Reviewerly/exHarmony huggingface dataset:
LPPN: A Lightweight Network for Fast Phase Picking

Abstract We here present one lightweight phase picking network (LPPN) to pick P/S phases from continuous seismic recordings. It first classifies the phase type for a segment of waveform, and then performs regression to get accurate phase arrival time. The network is optimized using deep separable convolution to reduce the number of trainable parameters and improve its computation efficiency. Experiments using the STanford EArthquake Dataset (STEAD) show that the precision of LPPN can reach 95.2% and 83.7% with the recalls 94.4% and 84.7% for P and S phases, respectively. The classification–regression approach shows comparable performance to traditional point-to-point methods with lower computation cost. LPPN can be configured to have different model size and run on a wide range of devices. It is open-source and can support phase picking for large-scale dataset or in other speed sensitive scenarios.
Technical Job Recommendation System Using APIs and Web Crawling

There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.
SMS-Net: Sparse multi-scale voxel feature aggregation network for LiDAR-based 3D object detection

Real-time three-dimensional (3D) object detection has become a crucial component of autonomous driving applications. Recent research demonstrates that a voxel-based feature aggregation method is accurate and efficient in large 3D scenes. However, the parameter choice of voxel size has become a sensitive issue because of the contradiction between its detection performance and inference speed. To alleviate this problem, in this paper we propose a sparse multi-scale voxel feature aggregation network (SMS-Net), a novel one-stage, end-to-end network that primarily contains a sparse multi-scale-fusion (SMSF) module and shallow-to-deep regression (SDR) module. First, the raw point clouds are divided into different scales of voxels to construct diverse 3D sparse feature maps. Then, the SMSF module attentively aggregates the point-wise features with a perspective-channel attention mechanism and fuses multi-scale features at 3D sparse feature-map level to achieve more fine-grained shape information. In addition, the new SDR module boosts the localization accuracy and 3D box estimation accuracy through multiple aggregation at feature-map level, which requires less computational overhead. Extensive experiments demonstrate the remarkable performance improvements from each module of the proposed method. On the KITTI 3D object detection benchmark, for example, SMS-Net outperforms most one-stage, state-of-the-art methods and its performance can even be compared to that of two-stage methods. These detection results are achieved with a real-time inference speed of 42 Hz. SMS-Net also achieves state-of-the-art performance on the nuScenes 3D benchmark.
An Improved Arithmetic Optimization Algorithm for Numerical Optimization Problems

The arithmetic optimization algorithm is a recently proposed metaheuristic algorithm. In this paper, an improved arithmetic optimization algorithm (IAOA) based on the population control strategy is introduced to solve numerical optimization problems. By classifying the population and adaptively controlling the number of individuals in the subpopulation, the information of each individual can be used effectively, which speeds up the algorithm to find the optimal value, avoids falling into local optimum, and improves the accuracy of the solution. The performance of the proposed IAOA algorithm is evaluated on six systems of nonlinear equations, ten integrations, and engineering problems. The results show that the proposed algorithm outperforms other algorithms in terms of convergence speed, convergence accuracy, stability, and robustness.
EBDF: The enterprise blockchain design framework and its application to an e-Procurement ecosystem

Blockchain technologies have seen a steady growth in interest from industries as the technology is gaining maturity. It is offering a novel way to establish trust amongst multiple stakeholders without relying or trusting centralised authorities. While its use as a decentralised store of value has been validated through the emergence of cryptocurrencies, its use case in industrial applications with multiple stakeholder ecosystems such as industrial supply chain management, is still at an early stage of design and experimentation where private blockchains are used as opposed to public blockchains. Many enterprise blockchain projects failed to gain traction after initial launches, due to inefficient design, lack of incentives to all stakeholders or simply because the use of blockchain was not really necessary in the first place. There has been a need for a framework that allows blockchain designers and researchers to evaluate scenarios when a blockchain solution is useful and design the key configurations for an enterprise blockchain solution. Literature on blockchain architectures are sparse and only applicable to specific use cases or functionalities. This paper proposes a comprehensive Enterprise Blockchain Design Framework (EBDF), that not only identifies the relevant use cases when a blockchain must be utilised, but also details all the characteristics and configurations for designing an enterprise blockchain ecosystem, applicable to multiple industries. To validate the EBDF, we apply the same to the Vortal e-Procurement ecosystem allowing for multiple platforms to interoperate with greater transparency and accountability over the proposed blockchain framework. In this use case, many vendors bid for procurement procedures, often for publicly managed funds where it is extremely vital that full transparency and accountability is ensured in the entire process. Ensuring that certain digital certification functions, such as timestamps are independent from e-Procurement platform owners has been a challenge. Blockchain technology has emerged as a promising solution for not only ensuring transparency and immutability of records, but also providing for interoperability across different platforms by acting as a trusted third-party. The applied framework is used to design a Hyperledger based blockchain solution with some of the key architectural elements that could fulfil these needs while presenting the advantages of such a solution.
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning. In the last decade, deep learning based freespace detection methods have been proved feasible. However, these efforts were focused on urban road environments and few deep learning based methods were specifically designed for off-road freespace detection due to the lack of off-road dataset and benchmark. In this paper, we present the ORFD dataset, which, to our knowledge, is the first off-road freespace detection dataset. The dataset was collected in different scenes (woodland, farmland, grassland and countryside), different weather conditions (sunny, rainy, foggy and snowy) and different light conditions (bright light, daylight, twilight, darkness), which totally contains 12,198 LiDAR point cloud and RGB image pairs with the traversable area, non-traversable area and unreachable area annotated in detail. We propose a novel network named OFF-Net, which unifies Transformer architecture to aggregate local and global information, to meet the requirement of large receptive fields for freespace detection task. We also propose the cross-attention to dynamically fuse LiDAR and RGB image information for accurate off-road freespace detection. Dataset and code are publicly available at https://github.com/chaytonmin/OFF-Net.
Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review

Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF.PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction.AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.
Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate.
Flexible Interactive Retrieval SysTem 3.0 for Visual Lifelog Exploration at LSC 2022

Building a retrieval system with lifelogging data is more complicated than with ordinary data due to the redundancies, blurriness, massive amount of data, various sources of information accompanying lifelogging data, and especially the ad-hoc nature of queries. The Lifelog Search Challenge (LSC) is a benchmarking challenge that encourages researchers and developers to push the boundaries in lifelog retrieval. For LSC'22, we develop FIRST 3.0, a novel and flexible system that leverages expressive cross-domain embeddings to enhance the searching process. Our system aims to adaptively capture the semantics of an image at different levels of detail. We also propose to augment our system with an external search engine to help our system with initial visual examples for unfamiliar concepts. Finally, we organize image data in hierarchical clusters based on their visual similarity and location to assist users in data exploration. Experiments show that our system is both fast and effective in handling various retrieval scenarios.
HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception

Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perception across a wide range of driving contexts remains a significant challenge. Techniques to fuse sensor data from camera, radar, and lidar sensors have been proposed to improve AV perception. However, existing methods are insufficiently robust in difficult driving contexts (e.g., bad weather, low light, sensor obstruction) due to rigidity in their fusion implementations. These methods fall into two broad categories: (i) early fusion, which fails when sensor data is noisy or obscured, and (ii) late fusion, which cannot leverage features from multiple sensors and thus produces worse estimates. To address these limitations, we propose HydraFusion: a selective sensor fusion framework that learns to identify the current driving context and fuses the best combination of sensors to maximize robustness without compromising efficiency. HydraFusion is the first approach to propose dynamically adjusting between early fusion, late fusion, and combinations in-between, thus varying both how and when fusion is applied. We show that, on average, Hydrafusionoutperforms early and late fusion approaches by 13.66% and 14.54%, respectively, without increasing computational complexity or energy consumption on the industry-standard Nvidia Drive PX2 AV hardware platform. We also propose and evaluate both static and deep-learning-based context identification strategies. Our open-source code and model implementation are available at https://github.com/AICPS/hydrafusion.
vitrivr at the Lifelog Search Challenge 2022

In this paper, we present the iteration of the multimedia retrieval system vitrivr participating at LSC 2022. vitrivr is a general-purpose retrieval system which has previously participated at LSC. We describe the system architecture and functionality, and show initial results based on the test and validation topics.
Fuzzy Cognitive Map-Enabled Approach for Investigating the Relationship between Influencing Factors and Prefabricated Building Cost Considering Dynamic Interactions

Prefabricated buildings have the advantages of efficiency, green, and sustainability. However, high cost seriously restricts the development and application of prefabricated buildings. The existing literature mainly focuses on a comparison of the incremental cost of prefabricated buildings and conventional buildings, and because most of the studies on the influencing factors of prefabricated cost are static analyses, the guiding role of prefabricated building cost control is inadequately explored. This study presents a fuzzy cognitive map (FCM)-enabled approach to investigate the relationship between influencing factors and prefabricated building cost, considering dynamic interactions. Content analysis and the Delphi method are combined to identify influencing factors, and the degree of influence was determined with the help of domain experts; thus, a cause-effect model consisting of nine concepts is established for analyzing the influencing factors of prefabricated building cost based on FCM. Results indicate that (1) the factors that have the greatest impact on CT (prefabricated building cost) include C1 (scale effect), C6 (PC PE), C4 (standardization degree), and C8 (construction management level); (2) C1 is the possible root cause that affects the cost of prefabricated buildings, because it displays the strongest negative correlation with CT, and the work in the early decision-making and design stage is of great significance; and (3) when the factors that are difficult to control for construction enterprises are not considered, the cost is most sensitive to changes in C6 and C4. The novelty of the proposed approach is that it can model the dynamic interactions between influencing factors and prefabricated building cost, and perform many kinds of what-if scenario analyses, including predictive, diagnostic, and sensitivity analyses. The proposed approach can be used to identify influencing factors for enhancing the probability of success in cost control of prefabricated buildings.
A Comprehensive Bibliometric Assessment on Software Testing (2016–2021)

The research study provides a comprehensive bibliometric assessment in the field of Software Testing (ST). The dynamic evolution in the field of ST is evident from the publication rate over the last six years. The research study is carried out to provide insight into the field of ST from various research bibliometric aspects. Our methodological approach includes dividing the six-year time frame into the set of two symmetric but different periods (2016–2018) and (2019–2021) comprising a total of 75,098 records. VOSViewer is used to perform analysis with respect to collaboration network of countries and co-word assessment. Bibliometrix (Studio R) analysis tool is used to evaluate research themes/topics. The year 2019 leads the publication rate whereas a decrement in publication frequency is observed for the years 2020 and 2021. Our research study shows the influence of ST in other research domains as depicted in different research areas. Especially the impact of ST in the Electrical and Electronics Domain is quite notable. Most of the research publications are from the USA and China as they are among the most resourceful countries. On the whole, the majority of the publications are from Asian countries. Collaboration networks amongst countries demonstrate the fact that the higher the collaboration network, the greater would be the research output. Co-word analysis presents the relatedness of documents based on the keywords. The topic dendrogram is generated based on the identified research themes. Although English is the leading language, prominent studies are present in other languages also. This research study provides a comprehensive analysis based on 12 informative research questions
Research into an Image Inpainting Algorithm via Multilevel Attention Progression Mechanism

Existing image inpainting schemes generally have the problems of structural disorder and blurred texture details. This is mainly because, in the reconstruction process of the damaged area of the image, it is difficult for the inpainting network to make full use of the information in the nondamaged area to accurately infer the content of the damaged area. Therefore, the paper has proposed an image inpainting network driven by multilevel attention progression mechanism. The proposed network has compressed the high-level features extracted from the full-resolution image into multiscale compact features and then drives the compact features to perform multilevel order according to the scale size. Attention feature progression is to achieve the goal of the full progression of high-level features including structure and details in the network. To further realize fine-grained image inpainting and reconstruction, the paper has also proposed a composite granular discriminator to achieve image inpainting process performing global semantic constraints and nonspecific local dense constraints. The related experimental results in the paper can show that the proposed method can achieve higher quality repair results than state-of-the-art ones.
Quality of randomness and node dropout regularization for fitting neural networks

Quality of randomness in generating random numbers is an attribute manifested by a sufficiently random process, and a sufficiently large sample size. To assess it, various statistical tests for it have been proposed in the past. The application area for random number generation is wide in natural sciences, and one of the more prominent and widely adopted is machine learning, where bounded randomness or stochastic random number generation has been utilized in various tasks. The artificial neural networks used for example in deep learning use random number generation for weight initialization, optimization and in methods that aim to reduce the overfitting phenomena of these models. One of these methods include node dropout, which has been widely adopted. The method’s internal logic is heavily dictated by a random number generator it utilizes. This study investigated the relationship of quality of randomness and the node dropout regularization in terms of reducing overfitting of neural networks. Our experimentation included five different random number generators, which output were tested for quality of randomness by various statistical tests. These sets of random numbers were then used to dictate the internal logic of a node dropout layer in a neural network model, in four different classification tasks. The impact of data size and relevant hyperparameters were tested, and the overall amount of overfitting, which was compared against the randomness results of a generator. The results suggest that true random number generation in node dropout can be both advantageous and disadvantageous, depending on the dataset and prediction problem at hand. These findings suggest that fitting neural networks in general can be improved by adding random number generation experimentation to modelling.