35 accepted papers
LLMs have transformed NLP, yet deploying them on edge devices poses great carbon challenges. Prior estimators remain incomplete, neglecting peripheral energy use, distinct prefill/decode behaviors, and SoC design complexity. This paper presents CO2-Meter, a unified framework for estimating operation
With the advancement of information retrieval (IR) technologies toward deep semantic understanding, reasoning-based methods—featuring explicit chain-of-thought generation—have demonstrated significant advantages in multi-hop and causal reasoning tasks. However, in complex clinical case retrieval sce
Image Super-Resolution (SR) aims to reconstruct high-resolution images from degraded low-resolution inputs. While diffusion-based SR methods offer powerful generative capabilities, their performance heavily depends on how semantic priors are structured and integrated into the generation process. Exi
Reliable question answering with large language models (LLMs) is challenged by hallucinations, fluent but factually incorrect outputs arising from epistemic uncertainty. Existing entropy-based semantic-level uncertainty estimation methods are limited by sampling noise and unstable clustering of vari…
With the explosive growth of multimodal data streams on social media, the timely detection of emerging social events has become increasingly important. As a result, Multimodal Social Event Detection in open-world settings is receiving growing attention. However, most existing methods face two major
Multimodal aspect-based sentiment classification (MABSC) is a challenging task emerging in recent years, which aims to combine text and image to identify the sentiment polarity of each aspect. There exists a potential irrelevance between aspects and images, and mistakenly focusing on irrelevant imag…
Private Transformer inference using cryptographic protocols offers promising solutions for privacy-preserving machine learning; however, it still faces significant runtime overhead (efficiency issues) and challenges in handling long-token inputs (scalability issues). We observe that the Transformer'…
Large Vision-Language Models (LVLMs) achieve remarkable performance in multimodal tasks but suffer from high computational costs due to the large number of visual tokens. Existing pruning methods either apply after visual tokens enter the LLM or perform pre-pruning based solely on visual attention.
Ensuring that Large Language Models (LLMs) return just responses which adhere to societal values is crucial for their broader application. Prior research has shown that LLMs often fail to perform satisfactorily on tasks requiring moral cognizance, such as ethics-based judgments. While current approa
Current Emotion Recognition in Conversation (ERC) research follows a closed-domain assumption. However, there is no clear consensus on emotion classification in psychology, which presents a challenge for models when it comes to recognizing previously unseen emotions in real-world applications. To br
In industrial applications, precision and load capacity are critical performance metrics for robotic arms. These requirements are typically addressed using high-gear-ratio designs. However, robotic arms are increasingly tasked with high-dynamic operations, such as smashes in sports and object throwi
Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing (NLP) scenarios, but they still face challenges when handling complex arithmetic and logical reasoning tasks. While Chain-Of-Thought (CoT) reasoning, self-consistency (SC) and self-corre…
Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse. However, mainstream AI-generated text detection (AGTD) methods primarily target document-level long texts and struggle to generalize effectively to sentence-level short texts. And current sent
Aspect sentiment triplet extraction (ASTE) aims to extract triplets composed of aspect terms, opinion terms, and sentiment polarities from given sentences. The table tagging method is a popular approach to addressing this task, which encodes a sentence into a 2-dimensional table, allowing for the ta
Packing for Supervised Fine-Tuning (SFT) in autoregressive models involves concatenating data points of varying lengths until reaching the designed maximum length to facilitate GPU processing. However, randomly concatenating data points can lead to cross-contamination of sequences due to the signifi…
The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions. An essential aspect is accurately estimating the carbon impa…
It is challenging to design rules of local interactions for global patterns in self-organized robot swarm systems, especially in robots with limited capacities of sensing, communication, and computing. Many natural swarms, such as animal swarms and cell collectives, generate complex global patterns
"Self-supervised learning (SSL) is a prevalent approach for encoding data representations. Using a pre-trained SSL image encoder and subsequently training a downstream classifier, impressive performance can be achieved on various tasks with very little labeled data. The growing adoption of SSL has l…
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution. In this work, we consider the inpainting of semantic Bird’s-Eye-View maps. We propose S…
Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. However, the security issues, e.g., Trojan attacks, of prompt tuning on a few data samples are not well-studied. Transferr…
We propose a circuit-level backdoor attack, QTrojan, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by a few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthier than a prior Data-Poisoning-based Backdoor Attack (DPBA…
Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are…
Intent detection (ID) and slot filling (SF) are two major tasks for spoken language understanding (SLU). Recent joint learning approaches consider the relationship between intent detection and slot filling, which leverage the shared knowledge across two tasks to benefit each other. However, most exi…
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Most recent efforts adopt pre-trained model to classify the sentences with aspects. However, the aspect sentiment bias from pre-trained model brings some noise to the ABSA task. Besides, traditional methods using…
Data augmentation with mixup has shown to be effective on the NLP tasks. Although its great success, the mixup still has shortcomings. First, vanilla mixup randomly selects one sample to generate the mixup sample for a given sample. It remains unclear how to best choose the input samples for the mix…
Compressed sensing based algorithms are utilized to obtain high-resolution time-frequency distribution (TFD) with negligible cross-terms (CTs), however, performance deteriorates when the signal is composed of closely-located or overlapped components. Moreover, there is still an impressive resolution…
Homomorphic encryption (HE) and garbled circuit (GC) provide the protection for users’ privacy. However, simply mixing the HE and GC in RNN models suffer from long inference latency due to slow activation functions. In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit…
Recently Homomorphic Encryption (HE) is used to implement Privacy-Preserving Neural Networks (PPNNs) that perform inferences directly on encrypted data without decryption. Prior PPNNs adopt mobile network architectures such as SqueezeNet for smaller computing overhead, but we find naïvely using mobi…
The advances in neural networks have driven many companies to provide prediction services to users in a wide range of applications. However, current prediction systems raise privacy concerns regarding the user's private data. A cryptographic neural network inference service is an efficient way to al…
Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference…
Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy,…
Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in different types of stained datasets due to the appearance variations. In this paper, we design a robust and flexible mod…
Homomorphic Encryption (HE) based secure Neural Networks(NNs) inference is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). In the HE-based MLaaS setting, a client encrypts the sensitive data, and uploads the encrypted data to the server that directly p…
Because of the lack of expertise, to gain benefits from their data, average users have to upload their private data to cloud servers they may not trust. Due to legal or privacy constraints, most users are willing to contribute only their encrypted data, and lack interests or resources to join deep n…
Homomorphic Encryption (HE) is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). Several Leveled-HE (LHE)-enabled Convolutional Neural Networks (LHECNNs) are proposed to implement MLaaS to avoid the large bootstrapping overhead. However, prior LHECNNs ha…