Reviewerly/exHarmony huggingface dataset:
Evaluating Generative Models for Graph-to-Text Generation

Large language models (LLMs) have been widely employed for graph-to-text generation tasks. However, the process of finetuning LLMs requires significant training resources and annotation work. In this paper, we explore the capability of generative models to generate descriptive text from graph data in a zero-shot setting. Specifically, we evaluate GPT-3 and ChatGPT on two graph-to-text datasets and compare their performance with that of finetuned LLM models such as T5 and BART. Our results demonstrate that generative models are capable of generating fluent and coherent text, achieving BLEU scores of 10.57 and 11.08 for the AGENDA and WebNLG datasets, respectively. However, our error analysis reveals that generative models still struggle with understanding the semantic relations between entities, and they also tend to generate text with hallucinations or irrelevant information. As a part of error analysis, we utilize BERT to detect machine-generated text and achieve high macro-F1 scores. We have made the text generated by generative models publicly available.
SEV-Step: A Single-Stepping Framework for AMD-SEV

The ever increasing popularity and availability of Trusted Execution Environments (TEEs) had a stark influence on microarchitectural attack research in academia, as their strong attacker model both boosts existing attack vectors and introduces several new ones. While many works have focused on Intel SGX, other TEEs like AMD SEV have recently also started to receive more attention. A common technique when attacking SGX enclaves is single-stepping, where the system's APIC timer is used to interrupt the enclave after every instruction. Single-stepping increases the temporal resolution of subsequent microarchitectural attacks to a maximum. A key driver in the proliferation of this complex attack technique was the SGX-Step framework, which offered a stable reference implementation for single-stepping and a relatively easy setup. In this paper, we demonstrate that SEV VMs can also be reliably single-stepped. To lay the foundation for further microarchitectural attack research against SEV, we introduce the reusable SEV-Step framework. Besides reliable single-stepping, SEV-Step provides easy access to common attack primitives like page fault tracking and cache attacks against SEV. All features can be used interactively from user space. We demonstrate SEV-Step's capabilities by carrying out an end-to-end cache attack against SEV that leaks the volume key of a LUKS2-encrypted disk. Finally, we show for the first time that SEV is vulnerable to Nemesis-style attacks, which allow to extract information about the type and operands of single-stepped instructions from SEV-protected VMs.
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning

Depth-aware panoptic segmentation is an emerging topic in computer vision which combines semantic and geometric understanding for more robust scene interpretation. Recent works pursue unified frameworks to tackle this challenge but mostly still treat it as two individual learning tasks, which limits their potential for exploring cross-domain information. We propose a deeply unified framework for depth-aware panoptic segmentation, which performs joint segmentation and depth estimation both in a per-segment manner with identical object queries. To narrow the gap between the two tasks, we further design a geometric query enhancement method, which is able to integrate scene geometry into object queries using latent representations. In addition, we propose a bi-directional guidance learning approach to facilitate cross-task feature learning by taking advantage of their mutual relations. Our method sets the new state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS and SemKITTI-DVPS datasets. Moreover, our guidance learning approach is shown to deliver performance improvement even under incomplete supervision labels.
Multilingual Code Co-Evolution Using Large Language Models

Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a bug fix or a new feature) is being propagated, timely and without errors, to implementations in other programming languages. In the world of ever-changing software, using rule-based translation tools (i.e., transpilers) or machine learning models for translating code from one language to another provides limited value. Translating each time the entire codebase from one language to another is not the way developers work. In this paper, we target a novel task: translating code changes from one programming language to another using large language models (LLMs). We design and implement the first LLM, dubbed Codeditor, to tackle this task. Codeditor explicitly models code changes as edit sequences and learns to correlate changes across programming languages. To evaluate Codeditor, we collect a corpus of 6,613 aligned code changes from 8 pairs of open-source software projects implementing similar functionalities in two programming languages (Java and C#). Results show that Codeditor outperforms the state-of-the-art approaches by a large margin on all commonly used automatic metrics. Our work also reveals that Codeditor is complementary to the existing generation-based models, and their combination ensures even greater performance.
Linear Termination over N is Undecidable

Recently it was shown that it is undecidable whether a term rewrite system can be proved terminating by a polynomial interpretation in the natural numbers. In this paper we show that this is also the case when restricting the interpretations to linear polynomials, as is often done in tools, and when only considering single-rule rewrite systems. What is more, the new undecidability proof is simpler than the previous one. We further show that polynomial termination over the rationals/reals is undecidable.
Kernelised Normalising Flows

Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation. This duality requires an inherently invertible architecture. However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of parameters and innovative architectural designs to achieve good results. Whilst flow-based models predominantly rely on neural-network-based transformations for expressive designs, alternative transformation methods have received limited attention. In this work, we present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework. Our results demonstrate that a kernelised flow can yield competitive or superior results compared to neural network-based flows whilst maintaining parameter efficiency. Kernelised flows excel especially in the low-data regime, enabling flexible non-parametric density estimation in applications with sparse data availability.
Decomposing and Routing Quantum Circuits Under Constraints for Neutral Atom Architectures

Quantum computing is in an era defined by rapidly evolving quantum hardware technologies, combined with persisting high gate error rates, large amounts of noise, and short coherence times. Overcoming these limitations requires systems-level approaches that account for the strengths and weaknesses of the underlying hardware technology. Yet few hardware-aware compiler techniques exist for neutral atom devices, with no prior work on compiling to the neutral atom native gate set. In particular, current neutral atom hardware does not support certain single-qubit rotations via local addressing, which often requires the circuit to be decomposed into a large number of gates, leading to long circuit durations and low overall fidelities. We propose the first compiler designed to overcome the challenges of limited local addressibility in neutral atom quantum computers. We present algorithms to decompose circuits into the neutral atom native gate set, with emphasis on optimizing total pulse area of global gates, which dominate gate execution costs in several current architectures. Furthermore, we explore atom movement as an alternative to expensive gate decompositions, gaining immense speedup with routing, which remains a huge overhead for many quantum circuits. Our decomposition optimizations result in up to ~3.5x and ~2.9x speedup in time spent executing global gates and time spent executing single-qubit gates, respectively. When combined with our atom movement routing algorithms, our compiler achieves up to ~10x reduction in circuit duration, with over ~2x improvement in fidelity. We show that our compiler strategies can be adapted for a variety of hardware-level parameters as neutral atom technology continues to develop.
State preparation by shallow circuits using feed forward

In order to achieve fault-tolerant quantum computation, we need to repeat the following sequence of four steps: First, perform 1 or 2 qubit quantum gates (in parallel if possible). Second, do a syndrome measurement on a subset of the qubits. Third, perform a fast classical computation to establish which errors have occurred (if any). Fourth, depending on the errors, we apply a correction step. Then the procedure repeats with the next sequence of gates. In order for these four steps to succeed, we need the error rate of the gates to be below a certain threshold. Unfortunately, the error rates of current quantum hardware are still too high. On the other hand, current quantum hardware platforms are designed with these four steps in mind. In this work we make use of this four-step scheme not to carry out fault-tolerant computations, but to enhance short, constant-depth, quantum circuits that perform 1 qubit gates and nearest-neighbor 2 qubit gates. To explore how this can be useful, we study a computational model which we call Local Alternating Quantum Classical Computations (LAQCC). In this model, qubits are placed in a grid allowing nearest neighbor interactions; the quantum circuits are of constant depth with intermediate measurements; a classical controller can perform log-depth computations on these intermediate measurement outcomes to control future quantum operations. This model fits naturally between quantum algorithms in the NISQ era and full fledged fault-tolerant quantum computation. We show that LAQCC circuits can create long-ranged interactions, which constant-depth quantum circuits cannot achieve, and use it to construct a range of useful multi-qubit gates. With these gates, we create three new state preparation protocols for a uniform superposition over an arbitrary number of states, W-states and Dicke states.
Impact of Black Swan Events on Ethereum Blockchain ERC20 Token Transaction Networks

The Ethereum blockchain and its ERC20 token standard have revolutionized the landscape of digital assets and decentralized applications. ERC20 tokens developed on the Ethereum blockchain have gained significant attention since their introduction. They are programmable and interoperable tokens, enabling various applications and token economies. Transaction graphs, representing the flow of the value between wallets within the Ethereum network, have played a crucial role in understanding the system's dynamics, such as token transfers and the behavior of traders. Here, we explore the evolution of daily transaction graphs of ERC20 token transactions, which sheds light on the trader's behavior during the Black Swan Events -- 2018 crypto crash and the COVID-19 pandemic. By using the tools from network science and differential geometry, we analyze 0.98 billion of ERC20 token transaction data from November 2015 to January 2023. Our analysis reveals that ERC20 financial ecosystem has evolved from a localized wealth formation period to a more mature financial ecosystem where wealth has dispersed among the traders in the network after the crypto crash and during the pandemic period. Before the crash, most sellers only sell the tokens, and buyers only buy the tokens. However, after the crash and during the pandemic period, sellers and buyers both performed buying and selling activities. In addition, we observe no significant negative impact of the COVID-19 pandemic on user behavior in the financial ecosystem.
ArcGPT: A Large Language Model Tailored for Real-world Archival Applications

Archives play a crucial role in preserving information and knowledge, and the exponential growth of such data necessitates efficient and automated tools for managing and utilizing archive information resources. Archival applications involve managing massive data that are challenging to process and analyze. Although LLMs have made remarkable progress in diverse domains, there are no publicly available archives tailored LLM. Addressing this gap, we introduce ArcGPT, to our knowledge, the first general-purpose LLM tailored to the archival field. To enhance model performance on real-world archival tasks, ArcGPT has been pre-trained on massive and extensive archival domain data. Alongside ArcGPT, we release AMBLE, a benchmark comprising four real-world archival tasks. Evaluation on AMBLE shows that ArcGPT outperforms existing state-of-the-art models, marking a substantial step forward in effective archival data management. Ultimately, ArcGPT aims to better serve the archival community, aiding archivists in their crucial role of preserving and harnessing our collective information and knowledge.
MatrixWorld: A pursuit-evasion platform for safe multi-agent coordination and autocurricula

Multi-agent reinforcement learning (MARL) has achieved encouraging performance in solving complex multi-agent tasks. However, the safety of MARL policies is one critical concern that impedes their real-world applications. Furthermore, popular multi-agent benchmarks provide limited safety support for safe MARL research, where negative rewards for collisions are insufficient for guaranteeing the safety of MARL policies. Therefore, in this work, we propose a new safety-constrained multi-agent environment: MatrixWorld, based on the general pursuit-evasion game. In particular, a safety-constrained multi-agent action execution model is proposed for the software implementation of safe multi-agent environments. In addition, MatrixWorld is a lightweight co-evolution framework for the learning of pursuit tasks, evasion tasks, or both, where more pursuit-evasion variants are designed based on different practical meanings of safety. As a brief survey, we review and analyze the co-evolution mechanism in the multi-agent setting, which clearly reveals its relationships with autocurricula, self-play, arms races, and adversarial learning. Thus, we argue that MatrixWorld can serve as the first environment for autocurriculum research, where ideas can be quickly verified and well understood. Finally, based on the above problems concerning safe MARL and autocurricula, our experiments show the difficulties of general MARL in guaranteeing safe multi-agent coordination with only negative rewards for collisions and the potential of MatrixWorld in autocurriculum learning, where practical suggestions for successful multi-agent adversarial learning and arms races are given.
Diverse Inpainting and Editing with GAN Inversion

Recent inversion methods have shown that real images can be inverted into StyleGAN's latent space and numerous edits can be achieved on those images thanks to the semantically rich feature representations of well-trained GAN models. However, extensive research has also shown that image inversion is challenging due to the trade-off between high-fidelity reconstruction and editability. In this paper, we tackle an even more difficult task, inverting erased images into GAN's latent space for realistic inpaintings and editings. Furthermore, by augmenting inverted latent codes with different latent samples, we achieve diverse inpaintings. Specifically, we propose to learn an encoder and mixing network to combine encoded features from erased images with StyleGAN's mapped features from random samples. To encourage the mixing network to utilize both inputs, we train the networks with generated data via a novel set-up. We also utilize higher-rate features to prevent color inconsistencies between the inpainted and unerased parts. We run extensive experiments and compare our method with state-of-the-art inversion and inpainting methods. Qualitative metrics and visual comparisons show significant improvements.
A Sparse Quantized Hopfield Network for Online-Continual Memory

An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed (non-i.i.d.) way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typically use non-local learning algorithms and are trained in an offline, non-noisy, i.i.d. setting. Understanding how neural networks learn under the same constraints as the brain is an open problem for neuroscience and neuromorphic computing. A standard approach to this problem has yet to be established. In this paper, we propose that discrete graphical models that learn via an online maximum a posteriori learning algorithm could provide such an approach. We implement this kind of model in a novel neural network called the Sparse Quantized Hopfield Network (SQHN). We show that SQHNs outperform state-of-the-art neural networks on associative memory tasks, outperform these models in online, non-i.i.d. settings, learn efficiently with noisy inputs, and are better than baselines on a novel episodic memory task.
A Transformer-based Approach for Arabic Offline Handwritten Text Recognition

Handwriting recognition is a challenging and critical problem in the fields of pattern recognition and machine learning, with applications spanning a wide range of domains. In this paper, we focus on the specific issue of recognizing offline Arabic handwritten text. Existing approaches typically utilize a combination of convolutional neural networks for image feature extraction and recurrent neural networks for temporal modeling, with connectionist temporal classification used for text generation. However, these methods suffer from a lack of parallelization due to the sequential nature of recurrent neural networks. Furthermore, these models cannot account for linguistic rules, necessitating the use of an external language model in the post-processing stage to boost accuracy. To overcome these issues, we introduce two alternative architectures, namely the Transformer Transducer and the standard sequence-to-sequence Transformer, and compare their performance in terms of accuracy and speed. Our approach can model language dependencies and relies only on the attention mechanism, thereby making it more parallelizable and less complex. We employ pre-trained Transformers for both image understanding and language modeling. Our evaluation on the Arabic KHATT dataset demonstrates that our proposed method outperforms the current state-of-the-art approaches for recognizing offline Arabic handwritten text.
A Comparative Study of Different Software Complexity Metrics in Measuring Software Interoperability

Software interoperability is crucial for organizations that rely on multiple software systems to perform their operations. However, due to the complexity and variety of software systems, ensuring interoperability can be difficult. Measuring software complexity metrics can be used to identify potential problems and assess how well different interoperability strategies work. In this study, we investigated and compared the effectiveness of different software complexity metrics in measuring software interoperability. We used statistical methods to analyze data collected from a sample of software systems. The results of our study show that certain metrics, such as coupling and cohesion, are more effective than others in measuring software interoperability. By selecting appropriate metrics, developers can ensure better productivity, lower costs, and more adaptable use of software systems. The findings of this study have implications for the creation of software and can guide businesses in choosing the right criteria to achieve software interoperability.
A new improved simulated annealing for traveling salesman problem

Simulated annealing algorithm is one of the most popular metaheuristics that has been successfully applied to many optimization problems. The main advantage of SA is its ability to escape from local optima by allowing hill-climbing moves and exploring new solutions at the beginning of the search process. One of its drawbacks is its slow convergence, requiring high computational time with a good set of parameter values to find a reasonable solution. In this work, a new improved SA is proposed to solve the well-known travelling salesman problem. In order to improve SA performance, a population-based improvement procedure is incorporated after the acceptance phase of SA, allowing the algorithm to take advantage of the social behavior of some solutions from the search space. Numerical results were carried out using known TSP instances from TSPLIB and preliminary results show that the proposed algorithm outperforms in terms of solution quality, the other comparison algorithms.
Multi-task Question Generation Based Data Augmentation for Biomedical Answer Generation

Limited by the corpus size and the annotation cost, biomedical question answering (BioQA) is a task of great research value. To generate professional biomedical answers, we first propose a text-to-text multi-task question generation model, which improves the accuracy of domain question generation with two auxiliary tasks. Based on this, a multi-task QA pipeline system with filtering is designed to synthesize high-quality biomedical data. Then, we use three data augmentation strategies to conduct generative BioQA experiments on original and synthetic data. The results on the factoid BioASQ 7b, 8b, and 9b datasets demonstrate the effectiveness of our method.
Code Mixing In Students’ Conversation at Grade VII in SMP N 2 Pematangsiantar

Code mixing can be happened in wide variety of language. It means that, mixing of two or morelanguages can be happened in morpheme/word, phrase, clause, and in sentence. According to Hamers(2000:270) “Code mixing is a language-contact communication strategy, but the speaker of a languagetransfers elements or rules of another language.”The researcher taught that English is important language,The reason to choose code mixing because the researcher found that students try to practice their Englishbut using sometime they have to use code mixing and swithing mostly because of lack of vocabulary.To describe the types of code mixing, to explain the functions of code mixing, and to tell the reasons ofusing code mixing. According to Siregar (1996:50) stated that Code mixing divided into two kinds, intrasentential mixing and extra-sentential mixing.Muysken (2000) defined three types of code mixing, thoseare: Insertion, Alternation, and Congruent Lexicalization.This situation is closer with daily conversationand also describe as their habitual communication. Speech situation of some people this phenomenoncould increase by status in society. In this case, for many young people code mixing becomes their ownstyle which is hoped to look like a modern and educational one.