Reviewerly/exHarmony huggingface dataset:
A Deep Learning Model to Generate Image Captions

How computers can automatically describe the substance of photographs using human language is a topic that interests us greatly. We choose to use the most advanced image caption generator currently available to obtain a deeper understanding of this computer vision topic. Show, attend and tell Visually attentive neural image caption generator [12]. Our machine learning-based neural network picture description generator is created in Python using the Pytorch ML framework. In our workﬂow, we’ve determined ﬁve key elements: Data preparation, a convolutional neural network (CNN) it helps in encoding, a recurrent neural network (RNN) it helps in decoding, a beam search to determine the best description, generation of sentences, and assessment make up the R1-R6 framework. The quality and correctness of the generated caption are evaluated using the BLEU-4 score. Each member of our group contributed equally to moving the project forward as we distributed the ﬁve elements mentioned above equally among ourselves. All ﬁve components have been completed successfully, and we can now use Kaggle Notebook to train our network. After the network has been trained and is performing satisfactorily, we continue to see the attention mechanism.
Estimation of Serum TLR-9,TNF-α, and IL-6 Levels in the Iraqi Patients Diagnosed as Acute Myelogenous Leukemia

Acute myelogenous leukemia (AML) is a varied group of biological and clinical diseases. It is distinguished by the fast proliferation of aberrant cells in the bone marrow, which interferes with normal blood cell formation. Several studies demonstrated that cytokines released by leukemic cells in an autocrine or paracrine manner influence the proliferation of AML cells. Our study aimed to assess serum levels of TLR-9, TNF-α and, IL-6 in patients of AML. The study was done via the ELISA Technique. The results show a highly significant difference in all parameters in Patients and control. The correlation between immunological parameters using the Pearson correlation coefficient for patients showed that there is a weak correlation between TLR-9 and TNF- α, IL-6 with P-values of 0.47 and 0.23 respectively. This may indicate the complex environment of inflammation. However, there was a strong correlation between TNF- α and IL-6 with a P-value (0.001). This indicates that the secretion of these cytokines was high in patients. All immunological parameters of patients were evaluated using the receiver operating characteristic (ROC) curve to demonstrate that any one of them is a useful tool for identifying and tracking inflammation. The ROC curve's findings revealed that all parameters have good sensitivity and specificity for detecting inflammation and disease activity in AML patients. According to the current study, AML was related to higher levels of TLR-9, TNF-α, and, IL-6 may be affected the prognosis of the disease, increase the risk of the disease progression, and may be used as a biomarker to target AML cells.
Pengembangan Kualitas Produk Dalam Upaya Meningkatkan Volume Penjualan Produk Home Industry Madu Lebah Kelulut “Nano Alami”

Product quality is seen as being able to influence product purchasing decisions by prospective consumers so as to increase the sales volume of a product. Previously, the "Nano Alami" kelulut bee honey home industry product experienced a decline in sales volume in 2022 due to the emergence of similar products under different brands, leading to market competition. It has become one of the driving factors for the "Nano Alami" kelulut bee honey home industry in developing product quality to increase product sales volume. So, this study aims to find out how product quality development is carried out to increase product sales volume. The research used is a qualitative method with the collection of data by observation, interview, and documentation techniques. Data analysis was carried out in three stages sequentially, they are data reduction, data presentation, and drawing conclusion.The results showed that the quality development of the home industry "Nano Alami" kelulut bee honey product which in the production system includes input processes, production processes, and output. In the input process, development is carried out using portable refractometer technology tools to measure water content in order to improve the quality of raw materials. Furthermore, in the production process, there are three development aspects which are carried out namely post-harvest by increasing the duration of the standing period to reduce water content; addition of variations of packaging bottles; as well as renewing packaging containers in the form of cardboard boxes which are specifically designed to increase security during product distribution. Besides, the output in in the form of improved products in order to increase the trust of potential customers, namely by including the product distribution permit number on the "Nano Alami" kelulut bee honey home industry. So that the development of product quality that has been carried out is able to support the increase in product sales volume. Keywords : Product Quality Development, Increase in Sales Volume, Home Industry
An Interactive Conversation with a Chatbot: Does ChatGPT Know Standard Phraseology in Aviation English?

This research investigates the profound effects of ChatGPT, an artificial intelligence (AI) language model, on the domain of aviation English. Within an industry that places significant emphasis on safety and accuracy, this study utilizes a qualitative case study methodology to evaluate the performance of ChatGPT. There has been an interactive conversation with ChatgGPT acting as an air-traffic controller. The observations based on the instant responses of the chatbot following the prompts demonstrated that ChatGPT shows exceptional competence in conforming to conventional aviation phraseology throughout different flight stages, ranging from ground clearances to landing, by using role-play situations. Beyond linguistic accuracy, ChatGPT facilitates dynamic and contextually relevant dialogues, enhancing aviation education and training experiences.
Image Manipulation Localization Based on Multiscale Convolutional Attention

Current developments in image editing technology bring considerable challenges to the trustworthiness of multimedia data. Recent research has shown that convolutional attention mechanism can encode contextual information more effectively and efficiently than attentional mechanism, which has been validated in the field of semantic segmentation of images. However, convolutional attention-based networks focus only on the semantic information at the object level. The anomalous features introduced by tampering manipulation in the low-level information are ignored, resulting in poor localization of tampered regions. In this paper, leveraging convolutional attention mechanism, we propose an image tampering localization method based on multi-scale convolutional attention mechanism (IMLMCA), which fuses the low-level information to improve its capability to recognize the low-level anomalous information. To overcome the problems such as the unbalanced distribution of training positive and negative samples caused by the tampered regions, which usually occupy only a tiny part of the whole image, we introduce a mixed loss function consisting of focal-loss and Lovászloss to assist the method in learning the tampered features. The experimental results show that the proposed method achieves state-of-the-art performance on several publicly available tampered image datasets. For example, on CASIA dataset, the F1 score of the proposed method is 0.601, while the F1 score of the current best Transformer-based method, Objectformer, is 0.579. Codes are available at https://github.com/AlchemistLiu/IMLMCA .
CLIP for Lightweight Semantic Segmentation

The large-scale pretrained model CLIP, trained on 400 million image-text pairs, offers a promising paradigm for tackling vision tasks, albeit at the image level. Later works, such as DenseCLIP and LSeg, extend this paradigm to dense prediction, including semantic segmentation, and have achieved excellent results. However, the above methods either rely on CLIP-pretrained visual backbones or use none-pretrained but heavy backbones such as Swin, while falling ineffective when applied to lightweight backbones. The reason for this is that the lightweitht networks, feature extraction ability of which are relatively limited, meet difficulty embedding the image feature aligned with text embeddings perfectly. In this work, we present a new feature fusion module which tackles this problem and enables language-guided paradigm to be applied to lightweight networks. Specifically, the module is a parallel design of CNN and transformer with a two-way bridge in between, where CNN extracts spatial information and visual context of the feature map from the image encoder, and the transformer propagates text embeddings from the text encoder forward. The core of the module is the bidirectional fusion of visual and text feature across the bridge which prompts their proximity and alignment in embedding space. The module is model-agnostic, which can not only make language-guided lightweight semantic segmentation practical, but also fully exploit the pretrained knowledge of language priors and achieve better performance than previous SOTA work, such as DenseCLIP, whatever the vision backbone is. Extensive experiments have been conducted to demonstrate the superiority of our method.
Effective Small Ship Detection with Enhanced-YOLOv7

Small ship detection is widely used in marine environment monitoring, military applications and so on, and it has gained increasing attentions both in industry and academia. In this paper, we propose an effective small ship detection algorithm with enhanced-YOLOv7. Specifically, to reduce the feature loss of small ships and the impact of marine environment, we firstly design a small object-aware feature extraction module by considering both small-scale receptive fields and multi-branch residual structures. In addition, we propose a small object-friendly scale-insensitive regression scheme, to strengthen the contributions of both bounding box distance and difficult samples on regression loss as well as further increase learning efficiency of small ship detection. Moreover, based on the formulated penalty model, we design a geometric constraint-based Non-Maximum Suppression (NMS) method, to effectively decrease small ship detection omission rate. Finally, extensive experiments are implemented, and corresponding results confirm the effectiveness of the proposed algorithm.
Exocentric and Egocentric Views for Biomedical Data Analytics in Virtual Environments—A Usability Study

Biomedical datasets are usually large and complex, containing biological information about a disease. Computational analytics and the interactive visualisation of such data are essential decision-making tools for disease diagnosis and treatment. Oncology data models were observed in a virtual reality environment to analyse gene expression and clinical data from a cohort of cancer patients. The technology enables a new way to view information from the outside in (exocentric view) and the inside out (egocentric view), which is otherwise not possible on ordinary displays. This paper presents a usability study on the exocentric and egocentric views of biomedical data visualisation in virtual reality and their impact on usability on human behaviour and perception. Our study revealed that the performance time was faster in the exocentric view than in the egocentric view. The exocentric view also received higher ease-of-use scores than the egocentric view. However, the influence of usability on time performance was only evident in the egocentric view. The findings of this study could be used to guide future development and refinement of visualisation tools in virtual reality.
A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology

Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology. Therefore, based on the ConvNeXt architecture, our study performs a systematic comparison of nine task adaptation techniques, namely, DELTA, L2-SP, MARS-PGM, Bi-Tuning, BSS, MultiTune, SpotTune, Co-Tuning, and vanilla fine-tuning, on five histopathological classification tasks using eight datasets. The results are based on external testing and statistical validation and reveal a multifaceted picture: some techniques are better suited for histopathology than others, but depending on the classification task, a significant relative improvement in accuracy was observed for five advanced task adaptation techniques over the control method, i.e., vanilla fine-tuning (e.g., Co-Tuning: P(≫) = 0.942, d = 2.623). Furthermore, we studied the classification accuracy for three of the nine methods with respect to the training set size (e.g., Co-Tuning: P(≫) = 0.951, γ = 0.748). Overall, our results show that the performance of advanced task adaptation techniques in histopathology is affected by influencing factors such as the specific classification task or the size of the training dataset.
A Flexible Two-tower Model for Item Cold-start Recommendation

One of the main challenges in recommendation system is the item cold-start problem, where absence of historical interactions or ratings in new items makes recommendation difficult. In order to solve the cold-start problem, hybrid neural network models using meta data of the item as a feature is widely used. However, existing cold-start models tend to focus too much on utilizing the side information of items, which may not be flexible enough to capture the interaction information of users. In this study, we propose a flexible framework for better capturing the interaction information of users. Specifically, we incorporate the multiple choice learning scheme into the two-tower neural network which is a popular recommendation model that consists of two towers - one for users and one for items. In our proposed framework, we construct two encoders. One of the two encoders, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tightly-coupled</i> encoder, focuses on the side information of items with which the user has interacted, the other one, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">loosely-coupled</i> encoder, focuses the user’s interaction information. We utilize Gumbel-Softmax to stochastically select the encoder, enhancing the flexibility that considers not only item feature but also user interaction information. We evaluate our proposed framework on two datasets - the MLIMDb dataset which is a combination of widely used the MovieLens and IMDb datasets based on common movies, and the CiteULike dataset. The experimental results show that our proposed framework achieves state-of-the-art results on cold-start recommendation. In the Recall@150 experiments on the CiteULike dataset, we achieved improvement of approximately 2.7% compared to the base model. In the Recall@150 experiments on the MLIMDb dataset, we achieved improvement of approximately 5.2% compared to the base model. We further show our proposed model improves the performance in the warm-start settings. In the Recall@100 experiments on the Citeulike dataset, we observed an improvement of approximately 1.3% compared to the base model. In the Recall@100 experiments on the MLIMDb dataset, we observed an improvement of approximately 3.9% compared to the base model. Our proposed framework provides a flexible approach for capturing the diverse aspects of users in recommendation systems, even for cold-start items. As demonstrated through extensive experiments, our proposed model outperforms several state-of-the-art models on both datasets.
Multi-Modal Remote Sensing Image Matching Based on Weighted Structure Saliency Feature

Matching multi-modal remote sensing images (MRSI) is a challenging task. Due to significant nonlinear radiation differences (NRD), traditional image matching methods cannot achieve satisfactory results. Current research shows that structural information can get more robust matching results compared with texture information (i.e., gradient features) from images. In order to better explore the structural information of images, this paper proposes a MRSIs matching method using structure saliency features, called weighted structure saliency feature (WSSF). Two strategies are investigated and integrated in WSSF to improve the matching performance. The scale space is constructed based on the pointwise shape-adaptive texture scale filtering, which can better retain the structure features, and the second-order Gaussian steerable filtering, edge confidence map and phase features are combined to establish the structural saliency map combined with second-order Gaussian steerable filtering, which is much more robust to NRD than traditional gradient map. The performance of the proposed method was evaluated on a total of 120 image pairs from two MRSI datasets, and compared with state-of-the-art matching methods including the histogram of the orientation of weighted phase (HOWP), locally normalized image feature transform (LNIFT), co-occurrence filter space matching (CoFSM), radiation-variation insensitive feature transform (RIFT), local phase sharpness orientation (LPSO), and position-scale-orientation SIFT (PSO-SIFT). Experimental results indicate that WSSF obtains satisfactory and reliable results in terms of success rate and matching accuracy. Compared with the above six methods, the matching accuracy of WSSF is improved by more than 20.275%, and the success rate is improved by over 5.833%. The source code will be publicly available in https://github.com/WGY-RS/WSSF.
SRRA: A Novel Skewness-Based Algorithm for Cloudlet Scheduling

Cloud computing enables developers to deploy and host applications without focusing on installing and maintaining the infrastructure. The developers can utilize the services provided by the cloud service providers (CSPs) to offer scalable solutions to customer applications. As a result, CSPs are deluged with different batches of cloudlets (tasks) from diverse customer applications. Therefore, developing an algorithm that selects and processes applications intelligently to minimize the execution time and maximize the throughput becomes challenging. Many researchers have shown the round-robin (RR) scheduling algorithm variants to tackle this problem. One such variant is the dynamic RR heuristic algorithm (DRRHA) that utilizes the mean of the burst times (BTs) of cloudlets in the ready queue (RQ) to calculate the time quantum (TQ). However, DRRHA has not considered skewness. This paper introduces a novel skewness-based RR algorithm (SRRA) for cloudlet scheduling. The algorithm dynamically determines the TQ for each cloudlet based on the skewness of the BTs of cloudlets in the RQ. The algorithm has two variants: SRRA with minimum TQ (SRRA-Min) and SRRA with median TQ (SRRA-Med). The two variants of the proposed algorithm exhibit improved performance in terms of total execution time (TET) and throughput compared to DRRHA, individually and collectively. These comparisons are conducted using CloudSim Plus under two scenarios: constant skewness with varying cloudlets and constant cloudlets with varying skewness.
Image Super Resolution Using ESRGAN: A Preliminary Experimental Study

The ongoing progression of technology has led to substantial advancements in image and video quality. Elevated image and video quality inherently enhance visual experiences. However, not all visual content maintains a high standard. Diverse factors contribute to the degradation of image and video quality. Nevertheless, super-resolution techniques offer a solution to restore compromised quality in images and videos. Super-resolution involves enhancing low-resolution images into high-resolution versions, often achieved through deep learning-based methodologies. This research focuses on utilizing the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to elevate image quality, providing the potential for generating images of superior quality from low-resolution sources.
Hardware Root-of-Trust Support for Operational Technology Cybersecurity in Critical Infrastructures

Operational technology (OT) systems use hardware and software to monitor and control physical processes, devices, and infrastructure - often critical infrastructures. The convergence of information technology (IT) and OT has significantly heightened the cyber threats in OT systems. Although OT systems share many of the hardware and software components in IT systems, these components often operate under different expectations. In this work, several hardware root-of-trust architectures are surveyed and the attacks each one mitigates are compared. Attacks spanning the design, manufacturing, and deployment life cycle of safety-critical operational technology are considered. The survey examines architectures that provide a hardware root-of-trust as a peripheral component in a larger system, SoC architectures with an integrated hardware root-of-trust, and FPGA-based hardware root-of-trust systems. Each architecture is compared based on the attacks mitigated. The comparison demonstrates that protecting operational technology across its complete life cycle requires multiple solutions working in tandem.
An Enhanced Deepfake Video Detection Technique

A deepfake is a piece of digital material that has been altered, such as a picture or a video in which the subject’s likeness has been substituted. Deepfake is a real danger to society since it distorts the opinions and perceptions of those around us. Deep learning, which is referred to as “a subset of AI,” is used to create deepfake. Deep learning is a combination of algorithms with the ability to learn and make sensible decisions on their own. People may be led astray by deepfake into believing something to be genuine when it is not. In the hands of internet scammers and cybercriminals, such cutting-edge technologies as deepfake can become dangerous tools. Deepfakes can be hard to spot, making it tough to stop the dissemination of fake content. Additionally, anyone with little programming knowledge can make deepfakes because they can be made with relatively simple tools. Understanding the dangers of deepfake, we are motivated to understand the technology behind deepfake generation, search for technologies to counter this threat, and develop effective detection techniques to accurately identify deepfake videos with high efficiency. In order to achieve this, we have conducted an extensive literature search on various algorithms for creating and detecting deepfake videos. We have identified their relative strengths and limitations, and areas for potential improvements. We have developed to enhance the current detection techniques by designing and developing a new technique that can effectively identify fake videos with multiple faces.