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Authentication mechanisms deployed on access control systems undertake the responsibility of judging user identity to prevent unauthorized individuals from illegally approaching. In this article, we propose LIPAuth leveraging hand-dependent L ight I ntensity P attern to Auth enticate users. To be specific, lights released by a screen, are blocked and reflected by one hand above it; in this propagation process, hands exhibit user-specific ability in driving light absorption and attenuation due to owning unique structures, thereby outputting discriminative intensity patterns representing user identity. To implement LIPAuth , we first study the impact of screen contents on light intensity patterns, also explore the possibility of embedding hand structure biometrics into these patterns. We then design a customized dynamic stimulus-response mechanism for LIPAuth and make it resilient to the risks of potential registration profile leakage. Subsequently, we construct a joint pipeline consisting of signal processing and a learning-based generative adversarial network to overcome interference from variable user behaviors. More importantly, LIPAuth just utilizes common sensors to capture light signals, hence achieving low cost. We finally conduct extensive experiments in three scenarios to evaluate the authentication performance of LIPAuth prototype.

Creating a good deep learning model is an art which requires expertise in deep learning and a large set of labeled data for training neural networks. Neither is readily available. In this paper, we introduce a method that enables us to recover messages of LWE/LWR-based PKE/KEMs using simple multilayer perceptron (MLP) models trained on a small dataset. The core idea is to extend the attack dataset so that at least one of its traces has the ground truth label to which the models are biased towards. We demonstrate the effectiveness of the presented method on the examples of CRYSTALS-Kyber and Saber algorithms implemented in ARM Cortex-M4 CPU on nRF52832 system-on-chip supporting Bluetooth 5.2.We use amplitude-modulated EM emanations which are typically weaker and noisier than power or near-field EM side channels, and thus more difficult to exploit. QC 20230328

Photons with spin angular momentum possess intrinsic chirality which underpins many phenomena including nonlinear optics1, quantum optics2, topological photonics3 and chiroptics4. Intrinsic chirality is weak in natural materials, and recent theoretical proposals5-7 aimed to enlarge circular dichroism by resonant metasurfaces supporting bound states in the continuum that enhance substantially chiral light-matter interaction. Those insightful works resort to three-dimensional sophisticated geometries, which are too challenging to be realized for optical frequencies8. Therefore, most of the experimental attempts9-11 showing strong circular dichroism rely on false/extrinsic chirality by employing either oblique incidence9, 10 or structural anisotropy11. Here, we report on the experimental realization of true/intrinsic chiral response with resonant metasurfaces where the engineered slant geometry breaks both in-plane and out-of-plane symmetries. Our result marks the first observation of intrinsic chiral bound states in the continuum with near-unity chiral dichroism of 0.93 and record-high quality factor exceeding 2663 for visible frequencies. Our chiral metasurfaces promise a plethora of applications in chiral light sources and detectors, chiral sensing, valleytronics and asymmetric photocatalysis.

An oxidized copper species (Cuδ+) on the metallic copper surface is critical to the activity and selectivity of electrochemical reduction of CO2 gas. However, Cuδ+ species are easily reduced under ...

Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difficult to interpret. We argue that translating each image into a graph-based semantic representation, i.e., scene graph, which explicitly encodes the objects and relationships detected within image, would benefit representing and describing images. To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs. To further model the interaction among images, on the cross-images level, a Temporal Convolution Network (TCN) is utilized to refine the region representations along the temporal dimension. Then the relation-aware representations are fed into the Gated Recurrent Unit (GRU) with attention mechanism for story generation. Experiments are conducted on the public visual storytelling dataset. Automatic and human evaluation results indicate that our method achieves state-of-the-art.

Referring expression comprehension aims to localize the target object in an image referred by a natural language expression. Most existing approaches neglect the implicit logical correlations among fine-grained cues, e.g., categories, attributes, which are beneficial for distinguishing objects. In this paper, we propose a logic-guided approach to explore logical knowledge for referring expression comprehension in a hierarchical modular-based framework. Specifically, we propose to extract fine-grained cues in visual and textual domains and perform logical reasoning over them with explicit logical expressions to regularize the matching process without extra parameters. Besides, we propose to improve existing modular-based methods by introducing context information of objects in the relationship module. Extensive experiments are conducted on three referring expression datasets, and the results demonstrate that our model can produce more consistent predictions and further achieve superior performance compared with previous methods.

Polar codes are used in 5G system for the transmission of control channels due to its excellent error correction capability for short sequence, and CRC assistant successive cancellation list (CA-SCL) decoders are commonly used in practical system. When applied in critical environment, e.g. space platform, the memories in the hardware polar decoder will suffer single-event upsets (SEUs) that can cause failures and disrupt communications. Therefore, analyzing the reliability of polar decoder to SEUs on memories is important. This paper first analyzes the effects of single event upsets (SEUs) on the different parts of the memories in a CA-SCL decoder based on the decoding principle, including the influence of main parameters to the reliability of each part of user memory. Then fault injection simulations are performed to check the conclusions of theoretical analysis. Experiment results show that about 99% of SEUs on the memories can be tolerated by the decoder itself, and the reliability of polar decoders is higher for longer code, lower code rate and larger number of parallel decoder units. These results will be valuable to efficient protection of polar decoders to SEUs.

With the rapid development of science and technology, network communication technology has been widely used in various industries, but there are many communication security problems. In order to further improve the security of information data, related staff should use data encryption technology (DES) to strengthen security protection and ensure the integrity of information transmission. In the information age, information can help and benefit groups or individuals. Similarly, information can also be used to threaten and destroy them. We often need a measure to protect our data from being seen or destroyed by people with ulterior motives. In this paper, combining the principle and thought of genetic algorithm (GA), DES is studied in computer network communication security, and its application in computer communication network security is discussed.

Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature but few of them have been successfully applied in practice. To address this problem, we propose K-AID, a systematic approach that includes a low-cost knowledge acquisition process for acquiring domain knowledge, an effective knowledge infusion module for improving model performance, and a knowledge distillation component for reducing the model size and deploying K-PLMs on resource-restricted devices (e.g., CPU) for real-world application. Importantly, instead of capturing entity knowledge like the majority of existing K-PLMs, our approach captures relational knowledge, which contributes to better-improving sentence-level text classification and text matching tasks that play a key role in question answering (QA). We conducted a set of experiments on five text classification tasks and three text matching tasks from three domains, namely E-commerce, Government, and Film&TV, and performed online A/B tests in E-commerce. Experimental results show that our approach is able to achieve substantial improvement on sentence-level question answering tasks and bring beneficial business value in industrial settings., CIKM 2021

Existing research for image captioning usually represents an image using a scene graph with low-level facts (objects and relations) and fails to capture the high-level semantics. In this paper, we propose a Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics. In practice, we model theme concepts as memory vectors and propose Transformer with Theme Nodes (TTN) to incorporate those vectors for image captioning. Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN. On the vision side, TTN is configured to take both scene graph based features and theme concepts as input for visual representation learning. On the language side, TTN is configured to take both captions and theme concepts as input for text representation re-construction. Both settings aim to generate target captions with the same transformer-based decoder. During the training, we further align representations of theme concepts learned from images and corresponding captions to enforce the cross-modality learning. Experimental results on MS COCO show the effectiveness of our approach compared to some state-of-the-art models., Comment: IJCAI2021

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, the historically injected knowledge would be flushed away. To address this, we propose K-Adapter, a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused model. Taking RoBERTa as the backbone model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus multiple adapters can be efficiently trained in a distributed way. As a case study, we inject two kinds of knowledge in this work, including (1) factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata and (2) linguistic knowledge obtained via dependency parsing. Results on three knowledge-driven tasks, including relation classification, entity typing, and question answering, demonstrate that each adapter improves the performance and the combination of both adapters brings further improvements. Further analysis indicates that K-Adapter captures versatile knowledge than RoBERTa.

We present the first deep learning-based side-channel attack on AES-128 using far field electromagnetic emissions as a side channel. Our neural networks are trained on traces captured from five different Bluetooth devices at five different distances to target and tested on four other Bluetooth devices. We can recover the key from less than 10K traces captured in an office environment at 15 m distance to target even if the measurement for each encryption is taken only once. Previous template attacks required multiple repetitions of the same encryption. For the case of 1K repetitions, we need less than 400 traces on average at 15 m distance to target. This improves the template attack presented at CHES'2020 which requires 5K traces and key enumeration up to 223.

When watching videos, the occurrence of a visual event is often accompanied by an audio event, e.g., the voice of lip motion, the music of playing instruments. There is an underlying correlation between audio and visual events, which can be utilized as free supervised information to train a neural network by solving the pretext task of audio-visual synchronization. In this paper, we propose a novel self-supervised framework with co-attention mechanism to learn generic cross-modal representations from unlabelled videos in the wild, and further benefit downstream tasks. Specifically, we explore three different co-attention modules to focus on discriminative visual regions correlated to the sounds and introduce the interactions between them. Experiments show that our model achieves state-of-the-art performance on the pretext task while having fewer parameters compared with existing methods. To further evaluate the generalizability and transferability of our approach, we apply the pre-trained model on two downstream tasks, i.e., sound source localization and action recognition. Extensive experiments demonstrate that our model provides competitive results with other self-supervised methods, and also indicate that our approach can tackle the challenging scenes which contain multiple sound sources., Accepted by the 28th ACM International Conference on Multimedia (ACM MM 2020)

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text., Comment: EMNLP2020;10 pages