Reviewerly/exHarmony huggingface dataset:
Multi-level Attention Network with Weather Suppression for All-Weather Action Detection in UAV Rescue Scenarios

Unmanned Aerial Vehicles (UAVs) possess significant advantages in terms of mobility and range compared to traditional surveillance cameras. Human action detection from UAV images has the potential to assist in various fields, including search and rescue operations. However, UAV images present challenges such as varying heights, angles, and the presence of small objects. Additionally, they can be affected by adverse illumination and weather conditions. In this paper, we propose a Multi-level Attention network with Weather Suppression for all-weather action detection in UAV rescue scenarios. The Weather Suppression module effectively mitigates the impact of illumination and weather, while the Multi-level Attention module enhances the model’s performance in detecting small objects. We conducted detection experiments under both normal and synthetic harsh conditions, and the results demonstrate that our model achieves state-of-the-art performance. Furthermore, a comparison of relevant metrics reveals that our model strikes a balance between size and complexity, making it suitable for deployment on UAV platforms. The conducted ablation experiments also highlight the significant contribution of our proposed modules.
Classification Under Partial Reject Options

Abstract In many applications there is ambiguity about which (if any) of a finite number N of hypotheses that best fits an observation. It is of interest then to possibly output a whole set of categories, that is, a scenario where the size of the classified set of categories ranges from 0 to N . Empty sets correspond to an outlier, sets of size 1 represent a firm decision that singles out one hypothesis, sets of size N correspond to a rejection to classify, whereas sets of sizes $$2,\ldots ,N-1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>N</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> represent a partial rejection to classify, where some hypotheses are excluded from further analysis. In this paper, we review and unify several proposed methods of Bayesian set-valued classification, where the objective is to find the optimal Bayesian classifier that maximizes the expected reward. We study a large class of reward functions with rewards for sets that include the true category, whereas additive or multiplicative penalties are incurred for sets depending on their size. For models with one homogeneous block of hypotheses, we provide general expressions for the accompanying Bayesian classifier, several of which extend previous results in the literature. Then, we derive novel results for the more general setting when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We also discuss how well-known methods of classification, such as conformal prediction, indifference zones, and hierarchical classification, fit into our framework. Finally, set-valued classification is illustrated using an ornithological data set, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function’s tuning parameters are chosen through cross-validation.
Many Is Better Than One: Multiple Covariation Learning for Latent Multiview Representation

Canonical correlation analysis is a typical multiview representation learning technique, which utilizes within-set and between-set covariance matrices to analyze the correlation between two multidimensional datasets. However, it is quite difficult for the covariance matrix to measure the nonlinear relationship between features because of its linear structure. In this paper, we propose a multiple covariation projection (MCP) method to learn latent two-view representation, which has the ability to model the complicated feature relationship. The proposed MCP first constructs multiple general covariance matrices for modeling diverse feature relations, and then integrates them together via a linear ensemble strategy. At last, an efficient two-stage algorithm is designed for solutions. In addition, we further present a multiview MCP for dealing with the case of multiple (more than two) views. Experimental results on benchmark datasets show the effectiveness of our proposed MCP method in multiview classification and clustering tasks.
Zero-Shot Relation Triplet Extraction via Retrieval-Augmented Synthetic Data Generation

In response to the challenge of existing relation triplet extraction models struggling to adapt to new relation categories in zero-shot scenarios, we propose a method that combines generated synthetic training data with the retrieval of relevant documents through a rank-based filtering approach for data augmentation. This approach alleviates the problem of low-quality synthetic training data and reduces noise that may affect the accuracy of triplet extraction in certain relation categories. Experimental results on two public datasets demonstrate that our model exhibits stable and impressive performance compared to the baseline models in terms of precision, recall, and F1 score, resulting in improved effectiveness for zero-shot relation triplet extraction.
Lightweight Public Key Encryption in Post-Quantum Computing Era

Confidentiality in our digital world is based on the security of cryptographic algorithms. These are usually executed transparently in the background, with people often relying on them without further knowledge. In the course of technological progress with quantum computers, the protective function of common encryption algorithms is threatened. This particularly affects public-key methods such as RSA and DH based on discrete logarithms and prime factorization. Our concept describes the transformation of a classical asymmetric encryption method to a modern complexity class. Thereby the approach of CramerShoup is put on the new basis of elliptic curves. The system is provable cryptographically strong, especially against adaptive chosen-ciphertext attacks. In addition, the new method features small key lengths, making it suitable for Internet-of-Things. It represents an intermediate step towards an encryption scheme based on isogeny elliptic curves. This approach shows a way to a secure encryption scheme for the post-quantum computing era.
GoatPose: A Lightweight and Efficient Network with Attention Mechanism

Keypoint detection is an essential part of human pose estimation. However, due to resource constraints, it's still a challenge to deploy complex convolutional networks to edge devices. In this paper, we present GoatPose: a lightweight deep convolutional model for real-time human keypoint detection incorporating attention mechanism. Since the high computational cost is associated with the frequently-use convolution block, we substitute it with our new-designed LiteConv block, which conducts cheap linear operation to generate rich feature maps from the intrinsic features with low cost. This method significantly accelerates the model while inevitably losing a part of spatial information. To compensate for the information loss, we introduce NAM attention mechanism. By applying channel weighting, the model can focus more on the important features and enhance the feature representation. Results on the COCO dataset show the superiority of our model. With the complexity of our model reduced by half and the computational speed doubled, the accuracy of our model is basically the same as that of the backbone model. We further deploy our model on NVIDIA Jetson TX2 to validate its real-time performance, indicating that our model is capable of being deployed and widely adopted in real-world scenarios.
Dy-KD: Dynamic Knowledge Distillation for Reduced Easy Examples

Knowledge distillation is usually performed by promoting a small model (student) to mimic the knowledge of a large model (teacher). The current knowledge distillation methods mainly focus on the extraction and transformation of knowledge while ignoring the importance of examples in the dataset and assigning equal weight to each example. Therefore, in this paper, we propose Dynamic Knowledge Distillation (Dy-KD). To alleviate this problem, Dy-KD incorporates a curriculum strategy to selectively discard easy examples during knowledge distillation. Specifically, we estimate the difficulty level of examples by the predictions from the superior teacher network and divide examples in a dataset into easy examples and hard examples. Subsequently, these examples are given various weights to adjust their contributions to the knowledge transfer. We validate our Dy-KD on CIFAR-100 and Tiny-ImageNet; the experimental results show that: (1) Use the curriculum strategy to discard easy examples to prevent the model's fitting ability from being consumed by fitting easy examples. (2) Giving hard and easy examples varied weight so that the model emphasizes learning hard examples, which can boost students' performance. At the same time, our method is easy to build on the existing distillation method.
Customized Anchors Can Better Fit the Target in Siamese Tracking

Most existing siamese trackers rely on some fixed anchors to estimate the scale and aspect ratio for all targets. However, in real tracking, different targets have different sizes and shapes, these predefined anchors are not enough to cover all possible scales and aspect ratios caused by various movement and deformation, so an adaptive scale and aspect ratio estimation method is expected for robust online tracking. In this paper, a customized anchor generation module is first proposed to estimate the shape of the target and generate customized anchors adapted to the target. Then, through an anchor adaptation module, each anchor information is embed into corresponding feature to learn more discriminative features. Finally, We design a Target-aware feature correlation module to reduce the interference of background information. It takes the region of interest of template as variable template and its central subregion as central template, and then performs global and local correlation operations, respectively. Experiments on benchmarks including OTB100, VOT2019, LaSOT, UAV123, and VOT2018 show that our tracker achieves promising performance.
SODet: A LiDAR-Based Object Detector in Bird’s-Eye View

LiDAR-based object detection is of paramount significance in the realm of autonomous driving applications. Nevertheless, the detection of small objects from a bird's-eye view perspective remains challenging. To address this issue, the paper presents SODet, an efficient single-stage 3D object detector designed to enhance the perception of small objects like pedestrians and cyclists. SODet incorporates several key components and techniques. To capture broader context information and augment the capability of feature representation, the model constructs residual blocks comprising large-kernel depthwise convolutions and inverted bottleneck structures, forming the foundation of the CSP-based NeXtDark backbone network. Furthermore, the NeXtFPN feature extraction network is designed with the introduced SPPF module and the proposed special residual blocks, enabling the extraction and fusion of multi-scale information. Additionally, training strategies such as mosaic data augmentation and cosine annealing learning rate are employed to further improve small object detection accuracy. The effectiveness of SODet is demonstrated through experimental results on the KITTI dataset, showcasing a remarkable enhancement in detecting small objects from a bird's-eye perspective while maintaining a detection speed of 20.6 FPS.
Heterogeneous Graph Prototypical Networks for Few-Shot Node Classification

The node classification task is one of the most significant applications in heterogeneous graph analysis, which is widely used for modeling multi-typed interactions. Meanwhile, Graph Neural Networks (GNNs) have aroused wide interest due to their remarkable effects on graph node classification. However, there are some challenges when applying GNNs to heterogeneous graph node classification: the cumbersome node labeling cost, and the heterogeneity of graphs. Existing GNNs require sufficient annotation while learning classifiers independently with node embeddings cannot exploit graph topology effectively. Recently, few-shot learning has achieved competitive results in homogeneous graphs to address the performance degradation in the label sparsity case. While heterogeneous graph few-shot learning is limited by the difficulties of extracting multiple semantics. To this end, we propose a novel Heterogeneous graph Prototypical Network (HPN) with two modules: Graph structural module generates node embeddings and semantics for meta-training by capturing heterogeneous structures. Meta-learning module produces prototypes with heterogeneous induced subgraphs for meta-training classes, which improves knowledge utilization compared with the traditional meta-learning. Experimental results on three real-world heterogeneous graphs demonstrate that HPN achieves outstanding performance and better stability.
Event-Aware Document-Level Event Extraction via Multi-granularity Event Encoder

Event extraction (EE) is a crucial task in natural language processing that entails identifying and extracting events from unstructured text. However, the prior research has largely concentrated on sentence-level event extraction (SEE), while disregarding the increasing requirements for document-level event extraction (DEE) in real-world scenarios. The latter presents two significant challenges, namely the arguments scattering problem and the multi-event problem, which are more frequently observed in documents. In this paper, we propose an event-aware document-level event extraction framework, which can accurately detect event locations throughout the entire document without triggers and encode information at three different granularities (i.e., event-level, document-level, and sentence-level) via a multi-granularity event encoder. The resulting event-related holistic representation is then utilized for subsequent event record generation, thereby improving the accuracy of argument classification. Our proposed model's effectiveness is demonstrated through experimental results obtained from a large Chinese financial dataset.
Can You Really Reason: A Novel Framework for Assessing Natural Language Reasoning Datasets and Models

Recent studies have illuminated a pressing issue in the domain of natural language understanding (NLU) and reasoning: many of these datasets are imbued with subtle statistical cues. These cues, often unnoticed, provide sophisticated models an unintended edge, allowing them to exploit these patterns, leading to a potentially misleading overestimation of their genuine capabilities. While the existence of these cues has been noted, a precise and systematic identification has remained elusive in existing literature. Addressing this gap, our paper presents a novel lightweight framework. This framework is meticulously designed to not only detect these hidden biases in multiple-choice NLU datasets but also rigorously evaluate the robustness of models that are developed based on these datasets. By unveiling these biases and assessing model integrity, we aim to pave the way for more genuine and transparent advancements in NLU research.
Cybersecurity Analysis and Phishing Attack

The paper deals with exponentially growing technology—the Internet of Things (IoT) in the field of healthcare. It discusses networked healthcare and medical architecture. Healthcare data has significant value as a potential target for hackers. Attention is given to the analysis of international regulations on medical and healthcare cybersecurity. A developed normative hierarchical model of the international cybersecurity standards is provided for building a trustworthy healthcare IoT solution. Phishing is a method of exploitation for malicious reasons using targeted communications (email and messaging). This study reports on an internal evaluation targeting hospital staff and summarizes peer-reviewed literature regarding phishing and healthcare. For cybersecurity assessment of such systems, the case-oriented technique, which includes Advanced Security Assurance Cases (ASAC) and an example of a wireless insulin pump, is provided.
Trolls to Cyber Mob: Reasons of Trolling on Women

When the entire world is shrinking into the internet, real-life misogyny is also spilling into the virtual world. From verbal abuse to obscene videos and morphed photos, there are many ways in which men harass women in cyberspace. This research intends to throw light on the reasons of gender-based trolls on women. It draws a link between gender trolling, cyber violence, cyber victimization, and psychological well-being of the respondents. The study uses qualitative methods. The responses were collected from three focus group discussion to find the impact of gender trolling on women in Kerala. The samples will be identified using purposive and convenient sampling methods. The names of the respondents will not be revealed to guarantee anonymity. There were 101 participants in total, with women accounting for more than half of them.
Leveraging OSINT and Artificial Intelligence, Machine Learning to Identify and Protect Vulnerable Sections of Society

The internet today is the global hub for immeasurable amounts of data storage and sharing. But certain sections of society are extremely vulnerable to cyberbullying, harassment, and cyberstalking on this massive information-sharing forum. Due to these psychological attacks, laws are being made and modified to better protect these vulnerable sections of the society against these attacks which also fall under the purview of cyberthreats. Open-Source Intelligence (OSINT) is the publicly available data like newspaper clipping and social media posts that can processed using tools to gather information. Artificial intelligence and machine learning (AI and ML) is a technology that aims to make computers more human like by making them learn human behavior. In this paper, we analyze the various possible solutions available and proposed to tackle cyberthreats against women and children. The prospects involve the open-source intelligence technology along with artificial intelligence to detect and tackle cases of breach in cybersecurity to protect the vulnerable sections of society.
Social Policy of Enterprise as Factor of Personnel Involvement

Enterprises still face the problem of shaping loyal and involved behavior of employees, which can affect economic results of the company work. The article shows the impact of in-company corporate social policy on employees’ involvement. The authors studied social policy of the enterprise and assessed its influence on shaping behavior of personnel involvement; they also highlighted the impact of conformity of social welfare measures and employees needs in shaping involvement relations. The research was carried out by using a whole complex of academic methods, such as system analysis, generalization and systematization of theoretical and practical data. The foundation of the empiric base was formed by findings of the research done in 2023 concerning the impact of corporate social policy of the company TMK CHERMET on workers’ involvement. The obtained information gave an opportunity to draw a conclusion about the positive effect of the availability of corporate social policy on workers’ involvement. The most serious influence of involvement is exercised by such measures of social welfare as help in improving housing conditions, granting 50% discount on food in the canteen, finance assistance in difficult circumstances. However, a third of respondents assessed certain measures as having no influence on involvement, which could be improved by including relevant measures of social welfare in corporate social policy. Therefore, the hypothesis that corporate social policy at the enterprise can increase the level of workers’ involvement by conformity of measures of social welfare offered by the enterprise and their individual needs found confirmation. The authors gave definition of the notion ‘corporate social policy’.
Technology and Gender Violence: Victimization Model, Consequences and Measures

The evolution of technology is playing a very vital role in the development of every sphere of life. This technical evolution is also contributing significantly to improving the quality of human life. On the one side, technology is causing enormous benefits, on the other hand, it is also facilitating violence. Women, girls, transgender, etc. are expected to be highly targeted in online violence, e.g., sex trolling, physical threats, stalking, sexual harassment, exploitation, and bullying. It has been analyzed those women and girls are at high risk of gender-specific technology-supported violence. In this work, the authors reviewed the literature and presented a technology-dependent gender violence model focusing on types of abuse, victim population, and technologies used. Different initiatives to avoid and manage technology-dependent gender violence are also presented in this work.
Image classification for historical documents: a study on Chinese local gazetteers

Abstract We present a novel approach for automatically classifying illustrations from historical Chinese local gazetteers using modern deep learning techniques. Our goal is to facilitate the digital organization and study of a large quantity of digitized local gazetteers. We evaluate the performance of eight state-of-the-art deep neural networks on a dataset of 4,309 manually labeled and organized images of Chinese local gazetteer illustrations, grouped into three coarse categories and nine fine classes according to their contents. Our experiments show that DaViT achieved the highest classification accuracy of 93.9 per cent and F1-score of 90.6 per cent. Our results demonstrate the effectiveness of deep learning models in accurately recognizing and categorizing historical local gazetteer illustrations. We also developed a user-friendly web service to enable researchers easy access to the developed models. The potential for extending this method to other collections of scanned documents beyond Chinese local gazetteers makes a significant contribution to the study of visual materials in the arts and history in the digital humanities field. The dataset used in this study is publicly available and can be used for further research in the field.
IFGLT: Information fusion guided lightweight Transformer for image denoising

Image denoising is a low-level computer vision task that aims to reconstruct high-quality images from noisy ones. However, large networks with a high computational burden have been employed in existing works in pursuit of high-quality images. This paper introduces an Information Fusion Guided Lightweight Transformer (IFGLT) that can lessen the computational burden and achieve superior restoration results. The Feature Enhancement Module (FEM) optimizes the computing cost of the Transformer layer by layer using various techniques such as group mapping, channel generation, fusion convolution, and window rearrangement. The Information Compensation Module (ICM) gradually compensates for missing information by leveraging the original image. The Lightweight Sample Module (LSM) performs up-sampling and down-sampling with the minimal computing cost by altering the order of feature transformation. The experimental results demonstrate that our proposed IFGLT attains higher objective indices and achieves better visual effects with reduced computing cost in comparison to conventional methods.