Reviewerly/exHarmony huggingface dataset:
Research on the Identifier Technology of Electric Internet of Things Based on IPv6

The Internet of Things identifier is the basic supporting technology to realize the precise interaction between things. With the advancement of the construction of the electric Internet of Things, the number of various terminal devices connected to the network has increased sharply, and the types are complex and diverse. There are many types of existing electric equipment identifier systems, and their compatibility is poor. With the access of a large number of terminal devices of the electric Internet of Things, the amount of data in the identifier system is huge, which will cause huge operation and maintenance costs and resource occupation. In response to the above problems, this paper proposes an IPv6 identifier scheme that supports large-scale electric equipment of the electric Internet of Things, designs the registration and network access procedures of the identifier, and then proposes an optimization mechanism for electric Internet of Things communication service transmission based on the ipv6 identifier. Experiments have proved that the identifier scheme and transmission optimization mechanism proposed in this paper can effectively reduce the communication delay and packet loss rate of the business in the electric Internet of Things, and achieve efficient support for the electric Internet of Things business.
Enhancing SVMs with Problem Context Aware Pipeline

In recent years, many data mining practitioners have treated deep neural networks (DNNs) as a standard recipe of creating the state-of-the-art solutions. As a result, models like Support Vector Machines (SVMs) have been overlooked. While the results from DNNs are encouraging, DNNs also come with their huge number of parameters in the model and overheads in long training/inference time. SVMs have excellent properties such as convexity, good generality and efficiency. In this paper, we propose techniques to enhance SVMs with an automatic pipeline which exploits the context of the learning problem. The pipeline consists of several components including data aware subproblem construction, feature customization, data balancing among subproblems with augmentation, and kernel hyper-parameter tuner. Comprehensive experiments show that our proposed solution is more efficient, while producing better results than the other SVM based approaches. Additionally, we conduct a case study of our proposed solution on a popular sentiment analysis problem---the aspect term sentiment analysis (ATSA) task. The study shows that our SVM based solution can achieve competitive predictive accuracy to DNN (and even majority of the BERT) based approaches. Furthermore, our solution is about 40 times faster in inference and has 100 times fewer parameters than the models using BERT. Our findings can encourage more research work on conventional machine learning techniques which may be a good alternative for smaller model size and faster training/inference.
Football Players Movement Analysis in Panning Videos

In this paper, we present an end-to-end application to perform automatic multiple player detection, unsupervised labelling, and a semi-automatic approach to finding homographies. We incorporate dense optical flow for modelling camera movement and user-assisted calibration on automatically chosen key-frames. Players detection is performed with a pre-trained YOLOv3 detector and player labelling is done using features in HSV colorspace. The experimental results demonstrate that our method is reliable with generating heatmaps from players’ positions in case of moderate camera movement. Major limitations of proposed method are the necessity of manual calibration of characteristic frames, inaccuracy with fast camera movements, and small tolerance of vertical camera movement.
Back to the Origin: An Intelligent System for Learning Chinese Characters

Learning Chinese characters is a challenging task for both native and foreign beginners. One major reason is that most Chinese characters in writing are distinct from each other and lack of directly phonetic clues. Fortunately, many Chinese characters' original forms have iconicity that indicates their meanings. By leveraging on these characteristics and the latest computer vision (CV) techniques, we design and build an intelligent system that could automatically retrieve the iconic and original forms of Chinese characters. Furthermore, the system could provide learners with different styles of the character in a chronological order to bridge the original form and the most commonly used one. Specifically, we adopt the SE-Resnet-50 classification model for both character recognition and style recognition tasks, and design a dedicated retrieval mechanism to properly select the representative characters in different styles for learners. A specific user interface is designed for beginners to upload, recognize, remember, and understand the Chinese characters.
First Passage Exponential Optimality Problem for Semi-Markov Decision Processes

This paper deals with the exponential utility maximization problem for semi-Markov decision process with Borel state and action spaces, and nonnegative reward rates. The criterion to be optimized is the expected exponential utility of the total rewards before the system state enters the target set. Under the regular and compactness-continuity conditions, we establish the corresponding optimality equation, and prove the existence of an exponential utility optimal stationary policy by an invariant embedding technique. Moreover, we provide an iterative algorithm for calculating the value function as well as the optimal policies. Finally, we illustrate the computational aspects of an optimal policy with an example.
Nonlinear differential dynamics of Gaussian States

The dynamics of Gaussian states for systems with Hamiltonians, quadratic in the position and momentum operators, gives rise to the definition of a system of nonlinear coupled differential equations for the density matrix parameters of the system states. In this work, we show that, using the Gaussian-state covariance-matrix evolution, it is possible to solve the system of these nonlinear equations. Some examples of applications of the method developed are given for one- and two-dimensional quadratic-system Gaussian states. It is noted that this formalism can also be used to find new constants of motion related to the covariance matrices of quadratic systems.
Transfer Learning on the Edge Networks

Transfer learning focuses on using extensive labeled data samples in the source domain to resolve a different yet related task for the target domain, even when there is no similarity among the training and testing problem's datasets and distribution of features. This paper will discourse the implementation of the transfer learning model on edge networks to improve the performance factors and communication delay times within different servers. Any extensive system working with embedded systems is considered a high-performance system. An embedded system aims to perform some specific tasks based on the microprocessors, works on low resources and have less power consumption. An embedded system has a functional mapping, and various environment states to generate significant results. For the edge networks, the description of tasks and the dynamics of outer environment is crucial. For further clarification, we developed the transfer learning model. We experimented it on the embedded system using edge device (edge networks) and the local system to compare the time latency of the transfer learning model's execution. As a result, we concluded that the transfer learning model works effectively and gives us decent accuracy. Implementing a transfer learning model on edge networks is better than implementing on a local system in terms of cost, performance and efficiency.
An Analysis of Rainstreak Modeling as a Noise Parameter Using Deep Learning Techniques

Outdoor vision systems (OVS) play a vital role in the surveillance of the environment. However, the images and videos captured by these systems could be severely tampered by the sharp intensity changes brought about by adverse weather and climatic conditions. In this work, synthetically prepared rain images are modeled to visualize the randomly distributed rainstreak patterns as noise. The analysis has been performed using various deep learning networks such as auto-encoders with and without skip connections and denoising convolutional neural networks (DnCNN). The best model for this process has been suggested based on mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) obtained by comparing the original and the reconstructed image.
Multi-Agent System Based Mobile Help Desk and Monitoring of Safety Measures to Combat COVID-19 and Future Pandemics

The outbreak of COVID-19 pandemic presents an unprecedented challenge for humanity. The historical data about the past pandemics indicates towards a high possibility of future pandemics. Moreover, current pandemic is expected to stay for longer time. In this situation, social robots hold a huge potential to assist in fighting against current and future pandemics by minimizing human-to-human interactions e.g., by providing digital help desk, and monitoring the compliance of people with required safety measures at public places. A huge number of robots are required to monitor large spaces like shopping malls, airports etc. Therefore, we present Multi-Agent System (MAS) to provide robot based mobile help desk and monitoring safety measure of wearing face mask, for large spaces in an efficient and cost-effective manner. Our proposed MAS is based on collaborative work of two agents: 1) CCTV Camera, and 2) Robot. Experimental results show efficiency and effectiveness of proposed MAS in navigation and control of robot based on visual information interpreted by CCTV camera to provide mobile help desk, and detecting face masks through robot by using state-of-the-art real-time computer vision algorithms in large spaces.
Unmanned vehicle’s control real-time method based on neural network and selection function

The article deals with the problem of controlling an unmanned vehicle in real time. We trained a convolution neural network (CNN) to display raw pixels from a single front camera directly into commands of control. This end-to-end approach is almost optimal control based on the selection function. The system automatically remembers internal representations of necessary steps, such as detecting useful road characteristics with restrictions only based on the MPC’s controller calculating control commands as a training signal. Compared to explicit problem decomposition, such as obstacle detection, lane marking, path planing, and managment, our system optimizes all processing step simultaneously. An example of using the method on real robot is given.
Building A Voice Based Image Caption Generator with Deep Learning

Image processing is used in various industries and it is remaining as one of the most advanced technologies used in Google, medical field etc. Recently, this technology has also attracted many programmers and developers due to its free and open source tool, which every developer can afford it. Image processing also helps in finding out lot of information from a single image since it is currently utilized as a primary method for collecting the information from image and processing it for some purpose and some operations will also be performed on the image. A voice based image caption generation is a task that involves the NLP (natural language processing) concept for understanding the description of an image. The combination of CNN and LSTM is considered as the best solution for this project; the main target of the proposed research work is to obtain the perfect caption for an image. After obtaining the description, it will be converted into text and the text into a voice. Image description is a best solution used for a visually impaired people who are unable to comprehend visuals. With the use of a voice based image caption generator, the descriptions can be obtained as a voice output, if their vision can't be resorted. In future, image processing will emerge as a significant research topic, which will be primarily utilized to save human lives.
Open Source Software Engineering the Eclipse Way

The author explains how open source software development works at the Eclipse Foundation. He dives into engineering best practices, providing insights from one of the most successful open source foundations of today. Next up will be community health metrics.
Process Mining Organization Email Data and National Security Implications

Many think of processes as sequential, deliberate activities which sustain businesses and government agencies; employees integrate themselves into defined organizational processes. From an ecosystem vantage, however, emergent processes exist and are discoverable. Emergent ecosystems form without human intention and may be especially influenceable. If emergent organizational processes–especially critical infrastructure processes–were explicit, they may be exploited. Tremendous intelligence is contained within semi-structured and unstructured organizational data sources. Properly analyzed, these data provide government and private organizations with actionable management and risk mitigation insights. Using explainable process technologies combined with natural language processing, a private critical infrastructure participant’s organizational process model is discovered from semi-structured email data. Data derived from the process model are presented which elucidate internal operations and contribute to automated situational awareness of dynamically evolving events. National security implications and future research needs are described.
<sup>14</sup>C Chronology and Circulation of Potassium‐rich Brine in Lop Nur Playa, Xinjiang, Northwestern China

Acta Geologica Sinica - English EditionVolume 95, Issue 4 p. 1423-1425 Research Advance 14C Chronology and Circulation of Potassium-rich Brine in Lop Nur Playa, Xinjiang, Northwestern China Chenglin LIU, Corresponding Author Chenglin LIU liuchengl@263.net MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037 ChinaCorresponding author. E-mail: liuchengl@263.netSearch for more papers by this authorPengcheng JIAO, Pengcheng JIAO MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037 ChinaSearch for more papers by this authorHua ZHANG, Hua ZHANG MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037 ChinaSearch for more papers by this authorBaoqian DUAN, Baoqian DUAN Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang, 050061 ChinaSearch for more papers by this author Chenglin LIU, Corresponding Author Chenglin LIU liuchengl@263.net MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037 ChinaCorresponding author. E-mail: liuchengl@263.netSearch for more papers by this authorPengcheng JIAO, Pengcheng JIAO MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037 ChinaSearch for more papers by this authorHua ZHANG, Hua ZHANG MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037 ChinaSearch for more papers by this authorBaoqian DUAN, Baoqian DUAN Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang, 050061 ChinaSearch for more papers by this author First published: 01 June 2021 https://doi.org/10.1111/1755-6724.14750Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume95, Issue4August 2021Pages 1423-1425 RelatedInformation
Multiagent Emergency Triage Classification System for Health Monitoring

In this paper, a multiagent classification system is proposed for the detection of cardiorespiratory abnormalities. The system takes as inputs, the blood oxygen saturation and heart rate and classifies the vital signs using the emergency triage used in Mexico. The complete system has two agents who are responsible for obtaining and classifying the information following the emergency triage. During the classification stage, the system integrates fuzzy logic that helps generate the categorization of the data; linguistic rules were generated for both the input values (oxygen saturation and heart rate) and for the output values (data classification according to the triage). The results obtained were subjected to validation by using metrics in classification systems.
Design and Implementation of Cloud Computing Platform Monitoring System Based on Nagios

With the rise of cloud computing technology, cloud environment has gradually become the mainstream way of application system deployment. Cloud computing provides fast allocation, dynamic scheduling and other capabilities to the application system provides a rapid development of basic capabilities. In order to continuously understand the operation of the cloud computing platform itself, it is necessary to build a set of monitoring system suitable for the cloud computing environment. In this study, the monitoring system under the cloud computing environment was studied from the open source monitoring technology, and a cloud computing platform monitoring system was designed and implemented based on Nagios technology. The cloud computing platform monitoring system designed and implemented by this research institute.
TS-Bert: Time Series Anomaly Detection via Pre-training Model Bert

Anomaly detection of time series is of great importance in data mining research. Current state of the art suffer from scalability, over reliance on labels and high false positives. To this end, a novel framework, named TS-Bert, is proposed in this paper. TS-Bert is based on pre-training model Bert and consists of two phases, accordingly. In the pre-training phase, the model learns the behavior features of the time series from massive unlabeled data. In the fine-tuning phase, the model is fine-tuned based on the target dataset. Since the Bert model is not designed for the time series anomaly detection task, we have made some modifications thus to improve the detection accuracy. Furthermore, we have removed the dependency of the model on labeled data so that TS-Bert is unsupervised. Experiments on the public data set KPI and yahoo demonstrate that TS-Bert has significantly improved the f1 value compared to the current state-of-the-art unsupervised learning models.
An XMG Account of Multiplicity of Meaning in Derivation

Abstract In this paper, we tackle the issue of multiplicity of meaning in derivation using Frame Semantics and eXtensible MetaGrammar (XMG). We use corpus extracted data to identify the range of readings -al derivatives exhibit and identify prominent constraints on the types of situations and entities -al targets. These constraints have the form of type constraints and specify which arguments in the frame of the verbal base are compatible with the referential arguments of the derivative. The introduction of these constraints into the semantics of an affix allows one to predict and generate those readings which are possible for a given derivative and, at the same time, rule out those readings which are not possible. Finally, as a proof of concept, we model these constraints using XMG, and check whether the output resulting of this XMG description is consistent with the range of readings observed in the corpus.
Data Privacy in IoT Equipped Future Smart Homes

Smart devices are becoming inseparable from daily lives and are improving fast for providing intelligent services and remote monitoring and control. In order to provide personalized and customized services more personal data collection is required. Consequently, intelligent services are becoming intensely personal and they raise concerns regarding data privacy and security. In this paper data privacy requirements in a smart home environment equipped with “Internet of Things” are described and privacy challenges for data and models are addressed.
Configuration optimization of photogrammetry system based on spectral radius for on-orbit measurement

Non-contact optical measurement is a potential approach to on-orbit vibration measurement for flexible appendages, providing dynamic information for spacecraft control system. Binocular photogrammetry system is a practical configuration to achieve this measurement. In this paper, optimization approach and strategy for configuration parameters of this system are raised. Measurement matrix is specially defined to obtain the objective function for the optimization. Successive linear programming algorithm is used for optimization iteration. Transient responses of flexible appendages calculated by finite element model and corresponding images generated by OpenGL help to achieve this simulation-based optimization. The feasibility and effectiveness of the optimization are verified both by numerical study and experiment. Error analysis of the optimal system reveals great improvement in accuracy and robustness after optimization. This optimization is a promising approach to designing the configuration of binocular photogrammetry system and helping to achieve reliable on-orbit dynamic measurement results.
Design Thinking Based Ontology Development for Robo-advisors

Robo advisors are used for matching financial products to customers by applying computer algorithms. Behaviours, perceptions, preferences form the development basis of these algorithms that rank, combine and recommend investment options to consumers granting better returns for a lower cost than human advisors. Trustworthiness and thoroughness are the key aspects of a competent robo-advisor. As new technological innovations appear on the market every day, citizens are increasingly exposed to malicious applications. Therefore, such systems should be developed that define an optimal balance of protecting customers and allowing robo-advisors to develop at the same time. First, this paper characterizes robo-advisors and compares them with human advisors, then a new methodology combining design thinking and ontology development methods will be introduced. Next, the process of developing an investment ontology that collects additional information on financial products and provides the foundations of recommender system (an advanced robo-advisor) is presented. A text-mining based approach used for the validation of this ontology is also discussed in this section of the paper. The purpose is to demonstrate how well-developed and validated ontology-enabled solutions can help to overcome difficulties in the use robo-advisors.