We will present different-type InAs/InP quantum dot (QD) coherent comb lasers (CCLs) and semiconductor optical amplifiers (SOAs) around 1550 nm with their detailed technical specifications. By using those QD-CCLs and ...
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It is broadly accepted that requirements engineering is one of the most important phases of a software project, and requires tools to be effective. For a variety of reasons, paper as a tool has lasted for millennia an...
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Poly(d,l-lactide) is a biocompatible and biodegradable polymer with applications in the biomedical field (drug delivery, implants) and packaging. Conventional synthesis with stannous octoate is slow (>4 h) and can ...
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Image segmentation is a fundamental component of either image processing or computer vision, finding its applications in medical image analysis, augmented reality, and video surveillance, among others. However, the cu...
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Image segmentation is a fundamental component of either image processing or computer vision, finding its applications in medical image analysis, augmented reality, and video surveillance, among others. However, the current research is paying too little attention to the robustness of such models, which is actually a factor that easily predisposes the model to adversarial perturbations caused by slight, imperceptible distortions added to the input images. In this work, we leverage Metamorphic Testing (MT) to evaluate and boost Segmentation models robustness. Our key innovation lies in using GA to intelligently evolve and optimize transformation sequences, systematically discovering the most effective combinations of spatial and spectral distortions while maintaining image fidelity. Our segmentation robustness metamorphic testing approach (SegRMT) generates adversarial examples that maintain the visual coherence of images while adhering to a predefined Peak Signal-to-Noise Ratio (PSNR) threshold, ensuring genuine disruptions. We use the Cityscapes dataset for our experiments, which consists of 5,000 images from diverse stereo video sequences in urban environments across 50 cities. Our findings show that by combining metamorphic testing and a genetic algorithm (GA), our approach can significantly reduce the mean Intersection over Union (mIoU) produced by the DeepLabV3 segmentation model to 6.4%, while other baseline adversaries decrease mIoU values between 21.7% and 8.5%. Other findings indicate that SegRMT and other baseline adversarial training achieve higher performance if training and testing occurred on their separate specific adversarial datasets, with mIoU values up to 73%. Other findings indicate that SegRMT adversarial training increases the mIoU of a segmentation model to 53.8% in cross-adversarial testings, while other baseline adversaries only increase mIoU values to between 2% and 10% on the SegRMT adversarial testing. This demonstrates that SegRMT effectiv
In recent years, cryptocurrencies have received much attention due to their recent price surge and crash. In fact, their prices have been volatile, making them very difficult to predict. Accordingly, various machine l...
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The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP ***,existing large language models have limitations in comprehending and reasoning in *** paper addresses these limitat...
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The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP ***,existing large language models have limitations in comprehending and reasoning in *** paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource *** propose LLaMA-LoRA,a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA)of Large Language Models technique for ***-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language *** reasoning resulting from conventional prompts negatively impacts model *** prompts are introduced to encourage reasoning chain generation and accurate answer *** the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning *** LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks,surpassing benchmark performance achieved by related language models such as GPT-3.5,Chat-GLM,and OpenAssistant,delivering accurate,comprehensive,and professional *** availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.
This paper provides an overview of the Internet of Things (IoT) and its significance. It discusses the concept of Man-in-the-Middle (MitM) attacks in detail, including their causes, potential solutions, and challenges...
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This article reviews the control and routing methods in Wireless Sensor Networks. These methods are able to increase the energy efficiency by using the reinforcement learning technique, considered as one of the means ...
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Context: Prior studies on mobile app analysis often analyze apps across different categories or focus on a small set of apps within a category. These studies either provide general insights for an entire app store whi...
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Context: Prior studies on mobile app analysis often analyze apps across different categories or focus on a small set of apps within a category. These studies either provide general insights for an entire app store which consists of millions of apps, or provide specific insights for a small set of apps. However, a single app category can often contain tens of thousands to hundreds of thousands of apps. For example, according to AppBrain, there are 46,625 apps in the "Sports" category of Google Play apps. Analyzing such a targeted category of apps can provide more specific insights than analyzing apps across categories while still benefiting many app developers interested in the category. Objective: This work aims to study a large number of apps from a single category (i.e., the sports category). Our work can provide two folds contributions: 1) identifying insights that are specific to tens of thousands of sports apps, and 2) providing empirical evidence on the benefits of analyzing apps in a specific category. Method: We perform an empirical study on over two thousand sports apps in the Google Play Store. We study the characteristics of these apps (e.g., their targeted sports types and main functionalities) through manual analysis, the topics in the user review through topic modeling, as well as the aspects that contribute to the negative opinions of users through analysis of user ratings and sentiment. Results: We identified sports apps that cover 16 sports types (e.g., Football, Cricket, Baseball) and 15 main functionalities (e.g., Betting, Betting Tips, Training, Tracking). We also extracted 14 topics from the user reviews, among which three are specific to sports apps (accuracy of prediction, up-to-dateness, and precision of tools). Finally, we observed that users are mainly complaining about the advertisements and quality (e.g., bugs, content quality, streaming quality) of sports apps. Conclusion: It is concluded that analyzing a targeted category of apps (e.g.,
Solar energy production has risen significantly globally in recent years. Solar Panels and Photovoltaic PV modules are the key components of solar energy production. Therefore, ensuring their health and efficiency and...
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