Tao Zhang

Gregory Ciccarelli, Jarred Barber, Arun Nair et al.

We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices". We also identify the datasets needed to support these research areas. Based on the review and our research experience in the multi-device domain, we conclude with an outlook on the future evolution

Youwei Huang, Sen Fang, Jianwen Li et al.

In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detecting developers' intents in smart contracts has become a more pressing issue, as malicious intents have caused substantial financial losses. Unfortunately, existing research lacks effective methods for detecting development intents in smart contracts. To address this gap, we propose \textsc{SmartIntentNN} (Smart Contract Intent Neural Network), a deep learning model designed to automatically detect development intents in smart contracts. \textsc{SmartIntentNN} leverages a pre-trained sentence encoder to generate contextual representations of smart contracts, employs a K-means clustering model to identify and highlight prominent intent features, and utilizes a bidirectional LSTM-based deep neural network for multi-label classification. We trained and evaluated \textsc{SmartIntentNN} on a dataset containing over 40,000 real-world smart contracts, employing self-comparison baselines in our experimental setup. The results show that \textsc{SmartIntentNN} achieves an F1-score of 0.8633 in identifying intents across 10 distinct categories, outperforming all baselines and addressing the gap in smart contract detection by incorporating intent analysis.

Tianpeng Li, Jun Liu, Tao Zhang et al.

We introduce Baichuan-Audio, an end-to-end audio large language model that seamlessly integrates audio understanding and generation. It features a text-guided aligned speech generation mechanism, enabling real-time speech interaction with both comprehension and generation capabilities. Baichuan-Audio leverages a pre-trained ASR model, followed by multi-codebook discretization of speech at a frame rate of 12.5 Hz. This multi-codebook setup ensures that speech tokens retain both semantic and acoustic information. To further enhance modeling, an independent audio head is employed to process audio tokens, effectively capturing their unique characteristics. To mitigate the loss of intelligence during pre-training and preserve the original capabilities of the LLM, we propose a two-stage pre-training strategy that maintains language understanding while enhancing audio modeling. Following alignment, the model excels in real-time speech-based conversation and exhibits outstanding question-answering capabilities, demonstrating its versatility and efficiency. The proposed model demonstrates superior performance in real-time spoken dialogue and exhibits strong question-answering abilities. Our code, model and training data are available at https://github.com/baichuan-inc/Baichuan-Audio

Jason Chou, Ao Liu, Yuchi Deng et al.

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have been proposed to evaluate their code generation abilities, these benchmarks face several critical limitations. First, they often rely on manual annotations, which are time-consuming and difficult to scale across different programming languages and problem complexities. Second, most existing benchmarks focus primarily on Python, while the few multilingual benchmarks suffer from limited difficulty and uneven language distribution. To address these challenges, we propose AutoCodeGen, an automated method for generating high-difficulty multilingual code generation datasets without manual annotations. AutoCodeGen ensures the correctness and completeness of test cases by generating test inputs with LLMs and obtaining test outputs through a multilingual sandbox, while achieving high data quality through reverse-order problem generation and multiple filtering steps. Using this novel method, we introduce AutoCodeBench, a large-scale code generation benchmark comprising 3,920 problems evenly distributed across 20 programming languages. It is specifically designed to evaluate LLMs on challenging, diverse, and practical multilingual tasks. We evaluate over 30 leading open-source and proprietary LLMs on AutoCodeBench and its simplified version AutoCodeBench-Lite. The results show that even the most advanced LLMs struggle with the complexity, diversity, and multilingual nature of these tasks. Besides, we introduce AutoCodeBench-Complete, specifically designed for base models to assess their few-shot code generation capabilities. We hope the AutoCodeBench series will serve as a valuable resource and inspire the community to focus on more challenging and practical multilingual code generation scenarios.

Tailin Wu, Takashi Maruyama, Long Wei et al.

Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical engineering to aerospace engineering. Inverse design is typically formulated as an optimization problem, with recent works leveraging optimization across learned dynamics models. However, as models are optimized they tend to fall into adversarial modes, preventing effective sampling. We illustrate that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples and significantly improve design performance. We further illustrate how such a design system is compositional, enabling us to combine multiple different diffusion models representing subcomponents of our desired system to design systems with every specified component. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes that are more complex than those in the training data. Our method generalizes to more objects for N-body dataset and discovers formation flying to minimize drag in the multi-airfoil design task. Project website and code can be found at https://github.com/AI4Science-WestlakeU/cindm.

Xiaoqi Li, Ting Chen, Xiapu Luo et al.

More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named STAN to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, STAN can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that STAN can generate adequate, accurate, and readable descriptions for contract's bytecodes, which have practical value for users.

Wei Bao, Mi Zhang, Tao Zhang et al.

Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's QAC systems in real-world scenarios face two major challenges:1)intention equivocality(IE): during the user's typing process,the prefix often contains a combination of characters and subwords, which makes the current intention ambiguous and difficult to model.2)intention transfer (IT):previous works make personalized recommendations based on users' historical sequences, but ignore the search intention transfer.However, the current intention extracted from prefix may be contrary to the historical preferences.

Jarred Barber, Yifeng Fan, Tao Zhang

We introduce a variant of the speaker localization problem, which we call device arbitration. In the device arbitration problem, a user utters a keyword that is detected by multiple distributed microphone arrays (smart home devices), and we want to determine which device was closest to the user. Rather than solving the full localization problem, we propose an end-to-end machine learning system. This system learns a feature embedding that is computed independently on each device. The embeddings from each device are then aggregated together to produce the final arbitration decision. We use a large-scale room simulation to generate training and evaluation data, and compare our system against a signal processing baseline.

Chen Wang, Yueqing Liang, Zhiwei Liu et al.

A recommender system predicts users' potential interests in items, where the core is to learn user/item embeddings. Nevertheless, it suffers from the data-sparsity issue, which the cross-domain recommendation can alleviate. However, most prior works either jointly learn the source domain and target domain models, or require side-features. However, jointly training and side features would affect the prediction on the target domain as the learned embedding is dominated by the source domain containing bias information. Inspired by the contemporary arts in pre-training from graph representation learning, we propose a pre-training and fine-tuning diagram for cross-domain recommendation. We devise a novel Pre-training Graph Neural Network for Cross-Domain Recommendation (PCRec), which adopts the contrastive self-supervised pre-training of a graph encoder. Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain. The experimental results demonstrate the superiority of PCRec. Detailed analyses verify the superiority of PCRec in transferring information while avoiding biases from source domains.

Tao Zhang, Timothy Lesch, Kenneth Koltermann et al.

Modern processors have suffered a deluge of threats exploiting branch instruction collisions inside the branch prediction unit (BPU), from eavesdropping on secret-related branch operations to triggering malicious speculative executions. Protecting branch predictors tends to be challenging from both security and performance perspectives. For example, partitioning or flushing BPU can stop certain collision-based exploits but only to a limited extent. Meanwhile, such mitigations negatively affect branch prediction accuracy and further CPU performance. This paper proposes Secret Token Branch Prediction Unit (STBPU), a secure BPU design to defend against collision-based transient execution attacks and BPU side channels while incurring minimal performance overhead. STBPU resolves the challenges above by customizing data representation inside BPU for each software entity requiring isolation. In addition, to prevent an attacker from using brute force techniques to trigger malicious branch instruction collisions, STBPU actively monitors the prediction-related events and preemptively changes BPU data representation.

Mengting Xu, Tao Zhang, Zhongnian Li et al.

A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker. However, most of these provable defense methods treat all examples equally during training process, which ignore the inconsistent constraint of certified robustness between correctly classified (natural) and misclassified examples. In this paper, we explore this inconsistency caused by misclassified examples and add a novel consistency regularization term to make better use of the misclassified examples. Specifically, we identified that the certified robustness of network can be significantly improved if the constraint of certified robustness on misclassified examples and correctly classified examples is consistent. Motivated by this discovery, we design a new defense regularization term called Misclassification Aware Adversarial Regularization (MAAR), which constrains the output probability distributions of all examples in the certified region of the misclassified example. Experimental results show that our proposed MAAR achieves the best certified robustness and comparable accuracy on CIFAR-10 and MNIST datasets in comparison with several state-of-the-art methods.

Yutian Tang, Hao Zhou, Zhou Xu et al.

Software reuse enables developers to reuse architecture, programs and other software artifacts. Realizing a systematical reuse in software brings a large amount of benefits for stakeholders, including lower maintenance efforts, lower development costs, and time to market. Unfortunately, currently implementing a framework for large-scale software reuse in Android apps is still a huge problem, regarding the complexity of the task and lacking of practical technical support from either tools or domain experts. Therefore, proposing a feature location benchmark for apps will help developers either optimize their feature location techniques or reuse the assets created in the benchmark for reusing. In this paper, we release a feature location benchmark, which can be used for those developers, who intend to compose software product lines (SPL) and release reuse in apps. The benchmark not only contributes to the research community for reuse research, but also helps participants in industry for optimizing their architecture and enhancing modularity. In addition, we also develop an Android Studio plugin named caIDE for developers to view and operate on the benchmark.

He Jiang, Dong Liu, Zhilei Ren et al.

The popularity of blockchain technology continues to grow rapidly in both industrial and academic fields. Most studies of blockchain focus on the improvements of security, usability, or efficiency of blockchain protocols, or the applications of blockchain in finance, Internet of Things, or public services. However, few of them could reveal the concerns of front-line developers and the situations of blockchain in practice. In this article, we investigate how developers use and discuss blockchain with a case study of Stack Overflow posts. We find blockchain is a relatively new topic in Stack Overflow but it is rising to popularity. We detect 13 types of questions that developers post in Stack Overflow and identify 45 blockchain relevant entities (e.g., frameworks, libraries, or tools) for building blockchain applications. These findings may help blockchain project communities to know where to improve and help novices to know where to start.

He Jiang, Jingxuan Zhang, Zhilei Ren et al.

Developers increasingly rely on API tutorials to facilitate software development. However, it remains a challenging task for them to discover relevant API tutorial fragments explaining unfamiliar APIs. Existing supervised approaches suffer from the heavy burden of manually preparing corpus-specific annotated data and features. In this study, we propose a novel unsupervised approach, namely Fragment Recommender for APIs with PageRank and Topic model (FRAPT). FRAPT can well address two main challenges lying in the task and effectively determine relevant tutorial fragments for APIs. In FRAPT, a Fragment Parser is proposed to identify APIs in tutorial fragments and replace ambiguous pronouns and variables with related ontologies and API names, so as to address the pronoun and variable resolution challenge. Then, a Fragment Filter employs a set of nonexplanatory detection rules to remove non-explanatory fragments, thus address the non-explanatory fragment identification challenge. Finally, two correlation scores are achieved and aggregated to determine relevant fragments for APIs, by applying both topic model and PageRank algorithm to the retained fragments. Extensive experiments over two publicly open tutorial corpora show that, FRAPT improves the state-of-the-art approach by 8.77% and 12.32% respectively in terms of F-Measure. The effectiveness of key components of FRAPT is also validated.

He Jiang, Xin Chen, Shuwei Zhang et al.

Wearable devices are a new form of mobile computer system that provides exclusive and user-personalized services. Wearable devices bring new issues and challenges to computer science and technology. This paper summarizes the development process and the categories of wearable devices. In addition, we present new key issues arising in aspects of wearable devices, including operating systems, database management system, network communication protocol, application development platform, privacy and security, energy consumption, human-computer interaction, software engineering, and big data.