Face recognition systems are widely deployed in safety-critical applications, including law enforcement, yet they exhibit bias across a range of socio-demographic dimensions, such as gender and race. Conventional wisdom dictates that model biases arise from biased training data. As a consequence, previous works on bias mitigation largely focused on pre-processing the training data, adding penalties to prevent bias from effecting the model during training, or post-processing predictions to debias them, yet these approaches have shown limited success on hard problems such as face recognition. In our work, we discover that biases are actually inherent to neural network architectures themselves. Following this reframing, we conduct the first neural architecture search for fairness, jointly with a search for hyperparameters. Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2. Furthermore, these models generalize to other datasets and sensitive attributes. We release our code, models and raw data files at //github.com/dooleys/FR-NAS.
相關內容
Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test, causing confusion and wasting development effort. While machine learning models have been used to predict flakiness and its root causes, there is much less work on providing support to fix the problem. To address this gap, in this paper, we focus on predicting the type of fix that is required to remove flakiness and then repair the test code on that basis. We do this for a subset of flaky test cases where the root cause of flakiness is in the test case itself and not in the production code. Our key idea is to guide the repair process with additional knowledge about the test's flakiness in the form of its predicted fix category. Thus, we first propose a framework that automatically generates labeled datasets for 13 fix categories and trains models to predict the fix category of a flaky test by analyzing the test code only. Our experimental results using code models and few-shot learning show that we can correctly predict most of the fix categories. To show the usefulness of such fix category labels for automatically repairing flakiness, in addition to informing testers, we augment a Large Language Model (LLM) like GPT with such extra knowledge to ask the LLM for repair suggestions. The results show that our suggested fix category labels significantly enhance the capability of GPT 3.5 Turbo, in generating fixes for flaky tests.
Multi-agent motion prediction is a crucial concern in autonomous driving, yet it remains a challenge owing to the ambiguous intentions of dynamic agents and their intricate interactions. Existing studies have attempted to capture interactions between road entities by using the definite data in history timesteps, as future information is not available and involves high uncertainty. However, without sufficient guidance for capturing future states of interacting agents, they frequently produce unrealistic trajectory overlaps. In this work, we propose Future Interaction modeling for Motion Prediction (FIMP), which captures potential future interactions in an end-to-end manner. FIMP adopts a future decoder that implicitly extracts the potential future information in an intermediate feature-level, and identifies the interacting entity pairs through future affinity learning and top-k filtering strategy. Experiments show that our future interaction modeling improves the performance remarkably, leading to superior performance on the Argoverse motion forecasting benchmark.
Dialogue systems controlled by predefined or rule-based scenarios derived from counseling techniques, such as cognitive behavioral therapy (CBT), play an important role in mental health apps. Despite the need for responsible responses, it is conceivable that using the newly emerging LLMs to generate contextually relevant utterances will enhance these apps. In this study, we construct dialogue modules based on a CBT scenario focused on conventional Socratic questioning using two kinds of LLMs: a Transformer-based dialogue model further trained with a social media empathetic counseling dataset, provided by Osaka Prefecture (OsakaED), and GPT-4, a state-of-the art LLM created by OpenAI. By comparing systems that use LLM-generated responses with those that do not, we investigate the impact of generated responses on subjective evaluations such as mood change, cognitive change, and dialogue quality (e.g., empathy). As a result, no notable improvements are observed when using the OsakaED model. When using GPT-4, the amount of mood change, empathy, and other dialogue qualities improve significantly. Results suggest that GPT-4 possesses a high counseling ability. However, they also indicate that even when using a dialogue model trained with a human counseling dataset, it does not necessarily yield better outcomes compared to scenario-based dialogues. While presenting LLM-generated responses, including GPT-4, and having them interact directly with users in real-life mental health care services may raise ethical issues, it is still possible for human professionals to produce example responses or response templates using LLMs in advance in systems that use rules, scenarios, or example responses.
Large Language Model as Synthesizer: Fusing Diverse Inputs for Better Automatic Vulnerability Repair
The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of its length. This model also integrates diverse information, encompassing vulnerable code structures and expert knowledge from the CWE system. We evaluated VulMaster on a real-world C/C++ vulnerability repair dataset comprising 1,754 projects with 5,800 vulnerable functions. The experimental results demonstrated that VulMaster exhibits substantial improvements compared to the learning-based state-of-the-art vulnerability repair approach. Specifically, VulMaster improves the EM, BLEU, and CodeBLEU scores from 10.2\% to 20.0\%, 21.3\% to 29.3\%, and 32.5\% to 40.9\%, respectively.
In the realm of security applications, biometric authentication systems play a crucial role, yet one often encounters challenges concerning privacy and security while developing one. One of the most fundamental challenges lies in avoiding storing biometrics directly in the storage but still achieving decently high accuracy. Addressing this issue, we contribute to both artificial intelligence and engineering fields. We introduce an innovative image distortion technique that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. From the theoretical perspective, we explore how reliable state-of-the-art biometrics recognition neural networks are by checking the maximal degree of image distortion, which leaves the predicted identity unchanged. On the other hand, applying this technique demonstrates a practical solution to the engineering challenge of balancing security, precision, and performance in biometric authentication systems. Through experimenting on the widely used datasets, we assess the effectiveness of our method in preserving AI feature representation and distorting relative to conventional metrics. We also compare our method with previously used approaches.
AI-controlled robotic systems pose a risk to human workers and the environment. Classical risk assessment methods cannot adequately describe such black box systems. Therefore, new methods for a dynamic risk assessment of such AI-controlled systems are required. In this paper, we introduce the concept of a new dynamic risk assessment approach for AI-controlled robotic systems. The approach pipelines five blocks: (i) a Data Logging that logs the data of the given simulation, (ii) a Skill Detection that automatically detects the executed skills with a deep learning technique, (iii) a Behavioral Analysis that creates the behavioral profile of the robotic systems, (iv) a Risk Model Generation that automatically transforms the behavioral profile and risk data containing the failure probabilities of robotic hardware components into advanced hybrid risk models, and (v) Risk Model Solvers for the numerical evaluation of the generated hybrid risk models. Keywords: Dynamic Risk Assessment, Hybrid Risk Models, M2M Transformation, ROS, AI-Controlled Robotic Systems, Deep Learning, Reinforcement Learning
Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media platforms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and elevated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technologies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These advances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to collect and curate the dataset, and the dataset's characteristics at the current stage.
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.
Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level. In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator. Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification. The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191