Reviewerly/exHarmony huggingface dataset:
Neural Network Backdoor Attacks Fully Controlled by Composite Natural Utterance Fragments

Since the popularity of deep neural networks, NLP models have played an increasingly important role in our lives and work. However, along with the widespread use of NLP models, backdoor attacks against NLP models have shown to be increasingly damaging, which can have extremely serious consequences. Backdoor attacks are generally used to implant backdoors into models by compromising the training phase, and then triggered by triggers in the inference phase to make the backdoored models exhibit abnormal behaviour. In this paper, we propose two backdoor attack methods that controlled by composite triggers, Enhanced Backdoor Attack (EBA) and Trigger Frequency Controlled Backdoor Attack (TFCBA), which extend the threatening nature of backdoor attacks by using composite natural utterance fragments as triggers, and they eliminate the shortcomings of currently proposed backdoor attacks such as triggers being easily used accidentally, the single function of the attack, and the over-association of trigger patches with the target class. We have experimentally evaluated our proposed attacks in multiple NLP task scenarios, and the experimental results demonstrate excellent feasibility and effectiveness.
Watch You Under Low-resolution and Low-illumination: Face Enhancement via Bi-factor Degradation Decoupling

Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor’s degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution.
Semantic Versioning for Python Programs

We propose a language-based approach to software versioning. Unlike the traditional approach of mainstream version control systems, where each evolution step is represented by a textual diff, we treat versions as programming elements. Each evolution step, merge operation, and version relationship, is represented explicitly in the program. This provides compile time guarantees for safety code reuse from previous versions, as well as forward and backwards compatibility between versions, allowing clients to use newly introduced code without needing to refactor their program. By lifting the versioning to the language level, we pave the way for tools that interact with software repositories to have more insight regarding the evolution of the software semantics.
Language Runtimes for the New Cloud Era (Invited Talk)

Programming languages offer a number of abstractions such as dynamic typing, sandboxing, and automatic garbage collection which, however, come at a performance cost. Looking back, the most influential programming languages were proposed at a time when Moore’s Law was still in place. Nowadays, post-Moore’s law, scalability, and elasticity become crucial requirements, leading to an increasing tension between programming language design and implementation, and performance. It is now time to discuss the impact of programming languages and language runtimes in the context of scalable and elastic cloud computing platforms with the goal of forecasting their role in the new cloud era.
The future has arrived for securing confidential data

Though quantum computers are still a decade or more away, NIST is finalizing new encryption standards now to replace current vulnerable protections.
The Effect of Difference In Press Time on Physical Properties and Mechanical Particle Board Without Adhesive From Coconut Fiber and Gambier Processed Pulp

This study aims to determine the effect of differences in the length of time when pressing for the manufacture of particle board without adhesive from a mixture of coconut coir fiber and processed gambir leaf dregs on the physical and mechanical properties of the resulting non-adhesive particle board. The method used in this study was a completely randomized design with 5 treatments and 3 replications. Data were analyzed using variance, followed by Duncan`s New Multiple Range Test (DNMRT) at 5% level. The comparison ratio used in the manufacture of particle board without adhesive from coconut coir fiber and processed gambir waste is 60% : 40%. The treatments in this study were A (8 minutes of pressing time), B (10 minutes of pressing time), C (12 minutes of pressing time), D (14 minutes of pressing time), E (16 minutes of pressing time). carried out is a test of physical properties and mechanical properties. The results showed that the length of time of compression affected the physical and mechanical properties of the non-adhesive particleboard. The best treatment was treatment A (pressing time 8 minutes). Observation of physical properties obtained density 0.68 g/cm3, moisture content 13.42%, water absorption 132.87%, thickness swelling 66.84%, mechanical properties fracture toughness (MOR) 90.40 kg/cm2, toughness compressive parallel to the surface of 50.07 kg/cm2, and internal bond (IB) 1.45 kg/cm2.
Mirrorverse: Live Tailoring of Video Conferencing Interfaces

How can we let users adapt video-based meetings as easily as they rearrange furniture in a physical meeting room? We describe a design space for video conferencing systems that includes a five-step “ladder of tailorability,” from minor adjustments to live reprogramming of the interface. We then present Mirrorverse and show how it applies the principles of computational media to support live tailoring of video conferencing interfaces to accommodate highly diverse meeting situations. We present multiple use scenarios, including a virtual workshop, an online yoga class, and a stand-up team meeting to evaluate the approach and demonstrate its potential for new, remote meetings with fluid transitions across activities.
The Effects of Energy Drinks on Concentration and Exercise Performance

Athletes\' concentration, lower limbs explosive power, lower limbs muscle strength and anaerobic exercise capacity are closely related to sports performance. This study explores the concentration, lower limb explosive power, lower limb muscle strength and anaerobic exercise capacity of 19 martial arts athletes after ingesting brewed energy drinks. The double-blind experimental design was carried out by means of balanced sequential crossover method and random assignment. All subjects will undergo the same experiment three times, each time ingesting low-dose energy drink, high-dose energy drink or placebo, with an interval of 7 days between each experiment. For each experiment, the subjects first performed a concentration pre-test, and then after ingesting different doses of energy drinks or placebos according to groups, they rested for 1 hour, and then implemented a concentration post-test. After a standardized warm-up, the sports performance will be tested sequentially. All data were analyzed by one-way ANOVA with repeated measures. There were no significant differences in concentration, lower limbs explosive power, and lower limbs muscle strength. However, the jump height and peak power of the lower limbs explosive power showed an upward trend. In the part of anaerobic power, the anaerobic peak power (734.4±42.0W), anaerobic power (11.7±0.3W/kg) and anaerobic capacity (9.0±0.3W/kg) of the high-dose group compared with the placebo group, there were significant differences (p&lt;0.05), but no significant difference in the fatigue index. Therefore, ingesting energy drinks before exercise can help improve the anaerobic performance of martial arts athletes. It is suggested that coaches and athletes can ingest energy drinks before training or competition as a supplement to improve exercise performance, which will help improve sports performance.
2S-BUSGAN: A Novel Generative Adversarial Network for Realistic Breast Ultrasound Image with Corresponding Tumor Contour Based on Small Datasets

Deep learning (DL) models in breast ultrasound (BUS) image analysis face challenges with data imbalance and limited atypical tumor samples. Generative Adversarial Networks (GAN) address these challenges by providing efficient data augmentation for small datasets. However, current GAN approaches fail to capture the structural features of BUS and generated images lack structural legitimacy and are unrealistic. Furthermore, generated images require manual annotation for different downstream tasks before they can be used. Therefore, we propose a two-stage GAN framework, 2s-BUSGAN, for generating annotated BUS images. It consists of the Mask Generation Stage (MGS) and the Image Generation Stage (IGS), generating benign and malignant BUS images using corresponding tumor contours. Moreover, we employ a Feature-Matching Loss (FML) to enhance the quality of generated images and utilize a Differential Augmentation Module (DAM) to improve GAN performance on small datasets. We conduct experiments on two datasets, BUSI and Collected. Moreover, results indicate that the quality of generated images is improved compared with traditional GAN methods. Additionally, our generated images underwent evaluation by ultrasound experts, demonstrating the possibility of deceiving doctors. A comparative evaluation showed that our method also outperforms traditional GAN methods when applied to training segmentation and classification models. Our method achieved a classification accuracy of 69% and 85.7% on two datasets, respectively, which is about 3% and 2% higher than that of the traditional augmentation model. The segmentation model trained using the 2s-BUSGAN augmented datasets achieved DICE scores of 75% and 73% on the two datasets, respectively, which were higher than the traditional augmentation methods. Our research tackles imbalanced and limited BUS image data challenges. Our 2s-BUSGAN augmentation method holds potential for enhancing deep learning model performance in the field.
Internet of Things access mechanism of university education information center based on distributed information system

With the continuous development of information technology, educational informatization has gradually become the mainstream of the future information technology industry. Building a complete, efficient, and flexible online teaching system based on distributed information systems is an inevitable trend. This article proposes a new idea based on the existing technology, that is, using the Internet of Things technology to create a complete and convenient network teaching resource library system to meet user needs. It focuses on students and teachers to establish an information platform and publish it to various regions, achieving issues such as interactivity and timeliness between educational data in this way. After that, this article also designed a test process for this access mechanism. The test results show that users have high requirements for the access mechanism of the Internet of Things in the education information center, with an average time between failures of 2356 hours to 2785 hours.
WorldFAIR month 12 deliverables

Abstract In June 2023 CODATA announced that all the WorldFAIR month 12 deliverables have been submitted to the EC HORIZON agency and are available in their Zenodo community: · WorldFAIR Project (D3.1) Digital recommendations for Chemistry FAIR data policy and practice: https://doi.org/10.5281/zenodo.7887283
From the Era of Print to the Reality of Electronic Publishing

Abstract In 1998, Michael Bowen summarized an ICSU Press workshop on electronic publishing [1]. It is reprinted below (in this issue of Chemistry International) and I have been invited to give a 2023 perspective on its conclusions. In 1998, Wendy Warr was chair of the IUPAC Committee on Printed and Electronic Publications (CPEP was the predecessor of the current CPCDS) and Mike Bowen was Secretary. It was an advisory function of the committee to keep-up with how the landscape of electronic publishing was evolving. Before I address those conclusions more specifically, it is worth mentioning some electronic publishing advances of the 1990s. Carnegie Mellon University advertised an opening for an “electronic librarian” as early as 1991. Some electronic products predate the World Wide Web (e.g., arXiv preprints which were first emailed using TeX in 1991). The Digital Object Identifier (DOI) was launched in October 1997. SpringerLink, Elsevier’s ScienceDirect, and PubMed were all available in 1998. The American Chemical Society (ACS) and Chemical Abstracts Service launched ChemPort in December 1997, in collaboration with seven other publishers. ACS had reinvented its journals for the Web with Articles as Soon as Possible (ASAP), search tools to find specific articles easily, and links to databases, and to cited articles through ChemPort. The scene was clearly set for electronic publishing, and so let’s move to the eight 1998 predictions from the ICSU Press workshop.
2330P Pre-test prediction of multiple druggable mutations based on H&amp;E image artificial intelligence (AI) analysis may enable more efficient clinical workflow for treatment decisions in non-small cell lung cancer (NSCLC)

AI may predict driver mutations from H&E slides, and this prediction may be useful as a screening tool to guide the efficient use of molecular testing. Here, we investigate the prediction performance for mutations in six driver oncogenes and explore the benefits to clinical workflow in NSCLC treatment decisions. NSCLC samples from Neogenomics (n=2,161) and TCGA (n=933) were used for model development. Four features were used: two convolutional neural networks trained for cell and tissue, a self-supervised vision transformer, and an AI-based pathology profiling analyzer for semantic contents. A set of classifiers was trained and ensembled for improved robustness. Performance of the model was validated in an independent dataset (n=792), then we simulated real-world impact based on the prevalence of driver mutations from AACR GENIE v13.1 NSCLC (n=19,722). Table 2330P shows the dataset and performance of the models. Given 2.8% prevalence of MET exon skipping mutations (MET-ex), 8.5% positive predictive value (PPV) means that a test-positive patient has a 3x higher chance of being true-MET-ex positive compared to the overall patient population. Moreover, given 99.2% specificity and 49.4% prevalence, PPV for selecting patients without mutations (All-WT) is 95.2%, enabling avoidance of unnecessary tests with potentially acceptable error (<5%).Table: 2330PInternal setExternal setn (%)AUCn (%)AUCSensitivitySpecificityEGFR-mt384 (12.4)0.841224 (28.3)0.72375.5%52.6%KRAS-mt516 (16.7)0.728130 (16.4)0.72186.2%22.5%ALK-tr310 (10.0)0.79546 (5.8)0.73839.1%81.8%ROS1-tr295 (9.5)0.79362 (7.8)0.60914.5%70.6%RET-tr307 (9.9)0.76311 (1.4)0.68318.2%96.4%MET-ex240 (7.8)0.78912 (1.5)0.84983.3%74.4%All-WT1045 (33.8)N/A277 (35.0)N/A15.9%99.2%AUC, area-under-the-curve; ex, exon skipping; mt, mutation; tr, translocation. Open table in a new tab AUC, area-under-the-curve; ex, exon skipping; mt, mutation; tr, translocation. A novel clinical workflow, using AI analysis of H&E to predict genomic profiles and identify which patients would benefit from confirmatory molecular testing, could enable better and more efficient treatment decisions.
Opto-thermal deformation fitting method based on a neural network and a transfer learning

The thermal deformation fitting result of an optical surface is an important factor that affects the reliability of optical-mechanical-thermal integrated analysis. The traditional numerical methods are challenging to balance fitting accuracy and efficiency, especially the insufficient ability to deal with high-order Zernike polynomials. In this Letter, we innovatively proposed an opto-thermal deformation fitting method based on a neural network and a transfer learning to overcome shortcomings of numerical methods. The one-dimensional convolutional neural network (1D-CNN) model, which can represent deformation of the optical surface, is trained with Zernike polynomials as the input and the optical surface sag change as the output, and the corresponding Zernike coefficients are predicted by the identity matrix. Meanwhile, the trained 1D-CNN is further combined with the transfer learning to efficiently fit all thermal deformations of the same optical surface at different temperature conditions and avoids repeated training of the network. We performed thermal analysis on the main mirror of an aerial camera to verify the proposed method. The regression analysis of 1D-CNN training results showed that the determination coefficient is greater than 99.9%. The distributions of Zernike coefficients predicted by 1D-CNN and transfer learning are consistent. We conducted an error analysis on the fitting results, and the average values of the peak-valley, root mean square, and mean relative errors of the proposed method are 51.56%, 60.51, and 45.14% of the least square method, respectively. The results indicate that the proposed method significantly improves the fitting accuracy and efficiency of thermal deformations, making the optical-mechanical-thermal integrated analysis more reliable.
Identity and Access Management in AWS

This chapter provides a comprehensive exploration of Identity and Access Management (IAM) in AWS. It delves into the detailed processes of creating IAM users, roles, and setting policies, emphasizing their significance in safeguarding the AWS account and its users. IAM proves invaluable in managing multiple users within a single account. The chapter also highlights the crucial role of Multi-Factor Authentication (MFA) in enhancing account security, particularly when collaborating with AWS Cloud Service Providers (CSP). Overall, this chapter serves as a fundamental guide for understanding and implementing IAM, establishing a strong foundation for working effectively within the AWS ecosystem.
Compute Services in AWS

This chapter provides a comprehensive study of container services, specifically focusing on Docker. It includes a detailed comparison between Docker implementation and virtual machines. The chapter offers an overview of various container services provided by AWS, such as Amazon Elastic Container Service (ECS), AWS Fargate, Amazon Elastic Container Registry, AWS Copilot, AWS Batch, and Kubernetes. Additionally, the chapter explores Lambda, a serverless service offered by AWS. It includes practical guidance on creating a lambda function. The chapter also touches upon Lightsail, catering to readers with less experience in AWS. By covering a wide range of topics, from Docker and container services to Lambda and Lightsail, this chapter equips readers with the knowledge and practical skills necessary to work effectively within the AWS ecosystem. It serves as a valuable resource for understanding and utilizing container services, serverless computing, and AWS services tailored for different user levels.
Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n

Deep-sea biological detection is essential for deep-sea resource research and conservation. However, due to the poor image quality and insufficient image samples in the complex deep-sea imaging environment, resulting in poor detection results. Furthermore, most existing detection models accomplish high precision at the expense of increased complexity, and leading cannot be well deployed in the deep-sea environment. To alleviate these problems, a detection method for deep-sea organisms based on lightweight YOLOv5n is proposed. First, a lightweight YOLOv5n is created. The proposed image enhancement method based on global and local contrast fusion (GLCF) is introduced into the input layer of YOLOv5n to address the problem of color deviation and low contrast in the image. At the same time, a Bottleneck based on the Ghost module and simAM (GS-Bottleneck) is developed to achieve a lightweight model while ensuring sure detection performance. Second, a transfer learning strategy combined with knowledge distillation (TLKD) is designed, which can reduce the dependence of the model on the amount of data and improve the generalization ability to enhance detection accuracy. Experimental results on the deep-sea biological dataset show that the proposed method achieves good detection accuracy and speed, outperforming existing methods.
Introduction To Cloud Computing and AWS

This chapter offers an in-depth exploration of cloud computing, encompassing various deployment models and services. It sheds light on the pressing concern of personal data privacy, as cloud technology enables data to traverse geographical boundaries, disregarding local regulations. The evolution of AWS since 2000 takes the center stage, emphasizing key elements such as regions, availability zones, and data centers. By providing comprehensive insights, the chapter equips users with the necessary knowledge to effectively navigate the intricacies of AWS. It also elucidates the AWS shared responsibility model, ensuring users understand their own responsibilities and those of AWS. Furthermore, the chapter focuses on six core competencies, enabling customers to fully harness the potential of AWS's offerings. This multifaceted chapter serves as a comprehensive guide, empowering readers to grasp the complexities and opportunities of cloud computing with AWS.
Storage Options with Amazon EC2 Instance

This chapter provides an overview of the storage options available with EC2 instances, namely EC2 Instance storage, EBS, EFS, and Amazon S3. It focuses on illustrating the creation of EBS volumes and attaching them to EC2 instances. The chapter highlights the unique features and uses cases of each storage option, empowering readers to make informed decisions based on their needs. By following the step-by-step instructions, readers gain practical guidance on configuring and utilizing EBS storage effectively within the EC2 environment. Overall, this chapter serves as a valuable guide for optimizing storage configurations and maximizing the benefits of EC2 instances in AWS.
Load Balancing and Auto Scaling in AWS

This chapter provides a thorough introduction to Autoscaling and Elastic Load Balancing (ELB) in the AWS cloud. It highlights the benefits of these services, which simplify workflows and automate tasks. The chapter offers an in-depth overview of ELB and Autoscaling, covering their basic uses and providing hands-on experience. By understanding and utilizing these services, users can optimize resource allocation, handle traffic fluctuations, and enhance application performance. This chapter serves as a comprehensive guide for effectively implementing Autoscaling and Elastic Load Balancing in the AWS environment.