Reviewerly/exHarmony huggingface dataset:
Soil copper concentration map in mining area generated from AHSI remote sensing imagery

Hyperspectral remote sensing has the advantages to predict and map soil heavy metal concentration over conventional monitoring methods and multispectral remote sensing. In quantitative applications of hyperspectral remote sensing imagery, the contribution of hyperspectral bands is different, and abnormal prediction values resulted from incorrectly classified bare soil images are a major problem. In this study, a variable weighting method was proposed to weight the hyperspectral bands, and a probability threshold was used to improve the classification to mitigate the problem of abnormal prediction values. The variable weighting was conducted by using the absorption depths obtained by continuum removal. Soil samples were collected from a mining area in southwestern China. Hyperspectral remote sensing imagery was acquired by the Advanced Hyperspectral Imager (AHSI) abroad on Geofen-5 (GF-5) satellite. Genetic algorithm and partial least squares regression (PLSR) were adopted to calibrate prediction models. In prediction of soil copper (Cu) concentration, root mean square error (RMSE) and coefficient of determination (R2) were 21.59 mg kg-1 and 0.60 for the prediction using raw reflectance spectra, and the values were improved to 18.33 mg kg-1 and 0.71 by using the weighted reflectance spectra. The developed prediction model was applied to the AHSI imagery to predict Cu concentration in bare soil areas. In prediction of Cu concentration using the AHSI imagery, negative prediction values were eliminated by using the bare soil image extracted by the improved classification. Based on the prediction, soil Cu concentration map was generated by kriging spatial interpolation. The result indicates that the proposed variable weighting method is effective and the problem of abnormal prediction values could be mitigated by using improved bare soil images. Further analysis indicates that some indices with proper thresholds also could be used to get improved bare soil images.
Generalizing Parikh’s Criterion for Relevance-Sensitive Belief Revision

Parikh proposed his relevance-sensitive axiom to remedy the weakness of the classical AGM paradigm in addressing relevant change. An insufficiency of Parikh’s criterion, however, is its dependency on the contingent beliefs of a belief set to be revised, since the former only constrains the revision process of splittable theories (i.e., theories that can be divided in mutually disjoint compartments). The case of arbitrary non-splittable belief sets remains out of the scope of Parikh’s approach. On that premise, we generalize Parikh’s criterion, introducing (both axiomatically and semantically) a new notion of relevance, which we call relevance at the sentential level . We show that the proposed notion of relevance is universal (as it is applicable to arbitrary belief sets) and acts in a more refined way as compared to Parikh’s proposal; as we illustrate, this latter feature of relevance at the sentential level potentially leads to a significant drop in the computational resources required for implementing belief revision. Furthermore, we prove that Dalal’s popular revision operator respects, to a certain extent, relevance at the sentential level. Last but not least, the tight relation between local and relevance-sensitive revision is pointed out.
Three-classification face manipulation detection using attention-based feature decomposition

Face manipulation detection has become a recent research hotpot, and many detection methods have been proposed. Most existing detection methods treat face manipulation detection as a vanilla binary classification problem. However, according to the visual effects of facial manipulation, face manipulation approaches are divided into face replacement and face reenactment. In this study, we propose a three-classification face manipulation detection method(TFMD). To implement three-classification face manipulation detection, we introduce a new face forgery feature representation, where the face forgery features are jointly represented by the identity-changing features and the face real-fake features. To decompose the face forgery features into the identity-changing features and the face real-fake features, we design an attention-based feature decomposition module (AFDM). Moreover, we extract high-frequency noise features from the shallow features of the RGB stream to enrich high-frequency information. Through extensive experiments on two datasets, FaceForensics++ and Celeb-DF, we achieve state-of-the-art performance and demonstrate the effectiveness and robustness of our proposed method.
Image quality assessment based on self-supervised learning and knowledge distillation

Deep neural networks have achieved great success in a wide range of machine learning tasks due to their excellent ability to learn rich semantic features from high-dimensional data. Deeper networks have been successful in the field of image quality assessment to improve the performance of image quality assessment models. The success of deep neural networks majorly comes along with both big models with hundreds of millions of parameters and the availability of numerous annotated datasets. However, the lack of large-scale labeled data leads to the problems of over-fitting and poor generalization of deep learning models. Besides, these models are huge in size, demanding heavy computation power and failing to be deployed on edge devices. To deal with the challenge, we propose an image quality assessment based on self-supervised learning and knowledge distillation. First, the self-supervised learning of soft target prediction given by the teacher network is carried out, and then the student network is jointly trained to use soft target and label on knowledge distillation. Experiments on five benchmark databases show that the proposed method is superior to the teacher network and even outperform the state-of-the-art strategies. Furthermore, the scale of our model is much smaller than the teacher model and can be deployed in edge devices for smooth inference.
3DVAE-LSTM for Extremely Rare Anomaly Signal Generation

To overcome the uncontrolled quality output problem of data augmentation, many data generation frameworks have been proposed recently. The main concept of the data generation is for ensuring the quality of the output samples which maintain the original characteristics and highly provide the diversity of data. The benefit of this concept is improving the performance of deep learning tasks that suffer from the lack of available training samples, such as anomaly classification. Recently, 3D variational autoencoder for extremely rare case signal generation (3DVAE-ERSG) was introduced. This framework achieves the best synthesis samples for multi-class classification deep learning training. However, it is not so well applicable to sequential data. Therefore, this paper proposed a 3DVAE-LSTM framework. The new framework was replaced a VAE’s feed-forward neural network with a long short-term memory (LSTM) neural network that works well with time-series signals. The experimental results show that the classification models trained with data generated by 3DVAE-LSTM have better performance than 3DVAE-ERSG in every aspect.
Intelligent disaster safety warning system through risk level analysis

At present, crisis warnings are issued, upgraded, and canceled collectively based on risk information that can be obtained immediately without considering the mechanism according to situation development by disaster type and risk level characteristics by disaster type, and crisis warning is operated only as an administrative procedure. As a result, even though there are signs of a crisis, the judgment is delayed, and the damage tends to spread. In addition, as it is urgent to come up with a countermeasure due to the missed timing of actual crisis signs and judgments, automation technology for crisis signs monitoring, analysis, evaluation, dissemination (transmission), and crisis alert operation is required. Through analysis and learning process, we propose a technique for developing a system that enables rapid response measures by recognizing and judging risks in advance.
A General and Robust Blockchain Storage System based on External Storage Service

Recently, blockchain technology has emerged as an important technology for executing smart contracts and storing consensus data reliably in decentralized manner. On the other hand, it is required that each node has to maintain the consensus ledger in its local storage. Due to limited storage capacity, most blockchain platforms typically adopt the techniques of checkpointing and pruning the consensus ledger database. However, in case of sensitive data such as financial, healthcare or identity information, there may be some regulations on data maintenance. Thus, we have to keep those data until a specified time interval to meet the regulation compliance. In this paper, we propose a general and robust blockchain storage system, BSS, exploiting large-scale external storage services such as Amazon S3, which stores the entire blockchain consensus ledger from the genesis block. It is general in the sense that BSS is designed to be compatible with any blockchain platform. It is robust in the sense that BSS supports the f-tolerant write operation, which tolerates the malicious behavior of blockchain nodes and external storage service. We show that the BSS meets three security properties: safety, liveness, and external validity.
A multikey fully homomorphic encryption privacy protection protocol based on blockchain for edge computing system

Summary In order to protect private data in edge computing system and ensure that ciphertexts encrypted under different keys can be processed in the cloud, we propose a privacy protection protocol for edge computing, which is based on blockchain technology and multikey fully homomorphic encryption (MFHE). By constructing a Merkle hash tree and saving the Merkle root node to the blockchain, the integrity of the data in edge nodes is protected. For the data with high computational complexity in edge nodes, the GSW fully homomorphic encryption scheme is employed to process the encrypted private data of a single edge node by the CMS. The LinkAlgo algorithm is applied to link different keys by the relation between them, which enables the CMS to process the encrypted private data of multiple edge nodes. The security analysis and performance evaluation of the proposed privacy protection protocol show that our protocol has high security and good efficiency.
State-of-the-Art Object Detectors for Vehicle, Pedestrian, and Traffic Sign Detection for Smart Parking Systems

To meet the safety requirements of pedestrians and other vehicles in smart parking systems, vehicles rely heavily on visual data to classify and detect target objects. In real-time, deep learning (DL)-based algorithms have shown excellent results in object detection. While several studies have thoroughly investigated various DL-based object detection methods, only a few have focused on multi-detection, encompassing pedestrian detection, vehicle detection, and traffic sign detection. This work provided a comparative analysis of six independent variants of Faster-RCNN and SSD Models for pedestrians, vehicles, and traffic sign detection. There is a lack of comparison leveraging on detailed evaluation metrics among existing models. To address the issue of little or non-availability of datasets for multi-detection, this work provided a mini dataset for task (TraPedesVeh). Experimental results analyzed using detection accuracy, average precision (AP), average recall (AR), training time, and loss show that the Faster-RCNN model with the Inception backbone outperformed all other models.
Improved Generative Convolution Method for Image Generation

In our recent study, we introduced a novel convolution method, called GConv, which improves the performance of generative adversarial networks (GAN) by modulating the convolution kernels following the given latent vector. In this paper, we analyze the limitations of GConv, and propose an improved GConv to address those problems. While GConv modulates the convolution kernel equally at all pixels, the proposed method produces pixel-wise different kernels following not only the given latent vector but also the feature in each pixel. Even though the proposed method is a simple modification of GConv, it shows better performance compared to the standard convolution as well as GConv. To show the superiority of the proposed method, this paper provides experimental results on the CIFAR-10 and CIFAR-100 datasets. Quantitative evaluations reveal that the proposed method improves both GAN and conditional GAN (cGAN) performance in terms of Frechet inception distance (FID).
Improving Text Classification Performance through Data Labeling Adjustment

The data labeling process in natural language processing (NLP) technology is an essential process for identifying raw text data and adding information labels to provide context for machines to learn. However, labeling set of data is one of the most time-consuming parts of any machine learning application, and requires to verify the accuracy of labels and adjusts them correctly as necessary. In this paper, we propose a method to adjust data labels to better fit the meaning of sentences to improve model performance. In addition, we show that our approach is feasible to improve model performance through various experiments
Laplace transform: basics and main properties

In this article, the Laplace transform is studied, undertaking the conditions for its existence and the basic properties of the method. For example, properties such as linearity, frequency shifting, time shifting, scaling, etc. are the key to employ and use this powerful method in mathematics and different engineering ambient. A highlight is also provided on the main theorems which define the different Laplace transform properties. Later, the Inverse Laplace transform is also presented, similarly highlighting its corresponding fundamental properties and theorems. Moreover, the inverse Laplace of some basic functions are summarized for reference. And finally, a special focus is given to the use of the Laplace transform in circuit theory to solve electrical circuits.
FPDM: Fisheye Panoptic segmentation dataset for Door Monitoring

Most existing panoptic segmentation datasets are not suited for applications in the railway environment. This paper introduces a new dataset composed of video feeds taken in the vicinity of train doors. It is aimed at the training of deep learning algorithms to identify the obstacles between doors to ensure passenger safety during boarding and to reduce boarding time. The dataset is acquired from fisheye cameras located at the train doors. The data is annotated entirely manually. The Fisheye Panoptic Door Monitoring dataset (FPDM) contains 3952 images with their annotation masks featuring 18 of the most frequent instance categories in the vicinity of train doors. FPDM answers the panoptic segmentation challenge by offering a new challenging dataset for the computer vision community. We present detailed information on the process of acquisition, annotation, and division of the data into training and validation sets in addition with an evaluation of an existing deep learning method.
Driver Monitoring System based on Distracted Driving Decision Algorithm

Distracted driving and drowsy driving are the main causes of car accidents. We proposed the Distracted Driving Decision Algorithm (DDDA) proposed whereby binary results from blink detection and gaze estimation are computed with the sliding window algorithm. The proposed algorithm is expected to be efficient in a vehicle environment system. The DDDA runs based on computational algorithms instead of using a machine learning model; ultimately, the process of determining the status of a driver can be performed in an efficient and accurate way. The average accuracy of the algorithm and time for it to process one frame was 83.5% and 42ms respectively. By improving the accuracy, the DDDA can contribute to provide easier access to the DMS (Driver Monitoring System).
Inspection System of Firefighting Facilities Based on YOLOv4 Using Augmented Reality Glass

Growing number of new building in recent years has led to increased installation of firefighting systems that require safety inspections. This has resulted in shortage of firefighting equipment safety inspection manpower and need for a digitized data management system for efficient inspections and post-inspection management. That is, periodic inspections of firefighting equipment installed in building are absolutely necessary for safety against fire and this study proposes some technological improvements for firefighting equipment inspections. In particular, augmented reality and cognitive intelligence are combined in a firefighting equipment inspection system and wearable devices are used to boost easiness and quality of inspections. Furthermore, the proposed system aims for not only the firefighting experts but the general public to have convenient access to learn more easily about fire prevention and firefighting inspection system.
Application of Deep Learning Model Inference with Batch Size Adjustment

The demand for deep learning model applications by users on edge devices is increasing. If the AI model inferences are directly operated on the edge device rather than the cloud or server, it allows customers to proceed additional tasks more conveniently. In order for a deep learning model to be used more efficiently in an embedded environment, studies such as model weight reduction and network compression have been conducted. In addition, studies related to GPU optimization that maximize the usage of limited GPU resources are also attracting a lot of attention. In this paper, in order to maximize the GPU allocation of edge resources, we conducted some experiments to infer images of multiple frames at once, not just one input. For the experiments, the YOLOv4 model was converted into several models with different input layer shapes, and the inference was performed by adjusting the input batch size. NVIDIA Jetson AGX Xavier was used as the edge device, and all other models with different batch sizes obtained the same inference results. As the number of batches increases as much as possible according to the resource limit, the inference speed increases.
GAN based Hairstyle Generation Framework for Standardization of Lightweight-model

Recently GAN-based image generation models are not properly generating local image accurately, especially for a hairstyle generation task. For the reason, we propose a new hairstyle generation framework based on GAN method. The framework consists of the Face segmentation module (F) with the Hairstyle Generator (HG) module, and network interface to control internal modules. The F module detects hairstyle and facial features, and then extracts global and facial feature masks. The HG module using a GAN method based on transformer to generate hairstyle image, while fixing the detailed fusion in the hairstyle generation process. We used FFHQ and CelebA-HQ datasets for training and evaluation of our framework. The evaluation process used FID, PSNR, SSIM methods and we compared our model with other hairstyle generation models. We get higher test score and robust generated image than other model, especially in lightness of model size.
Audio and ASR-based Filled Pause Detection

Filled pauses (or fillers) are the most common form of speech disfluencies and they can be recognized as hesitation markers (“um”, “uh” and “er”) made by speakers, usually to gain extra time while thinking their next words. Filled pauses are very frequent in spontaneous speech. Their detection is therefore rather important for two basic reasons: (a) their existence influences the performance of individual components, like Automatic Speech Recognition system (ASR), in human-machine interaction and (b) their frequency can characterize the overall speech quality of a particular speaker, as it can be strongly associated with the speaker's confidence. Despite that, only limited work has been published for the detection of filled pauses in speech, especially through audio. In this work, we propose a framework for filled pause detection using both audio and textual information. For the audio modality, we transfer knowledge from a plethora of supervised tasks, such as emotion or speaking rate, using Convolutional Neural Networks (CNNs). For the text modality, we develop a temporal Recurrent Neural Network (RNN) method that takes into account textual information derived from an ASR system. In addition, the proposed transfer learning approach for the audio classifier leads to better results when benchmarked on our internal dataset for which the text is not transcribed but estimated by an ASR system. In this case, a simple late fusion approach boosts the performance even further. This proves that the audio approach is suitable for real-world applications where the transcribed text is not available and has to leverage imperfect ASR results, or even the absence of textual information (to reduce computational cost).
Deployment Framework Design Techniques for Optimized Neural Network Applications

For companies with limited neural network-based technology, rapid and accurate development of AI services requires an integrated development framework that facilitates a device adaptive neural network model search and a target environment optimization code generation and deployment. For the efficient execution of neural network applications, it is necessary to have a technique capable of solving complex and difficult problems such as iterative application of various optimization techniques, verification of errors, and analysis of causes of performance degradation. This paper proposes the design of DeepFramework that can deploy neural network application services. DeepFramework generates neural networks according to user requirements. Next, the neural network generated according to the user requirements specification is optimized, and application templates are automatically generated and deployed. DeepFramework is currently under development, with plans to release later this year. Additionally, we experimented with vision recognition neural network applications for validation of DeepFramework. In the future, DeepFramework will open the results after excluding the technology dependency of a specific company. This makes it possible to develop neural network application services quickly and easily at low cost in various industries such as business, medical care, and smart factories.
Incremental Learning for Optical Flow

This paper presents a potential application of deep learning algorithms to the computation of optical flow in a temporal sequence of images. The motion information obtained by optical flow provides significant information regarding the analysis of video. The computational challenge for the estimation of optical flow makes it difficult for real-time applications. Thus, we explore a method that has a potential in improving computational efficiency for optical flow, in the framework of deep learning, by incorporating a learning technique that takes into consideration incremental warping. The presented paper is limited in that numerical results are not provided, yet it gives a direction for further studies.
Short-term Solar Power Generation Forecasting using Edge AI

Forecasting techniques for renewable energy offer useful information about the anticipated changes in the energy that will be generated in the near future. Besides, the emergence of edge computing is playing an important role to provide efficient and low latency information transmission to the smart grid. This study introduces the volatility of photovoltaic (PV) power generation forecasting based on a deep learning method to predict 1-day ahead hourly-average PV output using weather conditions as the input data. The quantization method is deployed to minimize the size of the model in embedded devices at the edge of the energy system.