We study the vulnerability of Cyber-physical systems (CPS) under stealthy sensor attacks in black-box scenarios. “Black-box” refers to scenarios where the attacker has minimal knowledge of the target system. Designing a stealthy sensor attack sequence under this scenario has two main challenges. The first one lies in ensuring the stealthiness of the sensor attack, meaning does not trigger an alert when applying the generated sensor attack sequence to the CPS. The second one is maintaining stealthiness throughout the attack generation process, indicating the limitation on the alarm frequency when generating the attack sequence. To address the above challenges, we develop a querybased black-box stealthy attack framework to violate the safety of the CPS. To maintain stealthiness during training, an active learning method has been introduced to extract the detector’s information to a time series model. The stealthy attack sequence is then generated from that model. Experiments on four numerical simulations and a high-fidelity simulator demonstrate the effectiveness of the proposed framework.

Sensor attacks on Cyber-Physical Systems (CPS) can cause substantial damage in the physical world, which motivates two major threads of defense works including attack detection and attack recovery. The former aims to identify whether any sensors are compromised while the latter seeks to restore a system to safety once an attack is detected. Although either thread has drawn many efforts, how to coordinate the detection and recovery has been barely studied. Overlooking the coordination, existing works may result in ineffective and even failed defense. For example, if a detector raises an alarm too late, there may not be enough time for a system to recover but reach the unsafe region anyway, even though the detection result is accurate. By contrast, raising an alarm earlier allows more time for recovery, but may come with more false positives and thus unnecessarily trigger the recovery. To fill this gap, we aim to co-design attack detection and recovery, and propose a novel recovery-guaranteed sensor attack detection framework. The framework dynamically adjusts the detection sensitivity and authenticates state estimates at run time to guarantee timely and safe recovery once an attack is detected. The detection will always reserve sufficient time for the recovery while minimizing unnecessary activation of recovery. We conduct extensive simulations and real-world testbed experiments to show the efficiency of our solution.

OBJECTIVE: Semantic coherence in speech is characterized by a logical, connected flow of ideas. A lack of coherence in speech may reflect disorganized thinking, a core feature of psychosis in schizophrenia spectrum disorders (SSDs). Developing tools that could help with automated assessment of semantic coherence in language could facilitate early detection of SSDs and improved monitoring of symptoms, enabling more timely intervention. Large language models (LLMs) have demonstrated strong capabilities on numerous language-centric tasks and have shown promise for analyzing semantic coherence due to the natural fit between their innate measures of language perplexity and the surprising turns that incoherent narrative often takes. This study aims to develop a novel representation and associated measure of semantic coherence using LLM-based perplexity metrics and to compare this measure with traditional vector distance-based coherence metrics. METHOD: We evaluated "bag" and "chain" models based on LLM perplexities as measures of semantic coherence. Regression models were trained using both single and paired combinations of perplexity- and proximity-based features to predict human ratings of semantic coherence using standardized instruments. Performance was evaluated on held-out examples from a training set of speeches from individuals experiencing psychotic symptoms and a test set of clinical interviews with patients diagnosed with SSDs, both with labels from human assessments of disorganized thinking severity. RESULTS: The best performance was achieved using a combination of perplexity and proximity features, yielding a Spearman correlation with human ratings of 0.61 (vs. 0.56 with proximity features alone) on leave-one-out cross-validation in the training set, and 0.54 (vs. 0.52 with proximity features alone) on the test set. CONCLUSION: We developed novel methods for assessing semantic coherence using LLM perplexities and found them complementary to proximity-based methods. Combined, these methods showed improved performance across two datasets, highlighting LLM's potential in enhancing automated diagnosis and monitoring of SSDs.

There are various applications of Cyber-Physical systems (CPSs) that are life-critical where failure or malfunction can result in significant harm to human life, the environment, or substantial economic loss. Therefore, it is important to ensure their reliability, security, and robustness to the attacks. However, there is no widely used toolbox to simulate CPS and target security problems, especially the simulation of sensor attacks and defense strategies against them. In this work, we introduce our toolbox CPSim, a user-friendly simulation toolbox for security problems in CPS. CPSim aims to simulate common sensor attacks and countermeasures to these sensor attacks. We have implemented bias attacks, delay attacks, and replay attacks. Additionally, we have implemented various recovery-based methods against sensor attacks. The sensor attacks and recovery methods configurations can be customized with the given APIs. CPSim has built-in numerical simulators and various implemented benchmarks. Moreover, CPSim is compatible with other external simulators and can be deployed on a real testbed for control purposes. 1

Deep learning (DL) techniques involving finetuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD).However, questions remain about their ability to generalize beyond the small reference sets that are publicly available for research.As an alternative to fitting model parameters directly, we propose a novel method by which a Transformer DL model (GPT-2) pre-trained on general English text is paired with an artificially degraded version of itself (GPT-D), to compute the ratio between these two models' perplexities on language from cognitively healthy and impaired individuals.This technique approaches state-ofthe-art performance on text data from a widely used "Cookie Theft" picture description task, and unlike established alternatives also generalizes well to spontaneous conversations.Furthermore, GPT-D generates text with characteristics known to be associated with AD, demonstrating the induction of dementia-related linguistic anomalies.Our study is a step toward better understanding of the relationships between the inner workings of generative neural language models, the language that they produce, and the deleterious effects of dementia on human speech and language characteristics.

Fog degrades the quality of road images causing errors in stereo matching and road segmentation for Advanced Driver Assistance Systems (ADAS). Accident rates can be reduced if robust and efficient algorithms are applied for road image fog removal. Many studies have been conducted on this subject to date, but existing methods are not optimized for road images. Several daytime models cause local darkness, and blurring artifacts result in low quality haze-free images, and nighttime models are altogether inefficient. This study focuses on dehazing daytime and nighttime images by utilizing chromatic properties to remove haze from images. The proposed method treats fog as a specular pixels of dual consistency and physical properties, and the dehazing reflection model is suitable for parallel implementation to efficiently detect fog pixels. An edge-preserving low-pass filter (a fast-bilateral filter running 230x faster than an average CPU) is used to smooth the color components' original image maximum fraction to remove noise among fog pixels. The method significantly outperforms existing baselines in regards to both efficiency and dehazing.

To improve the intra lossless video coding efficiency by introducing frequency domain compression, this paper presents a new intra coding scheme for lossless mode in HEVC. The proposed method utilizes reversible lifting DWT and DC level shifting to transform one frame into four sub-bands of frequency coefficients for the original coding tools to process. To recover the transformed frequency coefficients of signed type loss-lessly, a modified reconstruction are applied to HEVC. By implementing the method in HM-16.5 reference software, the experimental results show that compared with the original HEVC lossless intra coding, the new scheme can achieve 7.122% bitrate saving and take 92.443% encoding time averagely.