Reviewerly/exHarmony huggingface dataset:
A survey of literature on automated storage and retrieval systems from 2009 to 2019

This paper provides a literature review on automated storage and retrieval systems based on the Scopus database. The last comprehensive literature review that analyses the subject in its entirety dates back to 2009 by Roodbergen and Vis. Since then, the scientific community has provided many new efficient solutions, most of which are collected in Scopus. All these new published solutions need to be classified and summarised; thus, for this paper, more than one thousand scientific publications were analysed in order to classify the proposed solutions, deliver new academic insights to the field, and provide clear guidelines for researchers and practitioners.
GlobalAP: Global average precision optimization for person re-identification

Average Precision (AP) measures the overall performance on the Person Re-Identification (ReID) task. Optimizing AP using all instances in the training set is accordingly an excellent choice for learning a discriminative ReID model. However, exploiting this method directly is unacceptable in practice due to the high cost of computation on the entire dataset. To this end, this paper proposes an effective and easy-to-use approach called GlobalAP that optimizes AP globally at negligible computational cost. More specifically, GlobalAP adopts a memory module to acquire the embedding features of all instances in the training set. To reduce the required computational complexity, GlobalAP utilizes only a few instances with high similarities to the query, to compute AP; this is because we observe that only these instances significantly affect AP and model optimization. Moreover, we propose to gradually increase the difficulty of GlobalAP to further encourage intra-class compactness and inter-class separability. Ultimately, GlobalAP can globally optimize AP and dramatically boost the model performance at negligible computational cost. We evaluate GlobalAP on six large-scale ReID datasets. Experimental results show that GlobalAP exhibits obvious advantages in terms of both computational efficiency and ReID accuracy.
Safety Helmet Wearing Detection Based on Jetson Nano and Improved YOLOv5

Aiming to address the current shortcomings of the existing safety helmet wearing detection algorithms, including a slow reasoning speed, a large model size, and high hardware requirements, this study proposes an improved safety helmet wearing detection network named YOLOv5-SN, which is suitable for embedded deployment on Jetson Nano. First, the backbone of the YOLOv5 network is modified using the model lightweight method introduced by ShuffleNetV2. Next, the model size and number of parameters in the trained model are reduced to about one-tenth of those of the YOLOv5 network, and the reasoning speed is improved by 72 ms/f when tested on Jetson Nano. Then, the modified model is optimized using the quantification and layer fusion operations, further reducing the computing power and accelerating the reasoning speed. Finally, the YOLOv5-SN network is obtained by improving the YOLOv5 model, and the optimized model is deployed on Jetson Nano for testing. The average reasoning speed of the YOLOv5s-SN network reaches 32.2 ms/f, which is 84.7 ms/f faster compared to that of the YOLOv5s model. This demonstrates an obvious advantage of the proposed model in reasoning speed compared to the existing YOLOv5 models. Finally, the proposed model can perform real-time and effective target detection on the Jetson Nano embedded terminal.
Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Ontology generation is a process of relationship analysis and representation for multiple data categories using automatic or semi-automatic approaches. Thus, the main contribution of this paper is the design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification (BOGMAS) that can overcome existing issues. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. This model uses a combination of linear support vector machine, and extra trees stratifies for variance estimation, which makes the model highly efficient, and reduces redundant features from the output ontology. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending on incoming data. The performance of the proposed BOGMAS model is evaluated in terms of precision and recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly efficient in terms of precision, recall and accuracy performance, but has incrementally higher computational complexity and delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things ontology generation with high-security levels.
Application and Improvement of Agile Development in Intelligent Health Hut Software Project

As a new software system, the Intelligent Health Hut software project is characterized by exploration, creativity, and uncertainty. This makes the traditional waterfall development model unapplicable, thus allowing the agile development idea that is lighter, more flexible, and more adaptable to dynamic changes to become the preferred management mode of the project. This paper introduces the application and improvement of the Scrum method based on the agile development idea in software project management from the aspects of team building, requirements management, change management, and iterative process management, along with the characteristics of the Intelligent Health Hut software project. So as to improve the project management efficiency and project quality of Intelligent Health Hut.
Language as a Latent Sequence: Deep Latent Variable Models for Semi-Supervised Paraphrase Generation

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction (VSAR), which performs latent sequence inference given an observed text. To leverage information from text pairs, we additionally introduce a novel supervised model we call dual directional learning (DDL), which is designed to integrate with our proposed VSAR model. Combining VSAR with DDL (DDL+VSAR) enables us to conduct semi-supervised learning. Still, the combined model suffers from a cold-start problem. To further combat this issue, we propose an improved weight initialisation solution, leading to a novel two-stage training scheme we call knowledge-reinforced-learning (KRL). Our empirical evaluations suggest that the combined model yields competitive performance against the state-of- the-art supervised baselines on complete data. Furthermore, in scenarios where only a fraction of the labelled pairs are available, our combined model consistently outperforms the strong supervised model baseline (DDL) by a significant margin (𝑝 < .05; Wilcoxon test).
Ansible for Kubernetes Cloud Providers

Cloud providers are a great resource for propelling your business to a new level. The need for more resources is just a few clicks away. Cloud computing is a commercial offer from a vendor that offers a pool of on-demand computing, storage, database, and network shared resources sold as a service that you can rapidly deploy at scale. In recent times also, Machine Learning and Artificial Intelligence resources are becoming popular.
Twin-chain PBFT consensus for blockchain-based Non-Terrestrial Networks

Blockchain-based non-terrestrial networks (NTN) are expected to ensure the reliability of data in the process of data sharing between untrusted entities. Nevertheless, the low transactions per second (TPS) of blockchain makes it unable to support a large number of data sharing tasks in NTN. One of the key factors for low TPS is the single-chain structure of blockchain. In this way, block generation and block verification are only permitted to be executed sequentially, that means only a single valid block can be generated in the same period. To resolve this dilemma, we propose a twin-chain practical byzantine fault tolerance consensus strategy (TPBFT), in which the consensus process is executed in parallel at two aspects. The first parallel aspect permits block generation and block verification being executed in parallel by twin-chain structure. The second parallel aspect is that the block can be generated with sub-blocks being generated in parallel by replacing transactions with sub-blocks. Finally, the effectiveness of TPBFT is verified by simulation, and the TPS can reach up to 3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> , which means the problem of low TPS of blockchain-based NTN is solved.
Autoencoder-Based Collaborative Attention GAN for Multi-Modal Image Synthesis

Multi-modal images are required in a wide range of practical scenarios, from clinical diagnosis to public security. However, certain modalities may be incomplete or unavailable because of the restricted imaging conditions, which commonly leads to decision bias in many real-world applications. Despite the significant advancement of existing image synthesis techniques, learning complementary information from multi-modal inputs remains challenging. To address this problem, we propose an autoencoder-based collaborative attention generative adversarial network (ACA-GAN) that uses available multi-modal images to generate the missing ones. The collaborative attention mechanism deploys a single-modal attention module and a multi-modal attention module to effectively extract complementary information from multiple available modalities. Considering the significant modal gap, we further developed an autoencoder network to extract the self-representation of target modality, guiding the generative model to fuse target-specific information from multiple modalities. This considerably improves cross-modal consistency with the desired modality, thereby greatly enhancing the image synthesis performance. Quantitative and qualitative comparisons for various multi-modal image synthesis tasks highlight the superiority of our approach over several prior methods by demonstrating more precise and realistic results. The source code will be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/caohaifang123/ACA-GAN</uri> .
Defeasible linear temporal logic

After the seminal work of Kraus, Lehmann and Magidor (formally known as the KLM approach) on conditionals and preferential models, many aspects of defeasibility in more complex formalisms have been studied in recent years. Examples of these aspects are the notion of typicality in description logic and defeasible necessity in modal logic. We discuss a new aspect of defeasibility that can be expressed in the case of temporal logic, which is the normality in an execution. In this contribution, we take Linear Temporal Logic (LTL) as case study for this defeasible aspect. LTL has found extensive applications in Computer Science and Artificial Intelligence, notably as a formal framework for representing and verifying computer systems that vary over time. However, some systems may presents exceptions at some innocuous time points where they can be tolerated, or conversely, exceptions at other crucial time points where they need to be addressed. In order to ensure the reliability of such systems, we study a preferential extension of LTL, called defeasible linear temporal logic (LTL ~ ). In the first part of this paper, we show how semantics of KLM's preferential models can be integrated with LTL. We also discuss the addition of non-monotonic temporal operators as a way to formalise defeasible properties of these systems. The second part of this paper is a study of the satisfiability problem of LTL ~ sentences. Based on Sistla and Clarke's work on the complexity of the classical LTL language, we show the bounded-model property of two fragments of LTL ~ language. Moreover, we provide a procedure to check the satisfiability of sentences in both of these fragments.
Combining Contexts from Multiple Sources for Documentation-Specific Code Example Generation

Code example is a crucial part of good documentation. It helps the developers to understand the documentation easily and use the corresponding code unit (e.g., method) properly. However, many official documentation still lacks (good) code example and it is one of the common documentation issues as found by several studies. Hence in this paper, we consider automatic code example generation for documentation, a direction less explored by the existing research. We employ Codex, a GPT-3 based model, pre-trained on both natural and programming languages to generate code examples from source code and documentation given as input. Our preliminary investigation on 40 scikit-learn methods reveals that this approach is able to generate good code examples where 72.5% code examples were executed without error (passability) and 82.5% properly dealt with the target method and documentation (relevance). We also find that incorporation of error logs (produced by the compiler while executing a failed code example) in the input further improves the passability from 72.5% to 87.5%. Thus, our investigation sets the base of documentation-specific code example generation and warrants in-depth future studies.
Lightweight Mask Detection Algorithm Based on Improved YOLOv4

In view of the fact that the current YOLOv4 target detection network structure is complex and difficult to be applied to low-computing hardware platforms such as mobile terminals, a lightweight mask detection algorithm based on improved YOLOv4 is proposed. Using GhostNet to replace the backbone feature extraction network of YOLOv4, the number of network parameters is greatly reduced and the detection speed is improved. The ordinary convolution of YOLOv4 is replaced with a depthwise separable convolution to further reduce the amount of model parameters. It is proposed to introduce the CBAM attention mechanism into the feature fusion network part to enhance the feature extraction ability of space and channel. The label smoothing and learning rate cosine annealing decay algorithms are used to optimize the convergence effect of the model, and Mosaic data is used to enhance the robustness of the model. Experiments on the constructed face mask dataset show that compared with the original YOLOv4, the proposed algorithm sacrifices 1.92% of mAP, the detection speed (FPS) is increased by about 71%, and the size of model is reduced by 201. 86M. Compared with other mainstream target detection algorithms, this algorithm also has better detection effect and can meet the accuracy and real-time requirements of mask detection tasks.
Current-best Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a metaheuristic optimization method based on swarm intelligence. Due to its flexibility and ability to produce optimum performance, it is commonly used in various applications. While PSO has been used extensively to provide solutions to various complicated problems in engineering, it has also many deficiencies. Several improved PSO techniques have been proposed to compensate these deficiencies. However, there are still some scopes of improvement in its components. In this work, we have proposed an improvised PSO called Current-best Particle Swarm Optimization (CPSO) which introduces a new parameter called “cbest” that has been used in the social component of PSO to overcome the local minima issue. The suggested model, CPSO, has differentiate with the basic PSO method and the Iterative (ibest) PSO method using some optimization functions. The findings indicate that the recommended model outperforms the other models.
Deep Sentient network with multifarious features and inter-mutual attention mechanism for target-specific sentiment classification

Target-based aspect level sentiment analysis (TBASA) seeks to discover the polarity of the text towards certain aspect terms in each text. Most of the recent studies utilize deep learning (DL) frameworks like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to predict the influences of multiple contextual aspects on sentiment polarity. Both CNN and RNN are successfully used earlier to create complicated semantic representations. However, existing approaches fail to capture the sequence information due to the high dimensionality. In this paper, a typical approach called a Deep Sentient network with a novel inter-mutual attention mechanism is proposed to tackle this issue. The proposed model adds the sequence information identified with RNN into CNN to consistently anticipate the polarity. It also learns the contextual and target terms sequentially to understand the mutual impact between the features. Furthermore, both Part-of-Speech (POS) and position information are also included in the input layer as background knowledge. Finally, a series of experiments are performed on various benchmark datasets to verify the efficacy of our proposed approach.
Physical layer secure key generation and distribution based on noise variances in optical fiber communications systems

Considering the key demand in the secure optical fiber communication, a key generation and distribution scheme based on random noise variances is proposed. The noise of optical communication systems is inevitable due to the influence of various factors. The additive noise that cannot be eliminated is random after digital signal processing, which ensures the randomness of the key. However, the range of system noise variances is small and the noise of links is exposed. A random Gaussian white noise is added at receivers, which increases the noise fluctuation and enhances security. Legitimate parties obtain secure keys by calculating the data inconsistency rate, where the consistency comes from the correlation of noise variances in adjacent time slots over a period of time. Experimental results demonstrate that the error-free key is obtained with the key generation rate of 1.6 Mbit/s over 105 km standard single-mode fiber. Moreover, the security analysis shows that the proposed key generation and distribution scheme can resist fiber-tapping attacks.
Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment

Gastric endoscopic screening is an effective way to decide appropriate gastric cancer treatment at an early stage, reducing gastric cancer-associated mortality rate. Although artificial intelligence has brought a great promise to assist pathologist to screen digitalized endoscopic biopsies, existing artificial intelligence systems are limited to be utilized in planning gastric cancer treatment. We propose a practical artificial intelligence-based decision support system that enables five subclassifications of gastric cancer pathology, which can be directly matched to general gastric cancer treatment guidance. The proposed framework is designed to efficiently differentiate multi-classes of gastric cancer through multiscale self-attention mechanism using 2-stage hybrid vision transformer networks, by mimicking the way how human pathologists understand histology. The proposed system demonstrates its reliable diagnostic performance by achieving class-average sensitivity of above 0.85 for multicentric cohort tests. Moreover, the proposed system demonstrates its great generalization capability on gastrointestinal track organ cancer by achieving the best class-average sensitivity among contemporary networks. Furthermore, in the observational study, artificial intelligence-assisted pathologists show significantly improved diagnostic sensitivity within saved screening time compared to human pathologists. Our results demonstrate that the proposed artificial intelligence system has a great potential for providing presumptive pathologic opinion and supporting decision of appropriate gastric cancer treatment in practical clinical settings.
Metaverse for Education: Technical Framework and Design Criteria

As a third-generation Internet form, the Metaverse has received widespread attention for its spatio-temporal expansion, high immersion, sensory extension, and human-computer integration. Considering the prospect of application in the field of education, a series of studies on the design of Education Metaverse frameworks and applications have sprung up. However, there is no consensus on the technical framework of the Education Metaverse, and how to design the key elements is still an outstanding challenge. In this paper, we propose a novel 5-layer framework for the Education Metaverse guaranteed by management and incentives mechanisms, including physical layer, data layer, computational layer, interaction layer, and application layer. Moreover, the design criteria of three key elements (virtual avatars, virtual learning resources, and virtual teaching scenarios) in the Education Metaverse are presented, as well as interaction modes. Based on this technical framework and design criteria, we develop a prototype system to verify its feasibility and ease of use. This article provides a practical reference for the development and design of Education Metaverse applications, facilitating a step forward in the standardization of Education Metaverse.
Q-Learning Based System for Path Planning with Unmanned Aerial Vehicles Swarms in Obstacle Environments

Path Planning methods for autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise because of all the advantages they bring. There are more and more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios present a large number of obstacles, such as power lines or trees. If all UAVs can be operated autonomously, personnel expenses can be decreased. In addition, if their flight paths are optimal, energy consumption is reduced. This ensures that more battery time is left for other operations. In this paper, a Reinforcement Learning based system is proposed for solving this problem in environments with obstacles by making use of Q-Learning. This method allows a model, in this particular case an Artificial Neural Network, to self-adjust by learning from its mistakes and achievements. Regardless of the size of the map or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks, like field prospecting. Setting goals or having any prior information aside from the provided map is not required. For experimentation, five maps of different sizes with different obstacles were used and with different number of UAVs. For the calculation of the results, the number of actions taken by all UAVs to complete the task in each experiment is taken into account. The results show that the system obtains solutions in fewer movements the more UAVs there are.