1,988 research outputs found
Signature Verification Using Siamese Convolutional Neural Networks
This research entails the processes undergone in building a Siamese Neural Network for Signature Verification. This Neural Network which uses two similar base neural networks as its underlying architecture was built, trained and evaluated in this project. The base networks were made up of two similar convolutional neural networks sharing the same weights during training. The architecture commonly known as the Siamese network helped reduce the amount of training data needed for its implementation and thus increased the model’s efficiency by 13%. The convolutional network was made up of three convolutional layers, three pooling layers and one fully connected layer onto which the final results were passed to the contrastive loss function for comparison. A threshold function determined if the signatures were forged or not. An accuracy of 78% initially achieved led to the tweaking and improvement of the model to achieve a better prediction accuracy of 93%
Automated Deductive Content Analysis of Text: A Deep Contrastive and Active Learning Based Approach
Content analysis traditionally involves human coders manually combing through text documents to search for relevant concepts and categories. However, this approach is time-intensive and not scalable, particularly for secondary data like social media content, news articles, or corporate reports. To address this problem, the paper presents an automated framework called Automated Deductive Content Analysis of Text (ADCAT) that uses deep learning-based semantic techniques, ontology of validated construct measures, large language model, human-in-the-loop disambiguation, and a novel augmentation-based weighted contrastive learning approach for improved language representations, to build a scalable approach for deductive content analysis. We demonstrate the effectiveness of the proposed approach to identify firm innovation strategies from their 10-K reports to obtain inferences reasonably close to human coding
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New Methods Using Rigorous Machine Learning for Coarse-Grained Protein Folding and Dynamics
Graph Positional and Structural Encoder
Positional and structural encodings (PSE) enable better identifiability of
nodes within a graph, as in general graphs lack a canonical node ordering. This
renders PSEs essential tools for empowering modern GNNs, and in particular
graph Transformers. However, designing PSEs that work optimally for a variety
of graph prediction tasks is a challenging and unsolved problem. Here, we
present the graph positional and structural encoder (GPSE), a first-ever
attempt to train a graph encoder that captures rich PSE representations for
augmenting any GNN. GPSE can effectively learn a common latent representation
for multiple PSEs, and is highly transferable. The encoder trained on a
particular graph dataset can be used effectively on datasets drawn from
significantly different distributions and even modalities. We show that across
a wide range of benchmarks, GPSE-enhanced models can significantly improve the
performance in certain tasks, while performing on par with those that employ
explicitly computed PSEs in other cases. Our results pave the way for the
development of large pre-trained models for extracting graph positional and
structural information and highlight their potential as a viable alternative to
explicitly computed PSEs as well as to existing self-supervised pre-training
approaches
Three Essays on the Role of Unstructured Data in Marketing Research
This thesis studies the use of firm and user-generated unstructured data (e.g., text and videos) for improving market research combining advances in text, audio and video processing with traditional economic modeling. The first chapter is joint work with K. Sudhir and Minkyung Kim. It addresses two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, we develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, we address the problem of missing attributes in text in constructing attribute sentiment scores---as reviewers write only about a subset of attributes and remain silent on others. We develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, we show superior accuracy in converting text to numerical attribute sentiment scores with our model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. The second essay, which is joint work with Aniko Oery and Joyee Deb is an information-theoretic model to study what causes selection in valence in user-generated reviews. The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the strength of brand image (dispersion of consumer beliefs about quality) and the informativeness of good and bad experiences impacts selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. If the brand image is strong (consumer beliefs are homogeneous), only negative WOM can arise. With a weak brand image or heterogeneous beliefs, positive WOM can occur if positive experiences are sufficiently informative. Using data from Yelp.com, we show how strong brands (chain restaurants) systematically receive lower evaluations controlling for several restaurant and reviewer characteristics. The third essay which is joint work with K.Sudhir and Khai Chiong studies success factors of persuasive sales pitches from a multi-modal video dataset of buyer-seller interactions. A successful sales pitch is an outcome of both the content of the message as well as style of delivery. Moreover, unlike one-way interactions like speeches, sales pitches are a two-way process and hence interactivity as well as matching the wavelength of the buyer are also critical to the success of the pitch. We extract four groups of features: content-related, style-related, interactivity and similarity in order to build a predictive model of sales pitch effectiveness
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability
of spiking activity in the brain is not mere noise, but rather the reflection
of a sampling-based encoding scheme for probabilistic computing. Since the
precise statistical properties of neural activity are important in this
context, many models assume an ad-hoc source of well-behaved, explicit noise,
either on the input or on the output side of single neuron dynamics, most often
assuming an independent Poisson process in either case. However, these
assumptions are somewhat problematic: neighboring neurons tend to share
receptive fields, rendering both their input and their output correlated; at
the same time, neurons are known to behave largely deterministically, as a
function of their membrane potential and conductance. We suggest that spiking
neural networks may, in fact, have no need for noise to perform sampling-based
Bayesian inference. We study analytically the effect of auto- and
cross-correlations in functionally Bayesian spiking networks and demonstrate
how their effect translates to synaptic interaction strengths, rendering them
controllable through synaptic plasticity. This allows even small ensembles of
interconnected deterministic spiking networks to simultaneously and
co-dependently shape their output activity through learning, enabling them to
perform complex Bayesian computation without any need for noise, which we
demonstrate in silico, both in classical simulation and in neuromorphic
emulation. These results close a gap between the abstract models and the
biology of functionally Bayesian spiking networks, effectively reducing the
architectural constraints imposed on physical neural substrates required to
perform probabilistic computing, be they biological or artificial
The Power of Patents: Leveraging Text Mining and Social Network Analysis to Forecast IoT Trends
Technology has become an indispensable competitive tool as science and
technology have progressed throughout history. Organizations can compete on an
equal footing by implementing technology appropriately. Technology trends or
technology lifecycles begin during the initiation phase. Finally, it reaches
saturation after entering the maturity phase. As technology reaches saturation,
it will be removed or replaced by another. This makes investing in technologies
during this phase unjustifiable. Technology forecasting is a critical tool for
research and development to determine the future direction of technology. Based
on registered patents, this study examined the trends of IOT technologies. A
total of 3697 patents related to the Internet of Things from the last six years
of patenting have been gathered using lens.org for this purpose. The main
people and companies were identified through the creation of the IOT patent
registration cooperation network, and the main groups active in patent
registration were identified by the community detection technique. The patents
were then divided into six technology categories: Safety and Security,
Information Services, Public Safety and Environment Monitoring, Collaborative
Aware Systems, Smart Homes/Buildings, and Smart Grid. And their technical
maturity was identified and examined using the Sigma Plot program. Based on the
findings, information services technologies are in the saturation stage, while
both smart homes/buildings, and smart grid technologies are in the saturation
stage. Three technologies, Safety and Security, Public Safety and Environment
Monitoring, and Collaborative Aware Systems are in the maturity stage
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