Reviewerly/exHarmony huggingface dataset:
Improving Transferbility of Adversarial Attack on Face Recognition with Feature Attention

The development of deep convolutional neural networks has greatly improved the face recognition (FR) technique that has been used in many applications, which raises concerns about the fact that deep convolutional neural networks (DCNNs) are vulunerable to adversarial examples. In this work, we explore the robustness of FR models based on the transferbility of adversarial examples. Due to overfitting on the specific architecture of a surrogate model, adversarial examples tend to exhibit poor transferability. On the contrary, we propose to use diverse feature representation of a surrogate model to enhance transferbility. First, we argue that compared with adversarial examples generated by modelling output features of deep face models, the examples generated by modelling internal features have stronger transferbility. After that, we propose to leverage attention of the surrogate model to a pre-determined intermediate layer to seek the key features that different deep face models may share, which avoids overfitting on the surrogate model and narrows the gap between surrogate model and target model. In addition, in order to further enhance the black-box attack success rate, a multi-layer attack strategy is proposed, which enables the algorithm to generate perturbations guided by features with model's general interest. Extensive experiments on four deep face models show the effectiveness of our method.
Generating Pseudo-labels for Car Damage Segmentation Using Deep Spectral Method

Car damage segmentation, an integral part of vehicle damage assessment, involves identifying and classifying various types of damages from images of vehicles, thereby enhancing the efficiency and accuracy of assessment processes. This paper introduces an efficient approach for car damage assessment by combining pseudo-labeling and deep learning techniques. The method addresses the challenge of limited labeled data in car damage segmentation by leveraging unlabeled data. Pseudo-labels are generated using a deep spectral approach and refined through merge and flip-bit operations. Two models, i.e., Mask R-CNN and SegFormer, are trained using a combination of ground truth labels and pseudo-labels. Experimental evaluation of the CarDD dataset demonstrates the superior accuracy of our method, achieving improvements of 12.9% in instance segmentation and 18.8% in semantic segmentation when utilizing a 1/2 ground truth ratio. In addition to enhanced accuracy, our approach offers several benefits, including time savings, cost reductions, and the elimination of biases associated with human judgment. By enabling more precise and reliable identification of car damages, our method enhances the overall effectiveness of the assessment process. The integration of pseudo-labeling and deep learning techniques in car damage assessment holds significant potential for improving efficiency and accuracy in real-world scenarios.
Impulsion of Movie’s Content-Based Factors in Multi-modal Movie Recommendation System

Nowadays the Recommendation System, a subclass of information filtering system does not require any introduction, and the movie recommendation system plays an important role in the streaming platform where a huge number of movies are required to be analyzed before showcasing a perfectly matched subset of them to its users. The existing works in this domain focus only on the output and consider the model's input similar for all users. But actually, the movie embedding input vector varies on a user basis. A user's perception of a movie depends on the movie's genre as well as its meta information (story, director, and cast). To formulate the fact, we have introduced two scores, (i) TextLike_score (TL_score) and (ii) GenreLike_score (GL_score). Our proposed Cross-Attention-based Model outperforms the SOTA (state-of-the-art) by leveraging the effect of the scores and satisfying our factual notion. In this paper, we have evaluated our model's performance over two different datasets, (i) MovieLens-100K(ML-100K) and (ii) MFVCD-7K. Regarding multi-modality, the audio-video information of movies' are used and textual information has been employed for score calculation. Finally, it is experimentally proved that the Cross-Attention-based multi-modal movie recommendation system with the proposed Meta_score successfully covers all the analytical queries supporting the purpose of the experiment.
Spatial Gene Expression Prediction Using Hierarchical Sparse Attention

Spatial Transcriptomics (ST) quantitatively interprets human diseases by providing the gene expression of each fine-grained spot (i.e., window) in a tissue slide. This paper focuses on predicting gene expression at windows on a tissue slide image of interest. However, gene expression related to image features usually exhibits diverse spatial scales. To spatially model these features, we newly introduced Hierarchical Sparse Attention Network (HSATNet). The core idea of HSATNet is to employ a two-level sparse attention mechanism, namely coarse (i.e., area) and fine attention. Each HSAT Block consists of two main modules: i) An adaptive sparse coarse attention filters out the most irrelevant areas, resulting in the acquisition of adaptive sparse areas. ii) An adaptive sparse fine attention module filters out the most irrelevant windows, leading to the acquisition of adaptive sparse windows. The first module aims to identify similarities among windows within the same area, while the second module captures the differences between different windows. Particularly, after fusing these two modules together, without any additional training data or pre-training, experiments conducted on 10X Genomics breast cancer data show that our HSATNet achieves an impressive PCC@S of 7.43 for gene expression prediction. This performance exceeds the current state-of-the-art model. Code is available at https://github.com/biyecc/HSATNet .
Tigrinya OCR: Applying CRNN for Text Recognition

Tigrinya is a language predominantly spoken in Eritrea and the Tigray region of Ethiopia. It is classified as a low resourced language when it comes to Natural Language Processing (NLP) and its documents are not widely accessible due to the lack of printed material. Although the language has a rich cultural heritage, its literature hasn't been exposed to large-scale automated digitization when compared to other widely-spoken languages. In this paper, we design an end-to-end CRNN (Convolutional Recurrent Neural Network) to recognize machine-printed Tigrinya text from document images. This will help Tigrinya documents to be more accessible and also bridge the gap with languages that are rich in NLP resources. We have included all the 304 characters in Tigrinya and the network is trained on a total of over a million text-line images constructed from different domains. The majority of the data was synthesized to augment the limited real data to help the model generalize better. We employed two external datasets (ADOCR and GLOCR) in addition to ours to train the network. Furthermore, to improve the performance of the model, extensive parameter tuning was conducted. Without the use of post processing techniques, the model has achieved a 2.32% Character Error Rate (CER). The learning curve shows that given more data, the model can improve the CER. We finally managed to get a lightweight model that achieves comparable results to state-of-the-art results. This result implies that augmenting low resource data with synthetic data can significantly reduce the error rate in text recognition and proper hyperparameter tuning can find us lightweight models without compromising much accuracy.
TEZARNet: TEmporal Zero-Shot Activity Recognition Network

Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensor data has practical applications in healthcare and assisted living environments. However, its use in real-world scenarios has been limited due to the lack of comprehensive IMU-based HAR datasets covering various activities. Zero-shot HAR (ZS-HAR) can overcome these data limitations. However, most existing ZS-HAR methods based on IMU data rely on attributes or word embeddings of class labels as auxiliary data to relate the seen and unseen classes. This approach requires expert knowledge and lacks motion-specific information. In contrast, videos depicting various human activities provide valuable information for ZS-HAR based on inertial sensor data, and they are readily available. Our proposed model, TEZARNet: TEmporal Zero-shot Activity Recognition Network, uses videos as auxiliary data and employs a Bidirectional Long-Short Term IMU encoder to exploit temporal information, distinguishing it from current work. The proposed model outperforms the state-of-the-art accuracy by 4.7%, 7.8%, 3.7%, and 9.3% for benchmark datasets PAMAP2, DaLiAc, UTD-MHAD, and MHEALTH, respectively. The code is available at https://github.com/nipdep/TEZARNet
Basic Concepts of Neural Networks and Fuzzy Logic

In this chapter, we describe the basic concepts and techniques of computer science, which are crucial to the development of this book.
Introduction to the Hybrid Method Between Fireworks Algorithm and Competitive Neural Network

The Computer Science concept encompasses the combined study of information and computation. We also have learned that the theory of computation describes concepts as algorithms, computation problems, and artificial intelligence, among others.
Language Guided Graph Transformer for Skeleton Action Recognition

The Transformer model is a novel neural network architecture based on a self-attention mechanism, primarily used in the field of natural language processing and is currently being introduced to the computer vision domain. However, the Transformer model has not been widely applied in the task of human action recognition. Action recognition is typically described as a single classification task, and the existing recognition algorithms do not fully leverage the semantic relationships within actions. In this paper, a new method named Language Guided Graph Transformer (LGGT) for Skeleton Action Recognition is proposed. The LGGT method combines textual information and Graph Transformer to incorporate semantic guidance in skeleton-based action recognition. Specifically, it employs Graph Transformer as the encoder for skeleton data to extract feature representations and effectively captures long-distance dependencies between joints. Additionally, LGGT utilizes a large-scale language model as a knowledge engine to generate textual descriptions specific to different actions, capturing the semantic relationships between actions and improving the model's understanding and accurate recognition and classification of different actions. We extensively evaluate the performance of using the proposed method for action recognition on the Smoking dataset, Kinetics-Skeleton dataset, and NTU RGB $$+$$ D action dataset. The experimental results demonstrate significant performance improvements of our method on these datasets, and the ablation study shows that the introduction of semantic guidance can further enhance the model's performance.
Learning Discriminative Semantic and Multi-view Context for Domain Adaptive Few-Shot Relation Extraction

Few-shot relation extraction enables the model to extract new relations and achieve impressive success. However, when new relations come from new domains, semantic and syntactic differences cause a dramatic drop in model performance. Therefore, the domain adaptive few-shot relation extraction task becomes important. However, existing works identify relations more by entities than by context, which makes it difficult to effectively distinguish different relations with similar entity semantic backgrounds in professional domains. In this paper, we propose a method called multi-view context representation with discriminative semantic learning (MCDS). This method learns discriminative entity representations and enhances the use of relational information in context, thus effectively distinguishing different relations with similar entity semantics. Meanwhile, it filters partial entity information from the global information through an information filtering mechanism to obtain more comprehensive global information. We perform extensive experiments on the FewRel 2.0 dataset and the results show an average gain of 2.43% in the accuracy of our model on all strong baselines.
NDGR: A Noise Divide and Guided Re-labeling Framework for Distantly Supervised Relation Extraction

Distant supervision (DS) is widely used in relation extraction to reduce the cost of annotation but suffers from noisy instances. Current approaches typically involve selecting reliable instances from the DS-built dataset for model training. However, these approaches often lead to the inclusion of numerous noisy instances or the disregard of a substantial number of valuable instances. In this paper, we propose NDGR, a novel training framework for sentence-level distantly supervised relation extraction. Initially, NDGR partitions the noisy data from the DS-built dataset by employing a Gaussian Mixture Model (GMM) to model the loss distribution. Afterwards, we utilize a guided label generation strategy to generate high-quality pseudo-labels for noisy data. By iteratively executing the processes of noise division and guided label generation, NDGR helps refine the noisy DS-built dataset and enhance the overall performance. Our method has been extensively evaluated on commonly used benchmarks, and the results demonstrate its substantial improvements in both sentence-level evaluation and noise reduction.
A Meta Learning-Based Training Algorithm for Robust Dialogue Generation

There are many low-resource scenarios in the field of dialogue generation, such as medical diagnosis. Many dialogue generation models in these scenarios are usually unstable due to the lack of training corpus. As one of the most popular training algorithms in recent years, meta learning has achieved remarkable results. MAML in meta learning can find a fast adaptive initialization parameter for a series of low resource tasks, which is often used to solve the problem of low resource, and has achieved excellent performance in image classification tasks. However, in the field of text generation, such as dialogue generation, because of the large vocabulary, long sequence and large number of parameters involved in text generation, the effect of MAML is unstable. Therefore, this paper proposes a high robust text generation training framework based on meta learning for the dialogue generation task. By identifying the significant information in the model parameters, the optimizer can train the important parameters more concentrated in the limited data in the bi-level optimization. Experiments show that our method has a good performance on the BLEU scores on the six single-domain datasets.
PSO-Enabled Federated Learning for Detecting Ships in Supply Chain Management

Supply chain management plays a vital role in the efficient and reliable movement of goods across various platforms, which involves several entities and processes. Ships in the supply chain are very important for the global economy to be connected. Detecting ships and their related activities is of paramount importance to ensure successful logistics and security. To improve logistics planning, security, and risk management, a strong framework is required that offers an efficient and privacy-preserving solution for identifying ships in supply chain management. In this paper, we propose a novel approach called Particle Swarm Optimization-enabled Federated Learning (PSO-FL) for ship detection in supply chain management. The proposed PSO-FL framework leverages the advantages of both Federated Learning (FL) and Particle Swarm Optimization (PSO) to address the challenges of ship detection in supply chain management. We can train a ship identification model cooperatively using data from several supply chain stakeholders, including port authorities, shipping firms, and customs agencies, thanks to the distributed nature of FL. By improving the choice of appropriate participants for model training, the PSO algorithm improves FL performance. We conduct extensive experiments using real-world ship data that is gathered from various sources to evaluate the effectiveness of our PSO-FL approach. The results demonstrate that our framework achieves superior ship detection accuracy of 94.88% compared to traditional centralized learning approaches and standalone FL methods. Furthermore, the PSO-FL framework demonstrates robustness, scalability, and privacy preservation, making it suitable for large-scale deployment in complex supply chain management scenarios.
Segment Anything Model for Semi-supervised Medical Image Segmentation via Selecting Reliable Pseudo-labels

Semi-supervised learning (SSL) has become a hot topic due to its less dependence on annotated data compared to fully supervised methods. This advantage becomes more evident in the field of medical imaging, where acquiring labeled data is challenging. Generating pseudo-labels for unlabeled images is one of the most classic and intuitive methods in semi-supervised segmentation. However, this method may also produce unreliable pseudo-labels that can provide incorrect guidance to the model and impair its performance. The reliability of pseudo-labels is difficult to evaluate due to the lack of ground truth labels for unlabeled images. In this paper, a SSL framework was presented, in which we proposed a simple but effective strategy to select reliable pseudo-labels by leveraging the Segment Anything Model (SAM) for segmentation. Concretely, the SSL model trained with domain knowledge provides the generated pseudo-labels as prompts to the SAM. Reliable pseudo-labels usually make SAM to conduct predictions consistent with the semi-supervised segmentation model. Based on this result, the reliable pseudo-labels are selected to further boost the existing semi-supervised learning methods. The experimental results show that the proposed strategy effectively improves the performance of different algorithms in the semi-supervised scenarios. On the publicly available ACDC dataset, the proposed method achieves 6.84% and 10.76% improvement over the advanced two baselines respectively on 5% of labeled data. The extended experiments on pseudo-labels verified that the quality of the selected reliable pseudo-labels by the proposed strategy is superior to that of the unreliable pseudo-labels. This study may provide a new avenue for SSL medical image segmentation.
Imperative and Event-Driven Programming of Interoperable Software Agents

Jadescript is a recent agent-oriented programming language conceived to support the effective development of agents and multi-agent systems based on JADE. Jadescript is designed to ease the development of agents by means of a tailored syntax matched with a programmer-friendly development environment. This paper presents a brief overview of Jadescript by describing its main features and abstractions and by comparing them with the corresponding features and abstractions advocated by other agent-oriented programming languages. Moreover, to show the applicability of Jadescript to the construction of interesting multi-agent systems, this paper concisely summarizes a case study in which Jadescript is used to implement agents that participate in English auctions. Finally, this paper is concluded with a brief overview of planned future developments of the language.
The Importance of Cybersecurity Awareness Training in the Aviation Industry for Early Detection of Cyberthreats and Vulnerabilities

Nowadays, cybercriminals are targeting organizations to launch cyberattacks by exploiting cyberthreats and vulnerabilities. Hackers keep increasing the sophistication of the cyberattacks to orchestrate the different criminal hacking phases from conducting target reconnaissance to successfully achieving the desired objectives that could result in exfiltrating more than one digital asset. Many times, the targets are critical assets that organizations utilize to run their daily operations. The aviation industry is not the exception to this, and it is a matter of time before aviation companies will be dealing with imminent cyberattacks that will impact its financial operations, business reputation, legal and compliance areas, not to mention that its stakeholders can be a victim of a cyberattack at any time. Early prevention, detection and cybersecurity awareness training are key to recognize the initial stages of most common cyberattacks. From airport personnel to aviation crews, it is crucial to professionally delivering comprehensive cyber awareness training that will enforce the main of objectives of cybersecurity to protect aeronautical assets based on the CIA triad – Confidentiality, Integrity, and Availability. This article reviews existing cybersecurity awareness training policies from the industry governance agencies and proposes a customized training program based on the CATRAM. The Cybersecurity Awareness TRAining Model (CATRAM) was developed to deliver cybersecurity training to different organizational audiences, each of these groups with specific content and separate objectives. CATRAM was originally conceived to deliver awareness training for the members of the Board of Directors, Top Executives, Managers, IT (Information Technology) staff and of course, end-users.
Machine Learning Techniques and Privacy Concerns in Human-Computer Interactions: A Systematic Review

With the recent development of artificial intelligence, the field of human-computer interaction (HCI) has received an increasing amount of attention. Among all topics under HCI, machine learning and privacy concerns are two of the most important ones. One of the machine learning techniques that possess a strong connection with privacy is federated learning, which distributes learning tasks to individual devices to reduce information sharing and protect privacy. In this study, a systematic literature review of our topic has been conducted using various tools including Harzing’s Publish or Perish, Google nGram, Vicinitas, VOSviewer, CiteSpace, BibExcel, and maxQDA. The software Mendeley is also used to help sort out citation items. Trend analysis, co-citation analysis, content analysis, and cluster analysis have been conducted to identify the most important articles in the literature. It has been found that the two topics have been well studied and together they have a variety of applications, including communication networks, the healthcare industry, and the Internet of Things. Finally, we discuss the potential future work beyond our topics which cover directions in application fields such as healthcare and finance, and other machine learning techniques developed based on federated learning.