Natural language processing models are vulnerable to adversarial examples. Previous textual adversarial attacks adopt gradients or confidence scores to calculate word importance ranking and generate adversarial examples. However, this information is unavailable in the real world. Therefore, we focus on a more realistic and challenging setting, named hard-label attack, in which the attacker can only query the model and obtain a discrete prediction label. Existing hard-label attack algorithms tend to initialize adversarial examples by random substitution and then utilize complex heuristic algorithms to optimize the adversarial perturbation. These methods require a lot of model queries and the attack success rate is restricted by adversary initialization. In this paper, we propose a novel hard-label attack algorithm named LimeAttack, which leverages a local explainable method to approximate word importance ranking, and then adopts beam search to find the optimal solution. Extensive experiments show that LimeAttack achieves the better attacking performance compared with existing hard-label attack under the same query budget. In addition, we evaluate the effectiveness of LimeAttack on large language models, and results indicate that adversarial examples remain a significant threat to large language models. The adversarial examples crafted by LimeAttack are highly transferable and effectively improve model robustness in adversarial training.

Effective representation of molecules is a crucial factor affecting the performance of artificial intelligence models. This study introduces a flexible, fragment-based, multiscale molecular representation framework called t-SMILES (tree-based SMILES) with three code algorithms: TSSA (t-SMILES with Shared Atom), TSDY (t-SMILES with Dummy Atom) and TSID (t-SMILES with ID). It describes molecules using SMILES-type strings obtained by performing a breadth-first search on a full binary tree formed from a fragmented molecular graph. Systematic evaluations using JTVAE, BRICS, MMPA, and Scaffold show the feasibility to construct a multi-code molecular description system, where various descriptions complement each other, enhancing the overall performance. Additionally, it exhibits impressive performance on low-resource datasets, whether the model is original, data augmented, or pre-training fine-tuned. It significantly outperforms classical SMILES, DeepSMILES, SELFIES and baseline models in goal-directed tasks. Furthermore, it surpasses start-of-the-art fragment, graph and SMILES based approaches on ChEMBL, Zinc, and QM9.

As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increased complexity of these HPC systems and application tuning, empirical performance tuning, such as autotuning, has emerged as a promising approach in recent years. Despite its effectiveness, autotuning is often a computationally expensive approach. Transfer learning (TL)-based autotuning seeks to address this issue by leveraging the data from prior tuning. Current TL methods for autotuning spend significant time modeling the relationship between parameter configurations and performance, which is ineffective for few-shot (that is, few empirical evaluations) tuning on new tasks. We introduce the first generative TL-based autotuning approach based on the Gaussian copula (GC) to model the high-performing regions of the search space from prior data and then generate high-performing configurations for new tasks. This allows a sampling-based approach that maximizes few-shot performance and provides the first probabilistic estimation of the few-shot budget for effective TL-based autotuning. We compare our generative TL approach with state-of-the-art autotuning techniques on several benchmarks. We find that the GC is capable of achieving 64.37% of peak few-shot performance in its first evaluation. Furthermore, the GC model can determine a few-shot transfer budget that yields up to 33.39$\times$ speedup, a dramatic improvement over the 20.58$\times$ speedup using prior techniques.

Even with the best and largest pre-trained protein language models such Advances in biotechnology have demonstrated human's unprecedented as ESM-1b [33] and ProGen2 [29], one often needs to explore capabilities to engineer proteins. They make it an almost unknown domain and learn a new function possible to directly design the amino acid sequences that map in order to discover new drugs. This is especially true encode proteins for desired functions, towards improving with antibody engineering. Antibodies have highly diverse biochemical or enzymatic properties such as stability, binding complementarity-determining region (CDR) sequences that affinity, or catalytic activity. Directed evolution (DE), can be altered, resulting in a huge sequence space to explore for example, is a method for exploring new protein designs for optimal properties. The binding of antibodies to their targets with properties of interest and maximal utility, by mimicking are extrinsic properties of antibodies and it is difficult to the natural evolution process. The development of DE accurately model the sequence-binding relationships solely was honored in 2018 with the awarding of the Nobel Prize from the sequences alone. Further, most of the exploration in Chemistry to Frances Arnold for the directed evolution strategies used in practice lack theoretical guarantees. of enzymes, and George Smith and Gregory Winter for the development of phage display [3, 41, 48].

This study develops a graph search algorithm to find the optimal discrimination path for the binary classification problem. The objective function is defined as the difference of variations between the true positive (TP) and false positive (FP). It uses the depth first search (DFS) algorithm to find the top-down paths for discrimination. It proposes a dynamic optimization procedure to optimize TP at the upper levels and then reduce FP at the lower levels. To accelerate computing speed with improving accuracy, it proposes a reduced histogram algorithm with variable bin size instead of looping over all data points, to find the feature threshold of discrimination. The algorithm is applied on top of a Support Vector Machine (SVM) model for a binary classification problem on whether a person is fit or unfit. It significantly improves TP and reduces FP of the SVM results (e.g., reduced FP by 90% with a loss of only\ 5% TP). The graph search auto-generates 39 ranked discrimination paths within 9 seconds on an input of total 328,464 objects, using a dual-core Laptop computer with a processor of 2.59 GHz.

We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and configurations in order to learn a performance function of the solver. Secondly, we formulate a mixed-integer nonlinear program where the objective/constraints explicitly encode the learnt information, and which we solve, upon the arrival of an unknown instance, to find the best solver configuration for that instance, based on the performance function. The main novelty of our approach lies in the fact that the configuration set search problem is formulated as a mathematical program, which allows us to a) enforce hard dependence and compatibility constraints on the configurations, and b) solve it efficiently with off-the-shelf optimization tools.

During the architectural analysis, abstract artifacts need to be precisely identified and specified in order to efficiently guide the development, evolution and deployment of the overall system. Considering such an early stage, architectural decisions become even more challenging due to the lack of knowledge about the system but, at the same time, they are crucial to fulfill the many quality criteria imposed [12]. Artificial intelligence techniques and, more specifically, metaheuristics, can support software engineers in their decision processes by providing them with effective methods to explore a great deal of software designs, each one determined by a different trade-off among the required quality aspects. Such a scenario can be viewed as one of the goals of the search-based software engineering (SBSE) field[14], in which optimization techniques are applied to the resolution of software engineering (SE) tasks conveniently reformulated as search problems. However, solving human-centered activities in a fully automated way seems to be unrealistic, especially for those related to the analysis phase. Certainly, trying to capture the richness of human knowledge only by means of software metrics still represents an unresolved matter to the SE community [32]. Hence, most of the evaluation methods proposed at the architectural level strongly rely on the expert's judgment [10], making extremely difficult to precisely formulate a quantitative fitness function. Given the relevance of the software architect for the design process, searchbased approaches should benefit from his/her knowledge and expertise in order to address the optimization problem in the same way s/he would do it. Interactive optimization [21] constitutes a compelling paradigm here.

Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of causal effect estimation, such as controlling propensity score, enforcing randomization, etc., by designing neural network (NN) architectures and regularizers. In this paper, we propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER), a generalized method for causal effect estimation. NESTER integrates the ideas used in existing methods based on multi-head NNs for causal effect estimation into one framework. We design a Domain Specific Language (DSL) tailored for causal effect estimation based on causal inductive biases used in literature. We conduct a theoretical analysis to investigate NESTER's efficacy in estimating causal effects. Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.

System-level testing of avionics software systems requires compliance with different international safety standards such as DO-178C. An important consideration of the avionics industry is automated test data generation according to the criteria suggested by safety standards. One of the recommended criteria by DO-178C is the modified condition/decision coverage (MC/DC) criterion. The current model-based test data generation approaches use constraints written in Object Constraint Language (OCL), and apply search techniques to generate test data. These approaches either do not support MC/DC criterion or suffer from performance issues while generating test data for large-scale avionics systems. In this paper, we propose an effective way to automate MC/DC test data generation during model-based testing. We develop a strategy that utilizes case-based reasoning (CBR) and range reduction heuristics designed to solve MC/DC-tailored OCL constraints. We performed an empirical study to compare our proposed strategy for MC/DC test data generation using CBR, range reduction, both CBR and range reduction, with an original search algorithm, and random search. We also empirically compared our strategy with existing constraint-solving approaches. The results show that both CBR and range reduction for MC/DC test data generation outperform the baseline approach. Moreover, the combination of both CBR and range reduction for MC/DC test data generation is an effective approach compared to existing constraint solvers.

Many adversarial attacks target natural language processing systems, most of which succeed through modifying the individual tokens of a document. Despite the apparent uniqueness of each of these attacks, fundamentally they are simply a distinct configuration of four components: a goal function, allowable transformations, a search method, and constraints. In this survey, we systematically present the different components used throughout the literature, using an attack-independent framework which allows for easy comparison and categorisation of components. Our work aims to serve as a comprehensive guide for newcomers to the field and to spark targeted research into refining the individual attack components.