Personalized activity recognition improves performance for diverse users.
problem Poor performance of impersonal algorithms for individual users.
method Personalized activity recognition using deep embeddings from a fully convolutional neural network with triplet loss.
result Novel subject triplet loss provides the best performance overall.
A new method for person recognition using cosine loss.
problem Recognizing the same identity across time and space with complicated scenes and similar appearance.
method Proposes a congenerous cosine loss to train a network for robust and representative features.
result The proposed method achieves better classification accuracy than previous state-of-the-arts.
Mobile training improves speech recognition for users with unique speech characteristics.
problem Limited generalization of speaker-independent speech recognition models for users with very different speech characteristics.
method Securely training personalized end-to-end speech recognition models on mobile devices, splitting gradient computation to reduce memory usage.
result On-device personalization achieved 58.1% relative word error rate reduction compared to 63.7% in a server environment, with 18.7% performance degradation.
CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
Mobile app improves speech recognition of names with user feedback.
problem Improving speech recognition of proper names on mobile devices.
method Data synthesis and user feedback for keyword-dependent metrics.
result User feedback can significantly improve speech recognition of names.
Experiment sets personalized emotion baselines for real-life prediction.
problem Establishing reliable ground-truth estimates for real-life emotion recognition.
method Controlled experiment with adaptive stimuli selection and user feedback.
result 85% accuracy in predicting emotional state with few features.
Real-time personalization for HAR models learns from new users without prior data.
problem Poor performance of HAR models on new users without labeled data.
method Incremental online domain adaptation using batch normalization.
result Personalized HAR models adapt to new users in real-time.
This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and su…
Deep learning gait recognition from smartphone data in unconstrained settings.
problem Gait recognition in unconstrained environments using smartphones.
method Hybrid deep neural network combining convolutional and recurrent neural networks for gait feature extraction.
result Achieves over 93% accuracy in person identification and authentication.
Personal VAD detects target speaker voice activity efficiently.
problem Efficiently detect target speaker voice activity for reduced computational cost and battery usage.
method Trains a neural network conditioned on speaker embedding or verification score, outputs probabilities for three speech classes.
result Trained model with 130K parameters outperforms combined standard VAD and speaker recognition networks.
Deep-learning model detects personality traits from short texts across multiple languages.
problem Recognizing author personality traits from short texts.
method Uses deep-learning-based models and atomic features of text to build hierarchical, vectorial word and sentence representations.
result Shows state-of-the-art performance across five traits and three languages (English, Spanish, and Italian).
Paper proposes a method to encrypt faces while maintaining visual similarity.
problem Protecting personal data from unauthorized face recognition.
method Targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
result TIP-IM provides 95%+ protection success rate against face recognition models.
Transfer learning improves activity recognition accuracy on smartwatches.
problem Inconsistent activity recognition accuracy across different users.
method On-device calibration of GBM by tuning parameters without retraining.
result Significant improvement in user-based accuracy for activity recognition.
We provide a benchmark dataset for hand gesture recognition using force myography.
problem Lack of publicly available benchmark data for force myography hand gesture recognition.
method Collected data from 20 persons covering 18 unique gestures using a commercially available sensor setup.
result Improved gesture recognition accuracy through transfer learning.
FMP improves personalized ASR models on private devices.
problem Limited accuracy and privacy in federated fine-tuning of ASR models.
method FMP estimates global and personalized marginal distributions and adjusts NNLM probabilities.
result FMP achieves modest WER reductions on ASR rescoring tasks.
New approach uses graphs for sign language recognition.
problem Challenges in recognizing sign language for deaf individuals.
method Spatial-Temporal Graph Convolutional Network.
result Improved sign language recognition using human skeletal movements.
Proposes a neural network for recognizing 3D skeleton-based interactions.
problem Recognizing two-person interactions from 3D skeleton sequences.
method Uses Gaussian distributions and Riemannian geometry of SPD matrices and matrix groups.
result Achieves competitive results on three benchmarks for 3D human activity understanding.
Noise makes data unlearnable by tricking models.
problem Unauthorized exploitation of personal data by deep learning models.
method Error-minimizing noise to reduce training examples' learnability.
result Error-minimizing noise can make training examples unlearnable by deep learning models.
A new method reduces redundancy in multimodal data for improved inference.
problem Understanding and optimizing the contribution of each modality in multimodal tasks.
method Modality-based Tensor Factorization (MRRF) for multimodal fusion.
result Improves multimodal inference tasks by 1% to 4% compared to state-of-the-art.
Study shows semi-supervised learning improves human activity recognition with minimal user input.
problem Improving human activity recognition models using incremental learning.
method Three approaches: non-supervised, semi-supervised, and supervised learning were compared.
result Semi-supervised learning achieves similar accuracy to supervised learning with minimal user input.
Paper tackles activity recognition from body-worn video footage.
problem Classifying frames of body-worn video footage according to the wearer's activity.
method Extract motion features and semi-supervised classification.
result Method achieves comparable results to supervised and deep learning methods using less training data.
Improved action detection for multi-person videos using attention filtering.
problem Difficulty in distinguishing relevant parts of multi-person videos for action detection.
method Fovea attention filtering and generalized binary loss function.
result 20% relative improvement in mAP over baseline in AVA dataset.
A novel tracking method for dense honeybee colonies using pixel personality.
problem Tracking large numbers of densely-arranged, interacting objects in a 2D environment.
method Segmentation-based object detection followed by adaptive object recognition through visual appearance.
result Reconstructed ~46% of trajectories in 5 minutes and 71% of tracks for at least 2 minutes.
Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative a…
Fawkes protects images from unauthorized facial recognition models.
problem Unauthorized training of facial recognition models poses privacy risks.
method Fawkes adds imperceptible pixel-level changes (cloaks) to images before release.
result Fawkes can protect images from misidentification by 95% and 80% even when clean images are leaked.
Study develops sign recognition system for DHH users.
problem Accessibility of voice-controlled devices for Deaf and Hard-of-Hearing users.
method Multimodal data (RGB video and skeletal data) for sign language recognition using deep learning.
result Validation on GMUASL51 dataset of 12 users and 13107 samples across 51 signs.
Study uses machine learning to detect pain from brain signals.
problem No validated objective measure of pain exists.
method Multi-task multiple kernel learning for personalized pain recognition.
result Supports use of fNIRS and machine learning for objective pain detection.
Deep learning identifies unique walking patterns from pressure data.
problem Tackling the challenge of accurately identifying individuals based on their walking style.
method Used deep learning, specifically convolutional neural networks (CNNs), to analyze the center-of-pressure trajectory of 36 adults walking on a treadmill.
result CNNs achieved 99.9% accuracy in classifying 2,250 segments and 100% accuracy in fine-tuning a subset of 4,500 segments, suggesting unique pressure patterns for each person.
Paper proposes DPN for encrypted speech recognition, maintaining privacy and security.
problem Privacy and security issues in cloud-based speech recognition.
method Deep Polynomial Network (DPN) for encrypted speech, cloud-local joint decoding.
result DPN can make frame-level predictions over encrypted speech and return them in encrypted form.
This paper analyzes the stability and generalization of triplet learning algorithms.
problem Lack of theoretical understanding of triplet learning's generalization performance.
method Stability analysis and high-probability generalization bounds for triplet learning algorithms.
result Established general high-probability generalization bound for triplet learning algorithms.
Privacy-preserving method protects user speech data from cloud services.
problem Privacy compromise in cloud-based speech analysis.
method Collects and sanitizes speech data before sharing, using transformation functions and voice conversion.
result Identification of sensitive emotional state reduced by ~96%.
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
A new deep learning framework improves HAR with user adaptation.
problem Sensor-based human activity recognition with long-term dependencies.
method Attention-based deep learning framework with user adaptation.
result Average increment of more than 7% on F1 score over state-of-the-art.
Deep learning improves gait biometric recognition accuracy.
problem Low accuracy in existing CSI-based gait identification systems.
method Developed an end-to-end deep CSI learning system using deep neural networks.
result Achieved a top-1 accuracy of 97.12% for a dataset of 30 people.
Study uses deep learning to quickly estimate tissue properties for personalized radio-frequency dosimetry.
problem Time-consuming tissue segmentation limits personalized radio-frequency dosimetry.
method Developed a learning-based approach using Convolutional Neural Networks (CNN) for magnetic resonance images.
result Smooth distribution of dielectric properties improves SAR distribution consistency.
A framework uses deep learning for activity recognition in IoT devices.
problem Activity recognition in IoT devices without physical contact.
method Background subtraction followed by 3D-Convolutional Neural Networks.
result Enhanced activity recognition using small IoT devices.
Improved speech recognition for voice assistants by analyzing speech data.
problem Reducing false triggers in speech-enabled assistants.
method Post-processing LVCSR hypothesis lattice with a Bidirectional Lattice Recurrent Neural Network (LatticeRNN).
result LatticeRNN significantly improves detection accuracy over traditional methods.
A deep network learns diverse contexts from multi-modal sensor data.
problem Recognizing diverse contexts and activities from multi-modal sensor data.
method Multi-stream temporal convolutional network with contextualization module.
result Deep network achieves optimal recognition rate.
Greek POS Tagger and Entity Recognizer built using spaCy.
problem Developing a machine learning model for Greek language POS tagging and named entity recognition.
method Machine learning approach with spaCy platform, focusing on morphological features and token classification.
result The Greek spaCy platform improved performance in part-of-speech tagging and named entity recognition.
Synthesizes faces from facial features, invariant to pose and expression.
problem Creating realistic face images from facial features.
method Learning facial landmarks and textures from facial-recognition features, training on frontal, neutral-expression images.
result Generated images are invariant to lighting, pose, and expression.
This paper converts speech to match a face image and vice versa.
problem Matching speech to a face image and vice versa.
method Proposes a model with speech converter, face encoder/decoder, and voice encoder.
result Trained model converts speech to match a face image and generates a face image that matches the voice of input speech.
Robots learn intentions from multiple cues to reduce uncertainty.
problem Uncertainty in human-robot interaction for vulnerable users.
method Multimodal classifier fusion using Bayesian Independent Opinion Pool.
result Fused classifiers outperform individual modalities in accuracy and uncertainty reduction.
SafeAccess identifies people in smart homes for safer access.
problem Enhancing safety and independence for people with disabilities.
method Change detection, Faster-RCNN, LBP/FaceNet, MTCNN, MMS.
result Average F-score of 0.97 for identifying friends/families/caregivers versus intruders/unknown.
Bayesian framework evaluates predictors of subjective visual tasks.
problem Evaluating uncertainty in machine learning predictors for tasks with subjective annotations.
method Bayesian framework to estimate epistemic uncertainty from human labels.
result Framework successfully applied to four image classification tasks.
Paper proposes singing voice conversion without parallel data.
problem Convert singing voices without parallel data.
method Phonetic posterior feature, DBLSTM, vocoder.
result Successfully converts singing voices without parallel data.
A novel method to prevent bias in authentication models.
problem Bias in data-driven authentication models trained in one domain but required to apply in others.
method Two-stage method involving one-versus-rest disentangle learning and additive adversarial learning.
result Demonstrated effectiveness and superiority of the proposed method through comprehensive evaluation.
Improved NER performance on imbalanced data.
problem Highly unbalanced training data in NER tasks.
method Adapted a neural architecture with CRF and BI-LSTM layers, using pre-trained embeddings. Introduced a two-class split to optimize performance.
result Significant improvement in performance for weak classes with minimal training data.
Improved multi-user VoiceFilter-Lite model for speech recognition.
problem Limited performance of multi-user VoiceFilter-Lite models.
method Dual learning rate schedule and FiLM for feature conditioning.
result Closed the performance gap between multi-user and single-user VoiceFilter-Lite models.