Reviewerly/exHarmony huggingface dataset:
Consideration of a Supercomputing System with Cloud Bursting Functionality from an Operational Perspective

Cloud bursting functionality provides the administrators of on-premise supercomputing systems with an on-demand way of dynamically integrating the computing resources on the cloud into them when the computing demands from the users dramatically increase. In the Cybermedia Center at Osaka University, we have deployed a cloud bursting environment between a supercomputing system named SQUID and Microsoft Azure by respecting the transparency in usage rather than performance. Technically, the transparency allows users to execute their jobs on the cloud computing resources without having to be aware of the cloud in terms of usage. However, how we encourage users of our supercomputing system to use the cloud computing resources is a realistic and important problem to be solved from a practical operational perspective. In this paper, we investigate the cloud bursting environment on SQUID in terms of performance, monetary cost, and transparency on usage. Finally, we discuss how we as the administrators of on-premise supercomputing systems can provide a better way to encourage users to execute their jobs on the cloud computing resource instead of on the on-premise computing resource under our service policy.
Chinese Semantic Description Framework of Operational Planning Statements

As the operational relationship between each operational element in the context of military information system is more complex and the operational data is more massive, several attempts have been made in the past years to effectively conduct semantic analysis and extract key knowledge information from Chinese operational planning statements. This paper proposes to examine this question with the aim of provoking a discussion on how to bring the sciences of operational planning statements and and sematic description together to fulfill the expected potential. A series of natural language processing techniques such as lexical analysis, phrase structure analysis, and dependent syntax analysis are used to resolve this conundrum, to realize the analysis and and extraction of operational planning statements, and to store and display the structured knowledge information in the database. A platform corresponding to the algorithm process is given to show the results of the algorithm and the final implementation results. The paper then presents some observations and areas for further research.
End-to-End Efficient Indoor Navigation with Optical Flow

There has been a recent interest in employing reinforcement learning for training end-to-end goal-driven robot navigation policies. However, implementing reinforcement learning in end-to-end navigation may result in inefficient policies that exhibit redundant turning actions when attempting to avoid obstacles. This work proposes a two branches network to learn efficient policies with less turning action when robots cross the obstacles. We first employ supervised learning to train a robot action classification network with optical flow. We then combine this classifier with an RGBD optical encoder to develop an action-decision network. Ultimately, we evaluate our approach in a visually realistic simulation environment. The results show that our method can reduce unnecessary steering actions and improve efficiency while ensuring navigation capabilities. We further show that our approach can reduce energy consumption during navigation and extend the robot's work time. Experiment results in the iGibson® simulator over hand-made paths reveal that our method can reduce 13.1% of the action number in the training set and 12.9% in the testing set compared with the baseline approaches. It also can reduce 8.3% energy consumption in the training set and 9.6% in the testing set and only has a 4.2% and 8.1% difference compared with the human path.
Consensus latency of PoW blockchains

We analyze the consensus latency of Proof of Work (PoW) blockchains. A block reaches consensus if it is included in every miner’s longest chain. We define consensus latency in this paper as the duration from when a block is mined to when that block first reaches consensus. Understanding consensus latency helps us reason about when to confirm blocks for a series of confirmation rules. We find that blocks could reach consensus fast in practical scenarios. For instance, in a propagation network with maximum network delay delta, a block could reach consensus in about 1.3 deltas on average. Contrary to common expectations, average consensus latency even decreases when mining becomes easier. We establish an analytical model to understand the relation between consensus latency and various system parameters in various scenarios. We also build a blockchain simulator to validate the accuracy of our analysis. Our estimation closely tracks the simulation results.
GMLB Filter With Uncorrelated Conversion for Multi Nonlinear Targets Tracking

In the process of multi-target tracking, error observation and noise should be excluded and associated with the actual target observation value. Especially in the case of nonlinear motion, the difficulty of correlation rises sharply. To solve the decreasing correlation accuracy in nonlinear motion, a Generalized Labeled Multi-Bernoulli (GLMB) filter based on an Uncorrelated Conversion (UC) named UC-GLMB filter was proposed in this paper. Firstly, this method can effectively obtain more measurement information and is applied to the linear estimator. Secondly, it is an effective solution for multiple nonlinear moving target tracking problems based on random finite sets (RFS). Thus, the performance of the UC-GLMB filter may be continually improved. Simulation results demonstrate the effectiveness of the proposed estimator compared with some popular multi-target tracking algorithms.
A question answering model for electrical equipment standards based on subject object attention

In order to reduce the cost of manual question and answer for electric equipment standards and to solve the difficulties in querying and extracting answers to complex questions caused by the large number and many versions of electric equipment standards, a retriever-reader question-and-answer model EES-QA (Electrical Equipment Standards Question Answer) for electric equipment standards is proposed. Firstly, it identifies the names of electrical equipment and discriminates the types of questions. Then, it constructs coarse and fine-grained electrical equipment standards graphs to precisely locate the answer context related to the questions. Lastly, it predicts the span of answers by the proposed question subject-object attention mechanism combined with masking operations. The proposed question-answer model is validated on the Electrical Equipment Dataset (EED) and the public dataset SQuAD. The experimental results show that the accuracy of extracting answers to standard questions on electrical equipment reaches 94%. In addition, the performance is similar to other advanced models on the public dataset.
Learning a Bimodal Emotion Recognition System Based on Small Amount of Speech Data

This paper presents a bimodal emotion recognition system based on the voice and text information using small amount of speech data. Specifically, speech is divided into voice and text to learn emotion classifiers for each modal. The probabilities obtained from these emotion classifiers are weighted based on Mehrabian’s rule and summed up for each emotion to calculate the final score for bimodal emotion recognition. To create a highly accurate system while solving the problem that there are few Japanese speech data with emotion labels, we propose a novel data augmentation method and employ a transfer learning approach based on a pre-trained VGG16 model and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model on the tweets. In order to prove the effectiveness of the proposed method, we revealed the recognition results for 7 emotional states, i.e., anger, sadness, joy, fear, surprise, disgust, and neutral. The experiment result suggested that our novel data augmentation method improved the accuracy and that bimodal predictions based on voice and text-based outperformed the single-model predictions.
Internet of Wearables: Fog Extrapolation for Reduced Data Collection and Expanded Capture Volume in Real-Time Motion Capture Edge Devices

The range of applications that make up the Internet-of-Things ecosystem continues to grow. New opportunities present themselves along with new design challenges concerning the efficiency, portability, and processing capabilities of future Internet-of-Things systems. Thus, improving the operational metrics of individual Internet-of-Things devices, particularly across the edge and fog layers, is of paramount importance.In this manuscript, we present an approach for decreasing data collection at the edge, thus reducing form factor and power consumption of edge devices. This is particularly relevant for our application of interest, wearable motion capture, where human comfort and operational longevity are of prime importance. Our approach extrapolates from reduced edge data by leveraging prior physiological knowledge of the captured entity at the computational stage in the fog. By delegating computation to the fog, we also demonstrate the possibility for expanded capture volumes (operational areas) for future wearable motion capture systems and motion capture systems in general.Our approach, when prototyped on a millimeter wave sensor edge device and two fog node platforms of different processing tiers, shows that prior knowledge can facilitate a reduction in capture data dimensionality (and an associated decrease in power consumption) with little to no accuracy degradation, when compared to a more data-intensive edge system (Microsoft Kinect).
Movie Recommender System Based On Heterogeneous Graph Neural Networks

Heterogeneous Graph Neural Networks (GNNs) have shown good performance as a robust deep learning-based graph representation technique and have gained much research interest. Although it has adequately taken into account networks with a number of links and nodes, heterogeneity and the volume of semantic data provide significant obstacles. The attention mechanism, having great potential in a variety of areas, is one of the most interesting new developments in deep learning. This research demonstrates a system with two crucial attributes for embedding users and movies. The proposed framework achieves multi-level semantic attention using GNNs. We incorporated IMDB and Netflix Movie and TV Show datasets and merged them into a single consolidated dataset that was further utilized for results analysis. This paper mainly contributes a technique for movie recommendation using heterogeneous graphs and multi-level Semitics. We have proposed a framework that incorporates viewer and Director as an entity. During the research, we also combined two datasets in accordance with the proposed framework. After that, we evaluated the performance of the graph neural network on the heterogeneous graph. We discovered that the proposed model outperformed the current methodologies while using the proposed technique. Our model multilevel-Semitics-based framework shows effective results.
Scene Text Recognition with Multi-Encoders

Although text recognition has significantly evolved over the years, the current models still have huge challenges, especially for irregular text images, such as complex backgrounds, curved text, diverse fonts, distortions, etc. Currently, CNN-based text recognition networks have shown good performance but still face the above challenges. Recently, feature extractor based on transformer has shown excellent advantages for global feature extraction on images. Especially in irregular text images, which can use self-attention to establish the information connection of each part of the image, which can also reduce the influence of the irregular distribution of characters. Therefore, this paper proposes MESTR(Multi-Encoders Scene Text Recognition) that combines a CNN-based [1] [2] [6] feature extractor and a transformer-based feature extractor. MESTR can extract local and global features of text images at the same time and then integrate global features into local features. During training, we used CTC [6] as guide training in the decoder part, as the compensation training strategy for attentional decoder. Experimental results demonstrate that the proposed MESTR shows competitive results on all seven benchmarks. At the same time, we provide ablation experiments to show the effectiveness of the improved part on the text recognition model.
Identification for Drinking Spout of Plastic Bottle Using Multi Eff-UNet

Currently, the increase of general waste is a problem in Japan. It is garbage and human waste. One solution to this problem is to reuse the bodies of used plastic bottles to reduce the amount of plastic waste and the increase in general waste. For this purpose, some municipalities provide bags for disposing of plastic bottle bodies. However, in many cases, the bottles are not only discarded in these bags, but also with their caps attached. In such cases, the caps must be found and removed by hand in the sorting and processing facility at the waste treatment plant. This means that the same work is performed by person for a long time, causing problems such as overlooking caps due to fatigue. To solve these problems, we develop a method for automatic identification of plastic bottle bodies and caps using deep learning technique. In this paper, we propose a model that combines multiple Eff-UNets. Specifically, we combine EfficientNetB4 for local segmentation and EfficientNetB5 for global segmentation. By using our method, we conducted an experiment on images of plastic bottles collected from the internet and other sources. We obtained segmentation results of 97.9% for plastic bottle bodies and 86.0% for plastic bottle caps.
Image classification using DETR based object-level feature

The object in an image is the main information of image representation for image classification. In case that the background in the image is complex or an object size is small, the existing invariant feature, such as Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Features (SURF) is not easy to use for object-level representation. Because SIFT can not distinguish whether the feature includes relevant object information, it may consist of background or less informative features. We use Detection Transformer (DETR), the state of the art object detector to represent the object-level information. By visualizing the attention map of Transformer decoder, we find that each output vector indicates the region of objects effectively. Bag of visual words (BoVW) is applied to represent N output vectors of DETR as the feature of a query image. Based on scene-level and object-level datasets, we compare our method with SIFT based BoVW as an image classification task. We show that the proposed method perform better for object-level dataset than BoVW of SIFT.
Anomaly detection model using time serise dataset of small manufacturing industry

As artificial intelligence technique is generalized widely used in industry area. so, there are attempts to anomaly detect by using deep learning in small manufacturing industries. However, it is difficult for small manufacturing industries to have an artificial intelligence infrastructure. The nation support data set of open small manufacturing industries for solve these problems and help. This paper proposes an anomaly detection model for time series data using this data set. The propose LSTM-SVDD anomaly detection model is that combines the LSTM model widely used in time series data with the SVDD model widely used in anomaly detection. The propose model is that learns the range of normal data and detects data out of this range as abnormal data. It is confirmed that the data distribution of the test data not used for learning predicted similarly with prediction results. A performance indicator ROC is also high at 96.31. the proposed automatic anomaly classification model is expected that can be used in small manufacturing industries field that are limited in the construction of artificial intelligence infrastructure.
Automatic Identification of CTCs in Fluorescence Microscope Images Using Morphological Filtering to Detect Cell Nuclei

Currently, cancer is the leading cause of death in the world, and in 2020, about 10 million people died from cancer. Since cancer progresses by repeating metastasis, early detection and early treatment are required. There are various treatments for diagnosing cancer, but it is difficult to determine the presence or absence of metastasis. Therefore, as a new biomarker, analysis of CTCs (Circulating Tumor Cells) in blood including HL-60 and MCF-7 is drawing attention. However, since the proportion of CTCs in the blood is very small, there is concern that the burden on doctors will increase. Therefore, in order to detect CTC in blood, we propose a method that automatically detects CTCs from fluorescence microscope images and enables quantitative analysis by computer. First, rough extraction of cell candidate regions is performed by morphological filtering, and the watershed method using hue images is applied to the connected cells to set the ROI (region of interest). In this paper, the proposed method was applied to a total of 16 images, 8 for HL-60 and 8 for MCF-7, and CTCs detection experiment was performed. As a result, the number of detections was 882 (TPR $=98.4$%) for HL-60 and 275 (TPR $= 99.2$%) for MCF-7.
Development of Cleaning Module for Wall Climbing Drone with Bio-inspired Watering Mechanism

For many years, there has been significant progress in cleaning facilities in hazardous conditions using autonomous mechanical systems to keep humans away from potential danger. Especially, the cleaning method using a UAV (Unmanned Aerial Vehicle) has attracted much interest in the robotics field due to its high degree of freedom in maneuverability; however, a UAV has a payload limitation. In this paper, we propose a cleaning module for a specific drone platform “CAROS-Q” that can tilt its pose to perch on walls or tilted surfaces. The proposed module includes bio-inspired water flow control mechanism from the peristaltic movement of the Esophagus for improving cleaning performance while minimizing the complexity and weight. By comparing the cleaning performance between the proposed module and dry brush cleaning, it was confirmed that the proposed module outperforms the counterpart for cleaning various contaminants.
Learning to Box: Reinforcement Learning using Heuristic Three-step Curriculum Learning

The reinforcement learning paradigm is a widely used approach to solving sequential problems. In this paper, we utilized reinforcement learning with curriculum learning to train the agent to play boxing without any human demonstration. We presented as a curriculum three steps for a single agent to learn boxing: standing, walking and punching. The agent who learned through the proposed curriculum successfully threw a jab into a punching bag, but the agent who trained to punch from the beginning did not. Not only did we show the effectiveness of curriculum learning in single-agent boxing, but we also showed that the agent could play boxing without human demonstrations.
Environment Recognition from Spherical Camera Images Based on Multi-Attention DeepLab

Electric wheelchair is an easy-to-operate means of transportation that does not require physical strength. With the number of electric wheelchair users increasing in recent years, the increase in traffic accidents becomes a problem. Therefore, by developing an autonomous electric wheelchair, it is expected that the risk of accidents will be reduced and the convenience of the electric wheelchair will be improved. Environment recognition is indispensable for the development of autonomous electric wheelchairs. We propose a semantic segmentation method for recognizing 16 objects in traffic environment. This paper examines the improvement of problems such as the high price of autonomous electric wheelchairs due to the increase in the number of sensors used, which has been a concern in related research. Therefore, we use panoramic images acquired by a spherical camera as input data, and extern the Multi-Attention Deep Lab algorithms fitting for the recognition of distorted images. A new CNN model is constructed using Deep Lab v3+, scSE Block, Pairwise Self-Attention, and Joint Pyramid Up-sampling. We conducted a recognition experiment using images taken on campus and verified its effectiveness. (Comparing to DeepLab v3+, IoU and Dice showed a 3.5% and 3.6% improvement in accuracy, respectively.)
Semantic Segmentation with Perceiver IO

Recently, in deep learning, the transformer is replacing the convolutional neural network (CNN) due to its performance and simple design. In particular, in recent studies, constructing an encoder of the transformer that effectively extracts features on an image has been widely used. However, even in these cases, models utilizing existing deep neural network structures needed to use a form suitable for each data format according to input modality. Recently, the Perceiver IO [6] has been proposed to overcome this limitation. It can process various data formats through one structure to extract a characteristic value. Also, it uses an output query to output data as we want. In this paper, a semantic segmentation model using the characteristics of the Perceiver IO is presented. Two types of input configuration are suggested, and experimental results show the feasibility of the proposed method.
Extraction of Cervical Lymph Nodes using Improved U-Net++

Early detection and treatment of the lymph node are important since swelling of the neck is a likely factor in systemic metastasis of cancer. One of the diagnoses of cervical swelling is a CT scan, which has a beneficial influence on the diagnosis of the disease. However, the reading of CT images is burdened by the large number of images, which increases the physician's workload. In addition, since it is based on the subjective judgment of the physician, there may be discrepancies in diagnostic results and undetected cases due to differences in experience. A means of solving these problems requires a CAD system that provides a second opinion to the physician. Therefore, this paper proposes a segmentation method of cervical lymph node region for the purpose of developing a CAD system for the diagnosis of cervical lymphadenopathy from CT images. The proposal method is a CNN model with U-Net++ as the backbone, introducing CBAM (convolution block attention module) and dual-branch multi-scale attention module. The proposed method was applied to CT images of 11 cases, yielding IoU of 62.29, confirming its validity.
Semantic Foreground Feature Extraction and Camera-aware Re-allocation Clustering for Unsupervised Person Re-identification

Unsupervised person re-identification has reached an incredible developing process, especially the clustering-based method. However, there are still two challenging problems in this field. First, the majority of algorithms employ input samples that include background noise to generate pseudo labels. Second, the common clustering method cannot guarantee enough reliable clustering quality. In order to address these challenging issues, we propose a Semantic Feature Extraction-Camera-aware Re-allocation (SFE-CR) framework for unsupervised person re-identification. Especially, in the semantic feature extraction module, we use a parsing model to extract semantic local features for training samples, so as to eliminate the background noise. In the camera-aware re-allocation module, we split the samples using their camera id and re-allocate the pseudo label generated by the common clustering method. Further experiments on Market-1501 and DukeMTMC-reID datasets show that the proposed method's effection exceeds the baseline evidently.