Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation

Joint segmentation and classification of fine-grained actions is important for applications of human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large-scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier. Our spatiotemporal CNN is comprised of a spatial component that uses convolutional filters to capture information about objects and their relationships, and a temporal component that uses large 1D convolutional filters to capture information about how object relationships change across time. These features are used in tandem with a semi-Markov model that models transitions from one action to another. We introduce an efficient constrained segmental inference algorithm for this model that is orders of magnitude faster than the current approach. We highlight the effectiveness of our Segmental Spatiotemporal CNN on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important mathematical error and made the section on segmental inference cleare

Magnetoelectric Effect at the Ni/HfO\u3csub\u3e2\u3c/sub\u3e Interface Induced by Ferroelectric Polarization

Driven by the technological importance of the recently discovered ferroelectric HfO2, we explore a magnetoelectric effect at the HfO2-based ferroelectric-ferromagnetic interface. Using density-functionaltheory calculations of the Ni/HfO2/Ni (001) heterostructure as a model system, we predict a stable and sizable ferroelectric polarization in a few-nm-thick HfO2 layer. For the Ni/HfO2 interface with opposite polarization directions (pointing to or away from the interface), we find a sizable difference in the interfacial Ni—O bonding, resulting in dissimilar degrees of depletion of the electron density around the interface. The latter affects the relative population of the exchange-split majority and minority spin bands at the interface and thus the interfacial magnetic moments. The sizable change in the interface magnetization with ferroelectric polarization reversal of HfO2 manifests a significant ferroelectrically induced magnetoelectric effect at the Ni/HfO2 interface. Our results reveal promising prospects of ferroelectric-ferromagnetic composite multiferroics based on HfO2-based ferroelectric materials

A fingerprint based crypto-biometric system for secure communication

To ensure the secure transmission of data, cryptography is treated as the most effective solution. Cryptographic key is an important entity in this procedure. In general, randomly generated cryptographic key (of 256 bits) is difficult to remember. However, such a key needs to be stored in a protected place or transported through a shared communication line which, in fact, poses another threat to security. As an alternative, researchers advocate the generation of cryptographic key using the biometric traits of both sender and receiver during the sessions of communication, thus avoiding key storing and at the same time without compromising the strength in security. Nevertheless, the biometric-based cryptographic key generation possesses few concerns such as privacy of biometrics, sharing of biometric data between both communicating users (i.e., sender and receiver), and generating revocable key from irrevocable biometric. This work addresses the above-mentioned concerns. In this work, a framework for secure communication between two users using fingerprint based crypto-biometric system has been proposed. For this, Diffie-Hellman (DH) algorithm is used to generate public keys from private keys of both sender and receiver which are shared and further used to produce a symmetric cryptographic key at both ends. In this approach, revocable key for symmetric cryptography is generated from irrevocable fingerprint. The biometric data is neither stored nor shared which ensures the security of biometric data, and perfect forward secrecy is achieved using session keys. This work also ensures the long-term security of messages communicated between two users. Based on the experimental evaluation over four datasets of FVC2002 and NIST special database, the proposed framework is privacy-preserving and could be utilized onto real access control systems.Comment: 29 single column pages, 8 figure

Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

With the continuous growth in the number of parameters of transformer-based pretrained language models (PLMs), particularly the emergence of large language models (LLMs) with billions of parameters, many natural language processing (NLP) tasks have demonstrated remarkable success. However, the enormous size and computational demands of these models pose significant challenges for adapting them to specific downstream tasks, especially in environments with limited computational resources. Parameter Efficient Fine-Tuning (PEFT) offers an effective solution by reducing the number of fine-tuning parameters and memory usage while achieving comparable performance to full fine-tuning. The demands for fine-tuning PLMs, especially LLMs, have led to a surge in the development of PEFT methods, as depicted in Fig. 1. In this paper, we present a comprehensive and systematic review of PEFT methods for PLMs. We summarize these PEFT methods, discuss their applications, and outline future directions. Furthermore, we conduct experiments using several representative PEFT methods to better understand their effectiveness in parameter efficiency and memory efficiency. By offering insights into the latest advancements and practical applications, this survey serves as an invaluable resource for researchers and practitioners seeking to navigate the challenges and opportunities presented by PEFT in the context of PLMs.Comment: 20 pages, 4 figure