In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages.
Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products.
The use of word embeddings is an important NLP technique for extracting meaningful conclusions from corpora of human text.
1 code implementation • 16 Sep 2023 • Fudong Lin, Summer Crawford, Kaleb Guillot, Yihe Zhang, Yan Chen, Xu Yuan, Li Chen, Shelby Williams, Robert Minvielle, Xiangming Xiao, Drew Gholson, Nicolas Ashwell, Tri Setiyono, Brenda Tubana, Lu Peng, Magdy Bayoumi, Nian-Feng Tzeng
In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops.
Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders.
Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG.
Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization.
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process.
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness.
Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features.
We present a prototype ontology called Social Determinant of Health Ontology (SOHO) that covers relevant concepts and IS--A relationships describing impacts and associations of social determinants.
We use our implementation to study drift in model features, predictions, and performance on three real data sets.
Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption.
For the case with exact pseudo gradients, we design a two-loop algorithm by the equivalence of Nash equilibrium and variational inequality problems.
In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs).
In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data.
To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence.
Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products.
This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles.
This paper considers an unmanned vehicle-robot pickup and delivery system, in which a self-driving vehicle carrying multiple unmanned robots in the form of the mother ship travels from a depot to a number of stations distributed in a neighborhood to perform multiple pickup and delivery services.
To overcome such limitations, in this paper, we propose to utilize the radio signals, which can traverse obstacles and are unaffected by the lighting conditions to achieve silhouette segmentation.
The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices.
In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based onestage detectors and two-stage detectors.
For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models.
Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in.
A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection.
Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices.
To tackle this challenge, we propose an unsupervised domain adaptation framework for device free gesture recognition by making effective use of the unlabeled target domain data.
To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations.
To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i. e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes.
As an emerging technology that has attracted huge attention, non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface, with broad application prospects in the fields of autonomous driving, medical imaging, and defense.
We find that traders have executed 292, 606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue.
Reconfigurable metamaterials are constructed from tessellation of deformable modules that give rise to a set of tuneable properties.
In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP).
Natural Language Processing (NLP) is increasingly used as a key ingredient in critical decision-making systems such as resume parsers used in sorting a list of job candidates.
Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three.
from "The C Programming Language" course and "What are similarities and differences between packet switching and circuit switching?"
We observe that recent innovation in this area mainly focuses on new techniques that explicitly address the generalization issue when using this dataset, because this database is constructed in a highly controlled environment with limited human subjects and background variations.
Ranked #61 on 3D Human Pose Estimation on Human3.6M
To solve this problem, we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective.
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.
This paper proposes DroidNative, an Android malware detector that uses specific control flow patterns to reduce the effect of obfuscations, provides automation and platform independence, and as far as we know is the first system that operates at the Android native code level, allowing it to detect malware embedded in both native code and bytecode.
Cryptography and Security
Differential privacy has become the dominant standard in the research community for strong privacy protection.
Databases Cryptography and Security
In this paper, we develop differentially private algorithms that mirror real world empirical machine learning workflows.
In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game.