Reviewerly/exHarmony huggingface dataset:
Dataset classification: An efficient feature extraction approach for grammatical facial expression recognition

In this paper, an efficient features extraction using validated statistical approaches is proposed, along with a robust Grammatical Facial Expressions (GFEs) classifier in facial expression recognition systems. Accordingly, a new dataset was collected from 70 participants (33 males and 37 females) ranging in age from 18 to 46. The total number of video clips collected was 765. The features extracted in this study consist of 17 features associated with three categories of non-manual features: facial expression, head movement, and eye-gaze. Automatic recognition of nine classes of grammatical facial expressions in two languages (Arabic and Persian) is performed using a linear Support Vector Machine (SVM) classifier. The proposed system was also validated by testing it on the American Sign Language (ASL) dataset. In comparison to previous works on the ASL dataset, the results showed a higher accuracy rate of 95%.
A double transformer residual super-resolution network for cross-resolution person re-identification

Cross-resolution person re-identification is a challenging problem in the field of person re-identification. In order to solve the problem of resolution mismatch, many studies introduce super-resolution into person re-identification tasks. In this work, we propose a cross-resolution person re-identification method based on double transformer residual super-resolution network (DTRSR), which mainly includes super-resolution module and person re-identification module. In the super-resolution module, we propose the double transformer network as our attention module. First of all, we divide the features extracted by the residual network. Then calculate the similarity between each local feature and the global feature after average pooling and maximum pooling respectively, which makes our module quickly capture the hidden weight information in the spatial domain. In the person re-identification module, we propose an effective fusion method based on key point features (KPFF). The key point extraction model can not only solve the problem that local features can not be accurately aligned, but also remove the interference of background noise. In order to fully mine the relationship between the features of each key point, we calculate the two-way correlation between each key point feature and other features, and then superimpose the two-way correlation with the feature itself to get the superposition feature which contains global and local information. The effectiveness of this method is proved by extensive experiments. Compared with the most advanced methods, the test results in the three datasets show that our method improves rank-1 by 1.1%, 3.5% and 1.7%; and rank-5 by 1.3%, 1.7% and 0.3%; and rank-10 by 0.1%, 0.4% and 0.1%, respectively.
End-to-End One-Shot Human Parsing

Previous human parsing methods are limited to parsing humans into pre-defined classes, which is inflexible for practical fashion applications that often have new fashion item classes. In this paper, we define a novel one-shot human parsing (OSHP) task that requires parsing humans into an open set of classes defined by any test example. During training, only base classes are exposed, which only overlap with part of the test-time classes. To address three main challenges in OSHP, i.e., small sizes, testing bias, and similar parts, we devise an End-to-end One-shot human Parsing Network (EOP-Net). Firstly, an end-to-end human parsing framework is proposed to parse the query image into both coarse-grained and fine-grained human classes, which embeds rich semantic information that is shared across different granularities to identify the small-sized human classes. Then, we gradually smooth the training-time static prototypes to get robust class representations. Moreover, we employ a dynamic objective to encourage the network enhancing features' representational capability in the early training phase while improving features' transferability in the late training phase. Therefore, our method can quickly adapt to the novel classes and mitigate the testing bias issue. In addition, we add a contrastive loss at the prototype level to enforce inter-class distances, thereby discriminating the similar parts. For comprehensive evaluations on the new task, we tailor three existing popular human parsing benchmarks to the OSHP task. Experiments demonstrate that EOP-Net outperforms representative one-shot segmentation models by large margins and serves as a strong baseline for further research.
Review of Network Anomaly Detection in the High-speed Railway Signal System Based on Artificial Intelligence

The advent of network communication technology and associated equipment has significantly enhanced the operation efficiency and automation of the high-speed railway signal system. However, these open and complex networks also face increased security threats. To mitigate the risk of malicious network attacks, effective network anomaly detection methods are progressively being adopted. Artificial intelligence (AI), with its exceptional self-learning, environmental adaptability, and massive data processing capabilities, has emerged as the development trend for network anomaly detection in the high-speed railway signal system. This paper first provides an overview of the current state of network security in the high-speed railway signal system and examines potential network security threats. It then offers a detailed comparison of AI-based network anomaly detection methods. Lastly, it discusses future research directions in this field.
Security Robustness of Buried Power Rail Interconnect Technology: Modeling, Analysis and Countermeasures

Technology scaling gives rise to challenges in efficient power delivery for sub-5nm nodes. Buried Power Rail (BPR) interconnect technology is a promising solution for efficient monolithic power delivery. It provides power-performance-area (PP A) metric gains without actual dimensional scaling. With the introduction of any new technology, it is important to characterize it for potential security threats to critical blocks. This work evaluates the security robustness of BPR technology, providing insights on how the side-channel leakage changes with power delivery network (PDN) configuration, and comparing risks to critical private information. Choosing an open source 7nm FinFET as the baseline technology, three PDN variants are modelled by modifying the technology files. We design a crypto-IP (128-bit Advanced Encryption Standard) and extract their physical/electrical properties. Using custom simulators, the electromagnetic side channel attack vulnerability is analyzed and quantified. Our analysis reveals that BPRs tend to be more vulnerable when employed with backside metal routing, leaking side-channel information more strongly. The results suggest the need for robust pre-silicon design techniques and methodologies to mitigate the side channel attack vulnerabilities.
Privacy of Federated QR Decomposition Using Additive Secure Multiparty Computation

Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners' machine and thereby confidential.The clients compute local models and send them to an aggregator which computes a global model.In hybrid FL, the local parameters are additionally masked using secure aggregation, such that only the global aggregated statistics become available in clear text, not the client specific updates.In this context, we investigate the data leakage of three popular algorithms for QR decomposition, Gram-Schmidt orthonormalization, the Householder algorithm and Givens rotation.We show that, even when using additive SMPC, Givens rotation and the Householder matrix leak raw data and are therefore not suited for this computation paradigm.Gram-Schmidt orthonormalization relies on inner vector products and does not leak raw data points.
Perspective-Correct VR Passthrough Without Reprojection

Virtual reality (VR) passthrough uses external cameras on the front of a headset to allow the user to see their environment. However, passthrough cameras cannot physically be co-located with the user's eyes, so the passthrough images have a different perspective than what the user would see without the headset. Although the images can be computationally reprojected into the desired view, errors in depth estimation, view-dependent effects, and missing information at occlusion boundaries can lead to undesirable artifacts.
Entangled Hidden Markov Models

In this paper, we aim to expand upon our prior work on quantum hidden Markov processes by introducing the concept of entangled hidden Markov processes. These are quantum hidden processes in which the hidden processes themselves are entangled Markov processes. We provide an explicit expression for the joint expectation of these processes. Additionally, we show that our approach recovers the classical case.
Thinking Bateson with Deleuze and Guattari: Response-ability of Artisans-Artists-Designers in the Anthropocene

In this essay I bring Gregory Bateson together with Deleuze and Guattari (primarily with the latter) to show their ecological compatibility, especially with Guattari’s ecosophy. I do this against the backdrop of the Anthropocene which presents us not only with a ‘climate’ of post-truth and political corruption, but also with the so-called climate crisis. In the context of these two broad examinations, I ask what can an artisan-artist-designer do given this problematic context? My reply is to call on ‘speculative design’ by an ‘avant-garde without authority’ to expose the symptoms of the Anthropocene era.
External-Attentive Statistics Pooling for Text-Independent Speaker Verification

Speaker verification is an important biometric identification technique. In the neural network-based speaker feature extraction model, the pooling layer plays an important role. This layer aggregates frame-level features to obtain utterance-level features, and different pooling methods have different effects on the aggregation of frame-level features, which in turn affects the characterization ability of the final speaker features. In the existing work, some pooling methods with attention mechanisms have shown stronger feature aggregation capability than traditional pooling methods. In this paper, we combine a low-complexity External Attention with statistics pooling to design External-Attentive Statistics Pooling and propose Multi-Group External-Attentive Statistics Pooling considering the biological properties of human hearing. The two methods are used in text-independent speaker verification and tested on the VoxCeleb1 test set, VoxCeleb1-H, and VoxCeleb1-E. The test results show that the proposed method achieves more effective feature aggregation without significantly increasing the number of model parameters.
Consideration of Semantics between Q&amp;A Statements to Obtain Factor Score

In order to solve the issues of mismatches between the intentions of questioners and respondents at Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through factor analysis applied to the results of impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated by using the feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who are expected to appropriately answer a posted question. Nevertheless, up to now the meanings and contents of Q&A statements have not been taken into consideration. Therefore, this paper aims to consider the semantics between Q&A statements. The feature values are reviewed and narrowed down to syntactic information, closing sentence expressions, 2-gram, and word2vec. The analysis result conveys that all the trials show good estimation with the consideration of cross-validation. It has also been suggested that applying word2vec could play a vital role in estimating improved factor scores.
Global-Best Brain Storm Optimization Algorithm Based on Discussion Mechanism and Difference Step

Global-best brain storm optimization algorithm based on discussion mechanism and difference step (DDGBSO) is proposed in this paper to solve the problems that the traditional brain storm optimization algorithm has low convergence speed and poor optimization accuracy. The difference step is applied to replace the original mutation strategy, which improves the convergence speed by increasing the search space in the early iteration stage. The following global optimal strategy and discussion mechanism are innovatively combined to take full advantage of the global optimal information and to optimize the procedure of the BSO algorithm. Based on the CEC2013 benchmark test suit, 15 classical test functions are selected and multiple sets of simulations are conducted by Matlab. The simulation results show that DDGBSO has better performance than BSO and other improved BSO algorithms and improves the convergence speed and the optimization accuracy.
Commit Message Can Help: Security Patch Detection in Open Source Software via Transformer

As open source software is widely used, the vulnerabilities contained therein are also rapidly propagated to a large number of innocent applications. Even worse, many vulnerabilities in open-source projects are secretly fixed, which leads to affected software being unaware and thus exposed to risks. For the purpose of protecting deployed software, designing an effective patch classification system becomes more of a need than an option. To this end, some researchers take advantage of the recent advancements in natural language processing to learn both commit messages and code changes. However, they often incur high false positive rates. Not only that, existing works cannot yet answer how much the textual description (such as commit messages) alone can influence the final triage. In this paper, we propose a Transformer based patch classifier, which does not use any code changes as inputs. Surprisingly, the extensive experiment shows the proposed approach can significantly outperform other state-of-the-art work with a high precision of 93.0% and low false positive rate. Therefore, our research further confirms the critical importance of well-crafted commit messages for the later software maintenance. Finally, our case study also identifies 48 silent security patches, which can benefit those affected software.
Key-Locked Rank One Editing for Text-to-Image Personalization

Text-to-image models (T2I) offer a new level of flexibility by allowing users to guide the creative process through natural language. However, personalizing these models to align with user-provided visual concepts remains a challenging problem. The task of T2I personalization poses multiple hard challenges, such as maintaining high visual fidelity while allowing creative control, combining multiple personalized concepts in a single image, and keeping a small model size. We present Perfusion, a T2I personalization method that addresses these challenges using dynamic rank-1 updates to the underlying T2I model. Perfusion avoids overfitting by introducing a new mechanism that "locks" new concepts' cross-attention Keys to their superordinate category. Additionally, we develop a gated rank-1 approach that enables us to control the influence of a learned concept during inference time and to combine multiple concepts. This allows runtime efficient balancing of visual-fidelity and textual-alignment with a single 100KB trained model. Importantly, it can span different operating points across the Pareto front without additional training. We compare our approach to strong baselines and demonstrate its qualitative and quantitative strengths.
CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable and Controllable Text-Guided Face Manipulation

Recently introduced Contrastive Language-Image Pre-Training (CLIP) [Radford et al. 2021] bridges images and text by embedding them into a joint latent space. This opens the door to ample literature that aims to manipulate an input image by providing a textual explanation. However, due to the discrepancy between image and text embeddings in the joint space, using text embeddings as the optimization target often introduces undesired artifacts in the resulting images. Disentanglement, interpretability, and controllability are also hard to guarantee for manipulation. To alleviate these problems, we propose to define corpus subspaces spanned by relevant prompts to capture specific image characteristics. We introduce CLIP projection-augmentation embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation. Our method is a simple and general paradigm that can be easily computed and adapted, and smoothly incorporated into any CLIP-based image manipulation algorithm. To demonstrate the effectiveness of our method, we conduct several theoretical and empirical studies. As a case study, we utilize the method for text-guided semantic face editing. We quantitatively and qualitatively demonstrate that PAE facilitates a more disentangled, interpretable, and controllable face image manipulation with state-of-the-art quality and accuracy.
Networks and Communication Systems [Technical Activities]

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
High-Speed Autonomous Racing Using Trajectory-Aided Deep Reinforcement Learning

The classical method of autonomous racing uses real-time localisation to follow a precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) can train agents to race using only raw LiDAR scans. While classical methods prioritise optimization for high-performance racing, DRL approaches have focused on low-performance contexts with little consideration of the speed profile. This work addresses the problem of using end-to-end DRL agents for high-speed autonomous racing. We present trajectory-aided learning (TAL) that trains DRL agents for high-performance racing by incorporating the optimal trajectory (racing line) into the learning formulation. Our method is evaluated using the TD3 algorithm on four maps in the open-source F1Tenth simulator. The results demonstrate that our method achieves a significantly higher lap completion rate at high speeds compared to the baseline. This is due to TAL training the agent to select a feasible speed profile of slowing down in the corners and roughly tracking the optimal trajectory.
Differentiating Chat Generative Pretrained Transformer from Humans: Detecting ChatGPT-Generated Text and Human Text Using Machine Learning

Recently, the identification of human text and ChatGPT-generated text has become a hot research topic. The current study presents a Tunicate Swarm Algorithm with Long Short-Term Memory Recurrent Neural Network (TSA-LSTMRNN) model to detect both human as well as ChatGPT-generated text. The purpose of the proposed TSA-LSTMRNN method is to investigate the model’s decision and detect the presence of any particular pattern. In addition to this, the TSA-LSTMRNN technique focuses on designing Term Frequency–Inverse Document Frequency (TF-IDF), word embedding, and count vectorizers for the feature extraction process. For the detection and classification processes, the LSTMRNN model is used. Finally, the TSA is employed for selecting the parameters for the LSTMRNN approach, which enables improved detection performance. The simulation performance of the proposed TSA-LSTMRNN technique was investigated on benchmark databases, and the outcome demonstrated the advantage of the TSA-LSTMRNN system over other recent methods with a maximum accuracy of 93.17% and 93.83% on human- and ChatGPT-generated datasets, respectively.
Cuckoo-Store Engine: A Reed–Solomon Code-Based Ledger Storage Optimization Scheme for Blockchain-Enabled IoT

As the distributed ledger technology underlying cryptocurrencies such as Bitcoin and Ethereum, blockchain has empowered various industries, such as supply chain management, healthcare, government services, e-voting, etc. However, the ever-growing ledger on each node has been the main bottleneck for blockchain scalability as the network scale expands, which worsens in blockchain-enabled IoT scenarios with resource-limited devices. With the support of the Reed–Solomon (RS) code, the Cuckoo-Store (CS), a ledger storage optimization engine, is proposed in this paper to dramatically decrease the storage burden on each node by encoding the ledger as data segments with redundancy and distributing them to multiple nodes. These distributed data segments can be collected and decoded using RS code to recover the original ledger. Furthermore, the Cuckoo filter (CF) is used to guarantee the integrity of the encoded segments, which helps detect the forged segments and facilitates the process of ledger recovery. Theoretical analysis and simulation results show that the CS engine can decrease the storage in each node by more than 94%, and the original ledger can be recovered efficiently with acceptable communication overheads.
Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support

Society and the environment are severely impacted by catastrophic events, specifically floods. Inadequate emergency preparedness and response are frequently the result of the absence of a comprehensive plan for flood management. This article proposes a novel flood disaster management (FDM) system using the full lifecycle disaster event model (FLCNDEM), an abstract model based on the function super object. The proposed FDM system integrates data from existing flood protocols, languages, and patterns and analyzes viewing requests at various phases of an event to enhance preparedness and response. The construction of a task library and knowledge base to initialize FLCNDEM results in FLCDEM flooding response. The proposed FDM system improves the emergency response by offering a comprehensive framework for flood management, including pre-disaster planning, real-time monitoring, and post-disaster evaluation. The proposed system can be modified to accommodate various flood scenarios and enhance global flood management.
Edge Detection via Fusion Difference Convolution

Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These structures allow the model to better recognize the semantic and edge information in the images. Our model is trained from scratch on the BIPED dataset without any pre-trained weights and achieves promising results. Moreover, it generalizes well to other datasets without fine-tuning.