Reviewerly/exHarmony huggingface dataset:
<i>Correction to:</i> Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation, by Danielson and McKay <i>Ind Biotechnol</i> 2023;19(4):195–200; doi: 10.1089/ind.2023.29319.nda

Industrial BiotechnologyVol. 19, No. 5 CorrectionsOpen AccessCorrection to: Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation, by Danielson and McKay Ind Biotechnol 2023;19(4):195–200; doi: 10.1089/ind.2023.29319.ndais erratum ofBroad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive LegislationPublished Online:18 Oct 2023https://doi.org/10.1089/ind.2023.29319.nda.correxAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail In the August 2023 issue of Industrial Biotechnology (vol. 19, no. 4; 195–200), the article entitled “Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation,” by Danielson and McKay, requires correction. Due to an unfortunate administrative error during the publication process, seven names published in the original article were mistakenly included in the author byline.Paul Bloom, Jennifer B. Dunn, Neil Jakel, Timothy Bauer, John R. Hannon, Michael C. Jewett, and Brent H. Shanks are not associated with the article and have been removed from the byline.The two correct authors remain as Nathan A. Danielson and Sarah M. McKay.The online version has been corrected to reflect this. The publisher apologizes for this error.FiguresReferencesRelatedDetailsRelated articlesBroad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation17 Aug 2023Industrial Biotechnology Volume 19Issue 5Oct 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Correction to: Broad Geographic and Bi-Partisan Support of Industrial Biotechnology Indicates Timing May Be Right for Incentive Legislation, by Danielson and McKay Ind Biotechnol 2023;19(4):195–200; doi: 10.1089/ind.2023.29319.nda.Industrial Biotechnology.Oct 2023.301-301.http://doi.org/10.1089/ind.2023.29319.nda.correxcreative commons licensePublished in Volume: 19 Issue 5: October 18, 2023Online Ahead of Print:September 11, 2023PDF download
Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms

Object detection in 3D is a crucial aspect in the context of autonomous vehicles and drones. However, prototyping detection algorithms is time-consuming and costly in terms of energy and environmental impact. To address these challenges, one can check the effectiveness of different models by training on a subset of the original training set. In this paper, we present a comparison of three algorithms for selecting such a subset – random sampling, random per class sampling, and our proposed MONSPeC (Maximum Object Number Sampling per Class). We provide empirical evidence for the superior effectiveness of random per class sampling and MONSPeC over basic random sampling. By replacing random sampling with one of the more efficient algorithms, the results obtained on the subset are more likely to transfer to the results on the entire dataset. The code is available at: https://github.com/vision-agh/monspec.
DE-AE: A Dual-Encoder-Based Auto-encoder Framework with Improved Transformer for Anomaly Detection in Medical Imaging

Anomaly detection plays a crucial role in medical imaging because it does not require labeled samples. Existing anomaly detection methods are primarily based on image reconstruction techniques. However, image reconstruction-based methods face the issue of identity mapping. To solve the above issue, this paper presents a novel dual-encoder-based auto-encoder framework, which comprises two encoders sharing parameters and a decoder. The first encoder receives the raw medical image, while the second takes a synthesized noise image as an auxiliary input, aiding the first in better learning the manifold of normal samples. Further, an improved transformer is employed for modeling high-level features to mitigate the deficiencies of Convolutional Neural Networks (CNNs) in long-distance feature relationships. However, traditional Transformers bring a quadratic computational complexity. To balance model performance and computational cost, the framework uses CNNs to extract local features and the improved Transformer for modeling deep embedding features. The proposed method is validated on two public datasets, and the experimental results demonstrate the effectiveness of our approach.
Quasi-Time-Optimal Model Predictive Control Technology for Autonomous Pallet Picking

This paper proposes a pallet picking method based on time-optimal control addressing the high-precision control problem faced by automatic forklift in picking up pallet-loaded cargoes. In this paper, the local coordinate system is introduced, and the process of approaching the pallet and adjusting the orientation of the automatic forklift is transformed into a positional calming control problem through coordinate transformation, and the corresponding state regulator is further designed. Taking the consideration of pallet picking efficiency into account, the time-optimal performance metrics are fully investigated in constructing the optimization proposition, combined with the common quadratic optimal metrics. The control law follows the overall framework of open-loop optimization and closed-loop feedback with model predictive control, and generates control laws by rolling optimization. The control algorithm realizes the precise operation of the automatic forklift, leads which to accomplish the task of flexible pallet picking in the experiment.
Focusing on Cross Views Improves Reconstruction in Unsupervised Multi-View Stereo

The purpose of Multi-view Stereo(MVS) is to reproduce three-dimensional scenes through two-dimensional images. Unsupervised Multi-view Stereo has achieved remarkable progress both in terms of reconstruction accuracy and completeness. However, previous methods are mainly rely on the prerequisite that the corresponding pixels between different views share similar characteristics. These methods have certain limitations in the actual scene, especially for some special non-Lambert surface and specular area. In the process of 3D reconstruction of the above-mentioned scenes, the depth estimation work we want to adopt will be greatly affected. In order to improve the situation, we carry out relevant adjustment and optimization work on the self-supervised signal. We consider focusing on cross-views to improve the stability and anti-disturbance of self-supervised signals. Therefore, we design a novel self-supervised network to improve the quality of generated point clouds during reconstruction. In addition, we also introduce a new loss function to capture and characterize the relationship between cross-views to achieve the purpose of improving the completeness and accuracy of depth estimation in boundary regions. We carry out experiments and tests on DTU dataset and Tanks&Temples dataset respectively. The experimental results prove that our proposed method has good reconstruction ability and good generalization effect.
Motion Planning of Mobile Robot with Non-holonomic Constraints

This paper proposes a modified spatio-temporal A* path planning algorithm for the trajectory planning of mobile robots with non-holonomic constraints. Most path planning algorithms treat mobile robots as to be with holonomic constraints. However, wheeled mobile robots are affected by non-holonomic constraints, which leads to trajectory unreachability. To solve this problem, an improved spatio-temporal A* algorithm based on non-holonomic constraints is given, which considers the efficiency, completeness and smoothness of the path. At the same time, by improving the construction form of collision constraint and adding spatio-temporal constraints to the path, the success rate and solving speed of the path planning algorithm for non-holonomic constrained mobile robot are increased. Finally, simulation experiments are given to indicate that the proposed planning algorithm provides an optimized path, while improving the planning success rate and reducing the computational time.
Robust And Detailed Reconstuction Of Human Face From Single Image

Recently, 3D face reconstruction based on 3D Morphable Model (3DMM) has made great progress. However, 3D face reconstruction based on a single face image is always an ill-posed problem which can not capture detailed textures of faces in most cases. Recent works which recovered the faces with UV maps can not change in shape, and only have some visual details. In this paper, we introduce an end-to-end 3D face reconstruction framework in order to recover detailed 3D face from a single face image. The main idea is utilizing the facial details of the input image to refine the 3DMM-based rough mesh in order to make it more close to the real face in shape. We use the UV displacement map and UV visible mask to recover face details and make the model more robust to occlusions in face images. During the reconstruction, we use the multiple-level loss to make our training process unsupervised. This method essentially improves the problem that detailed faces are hard to be recovered through monocular 3D face reconstruction. We quantitatively evaluate our model on MICC Florence 3D Face dataset and finally get the acceptable results.
CPU and Memory Allocation Optimization using Fuzzy Logic Based Clustering

The allocation of CPU time and memory resources, are well known problems in organizations with a large number of users, and a single mainframe. Usually, the amount of resources given to a single user is based on its own statistics, not on the entire statistics of the organization therefore patterns are not well identified and the allocation system is prodigal. In this work the authors suggest a fuzzy logic-based algorithm to optimize the CPU and memory distribution between the users based on the history of the users. The algorithm works separately on heavy users and light users since they have different patterns to be observed. The result is a set of rules, generated by the fuzzy logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering in Tel Aviv University, demonstrate the abilities of the new algorithm. This paper also examines the subject of system investigation using structured analysis (represented by Data Flow Diagrams - DFD), and the Object-Oriented Analysis and Design approaches (OOAD). The structured approach focuses on the flow and processing of data. The OOAD partitions the problem with respect to objects when analyzing the problem domain. When analyzing a large system or trying to reconstruct one using one of the two approaches mentioned above, there is apt to be a degree of inefficiency. This paper suggests a unique solution to this inefficiency. The optimization approach is based upon fuzzy logic inference engine clustering techniques. The study also presents the means for grading a system and creating a simple base for a computerized tool. By implementing the algorithm in a defined case study, we improved the modeling of the system coupling by 28 %, the system complexity by 20% and the system size criteria by 36%, for the structured analysis. This leads to system improvement process.
Digitalizing the Dress-up Experience: An Exploration of Virtual Try-On for Traditional Chinese Costume

With the recent rapid developments in artificial intelligence (AI) technology, virtual try-on technology empowered by generative approaches has reached a level of maturity that is ripe for commercialization. However, the application of virtual try-on technology to traditional cultural costumes has not received adequate attention from research institutions and enterprises. To address this research gap, we present a novel virtual try-on system for traditional Chinese costume that incorporates diverse AI technologies, including generative models, pose estimation, human parsing, and image processing algorithms, to provide an accurate and immersive virtual try-on experience. In particular, we leverage five distinct techniques Human Keypoint Detection, Human Parsing, Densepose Parts Segmentation, Cloth Mask Extraction, and Clothing-Agnostic Representation - to realize a web service that can effectively handle the specific challenges of traditional Chinese costume, such as the need to preserve cultural authenticity and ensure accurate fit and drape. Finally, we conduct various experiments to evaluate the system’s performance and have received positive feedback from users, indicating that it provides a realistic and engaging virtual try-on experience. Our work contributes to the growing body of research on virtual try-on technology and has the potential to impact the way traditional cultural costumes are designed, marketed, and worn.
Int&amp;Int: A Two-Pathway Network for Skeleton-Based Action Recognition

Human action recognition has recently attracted a lot of researchers, while most of them mainly focus on the local dynamics or global information alone and cannot focus on both the intensity and the integrity of the action. In this work, we propose a two-pathway Int&Int network(Intensity&Integrity) for skeleton-based action recognition to satisfy both aspects, where the great complementarity between the two pathways further enhances the performance. Besides, for Integrity pathway, we apply the uniform sampling strategy. For Intensity pathway, we introduce the intensity-dependent sampling, where a clip composed of consecutive frames around the frame with the largest motion intensity is sampled. Moreover, we explain various definitions of the motion intensity containing different semantic information based on the extracted 2D human poses. For each pathway, the poses are represented by a 3D heatmap volume and 3D-CNNs of both pathways have the same architecture. Late fusion is used to ensemble them. The model is evaluated on two action recognition datasets, FineGYM-99 and HMDB-51, and it achieves superior performance on both of them. The code has been shown at https://github.com/SarahQi666/Int-and-Int.
Near-duplicate image removal method for aerial transmission line inspection

UAV inspection has become an important way of transmission line inspection. However, the large number of inspection images collected by UAVs presents a series of challenges for subsequent processing. Aiming at the problem that there are a large number of approximate duplicate images in the inspection data collected by UAV. This paper proposes a hierarchical clustering-based near-duplicate image deletion method that combines a "coarse grouping" strategy based on block feature extraction and a "fine grouping" strategy based on the Sobel operator of perceptual hashing. The hash index structure is constructed separately in two stages to improve matching efficiency. In the near-duplicate image removal stage, the content and attributes of the images are comprehensively evaluated to accurately identify the centroid image. Experimental results demonstrate that this method effectively removes near-duplicate images while improving overall efficiency and ensuring image deduplication accuracy. Thus, it is an effective method for near-duplicate image removal.
One software defective data augmentation method based on VAE and WGAN

In software metric datasets, the number of defective samples is always even fewer than that of non-defective samples, which makes follow-up research complex and difficult. Therefore, this essay provides a method of software defective data augmentation based on a variational autoencoder (VAE) and Wasserstein Generative Adversarial Network (WGAN). This process contains several steps. First, dimensionality reduction of software metric data is achieved by utilizing VAE, producing a set of codes (latent vectors); the distribution of the set is studied by WGAN and then “the code set (latent vectors)” is generated. Finally, the generated codes (latent vectors) are input into VAE to acquire defective data. This paper proposes a data augmentation method of a minority class, based on VAE and WGAN. The experiments performed in MINST, NSAS MDP confirm that 1) This data augmentation method is superior to WGAN, AE+WGAN, and SMOTE; 2) Introducing the variance of generated samples to the WGAN generator, the variety of those is efficiently improved.
Joint Workload Forecasting and Configuration Tuning to Achieve Cloud Database Performance Adaptation

Database parameter tuning has always been a research hotspot. In recent years, there have been some efforts emerging to tune the runtime parameters of the cloud database by leveraging the high-dimensional space search ability of reinforcement learning. However, the previous solutions either train a tuning model for each database from scratch online, or use offline workloads that deviate greatly from the actual workload for model training, resulting in expensive training costs and poor tuning effect, which fails to meet the requirements of cloud database automatic tuning. In this paper, we propose a cloud database parameter tuning approach JWFCT. By providing a set of offline pre-training models instead of one, and combining with the workload classification and prediction technologies, the optimal configuration is determined with the tuning model that best matches the database workload characteristics, thus the tuning performance can be greatly improved. In addition, the models are also fine-tuned periodically based on online data that can further improve the performance. Experimental results show that JWFCT can significantly improve the throughput of the cloud database and reduce the recommendation time.
Siamese Network for Visual Object Tracking with Improved Information Capture

Siamese network based trackers could enable end-to-end tracking and show excellent performance and tracking efficiency. However, the backbone network is simple which results in insufficient ability to extract the location and semantic information. Therefor, it is difficult to handle the situation such as scale variation and deformation. Aiming the problems above, a novel Siamese based tracker named as Infc-SiamFC is proposed in this paper to efficiently obtain object location and semantic information. Firstly, channel attention is embedded in the backbone network for modeling the interdependencies among feature channels explicitly and adjusting the response value of feature channels adaptively. Secondly, a novel lightweight network named SCNet is proposed to introduce spatial information into channel attention mechanism, and improve the ability of Infc-SiamFC for extracting the location information of objects without increasing the calculation costs. Finally, the features extract from SCNet and CRNet are fused to realize feature multiplexing and further enhance the representation capability of the proposed network. The proposed Infc-SiamFC could run at 66 FPS in real-time and achieved comparable results on challenging dataset, such as OTB50, OTB100, VOT2016 and VOT2018.
A Method for Automatic Code Comment Generation Based on Different Keyword Sequences

Automatic generation of code comments can help alleviate software project development and maintenance difficulties caused by insufficient, missing, or mismatched code comments. Previous works have demonstrated that identifying the intention of code and the corresponding comment categories can enhance the accuracy of automatic code comment generation. However, this method’s effectiveness relies on the expertise of programmers and requires a significant amount of human effort. We propose an automatic code comment generation method based on different keyword sequences to address this issue. We first investigate the verbs in code comments of mainstream public datasets and use automated methods to classify the comments into three categories. Then we extract and encode the keyword sequences of the code according to the comment category using different ways. The encoded sequences are combined with three classical comment generation models to generate comments automatically. Additionally, an algorithm for selecting API comments is proposed to address the issue of API comment quantity causing a decrease in the quality of generated comments during the keyword selection process. Experimental results indicate that the performance of the baseline model can be improved in terms of BLEU, ROUGE-L, and BLEU-DC metrics. In addition, compared with two models that combine other information, there is a certain degree of improvement in terms of three metrics, indicating that the proposed method can generate code comments more accurately.
Energy-Saving Combined Frequency Conversion and AI for Cloud and Edge Enabled Data Centers

With the gradual commercialization of 5G, the construction of the data center ushers in explosive expansion, which leads to higher energy consumption and is not conducive to energy protection, emission reduction and sustainable development. At the same time, the "dual carbon" goal is proposed, which requires green and low-carbon development of all industries. Therefore, this paper proposed an energy-saving scheme integrated frequency conversion and Artificial Intelligence (AI) to obtain higher energy-saving efficiency for Internet Data Center (IDC). Besides, the cloud-edge-end architecture is utilized to collect sufficient multi-type data for model training. Such a structure can help generates an overall strategy, and finely adjust the fan threshold of the frequency converter effectively solving the problems of local optimization and extensive parameter setting in the frequency conversion, The experiment conducted in the air-cooled constant frequency IDC room verifies that the energy saving rate of the proposed solution can reach 34.6%.
The Research on Security Technology of Earthquake Warning Information Release Based on Zero Trust MQTT Protocol

The national earthquake intensity rapid report and early warning project has been under construction since August 2018. Tianjin Earthquake Agency has built Tianjin Earthquake Early Warning System through this project. The system is composed of station technology system, central processing system and early warning information release system. The early warning information release system is an important part of the earthquake early warning system, and is also the key protection object of network security. At present, the release of Tianjin earthquake early warning information mainly adopts special early warning terminal, IPTV and other channels. The early warning information release system is based on MQTT. The release of early warning information depends on the Internet, and the end users who subscribe to early warning information through MQTT protocol are mostly zero trust users. This problem has become the biggest potential safety hazard endangering the early warning system. The project team proposed an MQTT dynamic access control model based on trust computing. This method realizes the safe release of earthquake warning information.