Reviewerly/exHarmony huggingface dataset:
BP neural network based on Qingdao City air logistics demand forecast

BP neural network is a kind of back-propagation artificial neural network, which can achieve simulation and prediction of complex systems through learning and training. Its powerful adaptive and self-learning capabilities enable it to play an important role in solving the prediction problem of air logistics industry. In order to predict the scale of air logistics demand in Qingdao more accurately, this paper combines theoretical analysis and empirical research to build a BP neural network prediction model. The results show that the scale of air logistics demand in Qingdao will show a steady growth in the next three years. The model can accurately predict the future trend and provide theoretical basis for the decision of enterprises and government. The study shows that the BP neural network prediction model is optimized through error analysis, and the prediction accuracy is high, and it is successfully applied to the prediction of air logistics demand, which can provide theoretical support for the planning of relevant departments.
Research on speech recognition technology for railway dispatching

The "Medium and Long-term Railway Network Plan" puts forward the needs of intelligent and information-based railway construction <sup>[1]</sup>.The dispatching order of railway dispatching is the medium for train dispatchers to direct train operation, organize construction and maintenance, and deal with emergencies, and is an important means of dispatching and commanding, in response to planning, speech recognition technology can be applied to it. The research is carried out from three aspects: original speech noise reduction, recognition and error correction of text after recognition. It can be applied to the railway train receiving and dispatching training system, and at the same time provides reference value for keyword search and positioning after machine translation and scheduling speech recognition in its field, and is also of great significance for improving the transportation efficiency of railway and ensuring its transportation safety.
Improved traffic flow estimation based on integrated learning methods

This paper aims to address the current lack of accurate hourly traffic flow measurement by proposing a speed-flow measurement method based on the two integrated learning methods of LightGBM and XGBoost. The road network information, traffic speed and flow data from intelligent monitoring and recording system were integrated to build the traffic speed-flow model. The tree parzen estimator (TPE), a Bayesian optimization algorithm, was performed for the hyperparameter search of LightGBM and XGBoost, which combined with cross-validation to finally determine the optimal parameters. In addition, we compared the simulated results of LightGBM and XGBoost with BP neural network, random forest, and Van Aerde model. These results indicated that the TPE-LightGBM and TPE-XGBoost based on Bayesian optimization had more accurate and better prediction results than the other models. The MAPE of TPE-XGBoost and TPE-LightGBM decreased 12.19% and 12.26% than Van Aerde model on average, respectively. Compared with the traditional deep learning algorithm, such as BP neural network, the MAPE of TPE-XGBoost and TPE-LightGBM decreased by 4.45% and 4.52%, respectively. Our results further showed that the TPE-XGBoost had higher accuracy for estimating the flow of expressway, while the TPE-LightGBM was more suitable for analysis in the main roads and secondary roads.
Path planning for obstacle avoidance of unmanned vehicles based on Frenet coordinates and B-spline curves

With the development of intelligent networking technology and computer information processing technology, the functions of autonomous vehicle are constantly enriched and improved. In order to improve the path planning processing ability of autonomous vehicle when encountering obstacles, the paper makes in-depth analysis and research on the path planning of autonomous vehicles encountering obstacles based on the Frenet coordinate system and quasi uniform B-spline curve. By introducing the central reference line of the road to automatically drive the vehicle, when encountering obstacles, the road ahead is uniformly sampled laterally, and the feasible path for obstacle avoidance is planned according to the vehicle obstacle information and real-time vehicle information. Finally, the optimal path for obstacle avoidance is selected through maximum curvature constraint, target cost function, collision detection, etc. In order to verify the feasibility of the method, First, the basic function of the algorithm is verified in the self-developed automatic driving simulation test tool terminal, and the vehicle obstacle avoidance function is realized under the condition of continuous static obstacles. Finally, the real vehicle experiment proves that this method can run stably and reliably in the aspect of obstacle avoidance.
Named entity recognition of railway dispatching command based on BiLSTM-CRF

With the continuous development of China's railway, the workload of train dispatching command compilation is also increasing, so it is necessary to conduct in-depth research on the text data of dispatching command to improve the efficiency of dispatching command issuance. As an important task of text mining, named entity recognition is also a key step in analyzing text data of railway dispatching commands. In this paper, a named entity recognition model of scheduling command is constructed by combining bidirectional short-term memory neural network and conditional random field. The model identifies five entity types from the text of the dispatching order, namely, location, time, dispatching order type and train number, which can effectively improve the efficiency of dispatching order issuance. The experiment shows that the proposed scheduling command entity recognition model is significantly better than the previous methods in terms of accuracy, recall rate, F1 value, and so on, without the need to manually build features.
Research and implementation of the blockchain-based supply chain information system

Addressing issues such as information symmetry, transparency, traceability, and data security in traditional supply chains, this study adopts Hyperledger Fabric as the blockchain technology platform, proposes a blockchain-based information traceability scheme, and designs and implements a blockchain-based supply chain information system. This system has undergone functional and performance tests. Tests indicate that this system not only achieves the storage of relevant data information in the blockchain, ensuring the security and traceability of system data, but also realizes functions such as account management for users on the supply chain, upload of organization peer information, and consumer information queries
Improved transcenter vehicle tracking algorithm based on attention mechanism feature fusion

Vehicle target tracking based on surveillance video has important application value for road running state perception and abnormal event detection, and has always been a research hotspot in the field of intelligent transportation. Since the advent of Transfomer, it has achieved superior performance in various tasks of computer vision. The multi-target tracking framework based on Transfomer has also received extensive attention. TransCenter algorithm, as a multi-target tracking network based on Transfomer, has achieved good results in pedestrian tracking tasks. However, in the top-down feature fusion process, the semantic information of high-level features is lost. Combined with the attention mechanism, we propose a multi-scale feature fusion strategy based on attention mechanism, which effectively utilizes the semantic information and detail information in multi-scale features. The effectiveness of the method is verified on the UA-DETRAC dataset. Experiments show that the vehicle tracking effect of this method is better than the benchmark model TransCenter
IPCADP-Equalizer: An Improved Multibalance Privacy Preservation Scheme against Backdoor Attacks in Federated Learning

Although there are some protection mechanisms in federated learning, its training process is still vulnerable to some powerful attacks, such as invisible backdoor attacks. Existing research work focuses more on how to prevent attacks in distributed training scenarios and improve the security of the FL training process, but it lacks consideration of utility and robustness, especially when the learning model of FL suffers from stealth backdoor attacks. This paper proposes an improved FL defense scheme IPCADP based on user-level differential privacy and variational autoencoders technology. The scheme can control and protect the privacy attribute of the image and can also eliminate the triggers that exist in the poisoned image. The experimental results show that compared with some existing defense schemes, IPCADP can defend against invisible backdoor attacks and improve the classification accuracy of the main task, while mitigating the impact of attacks on model robustness and stability. To a certain extent, the balance and unity of security, utility, and robustness are realized.
Progress Report on VVC Applications

Versatile video coding (VVC)1 is the latest video coding standard that can compress ultrahigh-definition (UHD)-1/4K and UHD-2/8K videos efficiently. Since its standardization in July 2020, various applications of VVC have been studied. This report introduces two types of VVC applications. First, in VVC, multilayer video coding is simple to use as opposed to previous standards. Herein, future service examples utilizing spatial and content scalability are provided to demonstrate its extensibility and user-selectable content, respectively. Second, quality assessment on 8K VVC video is conducted at various viewing conditions, and a simple regression equation reveals how the screen size and viewing distance affect subjective quality.
Applications of Generative AI to Media

When ChatGPT was released in November 2022, the public became aware of a new era of artificial intelligence (AI) built on a class of machine learning models known as foundation models (FMs). Although FMs burst upon the scene seemingly without precedent, the roots of FMs go back several years earlier. A key distinguishing factor of FMs versus older models is their powerful applications to Generative AI (GenAI), the use of models to generate new content and transform existing content. GenAI models tend to be general-purpose and more flexible than previous kinds of AI models, which are limited to specific predictive tasks such as object detection in images, text classification, forecasting, or detecting data anomalies. Although there were some other kinds of GenAI models before the rise of FMs, FMs are much more powerful and are an enormous leap over their predecessors.
Synthetic Speech Attribution: Highlights From the IEEE Signal Processing Cup 2022 Student Competition [SP Competitions]

The possibility of manipulating digital multimedia material is nowadays within everyone’s reach. In the audio case, anybody can create fake synthetic speech tracks using various methods with almost no effort <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . These methods range from simple waveform concatenation operations to more complex neural networks <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> .
Live Cloud Production

Live cloud production (LCP) is the production of live video events using virtualized processing on the cloud. It has several advantages over traditional, at-venue production, including ease and speed of launch, pay-as-you-go pricing, enablement of more and/or enhanced production, the ability to share streams with partners in the cloud, personnel quality of life enhancement, and carbon footprint reduction. This article describes these advantages and protocols for media transport with LCP, along with some real-world examples of LCP.
HHSD: Hindi Hate Speech Detection Leveraging Multi-Task Learning

Hate speech is now a frequent occurrence on social media. Recently, the majority of study was devoted to identifying hate speech in languages with abundant resources (e.g., English). However, relatively few works are developed for languages with limited resources (e.g., Hindi, the third most widely used language on earth). In this study, Hindi Hate Language Dataset (HHLD) is created following a novel hierarchical fine-grained four-layer annotation approach. The top layer separates the posts into hateful and non-hateful categories. The second layer further categorises hateful posts into explicit hate and implicit hate. The third layer is the multilabel tagging of the post into topics, such as political, religious, racism, or sexism. The fourth layer involves the identification of the targeted named entity, either explicitly or implicitly. Additionally, a thorough evaluation of the data annotation schema for trustworthy annotation is provided. The HHLD data is the largest multi-layer annotated corpora in Hindi compared with the existing multi-layer annotated data. Experiments on the dataset using the transformer-based approaches in single-task learning (STL) attain encouraging performances in accuracy and weighted-f1 score. The experiment leveraged multitask learning (MTL) by including multiple related hate speech detection tasks from high-resource English and languages from the same linguistic family such as Urdu and Bangla with a transformer encoder as the shared layers to obtain a significant increment of 5.31% and 5.35% over STL in accuracy and weighted-f1 for layer A, 8.20%, and 22.83% for layer B. The MTL surpasses STL by 8.58% and 4.07% in exact match and hamming loss for layer C.
Practicing Equity Diversity Inclusion (EDI) in Software Development Teams: A Systematic Literature Survey

Human factors in successful software projects have always been a critical element in software engineering, however, it has always been overshadowed by focusing more on technology and underlying process. This work is inspired by the recent increasing interest from the software engineering research community in human factors and software development by leveraging and understanding some examples of human factors such as Equity, Diversity, and Inclusion (EDI) which are not given due research consideration earlier. We performed a systematic literature review (SLR) to review the state-of-the-art literature on practicing EDI in software development teams despite of country or culture. We found that evidence of comprehensive research about practicing EDI in software development teams is limited, the up-to-date majority focus is on the topic of diversity, whereas research on topics of practicing equity and inclusion in software development teams is sporadic. It is expected that investigating the impact of human factors in the context of EDI’s triangle will generate new knowledge. This will allow software practitioners to understand the benefits of practicing EDI in managing software development teams as well as provides opportunities to incorporate them into the core development process activities. In the end, future research directions for EDI practices in software development teams are also identified.
<i>Cost-Effectively</i> Searchable Blackbox Data With <i>Unlinkability</i> Based on Public Blockchain

A blackbox data can be used to identify the cause of an accident. And we may need to find blackbox videos recorded by the third party to accurately analyze the cause of the accident. One of the basic requirements of blackbox data is integrity, and this integrity can be provided by uploading the data to blockchains. However, the existing blackbox data storage systems have two big problems, cost and privacy. The transaction fees are the costs necessary to upload data to the blockchains. The transaction fees of the major public blockchain are getting higher. Therefore, if a user uses Bitcoin or Ethereum to upload its blackbox video, the costs could be very expensive. On the other hand, the private information such as driving route could be revealed from the uploaded information to the blockchain. We suggest a cost-effective searchable blackbox data storage system with strong anonymity, i.e. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unlinkability</i> , using blockchains. In our scheme, the cost of registering a blackbox video is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cost<sub>tr</sub></i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cost<sub>tr</sub></i> is the cost necessary to upload a transaction to a blockchain and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is the number of uploading users at the same time. Therefore, our scheme is practical with public blockchains such as Bitcoin or Ethereum.
Balanced Binary Tree Schemes for Computing Zernike Radial Polynomials

Zernike radial polynomials play a significant role in application areas such as optics design, imaging systems, and image processing systems. Currently, there are two kinds of numerical schemes for computing the Zernike radial polynomials automatically with computer programs: one is based on the definition in which the factorial operations may lead to the overflow problem and the high order derivatives are troublesome, and the other is based on recursion which is either unstable or with high computational complexity. In this paper, our emphasis is focused on exploring the balanced binary tree (BBT) schemes for computing Zernike radial polynomials: firstly we establish an elegant formulae for computation; secondly we propose the recursive and iterative algorithms based-on BBT; thirdly we analyze the computational complexity of the algorithms rigorously; finally we verify and validate the performance of BBT schemes by testing the running time. Theoretical analysis shows that the computational complexity of BBT recursive algorithm and iterative algorithm are exponential and quadratic respectively, which coincides with the running time test very well. Experiments show that the time consumption is about 1 ~ 10 microseconds with different computation platforms for the BBT iterative algorithm (BBTIA), which is stable and efficient for real-time applications.
An Efficient Post-Quantum Attribute-Based Encryption Scheme Based on Rank Metric Codes for Cloud Computing

Attribute-based encryption is a valuable technique for ensuring data privacy and confidentiality in the realm of cloud computing. Using this cryptographic primitive, the data owner can securely store and share data within the cloud environment. On the other hand, in recent years, extensive advances have been made in quantum processors, which have raised hopes of solving certain mathematical problems includes factoring integers and computing discrete logarithms of large numbers. The advent of quantum computers has posed a significant security threat to existing cryptographic protocols. The existing post-quantum attribute-based encryption schemes have not satisfied the essential features such as verifiability, user privacy and user revocability, simultaneously. In this paper, we present the first secure, practical and post-quantum attribute-based encryption scheme based on rank metric codes. Our scheme enjoys all mentioned features due to utilization of low rank parity check codes. The proposed scheme provides security against chosen plaintext attacks in the standard model, as well as resistance against reaction attacks as a kind of chosen ciphertext attacks. Moreover, at the 256-bit security level, the key size is about 16.5 KB, with an execution time of around 31.2 ms on a desktop. Our implementation results confirm that the proposed scheme is more efficient than the existing post-quantum and classical schemes.
A Broad Sparse Fine-Grained Image Classification Model Based on Dictionary Selection Strategy

When the fine-grained recognition problems of image classification processed, broad learning system (BLS) is more efficient in classification, but has difficulty in distinguishing features with large similarities. Sparse representation classification (SRC) is more capable of handling similarity features, but is more computationally expensive. To better use the BLS model to tackle the fine-grained recognition problem and improve the ability to handle similarity features, this article combines the advantages of BLS and SRC, and proposes a broad sparse fine-grained image classification model based on dictionary selection strategy, dictionary broad sparse representation classification (DBSRC). First, to solve the parameter selection problem of the BLS model, leave one out cross validation (LOO) is introduced to quickly find the better regularization parameters and build a BLS-LOO coarse-grained classification model. Then propose reliability criteria based on a threshold selection strategy for the selection of fine-grained images. Next, an adaptive dictionary selection strategy is designed based on the output of the BLS-LOO to construct a sparse subdictionary for each fine-grained image that is not distinguished by the BLS-LOO. Finally, a sparse subdictionary based SRC model is used to classify fine-grained images. Experimental results show that DBSRC achieves good classification performance on three image datasets with different complex dimensions, ImageNet, USPS, and Pavia, and has strong processing capability for fine-grained features.
Systematic Optimization of Parameters for Adaptive Local Trajectory Planning Using Genetic Algorithm

The following paper deals with an assistance function based on genetic algorithms (GA) to optimize the weighting factors of the driving function “Adaptive Local Trajectory Planning (alTp)”, which serves as a part of the trajectory planning of autonomous driving to compensate for the inability of global trajectory planning in obstacle avoidance in the dynamic traffic environment. Considering the derived requirements, the considered optimization problem is coded and initialized. To ensure a steady optimization of the alTp with the specified weighting factors, the same objective criteria of the alTp are also applied in the fitness function of the assistance function for the quantitative evaluation of the respective solution candidates. Using a test scenario with driverless transport vehicles (AGV) for material transport in intralogistics, the developed assistance function is validated with the help of a virtual test bench and demonstrated.
Navigation of Ground Robots with Reinforcement Learning

Reinforcement learning (RL) is a machine learning technique that enables an agent to learn optimal behaviors within an environment through a process of trial and error. This is achieved by the agent receiving rewards or punishments based on its actions. In this study, we compare the performance of two RL algorithms, SARSA and Q-Learning, in a simulated environment using the Gazebo Simulator. The goal of the simulation is to navigate a ground robot towards pre-defined goals. By manipulating various training parameters, we investigate the impact on learning speed and robot behavior. To ensure meaningful comparisons, we vary the navigation goal and the complexity of the simulation environment. Through extensive simulations, our results highlight the effectiveness of RL-based navigation for ground robots and offer insights into the influential parameters that significantly affect optimal navigation performance. It underscores the importance of algorithm selection and parameter optimization in achieving optimal navigation performance.