Reviewerly/exHarmony huggingface dataset:
Person Re-Identification without Identification via Event Anonymization

Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset. The code is available at https://github.com/IIT-PAVIS/ReId_without_Id
CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition

The co-occurrence signals (e.g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR). Compared to RGB data, skeleton data provide a more efficient and concise option, and lay a good foundation for the co-occurrence exploration in CSLR. However, skeleton data are often used as a tool to assist visual grounding and have not attracted sufficient attention. In this paper, we propose a simple yet effective GCN-based approach, named CoSign, to incorporate Co-occurrence Signals and explore the potential of skeleton data in CSLR. Specifically, we propose a group-specific GCN to better exploit the knowledge of each signal and a complementary regularization to prevent complex co-adaptation across signals. Furthermore, we propose a two-stream framework that gradually fuses both static and dynamic information in skeleton data. Experimental results on three public CSLR datasets (PHOENIX14, PHOENIX14-T and CSL-Daily) show that the proposed CoSign achieves competitive performance with recent video-based approaches while reducing the computation cost during training.
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection

Task driven object detection aims to detect object instances suitable for affording a task in an image. Its challenge lies in object categories available for the task being too diverse to be limited to a closed set of object vocabulary for traditional object detection. Simply mapping categories and visual features of common objects to the task cannot address the challenge. In this paper, we propose to explore fundamental affordances rather than object categories, i.e., common attributes that enable different objects to accomplish the same task. Moreover, we propose a novel multi-level chain-of-thought prompting (MLCoT) to extract the affordance knowledge from large language models, which contains multi-level reasoning steps from task to object examples to essential visual attributes with rationales. Furthermore, to fully exploit knowledge to benefit object recognition and localization, we propose a knowledge-conditional detection framework, namely CoTDet. It conditions the detector from the knowledge to generate object queries and regress boxes. Experimental results demonstrate that our CoTDet outperforms state-of-the-art methods consistently and significantly (+15.6 box AP and +14.8 mask AP) and can generate rationales for why objects are detected to afford the task.
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution

Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on edge devices. This work investigates the potential of network pruning for super-resolution to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. Two main challenges remain in applying pruning methods for SR. First, the widely-used filter pruning technique reflects limited granularity and restricted adaptability to diverse network structures. Second, existing pruning methods generally operate upon a pre-trained network for the sparse structure determination, hard to get rid of dense model training in the traditional SR paradigm. To address these challenges, we adopt unstructured pruning with sparse models directly trained from scratch. Specifically, we propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly initialized network at each iteration and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly. We observe that the proposed ISS-P can dynamically learn sparse structures adapting to the optimization process and preserve the sparse model’s trainability by yielding a more regularized gradient throughput. Experiments on benchmark datasets demonstrate the effectiveness of the proposed ISS-P over diverse network architectures. Code is available at https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-SR
Temporal Collection and Distribution for Referring Video Object Segmentation

Referring video object segmentation aims to segment a referent throughout a video sequence according to a natural language expression. It requires aligning the natural language expression with the objects’ motions and their dynamic associations at the global video level but segmenting objects at the frame level. To achieve this goal, we propose to simultaneously maintain a global referent token and a sequence of object queries, where the former is responsible for capturing video-level referent according to the language expression, while the latter serves to better locate and segment objects with each frame. Furthermore, to explicitly capture object motions and spatial-temporal cross-modal reasoning over objects, we propose a novel temporal collection-distribution mechanism for interacting between the global referent token and object queries. Specifically, the temporal collection mechanism collects global information for the referent token from object queries to the temporal motions to the language expression. In turn, the temporal distribution first distributes the referent token to the referent sequence across all frames and then performs efficient cross-frame reasoning between the referent sequence and object queries in every frame. Experimental results show that our method outperforms state-of-the-art methods on all benchmarks consistently and significantly.
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups

Semi-supervised semantic segmentation methods use a small amount of clean pixel-level annotations to guide the interpretation of a larger quantity of unlabelled image data. The challenges of providing pixel-accurate annotations at scale mean that the labels are typically noisy, and this contaminates the final results. In this work, we propose an approach that is robust to label noise in the annotated data. The method uses two diverse learning groups with different network architectures to effectively handle both label noise and unlabelled images. Each learning group consists of a teacher network, a student network and a novel filter module. The filter module of each learning group utilizes pixel-level features from the teacher network to detect incorrectly labelled pixels. To reduce confirmation bias, we employ the labels cleaned by the filter module from one learning group to train the other learning group. Experimental results on two different benchmarks and settings demonstrate the superiority of our method over state-of-the-art approaches.
Mitigating Health Risks and Ensuring Safe Video Streaming Environments through Automated Video Content Moderation

Automated content moderation systems have become essential for maintaining a safe and healthy online environment with the ever-increasing amount of video content on the internet and social media platforms. Individuals are likely to see videos containing flashing lights that can trigger seizures, nudity, or videos that may evoke extreme emotions like anxiety, frustration, or despair. In this paper, we propose a comprehensive content moderation solution that incorporates several features. The system features epileptic seizure recognition, emotional dysregulation prevention, and kid-safe mode. Epilepsy is a chronic neurological disorder affecting millions worldwide. Various factors can trigger seizures, including flashing lights, which are prevalent in video content. The system analyzes videos for trigger segments and skips them to non-trigger segments, mitigating the risk of seizures in individuals with epilepsy. The proposed algorithm uses a sophisticated analysis of the differences in luminosity between each video frame. It identifies areas that represent potential seizure-inducing segments due to their high density of big changes in luminosity. Similarly, when a video triggers strong emotions, the emotional dysregulation prevention mechanism recognizes it and alerts the user with an emotional summary of the video. The emotional dysregulation algorithm uses a deep learning model for facial emotion identification based on the VGG16 architecture. In kid-safe mode, the nudity detection algorithm uses CNN architecture, recognizes explicit content, and blocks users, especially children, from seeing it. The system displays notifications and alerts to parents to establish restrictions on the content their children can access. The proposed mechanisms prevent potential harm to users with specific vulnerabilities and provide parents with tools to ensure a safe online streaming environment for their kids.
Corresponding expressions of resultative complements in Mongolian language and Chinese teaching strategies: an analysis from the perspective of Chinese language

Resultative complements have special and contextual features, which play a crucial role in teaching Chinese as a foreign language. There are many differences between the language structures of Mongolian and Chinese, especially in the application of resultative complements. Therefore, students can accurately describe their ideas in Mongolian if they can effectively grasp the characteristics and structures of resultative complements. Based on this, the author of this paper elaborates on the corresponding expressions of resultative complements in Mongolian from different perspectives, and provides teaching suggestions of Chinese for relevant educators.
Algorithm Skeleton Mining Based on Optimized Pre-training Mechanisms in Survey Paper

Analyzing the quality of an algorithm evolution process and constructing the knowledge body is a crucial research task with immense value. Thus, it becomes imperative to develop methods that can accurately predict the evolution of these algorithms. The paper creates a structured algorithm skeleton in survey papers to analyze lengthy texts effectively, focusing on key information and its context. It merges the reasoning abilities of extensive language models with optimized structured extraction techniques to enhance the understanding of essential information in lengthy texts. This study aims to facilitate the algorithm inventors to use the proper and high-quality way to evolve algorithms. Our research equips algorithm creators with tools and knowledge for the efficient development of high-quality evolution algorithms, contributing to the field’s advancement and real-world impact.
Type-n fuzzy logic - the next level of type-1 and type-2 fuzzy logic

The level of uncertainty in a system can be reduced by using type-2 fuzzy logic, which has a superior ability to handle linguistic uncertainties by modelling ambiguity and unreliability of information. Unfortunately, type-2 fuzzy sets are harder to use and understand than type-1 fuzzy sets. This article provides a comprehensive idea on non-stationary fuzzy inference system (FIS) as well as a generalised approach to the extended type-2 fuzzy approach. A new proposed breakdown of T2FS along with stationary and non-stationary fuzzy sets (FIS) is also described using the fuzzy inference system in this article. Besides that, it describes a new generalised FIS technique and ends with a generalised computation of the centroid of a type-2 fuzzy system. A new proposed breakdown of T2FS along with stationary and non-stationary fuzzy sets (FIS) is also described using the fuzzy inference system in this article.
Nonlinear images as art objects

Thanks to a powerful calculating tool - a computer, as well as the development of mathematical modeling methods, synergetics for the first time in the history of science began to describe and reproduce the processes of self-organization and development. On this path, magnificent, previously unseen images began to appear - strange attractors, fractals, cellular automata, which to a large extent contributed to the popularity of synergetics in the scientific and philosophical community. The article is devoted to images that arise in models, experiments and computational experiments developed and conducted by the author. Continual cellular automata with non-linear transition rules, cycles, fractal structures and chaos in non-linear mappings that describe social processes give rise to images that make you think, awaken aesthetic sensations and allow you to better understand, visualize how it all (surrounding world, the Universe) was created, organized and works. And this is the function of Science Art.
IoT-Enabled Cloud-Based Fair Provable Data Possession Scheme based on Blockchain

In traditional Provable Data Possession (PDP) schemes, the assumption of client honesty has posed a significant challenge, undermining the system's fairness. A fair Provable Data Possession scheme is proposed to address this issue, leveraging blockchain technology within an IoT-enabled cloud environment. In conventional PDP schemes, the server is unfairly assumed to be semi-honest, while the client is trusted. In the blockchain-based fair PDP scheme, the generation of verification metadata is shifted from the client to consensus among blockchain nodes, ensuring fairness and trustworthiness. The fair PDP scheme incorporates efficient hash functions and Pedersen commitments, enabling a robust and efficient verification process. A comprehensive analysis covers security, computational overhead, communication overhead, and redundancy. Results demonstrate that the proposed scheme surpasses similar approaches, exhibiting superior computational and communication efficiency while maintaining a reduced redundancy rate. By integrating blockchain technology into the PDP paradigm, the fair PDP scheme achieves fairness and enhances efficiency in data possession verification within the IoT-enabled cloud ecosystem. Furthermore, the mutual trust mechanism established through distributed blockchain properties ensures a reliable and equitable data possession verification process between the client and the cloud server.
A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models

Movie recommendation systems are useful tools that help users find relevant results and prevent information overload. On the other hand, the user cold-start issue has arisen because the system lacks sufficient user data. Furthermore, they are not very scalable for use in extensive real-world applications. One of the key strategies to address the sparsity and cold-start problems is to leverage other sources of information, including item or user profiles or user reviews. Processing client feedback is typically a challenging process that involves challenging the interpretation and analysis of the textual data. Thus, this research implements an efficient deep learning-based recommendation architecture. Following the acquisition of textual data from the Amazon product reviews database, stop word removal, lemmatization, and stemming techniques are applied to the data pre-processing which eliminate inconsistent and redundant data, facilitating the process of interpreting and utilising data. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract the feature values from the pre-processed text data. The extracted feature values are fed to the Self-Attention Bidirectional Long Short-Term Memory (SA-BiLSTM) that utilises the matrix factorization method framework's information sources. The SA-BiLSTM model obtained 95.93% of recall, 94.76% of precision, and 97.84% of accuracy on the amazon product reviews database.
A Secure IoT Based Wireless Sensor Network for an Efficient Modified User Authentication and Key Agreement Scheme

Medical and transportation systems are only two examples of the many domains in which the IoT has found success. To meet the growing demand for continuous health surveillance that is affordable, noninvasive, and long-term, smart healthcare platforms have emerged as the most efficient and useful alternatives. To improve the reliability of IoT devices in smart cities, this study first establishes a cloud-assisted dependability evaluation approach, as reliability examination for IoT gadgets is a crucial part of data source authentication. In addition, a non-interactive zero-knowledge argument-based anonymous verification and key agreement system is developed to address the issue of user privacy being compromised during the process of verification. The proposed method may safeguard sensitive information collected by Internet of Things gadgets in smart cities. The experimental modeling shows that the proposed method outperforms competing approaches by a significant margin. Lastly, the suggested strategy is demonstrated to be more practical and secure than the previous schemes by comparison of effectiveness.
Ensemble Text Classification with TF-IDF Vectorization for Hate Speech Detection in Social Media

The development of artificial intelligence (AI) has changed how hate speech is detected. In hate speech identification using machine learning, a number of methods are used to automatically find text that uses vocabulary that is considered to be derogatory, discriminatory, or motivated by hatred. Supervised learning techniques like neural networks, decision trees, and SVMs need a labelled dataset comprising samples of hate speech and non-hate speech. This project investigates the use of AI and machine learning techniques to automatically detect material that uses offensive, intolerant, or hostile words. A voting classifier and TF-IDF representations are combined to improve classification accuracy. The ensemble of classifiers, powered by AI approaches, shows impressive accuracy in identifying hate speech by training five different classifiers (Random Forest, Bagging, Support Vector Machine, AdaBoost, and Gradient Boosting) on a labelled dataset of tweets. The TF-IDF representation prioritises textual terms, whereas the ensemble method uses classifier diversity to capture distinctive patterns. Results from experiments show the strategy's effectiveness, with precision 0.95, recall 0.96, f1-score 0.95 and accuracy 0.97 for detecting hate speech. By successfully utilising AI's capacity to fight hate speech, this research helps the development of a diverse and secure online environment. The suggested approach works well for automatically identifying hate speech, making the internet a safer and more welcoming place for all users.
Advancing Clinical Text Summarization through Extractive Methods using BERT-Based Models on the NBME Dataset

Medical notes contains a vast amount of healthcare information. NLP techniques are essential for extracting the most relevant insights from abundance data, enabling healthcare professionals to make well-informed decisions.In the study, clinical text feature extraction was performed on the NBME dataset for extractive summarization using PubMedBERT, BioBERT, and DeBERTa. It was found that PubMedBERT achieved 92% accuracy, BioBERT achieved 95% accuracy, and DeBERTa achieved 86% accuracy. PubMedBERT and BioBERT performed well on clinical text summarization task these models have a slight edge over DeBERTa. These results suggest that domain-specific pretrained language models like PubMedBERT and BioBERT are useful for extractive summarization of clinical text. Performing feature extraction for extractive summarization of medical notes can make healthcare information more concise and accessible.Facilitates physicians and other healthcare professionals to improve their analysis on medical reports.Overall,advances in extractive summarisation of clinical text through NLP can lead to better-informed healthcare professionals and improved patient outcomes.
SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network

In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor selection method that is optimized to achieve the task with just a single click. Our method surpasses the precision and recall achieved by the traditional method of running panoptic segmentation and then selecting the segments containing the clicks. We also showcase how a transformer-based module can be used to identify more distracting regions similar to the user's click position. Our experiments demonstrate that the model can effectively and accurately segment unknown distracting objects interactively and in groups. By significantly simplifying the photo cleaning and retouching process, our proposed model provides inspiration for exploring rare object segmentation and group selection with a single click.
FPA-WAN:Feature Pyramid Attention Based Watermarking Attack Network

Digital watermarking technology is a method of embedding specific information in digital images and videos, often used for copyright protection and identity verification. However, unscrupulous users may also use this technique to falsify and tamper with data. Therefore, a reliable watermark attack network is needed to detect and remove watermarks embedded by unscrupulous users. In this paper, we propose a watermarking attack network based on feature pyramid attention, which can effectively remove watermarks embedded in digital images and greatly guarantee the image quality of carrier images. The network consists of two main modules: the feature extraction module and the watermark attack module. In the feature extraction module, we use a convolutional neural network and a residual block to extract the features of the image. Then, in the watermarking attack module, we use the pyramid attention mechanism to focus on the important regions in the feature map and apply the attention weights to the watermarking attack operation. To validate the effectiveness of this network, we conducted experiments using a variety of standard data sets. Experimental results show that the network can effectively attack the watermark information embedded in digital images while guaranteeing the quality of the images after the attack. Overall, the watermarking attack network based on feature pyramid attention proposed in this paper is an effective attack with high imperceptibility that can be applied in the field of digital media protection and security in practical scenarios.