276 research outputs found
Social Capital, Socioeconomic Status and Self-efficacy
This study internalized social capital on the basis of traditional study of the influence of economic factors on self-efficacy, and studied the relationship among the family socio-economic status, social capital and self-efficacy. Based on the theoretical analysis, with first-hand data collection and using multiple regression models, the paper studied the intermediate effect of social capital in the relationship between the socioeconomic status and self-efficacy. We draw on the following conclusions: (1) The family socio-economic status as well as all its dimensions (father’s degree of education, mother’s degree of education, the total annual income of the family, father’s occupation, mother’s occupation) is significantly positively related to social capital and all the dimensions of its proxy variable (peer support, kinship support and general support of others); (2) There is a significant positive correlation between the family socio-economic status as well as all its dimensions and self-efficacy; the socio-economic status, with its dimensions, is the predictive variable of self-efficacy; (3) Social capital, with dimensions of its proxy variable, is positively correlated with self-efficacy and has predictive effect on self-efficacy (4) Social capital plays a significant intermediate role between socio-economic status and self-efficacy, and the mediating effect size is about 51.75%
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Computer-aided pathology diagnosis based on the classification of Whole Slide
Image (WSI) plays an important role in clinical practice, and it is often
formulated as a weakly-supervised Multiple Instance Learning (MIL) problem.
Existing methods solve this problem from either a bag classification or an
instance classification perspective. In this paper, we propose an end-to-end
weakly supervised knowledge distillation framework (WENO) for WSI
classification, which integrates a bag classifier and an instance classifier in
a knowledge distillation framework to mutually improve the performance of both
classifiers. Specifically, an attention-based bag classifier is used as the
teacher network, which is trained with weak bag labels, and an instance
classifier is used as the student network, which is trained using the
normalized attention scores obtained from the teacher network as soft pseudo
labels for the instances in positive bags. An instance feature extractor is
shared between the teacher and the student to further enhance the knowledge
exchange between them. In addition, we propose a hard positive instance mining
strategy based on the output of the student network to force the teacher
network to keep mining hard positive instances. WENO is a plug-and-play
framework that can be easily applied to any existing attention-based bag
classification methods. Extensive experiments on five datasets demonstrate the
efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.Comment: Accepted by NeurIPS 202
Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry
Since its introduction in 2006, differential privacy has emerged as a
predominant statistical tool for quantifying data privacy in academic works.
Yet despite the plethora of research and open-source utilities that have
accompanied its rise, with limited exceptions, differential privacy has failed
to achieve widespread adoption in the enterprise domain. Our study aims to shed
light on the fundamental causes underlying this academic-industrial utilization
gap through detailed interviews of 24 privacy practitioners across 9 major
companies. We analyze the results of our survey to provide key findings and
suggestions for companies striving to improve privacy protection in their data
workflows and highlight the necessary and missing requirements of existing
differential privacy tools, with the goal of guiding researchers working
towards the broader adoption of differential privacy. Our findings indicate
that analysts suffer from lengthy bureaucratic processes for requesting access
to sensitive data, yet once granted, only scarcely-enforced privacy policies
stand between rogue practitioners and misuse of private information. We thus
argue that differential privacy can significantly improve the processes of
requesting and conducting data exploration across silos, and conclude that with
a few of the improvements suggested herein, the practical use of differential
privacy across the enterprise is within striking distance
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
This paper introduces the novel concept of few-shot weakly supervised
learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC.
A solution is proposed based on prompt learning and the utilization of a large
language model, GPT-4. Since a WSI is too large and needs to be divided into
patches for processing, WSI classification is commonly approached as a Multiple
Instance Learning (MIL) problem. In this context, each WSI is considered a bag,
and the obtained patches are treated as instances. The objective of FSWC is to
classify both bags and instances with only a limited number of labeled bags.
Unlike conventional few-shot learning problems, FSWC poses additional
challenges due to its weak bag labels within the MIL framework. Drawing
inspiration from the recent achievements of vision-language models (V-L models)
in downstream few-shot classification tasks, we propose a two-level prompt
learning MIL framework tailored for pathology, incorporating language prior
knowledge. Specifically, we leverage CLIP to extract instance features for each
patch, and introduce a prompt-guided pooling strategy to aggregate these
instance features into a bag feature. Subsequently, we employ a small number of
labeled bags to facilitate few-shot prompt learning based on the bag features.
Our approach incorporates the utilization of GPT-4 in a question-and-answer
mode to obtain language prior knowledge at both the instance and bag levels,
which are then integrated into the instance and bag level language prompts.
Additionally, a learnable component of the language prompts is trained using
the available few-shot labeled data. We conduct extensive experiments on three
real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer,
demonstrating the notable performance of the proposed method in bag and
instance classification. All codes will be made publicly accessible
A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging
The clustering technique has attracted a lot of attention as a promising
strategy for parallel debugging in multi-fault scenarios, this heuristic
approach (i.e., failure indexing or fault isolation) enables developers to
perform multiple debugging tasks simultaneously through dividing failed test
cases into several disjoint groups. When using statement ranking representation
to model failures for better clustering, several factors influence clustering
effectiveness, including the risk evaluation formula (REF), the number of
faults (NOF), the fault type (FT), and the number of successful test cases
paired with one individual failed test case (NSP1F). In this paper, we present
the first comprehensive empirical study of how these four factors influence
clustering effectiveness. We conduct extensive controlled experiments on 1060
faulty versions of 228 simulated faults and 141 real faults, and the results
reveal that: 1) GP19 is highly competitive across all REFs, 2) clustering
effectiveness decreases as NOF increases, 3) higher clustering effectiveness is
easier to achieve when a program contains only predicate faults, and 4)
clustering effectiveness remains when the scale of NSP1F is reduced to 20%
Comprehensive Evaluation of Driver's Propensity Based on Evidence Theory
Traffic safety is related closely with driver’s physiological and psychological characteristics. And the influence on traffic safety is represented as driver’s propensity. Evidence theory is introduced to the evaluation system of driver’s propensity in this paper, and it is utilized to combine the expert opinions, which can eliminate unavoidable uncertain elements in the traditional appraisal methods. The appraisal problems of subjective index can also be resolved by this method in the appraisal system. Results show that the method is objective and reasonable, and driver’s propensity can be evaluated effectively
SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum
Failure indexing is a longstanding crux in software testing and debugging,
the goal of which is to automatically divide failures (e.g., failed test cases)
into distinct groups according to the culprit root causes, as such multiple
faults in a faulty program can be handled independently and simultaneously.
This community has long been plagued by two challenges: 1) The effectiveness of
division is still far from promising. Existing techniques only employ a limited
source of run-time data (e.g., code coverage) to be failure proximity, which
typically delivers unsatisfactory results. 2) The outcome can be hardly
comprehensible. A developer who receives the failure indexing result does not
know why all failures should be divided the way they are. This leads to
difficulties for developers to be convinced by the result, which in turn
affects the adoption of the results. To tackle these challenges, in this paper,
we propose SURE, a viSUalized failuRe indExing approach using the program
memory spectrum. We first collect the run-time memory information at preset
breakpoints during the execution of failed test cases, and transform it into
human-friendly images (called program memory spectrum, PMS). Then, any pair of
PMS images that serve as proxies for two failures is fed to a trained Siamese
convolutional neural network, to predict the likelihood of them being triggered
by the same fault. Results demonstrate the effectiveness of SURE: It achieves
101.20% and 41.38% improvements in faults number estimation, as well as 105.20%
and 35.53% improvements in clustering, compared with the state-of-the-art
technique in this field, in simulated and real-world environments,
respectively. Moreover, we carry out a human study to quantitatively evaluate
the comprehensibility of PMS, revealing that this novel type of representation
can help developers better comprehend failure indexing results.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract here is shorter than that in the PDF fil
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