or the results are unsatisfactory. Our work explores the convergence of advanced natural language processing techniques with knowledge base question answering (KBQA), focusing on the automated generation of Cypher queries from natural language queries. By leveraging the capabilities of large language model (LLM), our approach bridges the gap between textual questions and structured knowledge representations. The proposed methodology showcases promising results in accurately formulating Cypher queries. We achieved substantial performance in the CCKS2023 Foreign Military Unmanned Systems Knowledge Graph Reasoning Question-Answering Evaluation Task. Our method achieved an F1 score of 0.94269 on the final testing dataset.
Single Source Path-Based Graph Neural Network for Inductive Knowledge Graph Reasoning

In this paper, we introduce our solution for the inductive knowledge graph reasoning task organized by the 2023 China Conference on Knowledge Graph and Semantic Computing (CCKS 2023). Specifically, this inductive knowledge graph reasoning task has two main challenges, namely 1) How to predict the entities that are not in the training set, and 2) How to train and reason more efficiently. To deal with these challenges, we adapt the Neural Bellman-Ford Networks (NBFNet) with grid search strategy for achieving the best performance. Along this line, we further refine this solution with ensemble learning and post-processing. Extensive experiments have demonstrated the effectiveness of our solution, which won the first place in the competition. Code is publicly available at https://github.com/smart-lty/CCKS-2023-Task2
LLM-Based SPARQL Generation with Selected Schema from Large Scale Knowledge Base

Knowledge base question answering (KBQA) aims to answer natural language questions using structured knowledge bases. Common approaches include semantic parsing-based approaches and retrieval-based approaches. However, both approaches have some limitations. Retrieval-based methods struggle with complex reasoning requirements. Semantic parsing approaches have a complex reasoning process and cannot tolerate errors in earlier steps when generating the final logical form. In this paper, we proposed a large language model (LLM)-based SPARQL generation model, which accepts multiple candidate entities and relations as inputs, reducing the reliance on mention extraction and entity linking performance, and we found an entity combination strategy based on mentions, which can produce multiple SPARQL queries for a single question to boost the chances of finding the correct answer. Finally, our model achieves state-of-the-art performance in the CCKS2023 CKBQA competition, F1 score is 75.63%.
Conditional Knowledge Graph: Design, Dataset and a Preliminary Model

Facts are conditionally established in most cases. However, current Knowledge Graph (KG) techniques only focus on the modeling and representations of facts, neglecting the presence of conditions, which are necessary to establish the validity of facts. In this paper, we propose Conditional Knowledge Graph (Conditional-KG), which employs a three-layer hierarchical network to incorporate both facts and conditions. To facilitate research on the automatic construction of Conditional-KG, we manually annotate an innovative large-scale dataset named HACISU. Based on the Conditional-KG design and HACISU, we propose a simple construction model to benchmark HACISU. Experimental results show that our benchmark model outperforms several baselines but still has a considerable margin with human performance. We highlight the significance of HACISU, as it is the first carefully annotated dataset with conditional information. Our dataset is publicly available in http://101.200.120.155:5555/ , hoping to serve as a challenging testbed and an ideal benchmark for Conditional-KG construction.
An automatic reduction method of <scp>3D</scp> bone fragments based on a novel section contour point descriptor

Abstract Comminuted fractures are orthopedic traumas with greater surgical difficulty. In clinical treatment, a great challenge is precise reduction of multiple broken bone fragments; Another great challenge is personalized and precise internal fixation after reduction. For these two issues, we designed an automated method framework for precise reduction and internal fixation of comminuted fractures. First, the Gaussian mixture model (GMM) is used to distinguish section points and noise points in a broken bone model; Second, ellipse fitting is carried out to achieve section points matching and a descriptor is proposed to describe the section features; Then, the Convolution Auto‐Encoder (CAE) and genetic algorithm are used to extract feature vectors; Finally, after broken bone models registration, internal fixed plate can be reconstructed. Three verification experiments for comminuted bone fracture show this method has high accuracy and good efficiency. It can provide support for minimally invasive treatment for comminuted fractures.
Analisis Pemanfaatan Media Permainan Kartu Huruf untuk Meningkatkan Pemahaman Siswa dalam Pembelajaran PAI pada Siswa Kelas Satu Materi Mari Mengenal Huruf Hijaiyah di SD Islam Khaira Ummah Padang

This research was motivated by the application of letter card learning media to improve the understanding of first grade elementary school students. This research aims (1) To find out how teachers design letter card game media in PAI learning materials, let's get to know the hijaiyah letters for class 1 B students at Khaira Ummah Islamic Elementary School in Padang. (2) To find out how teachers apply letter card game media to PAI learning materials, let's get to know the Hijaiyah letters for class 1 B students at Khaira Ummah Islamic Elementary School, Padang. (3) To find out the advantages and disadvantages of using letter card game media to improve students' understanding of PAI learning material, let's get to know the Hijaiyah Letters for class 1 B students at Khaira Ummah Islamic Elementary School, Padang. This research uses a qualitative descriptive method, the research object is class 1 B students of Khaira Ummah Islamic Elementary School in Padang and the research subject is students' understanding of learning. Then data was obtained from observation, interviews with informants, and documentation to see existing phenomena. The research results showed that first, the teacher designed card media using simple and affordable materials. Second, in the implementation process the teacher explains the learning material using the play while learning method, following the educational standards and curriculum that apply in the school. Third, the advantages and disadvantages of card game learning media are that children really like this media. And the downside is that students have their own way of learning so there are students who experience boredom while studying.
Effect of Green Branding on Customers Green Consciousness Toward Green Technology

The study aims to test the impact of green branding on customers green consciousness toward green technology. The study population involves buyers who purchased green technology products in Amman, A convenience sample was selected through distributing our questionnaire face to face approach at different companies and places in Amman. The study hypothesis was tested using structural equation modeling (AMOS). The results show an impact of GB on customers green consciousness. Based on the study results, managers and decision makers recommended enhancing communication about their green and ecofriendly products attributes in order to gain the trust of their customers, and increase their customer purchase intentions.