Backdooring Neural Code Search

Reusing off-the-shelf code snippets from online repositories is a common practice, which significantly enhances the productivity of software developers. To find desired code snippets, developers resort to code search engines through natural language queries. Neural code search models are hence behind many such engines. These models are based on deep learning and gain substantial attention due to their impressive performance. However, the security aspect of these models is rarely studied. Particularly, an adversary can inject a backdoor in neural code search models, which return buggy or even vulnerable code with security/privacy issues. This may impact the downstream software (e.g., stock trading systems and autonomous driving) and cause financial loss and/or life-threatening incidents. In this paper, we demonstrate such attacks are feasible and can be quite stealthy. By simply modifying one variable/function name, the attacker can make buggy/vulnerable code rank in the top 11%. Our attack BADCODE features a special trigger generation and injection procedure, making the attack more effective and stealthy. The evaluation is conducted on two neural code search models and the results show our attack outperforms baselines by 60%. Our user study demonstrates that our attack is more stealthy than the baseline by two times based on the F1 score

Porous chitosan by crosslinking with tricarboxylic acid and tuneable release

Chitosan hydrogels crosslinked with 1,3,5-benzene tricarboxylic acid (BTC) are readily prepared at room temperature by adding aqueous chitosan solution dropwise into BTC-ethanol solution. Highly interconnected porous chitosan materials are subsequently prepared by freeze-drying the chitosan hydrogels. These chitosan materials show porous structures with smaller pores than conventionally prepared chitosan hydrogels via crosslinking with NaOH, genipin or sodium triphosphate. This method of forming chitosan hydrogels with BTC provides the advantage of facile encapsulation of both hydrophobic and hydrophilic compounds, as demonstrated with the model dyes (Oil Red O and Rhodamine B). The release of the hydrophilic dye from the chitosan hydrogels is demonstrated and can be tuned by BTC/chitosan concentrations and the hydrogel drying methods. However, the release of encapsulated hydrophobic dye is negligible

Chemoselective Decarboxylative Oxygenation of Carboxylic Acids To Access Ketones, Aldehydes, and Peroxides.

Reported here is a photocatalytic strategy for the chemoselective decarboxylative oxygenation of carboxylic acids using Ce(III) catalysts and O2 as the oxidant. By simply changing the base employed, we demonstrate that the selectivity of the reaction can be channeled to favor hydroperoxides or carbonyls, with each class of products obtained in good to excellent yields and high selectivity. Notably, valuable ketones, aldehydes, and peroxides are produced directly from readily available carboxylic acid without additional steps

Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift

Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training. However, they are vulnerable to backdoor attacks as reported by recent studies. When a data input (e.g., some Gaussian noise) is stamped with a trigger (e.g., a white patch), the backdoored model always generates the target image (e.g., an improper photo). However, effective defense strategies to mitigate backdoors from DMs are underexplored. To bridge this gap, we propose the first backdoor detection and removal framework for DMs. We evaluate our framework Elijah on hundreds of DMs of 3 types including DDPM, NCSN and LDM, with 13 samplers against 3 existing backdoor attacks. Extensive experiments show that our approach can have close to 100% detection accuracy and reduce the backdoor effects to close to zero without significantly sacrificing the model utility.Comment: AAAI 202

An Approximate Solution for Predicting the Heat Extraction and Preventing Heat Loss from a Closed-Loop Geothermal Reservoir

Approximate solutions are found for a mathematical model developed to predict the heat extraction from a closed-loop geothermal system which consists of two vertical wells (one for injection and the other for production) and one horizontal well which connects the two vertical wells. Based on the feature of slow heat conduction in rock formation, the fluid flow in the well is divided into three stages, that is, in the injection, horizontal, and production wells. The output temperature of each stage is regarded as the input of the next stage. The results from the present model are compared with those obtained from numerical simulator TOUGH2 and show first-order agreement with a temperature difference less than 4°C for the case where the fluid circulated for 2.74 years. In the end, a parametric study shows that (1) the injection rate plays dominant role in affecting the output performance, (2) higher injection temperature produces larger output temperature but decreases the total heat extracted given a specific time, (3) the output performance of geothermal reservoir is insensitive to fluid viscosity, and (4) there exists a critical point that indicates if the fluid releases heat into or absorbs heat from the surrounding formation

Reducing Noise Level in Differential Privacy through Matrix Masking

Differential privacy schemes have been widely adopted in recent years to address issues of data privacy protection. We propose a new Gaussian scheme combining with another data protection technique, called random orthogonal matrix masking, to achieve $(\varepsilon, \delta)$-differential privacy (DP) more efficiently. We prove that the additional matrix masking significantly reduces the rate of noise variance required in the Gaussian scheme to achieve $(\varepsilon, \delta)-$DP in big data setting. Specifically, when $\varepsilon \to 0$, $\delta \to 0$, and the sample size $n$ exceeds the number $p$ of attributes by $(n-p)=O(ln(1/\delta))$, the required additive noise variance to achieve $(\varepsilon, \delta)$-DP is reduced from $O(ln(1/\delta)/\varepsilon^2)$ to $O(1/\varepsilon)$. With much less noise added, the resulting differential privacy protected pseudo data sets allow much more accurate inferences, thus can significantly improve the scope of application for differential privacy.Comment: 31 page

J2EE-based authentication system of expansive training for the university student

Conference Name:2013 3rd International Conference on Consumer Electronics, Communications and Networks, CECNet 2013. Conference Address: Xianning, China. Time:November 20, 2013 - November 22, 2013.The paper presents an authentication system that allows the user to query expansive training certification records for the university student. Firstly, we analyze the requirements of authentication system and come up with feasible solutions on the basis of learning the plan on expansive training to the cultivation of student quality. Before carrying out the system, the paper introduces the design thought of developing the system and the network technology it needs, and decides the design idea of oriented object, technique standard of J2EE and Web Service as technical solutions. In the end of the paper, it mainly discusses how to apply the J2EE technology to design the whole framework of authentication system. ? 2013 IEEE

Concise synthesis of pyrrolo[2,3-d]pyrimidine derivatives via the Cu-catalyzed coupling reaction

We reported a green and simple Cu-catalyzed method for the efficient synthesis of 2-chloro-7-cyclopentyl-N,N-dimethyl-7H-pyrrolo[2,3-d]pyrimidine-6-carboxamide as the key intermediate in the synthetic approaches to pyrrolo[2,3-d]pyrimidine derivatives from 5-bromo-2,4-dichloropyrimidine through two routes in four steps and five steps, respectively. This method provided green and economical approaches toward numerous pyrrolo[2,3-d]pyrimidine derivatives