2,683 research outputs found
Information and Collective Mindfulness - A Methodological Replication Study
This paper reinvestigates the cognitive theory of collective mindfulness on organizational Information Systems performance by conducting a methodological replication of Khan, Lederer, and Mirchandani’s (2013) study. Collective mindfulness in the context of organizational information systems (IS) has significant effects on effectiveness and performance. We found that upper management concern and support for IS influences organizational performance through collective mindfulness. Upper management concern for typical and atypical situations and their associated repercussions on performance require solutions in real-time and concern for alternative problem-solving methods. Collective mindfulness addresses the notion of a more in-depth and purposeful analysis of potential catalysts negatively affecting performance. Future studies are encouraged to strengthen this study through construct improvement including the addition of relevant dimensions to collective mindfulness
Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC
Fault tolerance is one of the major design goals for HPC. The emergence of
non-volatile memories (NVM) provides a solution to build fault tolerant HPC.
Data in NVM-based main memory are not lost when the system crashes because of
the non-volatility nature of NVM. However, because of volatile caches, data
must be logged and explicitly flushed from caches into NVM to ensure
consistence and correctness before crashes, which can cause large runtime
overhead.
In this paper, we introduce an algorithm-based method to establish crash
consistence in NVM for HPC applications. We slightly extend application data
structures or sparsely flush cache blocks, which introduce ignorable runtime
overhead. Such extension or cache flushing allows us to use algorithm knowledge
to \textit{reason} data consistence or correct inconsistent data when the
application crashes. We demonstrate the effectiveness of our method for three
algorithms, including an iterative solver, dense matrix multiplication, and
Monte-Carlo simulation. Based on comprehensive performance evaluation on a
variety of test environments, we demonstrate that our approach has very small
runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller
than that of traditional checkpoint, while having the same or less
recomputation cost.Comment: 12 page
DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning
Twenty-five hundred years ago, the paperwork of the Achaemenid Empire was
recorded on clay tablets. In 1933, archaeologists from the University of
Chicago's Oriental Institute (OI) found tens of thousands of these tablets and
fragments during the excavation of Persepolis. Many of these tablets have been
painstakingly photographed and annotated by expert cuneiformists, and now
provide a rich dataset consisting of over 5,000 annotated tablet images and
100,000 cuneiform sign bounding boxes. We leverage this dataset to develop
DeepScribe, a modular computer vision pipeline capable of localizing cuneiform
signs and providing suggestions for the identity of each sign. We investigate
the difficulty of learning subtasks relevant to cuneiform tablet transcription
on ground-truth data, finding that a RetinaNet object detector can achieve a
localization mAP of 0.78 and a ResNet classifier can achieve a top-5 sign
classification accuracy of 0.89. The end-to-end pipeline achieves a top-5
classification accuracy of 0.80. As part of the classification module,
DeepScribe groups cuneiform signs into morphological clusters. We consider how
this automatic clustering approach differs from the organization of standard,
printed sign lists and what we may learn from it. These components, trained
individually, are sufficient to produce a system that can analyze photos of
cuneiform tablets from the Achaemenid period and provide useful transliteration
suggestions to researchers. We evaluate the model's end-to-end performance on
locating and classifying signs, providing a roadmap to a linguistically-aware
transliteration system, then consider the model's potential utility when
applied to other periods of cuneiform writing.Comment: Currently under review in the ACM JOCC
Part-guided Relational Transformers for Fine-grained Visual Recognition
Fine-grained visual recognition is to classify objects with visually similar
appearances into subcategories, which has made great progress with the
development of deep CNNs. However, handling subtle differences between
different subcategories still remains a challenge. In this paper, we propose to
solve this issue in one unified framework from two aspects, i.e., constructing
feature-level interrelationships, and capturing part-level discriminative
features. This framework, namely PArt-guided Relational Transformers (PART), is
proposed to learn the discriminative part features with an automatic part
discovery module, and to explore the intrinsic correlations with a feature
transformation module by adapting the Transformer models from the field of
natural language processing. The part discovery module efficiently discovers
the discriminative regions which are highly-corresponded to the gradient
descent procedure. Then the second feature transformation module builds
correlations within the global embedding and multiple part embedding, enhancing
spatial interactions among semantic pixels. Moreover, our proposed approach
does not rely on additional part branches in the inference time and reaches
state-of-the-art performance on 3 widely-used fine-grained object recognition
benchmarks. Experimental results and explainable visualizations demonstrate the
effectiveness of our proposed approach. The code can be found at
https://github.com/iCVTEAM/PART.Comment: Published in IEEE TIP 202
Multimodal Adversarial Learning
Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models are susceptible to adversarial attacks. In the case of generative models, facial sketches drawn by artists mostly used by law enforcement agencies depend on the ability of the artist to clearly replicate all the key facial features that aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. However, the incorporation of hybrid discriminators, which perform attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network have shown to be powerful tools towards better multi modal learning techniques. In general, our overall approach was aimed at improving target detection systems and the visual appeal of synthesized images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesized image. We synthesized sketches using XDOG filter for the CelebA, Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results overall for different model applications are impressive compared to current state of the art
Transformer-empowered Multi-modal Item Embedding for Enhanced Image Search in E-Commerce
Over the past decade, significant advances have been made in the field of
image search for e-commerce applications. Traditional image-to-image retrieval
models, which focus solely on image details such as texture, tend to overlook
useful semantic information contained within the images. As a result, the
retrieved products might possess similar image details, but fail to fulfil the
user's search goals. Moreover, the use of image-to-image retrieval models for
products containing multiple images results in significant online product
feature storage overhead and complex mapping implementations. In this paper, we
report the design and deployment of the proposed Multi-modal Item Embedding
Model (MIEM) to address these limitations. It is capable of utilizing both
textual information and multiple images about a product to construct meaningful
product features. By leveraging semantic information from images, MIEM
effectively supplements the image search process, improving the overall
accuracy of retrieval results. MIEM has become an integral part of the Shopee
image search platform. Since its deployment in March 2023, it has achieved a
remarkable 9.90% increase in terms of clicks per user and a 4.23% boost in
terms of orders per user for the image search feature on the Shopee e-commerce
platform.Comment: Accepted by IAAI 202
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