855 research outputs found
On the decidability of linear bounded periodic cyber-physical systems
Cyber-Physical Systems (CPSs) are integrations of distributed computing systems with physical processes via a networking with actuators and sensors, where feedback loops among the components allow the physical processes to affect the computations and vice versa. Although CPSs can be found in several complex and sometimes critical real-world domains, their verification and validation often relies on simulation-test systems rather then automatic methodologies to formally verify safety requirements. In this work, we prove the decidability of the reachability problem for discrete-time linear CPSs whose physical process in isolation has a periodic behavior, up to an initial transitory phase
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Predicting the course of Alzheimer's progression.
Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5Â years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only
Opinion Mining for Software Development: A Systematic Literature Review
Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies.
SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in
code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take
considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils
these approaches entail.
We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion
mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in
other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4)
concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques.
The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide
critical insights for the further development of opinion mining techniques in the SE domain
The relevance of application domains in empirical findings
The term 'software ecosystem' refers to a collection of software systems that are related in some way. Researchers have been using different levels of aggregation to define an ecosystem: grouping them by a common named project (e.g., the Apache ecosystem); or considering all the projects contained in online repositories (e.g., the GoogleCode ecosystem). In this paper we propose a definition of ecosystem based on application domains: software systems are in the same ecosystem if they share the same application domain, as described by a similar technological scope, context or objective. As an example, all projects implementing networking capabilities to trade Bitcoin and other virtual currencies can be considered as part of the same "cryp-tocurrency" ecosystem. Utilising a sample of 100 Java software systems, we derive their application domains using the Latent Dirichlet Allocation (LDA) approach. We then evaluate a suite of object-oriented metrics per ecosystem, and test a null hypothesis: 'the OO metrics of all ecosystems come from the same population'. Our results show that the null hypothesis is rejected for most of the metrics chosen: the ecosystems that we extracted, based on application domains, show different structural properties. From the point of view of the interested stakeholders, this could mean that the health of a software system depends on domain-dependent factors, that could be common to the projects in the same domain-based ecosystem
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
In this work, we present a post-processing solution to address the hubness
problem in cross-modal retrieval, a phenomenon where a small number of gallery
data points are frequently retrieved, resulting in a decline in retrieval
performance. We first theoretically demonstrate the necessity of incorporating
both the gallery and query data for addressing hubness as hubs always exhibit
high similarity with gallery and query data. Second, building on our
theoretical results, we propose a novel framework, Dual Bank Normalization
(DBNorm). While previous work has attempted to alleviate hubness by only
utilizing the query samples, DBNorm leverages two banks constructed from the
query and gallery samples to reduce the occurrence of hubs during inference.
Next, to complement DBNorm, we introduce two novel methods, dual inverted
softmax and dual dynamic inverted softmax, for normalizing similarity based on
the two banks. Specifically, our proposed methods reduce the similarity between
hubs and queries while improving the similarity between non-hubs and queries.
Finally, we present extensive experimental results on diverse language-grounded
benchmarks, including text-image, text-video, and text-audio, demonstrating the
superior performance of our approaches compared to previous methods in
addressing hubness and boosting retrieval performance. Our code is available at
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.Comment: Accepted by EMNLP 202
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks
Text-video retrieval is a challenging task that aims to identify relevant
videos given textual queries. Compared to conventional textual retrieval, the
main obstacle for text-video retrieval is the semantic gap between the textual
nature of queries and the visual richness of video content. Previous works
primarily focus on aligning the query and the video by finely aggregating
word-frame matching signals. Inspired by the human cognitive process of
modularly judging the relevance between text and video, the judgment needs
high-order matching signal due to the consecutive and complex nature of video
contents. In this paper, we propose chunk-level text-video matching, where the
query chunks are extracted to describe a specific retrieval unit, and the video
chunks are segmented into distinct clips from videos. We formulate the
chunk-level matching as n-ary correlations modeling between words of the query
and frames of the video and introduce a multi-modal hypergraph for n-ary
correlation modeling. By representing textual units and video frames as nodes
and using hyperedges to depict their relationships, a multi-modal hypergraph is
constructed. In this way, the query and the video can be aligned in a
high-order semantic space. In addition, to enhance the model's generalization
ability, the extracted features are fed into a variational inference component
for computation, obtaining the variational representation under the Gaussian
distribution. The incorporation of hypergraphs and variational inference allows
our model to capture complex, n-ary interactions among textual and visual
contents. Experimental results demonstrate that our proposed method achieves
state-of-the-art performance on the text-video retrieval task
Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
While recent progress in video-text retrieval has been advanced by the
exploration of better representation learning, in this paper, we present a
novel multi-grained sparse learning framework, S3MA, to learn an aligned sparse
space shared between the video and the text for video-text retrieval. The
shared sparse space is initialized with a finite number of sparse concepts,
each of which refers to a number of words. With the text data at hand, we learn
and update the shared sparse space in a supervised manner using the proposed
similarity and alignment losses. Moreover, to enable multi-grained alignment,
we incorporate frame representations for better modeling the video modality and
calculating fine-grained and coarse-grained similarities. Benefiting from the
learned shared sparse space and multi-grained similarities, extensive
experiments on several video-text retrieval benchmarks demonstrate the
superiority of S3MA over existing methods. Our code is available at
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.Comment: Findings of EMNLP 202
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