In recent years, machine learning (ML) has relied heavily on crowdworkers both for building datasets and for addressing research questions requiring human interaction or judgment. The diverse tasks performed and uses of the data produced render it difficult to determine when crowdworkers are best thought of as workers (versus human subjects). These difficulties are compounded by conflicting policies, with some institutions and researchers regarding all ML crowdworkers as human subjects and others holding that they rarely constitute human subjects. Notably few ML papers involving crowdwork mention IRB oversight, raising the prospect of non-compliance with ethical and regulatory requirements. We investigate the appropriate designation of ML crowdsourcing studies, focusing our inquiry on natural language processing to expose unique challenges for research oversight. Crucially, under the U.S. Common Rule, these judgments hinge on determinations of aboutness, concerning both whom (or what) the collected data is about and whom (or what) the analysis is about. We highlight two challenges posed by ML: the same set of workers can serve multiple roles and provide many sorts of information; and ML research tends to embrace a dynamic workflow, where research questions are seldom stated ex ante and data sharing opens the door for future studies to aim questions at different targets. Our analysis exposes a potential loophole in the Common Rule, where researchers can elude research ethics oversight by splitting data collection and analysis into distinct studies. Finally, we offer several policy recommendations to address these concerns.
相關內容
The past decade has witnessed remarkable advancements in deep learning, owing to the emergence of various architectures, layers, objectives, and optimization techniques. These consist of a multitude of variations of attention, normalization, skip connections, transformer, and self-supervised learning methods, among others. Our goal is to furnish a comprehensive survey of significant recent contributions in these domains to individuals with a fundamental grasp of deep learning. Our aspiration is that an integrated and comprehensive approach of influential recent works will facilitate the formation of new connections between different areas of deep learning. In our discussion, we discuss multiple patterns that summarize the key strategies for many of the successful innovations over the last decade. We also include a discussion on recent commercially built, closed-source models such as OpenAI's GPT-4 and Google's PaLM 2.
Federated learning (FL), as a decentralized machine learning solution to the protection of users' private data, has become an important learning paradigm in recent years, especially since the enforcement of stricter laws and regulations in most countries. Therefore, a variety of FL frameworks are released to facilitate the development and application of federated learning. Despite the considerable amount of research on the security and privacy of FL models and systems, the security issues in FL frameworks have not been systematically studied yet. In this paper, we conduct the first empirical study on 1,112 FL framework bugs to investigate their characteristics. These bugs are manually collected, classified, and labeled from 12 open-source FL frameworks on GitHub. In detail, we construct taxonomies of 15 symptoms, 12 root causes, and 20 fix patterns of these bugs and investigate their correlations and distributions on 23 logical components and two main application scenarios. From the results of our study, we present nine findings, discuss their implications, and propound several suggestions to FL framework developers and security researchers on the FL frameworks.
Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as trustworthy data resources, and are accessible across different platforms. Therefore, researchers have developed many different PDF malware detection methods. Performance in detecting PDF malware is greatly influenced by feature selection. In this research, we propose a small features set that don't require too much domain knowledge of the PDF file. We evaluate proposed features with six different machine learning models. We report the best accuracy of 99.75% when using Random Forest model. Our proposed feature set, which consists of just 12 features, is one of the most conciseness in the field of PDF malware detection. Despite its modest size, we obtain comparable results to state-of-the-art that employ a much larger set of features.
Reliable and robust evaluation methods are a necessary first step towards developing machine learning models that are themselves robust and reliable. Unfortunately, current evaluation protocols typically used to assess classifiers fail to comprehensively evaluate performance as they tend to rely on limited types of test data, and ignore others. For example, using the standard test data fails to evaluate the predictions made by the classifier to samples from classes it was not trained on. On the other hand, testing with data containing samples from unknown classes fails to evaluate how well the classifier can predict the labels for known classes. This article advocates bench-marking performance using a wide range of different types of data and using a single metric that can be applied to all such data types to produce a consistent evaluation of performance. Using such a benchmark it is found that current deep neural networks, including those trained with methods that are believed to produce state-of-the-art robustness, are extremely vulnerable to making mistakes on certain types of data. This means that such models will be unreliable in real-world scenarios where they may encounter data from many different domains, and that they are insecure as they can easily be fooled into making the wrong decisions. It is hoped that these results will motivate the wider adoption of more comprehensive testing methods that will, in turn, lead to the development of more robust machine learning methods in the future. Code is available at: \url{//codeberg.org/mwspratling/RobustnessEvaluation}
Learning algorithms that divide the data into batches are prevalent in many machine-learning applications, typically offering useful trade-offs between computational efficiency and performance. In this paper, we examine the benefits of batch-partitioning through the lens of a minimum-norm overparameterized linear regression model with isotropic Gaussian features. We suggest a natural small-batch version of the minimum-norm estimator, and derive an upper bound on its quadratic risk, showing it is inversely proportional to the noise level as well as to the overparameterization ratio, for the optimal choice of batch size. In contrast to minimum-norm, our estimator admits a stable risk behavior that is monotonically increasing in the overparameterization ratio, eliminating both the blowup at the interpolation point and the double-descent phenomenon. Interestingly, we observe that this implicit regularization offered by the batch partition is partially explained by feature overlap between the batches. Our bound is derived via a novel combination of techniques, in particular normal approximation in the Wasserstein metric of noisy projections over random subspaces.
In recent years, more and more researchers have reflected on the undervaluation of emotion in data visualization and highlighted the importance of considering human emotion in visualization design. Meanwhile, an increasing number of studies have been conducted to explore emotion-related factors. However, so far, this research area is still in its early stages and faces a set of challenges, such as the unclear definition of key concepts, the insufficient justification of why emotion is important in visualization design, and the lack of characterization of the design space of affective visualization design. To address these challenges, first, we conducted a literature review and identified three research lines that examined both emotion and data visualization. We clarified the differences between these research lines and kept 109 papers that studied or discussed how data visualization communicates and influences emotion. Then, we coded the 109 papers in terms of how they justified the legitimacy of considering emotion in visualization design (i.e., why emotion is important) and identified five argumentative perspectives. Based on these papers, we also identified 61 projects that practiced affective visualization design. We coded these design projects in three dimensions, including design fields (where), design tasks (what), and design methods (how), to explore the design space of affective visualization design.
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. However, data heterogeneity, e.g., non-independently identically distributions and different sizes of training data among clients, poses major challenges to wireless FL. Limited communication resources complicate the implementation of fair scheduling which is required for training on heterogeneous data, and further deteriorate the overall performance. To address this issue, this paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. Specifically, we first develop a closed-form expression for an upper bound on the FL loss function, with a particular emphasis on data heterogeneity described by a dataset size vector and a data divergence vector. Then we formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE). Next, via the Lyapunov drift technique, we transform the CRE optimization problem into a series of tractable problems. Extensive experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.
In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order. Similar to the human learning process with the ability of learning, fusing, and accumulating new knowledge coming at different time steps, continual learning is considered to have high practical significance. Hence, continual learning has been studied in various artificial intelligence tasks. In this paper, we present a comprehensive review of the recent progress of continual learning in computer vision. In particular, the works are grouped by their representative techniques, including regularization, knowledge distillation, memory, generative replay, parameter isolation, and a combination of the above techniques. For each category of these techniques, both its characteristics and applications in computer vision are presented. At the end of this overview, several subareas, where continuous knowledge accumulation is potentially helpful while continual learning has not been well studied, are discussed.
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191