The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing

Most greybox fuzzing tools are coverage-guided as code coverage is strongly correlated with bug coverage. However, since most covered codes may not contain bugs, blindly extending code coverage is less efficient, especially for corner cases. Unlike coverage-guided greybox fuzzers who extend code coverage in an undirected manner, a directed greybox fuzzer spends most of its time allocation on reaching specific targets (e.g., the bug-prone zone) without wasting resources stressing unrelated parts. Thus, directed greybox fuzzing (DGF) is particularly suitable for scenarios such as patch testing, bug reproduction, and specialist bug hunting. This paper studies DGF from a broader view, which takes into account not only the location-directed type that targets specific code parts, but also the behaviour-directed type that aims to expose abnormal program behaviours. Herein, the first in-depth study of DGF is made based on the investigation of 32 state-of-the-art fuzzers (78% were published after 2019) that are closely related to DGF. A thorough assessment of the collected tools is conducted so as to systemise recent progress in this field. Finally, it summarises the challenges and provides perspectives for future research.Comment: 16 pages, 4 figure

Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10−5) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications

Linking Obesity with Colorectal Cancer: Epidemiology and Mechanistic Insights

The incidence of obesity and colorectal cancer (CRC) has risen rapidly in recent decades. More than 650 million obese and 2 billion overweight individuals are currently living in the world. CRC is the third most common cancer. Obesity is regarded as one of the key environmental risk factors for the pathogenesis of CRC. In the present review, we mainly focus on the epidemiology of obesity and CRC in the world, the United States, and China. We also summarize the molecular mechanisms linking obesity to CRC in different aspects, including nutriology, adipokines and hormones, inflammation, gut microbiota, and bile acids. The unmet medical needs for obesity-related CRC are still remarkable. Understanding the molecular basis of these associations will help develop novel therapeutic targets and approaches for the treatment of obesity-related CRC

Prediction of dynamic responses of FSRU-LNGC side-by-side mooring system

Floating Storage and Regasification Unit (FSRU) becomes one of the most popular equipment in the industry for providing clean energy because of its technical, economic and environmental features. The interaction between the FSRU and Liquified Natural Gas Carrier (LNGC) under the combined loads from wind, wave and current is quite complex to model. In this paper, a configuration for the offloading operation of the FSRU-LNGC side-by-side mooring system is proposed to predict the motion responses, forces on the cables and fenders of the multi-floating mooring system. The damping lid method is adopted to improve the overestimated hydrodynamic coefficients calculated from conventional potential flow theory in the frequency domain. The dynamic response of the side-by-side mooring system including six degrees of freedom motion, relative motions, cable tensions and fender forces are provided and analyzed. The numerical results are validated using the experimental data. The proposed coupled analysis model and the numerical analysis can properly predict the dynamic response of the multi-floating mooring. The sensitivity analysis of pretension of the connecting cables on the dynamic responses of the two vessels are provided. Moreover, the non-dimensional damping parameters can be acted as a good reference to the dynamic response analysis of similar multi-floating mooring systems

Superiority of Multi-Head Attention in In-Context Linear Regression

We present a theoretical analysis of the performance of transformer with softmax attention in in-context learning with linear regression tasks. While the existing literature predominantly focuses on the convergence of transformers with single-/multi-head attention, our research centers on comparing their performance. We conduct an exact theoretical analysis to demonstrate that multi-head attention with a substantial embedding dimension performs better than single-head attention. When the number of in-context examples D increases, the prediction loss using single-/multi-head attention is in O(1/D), and the one for multi-head attention has a smaller multiplicative constant. In addition to the simplest data distribution setting, we consider more scenarios, e.g., noisy labels, local examples, correlated features, and prior knowledge. We observe that, in general, multi-head attention is preferred over single-head attention. Our results verify the effectiveness of the design of multi-head attention in the transformer architecture

Data Poisoning for In-context Learning

In the domain of large language models (LLMs), in-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks, relying on examples rather than retraining or fine-tuning. This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks, an area not yet fully explored. We wonder whether ICL is vulnerable, with adversaries capable of manipulating example data to degrade model performance. To address this, we introduce ICLPoison, a specialized attacking framework conceived to exploit the learning mechanisms of ICL. Our approach uniquely employs discrete text perturbations to strategically influence the hidden states of LLMs during the ICL process. We outline three representative strategies to implement attacks under our framework, each rigorously evaluated across a variety of models and tasks. Our comprehensive tests, including trials on the sophisticated GPT-4 model, demonstrate that ICL's performance is significantly compromised under our framework. These revelations indicate an urgent need for enhanced defense mechanisms to safeguard the integrity and reliability of LLMs in applications relying on in-context learning

Social-specific impairment of negative emotion perception in alexithymia

Alexithymia has been characterized as an impaired ability of emotion processing and regulation. The definition of alexithymia does not include a social component. However, there is some evidence that social cognition may be compromised in individuals with alexithymia. Hence, emotional impairments associated with alexithymia may extend to socially relevant information. Here, we recorded electrophysiological responses of individuals meeting the clinically relevant cutoff for alexithymia (ALEX; n = 24) and individuals without alexithymia (NonALEX; n = 23) while they viewed affective scenes that varied on the dimensions of sociality and emotional valence during a rapid serial visual presentation task. We found that ALEX exhibited lower accuracy and larger N2 than NonALEX in the perception of social negative scenes. Source reconstruction revealed that the group difference in N2 was localized at the dorsal anterior cingulate cortex. Irrespective of emotional valence, ALEX showed stronger alpha power than NonALEX in social but not non-social conditions. Our findings support the hypothesis of social processing being selectively affected by alexithymia, especially for stimuli with negative valence. Electrophysiological evidence suggests altered deployment of attentional resources in the perception of social-specific emotional information in alexithymia. This work sheds light on the neuropsychopathology of alexithymia and alexithymia-related disorders

A two-stage data-driven multi-energy management considering demand response

This paper proposes an innovative two-stage data-driven optimization framework for a multi-energy system. Enormous energy conversion technologies are incorporated in the system to enhance the overall energy utilization efficiency, i.e., combined heat and power, power-to-gas, gas furnace, and ground source heat pump. Furthermore, a demand response program is adopted for stimulating the load shift of customers. Accordingly, both the economic performance and system reliability can be improved. The endogenous solar generation brings about high uncertainty and variability, which affects the decision making of the system operator. Therefore, a two-stage data-driven distributionally robust optimization (TSDRO) method is utilized to capture the uncertainty. A tractable semidefinite programming reformulation is obtained based on the duality theory. Case studies are implemented to demonstrate the effectiveness of applying the TSDRO on energy management.</p