Classification of Quantum Computer Fault Injection Attacks

The rapid growth of interest in quantum computing has brought about the need to secure these powerful machines against a range of physical attacks. As qubit counts increase and quantum computers achieve higher levels of fidelity, their potential to execute novel algorithms and generate sensitive intellectual property becomes more promising. However, there is a significant gap in our understanding of the vulnerabilities these computers face in terms of security and privacy attacks. Among the potential threats are physical attacks, including those orchestrated by malicious insiders within data centers where the quantum computers are located, which could compromise the integrity of computations and resulting data. This paper presents an exploration of fault-injection attacks as one class of physical attacks on quantum computers. This work first introduces a classification of fault-injection attacks and strategies, including the domain of fault-injection attacks, the fault targets, and fault manifestations in quantum computers. The resulting classification highlights the potential threats that exist. By shedding light on the vulnerabilities of quantum computers to fault-injection attacks, this work contributes to the development of robust security measures for this emerging technology.Comment: 7 pages, 4 figure

Hardware Architecture for a Quantum Computer Trusted Execution Environment

The cloud-based environments in which today's and future quantum computers will operate, raise concerns about the security and privacy of user's intellectual property. Quantum circuits submitted to cloud-based quantum computer providers represent sensitive or proprietary algorithms developed by users that need protection. Further, input data is hard-coded into the circuits, and leakage of the circuits can expose users' data. To help protect users' circuits and data from possibly malicious quantum computer cloud providers, this work presented the first hardware architecture for a trusted execution environment for quantum computers. To protect the user's circuits and data, the quantum computer control pulses are obfuscated with decoy control pulses. While digital data can be encrypted, analog control pulses cannot and this paper proposed the novel decoy pulse approach to obfuscate the analog control pulses. The proposed decoy pulses can easily be added to the software by users. Meanwhile, the hardware components of the architecture proposed in this paper take care of eliminating, i.e. attenuating, the decoy pulses inside the superconducting quantum computer's dilution refrigerator before they reach the qubits. The hardware architecture also contains tamper-resistant features to protect the trusted hardware and users' information. The work leverages a new metric of variational distance to analyze the impact and scalability of hardware protection. The variational distance of the circuits protected with our scheme, compared to unprotected circuits, is in the range of only $0.16$ to $0.26$. This work demonstrates that protection from possibly malicious cloud providers is feasible and all the hardware components needed for the proposed architecture are available today

Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning

Fine-tuning large pre-trained language models on various downstream tasks with whole parameters is prohibitively expensive. Hence, Parameter-efficient fine-tuning has attracted attention that only optimizes a few task-specific parameters with the frozen pre-trained model. In this work, we focus on prefix tuning, which only optimizes continuous prefix vectors (i.e. pseudo tokens) inserted into Transformer layers. Based on the observation that the learned syntax and semantics representation varies a lot at different layers, we argue that the adaptive prefix will be further tailored to each layer than the fixed one, enabling the fine-tuning more effective and efficient. Thus, we propose Adaptive Prefix Tuning (APT) to adjust the prefix in terms of both fine-grained token level and coarse-grained layer level with a gate mechanism. Experiments on the SuperGLUE and NER datasets show the effectiveness of APT. In addition, taking the gate as a probing, we validate the efficiency and effectiveness of the variable prefix.Comment: Accepted to ACL 2023 (Main conference

A review of building occupants adaptive behavior in buildings of China

In order to realize the sustainable development of society, building energy consumption has become a global concern. In buildings, occupants adaptive behaviors that means how to use the buildings have an very important influence on the building energy use. The researches of occupants adaptive behaviors have been carried out for more than 30 years in Europe, including England, Switzerland, Denmark and so on. In past 10 years, many Chinese scholars also started to study on this field. This paper reviewed the articles of occupants adaptive behaviors in china and summerized the current development situations then discussed the proper development direction in the future that can give some advises and references for the future study

DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors

Traditional neural objection detection methods use multi-scale features that allow multiple detectors to perform detecting tasks independently and in parallel. At the same time, with the handling of the prior box, the algorithm's ability to deal with scale invariance is enhanced. However, too many prior boxes and independent detectors will increase the computational redundancy of the detection algorithm. In this study, we introduce Dubox, a new one-stage approach that detects the objects without prior box. Working with multi-scale features, the designed dual scale residual unit makes dual scale detectors no longer run independently. The second scale detector learns the residual of the first. Dubox has enhanced the capacity of heuristic-guided that can further enable the first scale detector to maximize the detection of small targets and the second to detect objects that cannot be identified by the first one. Besides, for each scale detector, with the new classification-regression progressive strapped loss makes our process not based on prior boxes. Integrating these strategies, our detection algorithm has achieved excellent performance in terms of speed and accuracy. Extensive experiments on the VOC, COCO object detection benchmark have confirmed the effectiveness of this algorithm

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting

Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data. Existing dominant approaches usually suffer from the challenge that the target domain has different label sets compared with a resource-rich source domain, which can be concluded as class transfer and domain transfer. In this paper, we propose a lightweight tuning paradigm for low-resource NER via pluggable prompting (LightNER). Specifically, we construct the unified learnable verbalizer of entity categories to generate the entity span sequence and entity categories without any label-specific classifiers, thus addressing the class transfer issue. We further propose a pluggable guidance module by incorporating learnable parameters into the self-attention layer as guidance, which can re-modulate the attention and adapt pre-trained weights. Note that we only tune those inserted module with the whole parameter of the pre-trained language model fixed, thus, making our approach lightweight and flexible for low-resource scenarios and can better transfer knowledge across domains. Experimental results show that LightNER can obtain comparable performance in the standard supervised setting and outperform strong baselines in low-resource settings. Code is in https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot.Comment: Accepted by COLING 202

Fast and Efficient Hardware Implementation of HQC

This work presents a hardware design for constant-time implementation of the HQC (Hamming Quasi-Cyclic) code-based key encapsulation mechanism. HQC has been selected for the fourth round of NIST\u27s Post-Quantum Cryptography standardization process and this work presents the first, hand-optimized design of HQC key generation, encapsulation, and decapsulation written in Verilog targeting implementation on FPGAs. The three modules further share a common SHAKE256 hash module to reduce area overhead. All the hardware modules are parametrizable at compile time so that designs for the different security levels can be easily generated. The design currently outperforms the other hardware designs for HQC, and many of the fourth-round Post-Quantum Cryptography standardization process, with one of the best time-area products as well. For the combined HighSpeed design targeting the lowest security level, we show that the HQC design can perform key generation in 0.09ms, encapsulation in 0.13ms, and decapsulation in 0.21ms when synthesized for an Xilinx Artix 7 FPGA. Our work shows that when hardware performance is compared, HQC can be a competitive alternative candidate from the fourth round of the NIST PQC competition

A field study on occupants’ ventilation behaviour through balcony doors in university students’ apartments during transitional seasons in Beijing

Occupant behaviour has an important role in both the environmental performance and energy performance of buildings, which has been thoroughly demonstrated in the past several decades. Based on a review work, some research gaps have been identified in the area of occupants’ ventilation behaviour and to answer those gaps a field study was carried out in a student dormitory building in Beijing, China, over the period of one transitional season in 2015. The study monitored students’ ventilation behaviour dynamically with concurrent measurement of relevant influential factors that have been identified in existing studies carried out in conventional buildings. The analysis carried out in the study aimed to demonstrate the influence of those previously-identified factors in the case study building. The factors examined in the study included outdoor air temperature, indoor air temperature, occupant presence, and certain aspects relating to personal preferences. From the analysis, it was found that all these factors can influence students’ ventilation behaviour in dormitories. However, the influence of occupant presence seems to be different from the findings in conventional buildings which focused mainly on the use of external windows, and not balcony doors, which are included in this study

Energy waste in buildings due to occupant behaviour

Occupants’ behaviour has a significant impact on the energy performance of buildings. A good understanding of how occupants use a building provides a possibility of promoting the building’s energy efficiency through changing occupant behaviour. Building simulation has been adopted as a useful method by building engineers for quantifying the effects of changing occupant behaviour on the building’s energy consumption and indoor environment. However, due to the lack of real measured data with respect to how occupants use the building, such simulation work has relied on assumed behavioural patterns, which significantly reduces the reliability of the predicted results. This paper describes a longitudinal study monitoring occupants’ heating, window opening and cooling behaviour in an office building throughout summer, transitional and winter periods. These behavioural data were then used to drive dynamic building performance simulation to predict the energy saving potential of changing behaviour. Comparison with predicted results by assumed behavioural patterns reflected that improperly assumed behavioural patterns may either overestimate or underestimate the energy saving potential of changing behaviour, especially for unextreme behaviours

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}