Reviewerly/exHarmony huggingface dataset:
A Task Priority-based Resource Scheduling Algorithm for Container-based Clouds

Past military command systems were mostly based on control and scheduling methods based on preset rules, and it was difficult to dynamically adjust them quickly according to environmental changes during operation. With the continuous development and maturity of cloud computing technology, the current Kubernetes-based container cloud management technology has become the standard in the field of cloud computing cluster management, but there are problems such as single considerations in scheduling strategies. To this end, this paper proposes a container cloud computing power resource scheduling algorithm based on combat mission priority, which comprehensively considers combat mission priority and various combat node computing, storage and network factors, and designs and implements the algorithm. Finally, the effectiveness of the algorithm is verified by building a demonstration environment for testing.
A New Method for Predicting Failures in Cloud Computing

The big problem in Cloud Computing is fault tolerance. Fault tolerance is a major concern in order to ensure the availability and reliability of critical services. In order to minimize the failure impact on systems and applications execution, the solution is therefore to predict and reduce failures. In this paper, we propose a new method based on the junction temperature combined with a processor cooling technique which allows the prediction of failures in Cloud Computing which is composed of a set of unstructured resources classified into three 3 layers. In this work, we are interested in prediction at the hardware layer of the Cloud. By assuming that this layer contains a set of processors from different machines that work with this Cloud, the prediction is made compared with the temperature of the processors. When the processor temperature increases, the probability that the process will fail is high and vice versa. First, we will measure the junction temperature of each machine, Therefore, if it is high we will use a phase change material to absorb it by its latent heat so the material becomes liquid. And, to return it to its solid state, we will call it using a fan. The advantage of the proposed method, compared to the others, is that it helps the decision maker to make more flexible decisions when distributing loads in the Cloud. The simulation results are given to demonstrate the effectiveness and performance of the proposed method.
Public Sentiment on Ayodhya Verdict by the Supreme Court of India

Social media has become one of the most important sources in understanding opinion of people regarding important and trending topics. One such topic was the Supreme Court’s verdict on almost seven-decade old Ayodhya land dispute case. On 9th November 2019, the apex court ordered that the disputed land in question be used for construction of Ram Temple. As it was one of the most anticipated judgements in India’s history, Twitter, was buzzing long before the date of verdict. To perform sentiment analysis, around 70,000 tweets pertaining to the verdict were collected from 26th Oct 2019 to 23rd Nov 2019. The dataset was then divided into three phases i.e., pre-verdict, on day of verdict and post-verdict and NRC Lexicon has been used to analyze the results and extract positive, negative, and neutral tweets. Sentiment analysis of each phase indicates that the verdict has been accepted by the public.
Computer Intelligent Prediction Method for Hot Event by Long Short-Term Memory Model

At present, with the development of the Internet, people can timely post their cognitions, attitudes and emotions about hot events, which constitute the network public opinions. Through the analysis of the network public opinions and sentiments, the evolution of hot events can be judged and further provided suggestion to relevant departments to deal with hot event crisis. The exiting approaches still have some shortages, such as inaccurate classification of sentimants and topics, poor predicting capability of traditional prediction models. To sovle those problems, we propose a novel approach, which is based on sentiment analysis, text clustering and LSTM neural network model. First, we extracted emotional tendencies to classify text comments into positive and negative stentiments with SnowNLP, a Chinese natural language processing tool. Second, based on the Single-Pass clustering algorithm, we implemented text cluster analysis to explore hot topics. At last, we predicted the public emtional trends and the topic attention trends with a Long-Short-Term Memory model(LSTM). The experiments show that our approach has a high accuracy of prediction about the evolution trend of hot events.
Drone Clean-Zilla

The main reason for waste production is littering which also results in pollution. Knowingly or unknowingly, littering affects the climate and environment. And, it additionally costs the municipalities a huge amount of money every year to clean up the cities. Normally, garbage searching, collection and waste management is in the hands of the executives and authorities, for example, the municipality trucks work for urban communities, janitors for industries, etc. But, none of these systems are perfect. Because of the weaknesses of existing waste management system and less transparency in the garbage disposal system by the authorities, this paper aims to propose a solution for garbage checking in a wide manner. This paper presents an efficient solution for solid waste detection which will be made possible with the use of UAV i.e. Unmanned Aerial Vehicle (for example, drone) which will assist the society by distinguishing places polluted with waste materials and will send the area details as a warning in the “Drone Cleanzilla” website.This system will utilize the ideas of image stitching, deep learning, and convolutional neural networks to detect garbage from the pictures captured by the drone. These images will be sent to Firebase using Raspberry Pi which is going to be installed in the drone. From Firebase cloud, “Drone Cleanzilla” website will fetch the images with time and location. This project will bring more transparency to the wastage cleaning system as all the details of waste along with pictures and location will be uploaded in our website which will be accessible to everyone with internet. This will alert the municipality and officials and they will have to responsibly take actions to clean the garbage. In future, we will also examine the future uses of this project which will handle both trash identification and collection by the government authorities.
Customizing Mems Designs via Conditional Generative Adversarial Networks

We present a novel systematic MEMS structure design approach based on a “deep conditional generative model”. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as low anchor loss of micro resonators. Excellent agreements with experimental data, numerical simulations, and a previously reported machine learning-based analyzer are achieved for validation of our methodology. As such, the proposed scheme could open up a new class of data-driven, intelligent design systems for a wide range of MEMS applications.
Analysis of the Performance of a Cloud Computing Processing Queue with Correlated Reneging of Tasks and Resubmission

The Quality of Service (QoS) experienced by the packets that constitute a cloud computing task does not only depend on the performance of the cloud computing network infrastructure at the data centre but also on the performance of the access networks and the internet core networks traversed by the packets before the task is fully submitted to the cloud computing or processing servers for execution or processing. Sometimes, some of the tasks that are submitted and are waiting in queues to be processed could be removed from the queues without being processed (task reneging or dropping). Removing tasks from the queue could result from the user&#x0027;s impatience, missing execution deadline, security reasons, or an active queue management strategy. The reneged or dropped tasks could be resubmitted depending on the reason for which it was dropped. The resubmission of tasks decreases QoS and increases the energy consumption overhead as more energy is required to transport the resubmitted tasks through the access, core, and data centre networks. In this paper, we propose a multiserver queueing model with correlated reneging and resubmission to analyse the performance of a cloud computing server.
Real-Time Traffic Sign Recognition on Sipeed Maix AI Edge Computing

Convolutional neural network (CNN) has become popular and helpful model for classification of objects in the video stream, clustering, pattern recognition and prediction in many disciplines. Considering many systems solving the classification problem, the mobility is often required. This paper proposes an implementation of the Tiny-YOLOv3 (You Only Look Once) using CNN in the Internet of Things (IoT) devices to solve the problem of classification of traffic signs on the EDGE AI platform Sipeed MAIX. The main feature of this module is the availability of 1st RISC-V 64 AI Module include K210 KPU inside, which allows high-performance computing with low power consumption. The proposed method uses K-means algorithm to cluster our training set to find more excellent priori boxes of the targets. Performances of the proposed approach are evaluated on the Belgium Traffic Sign Detection (BTSD) dataset. Extensive experiments demonstrate that the proposed method achieves an excellent trade-off between speed and accuracy. The system executes the detection/recognition process in approximately 112 ms for each image in (KPU) and 9 FPS in video stream.
Rekiske: Interdisciplinary platform for sharing knowledge and experience of Japanese historical documents

Abstract Historical materials contain descriptions of various events involved in natural processes and human societies (such as weather, earthquakes, volcanic eruptions, harvests, market prices, and famines). They have been used for studies in diverse fields, including natural sciences (such as seismology, climatology, and astronomy), social sciences, and humanities. Hence, researchers have made various efforts, such as identifying and obtaining the materials, reading handwritten characters and transcribing them into digital texts, standardizing formats, extracting descriptions of relevant phenomena, and constructing integrated datasets, to use documentary information. Because scientists, other than historians, find it quite challenging even to ascertain where to find appropriate research materials, the Rekiske Development Group has, therefore, developed a system called Rekiske to facilitate interdisciplinary sharing of information available from historical materials. Rekiske is designed for sharing bibliographic and scientific information of historical materials, along with researchers’ experience and knowledge, through interdisciplinary collaboration. Rekiske can assist researchers in identifying and obtaining useful historical materials for research purposes. It also facilitates collaboration among experts to share their expertise and knowledge of historical information, materials, and recent studies. Using Rekiske, we register various types of information on historical materials regarding weather or earthquakes considered in geoscience research to utilize researchers’ experiences and to produce new knowledge through interdisciplinary collaborations. The developed system is designed to be used as a metadata portal and pathfinder to promote and preserve historical data and facilitate an understanding of the spatiotemporal connection between nature and human society. We have applied Rekiske to historical climatic studies and discuss its usage, benefits, usability, and other remaining issues.
Automated Detection of Spatial Memory Safety Violations for Constrained Devices

Software written for constrained devices, commonly used in the Internet of Things (IoT), is primarily written in C and thus subject to vulnerabilities caused by the lack of memory safety (e.g. buffer overflows). To prevent these vulnerabilities, we present a systematic approach for finding spatial memory safety violations in low-level code for constrained embedded devices. We propose implementing this approach using SystemC-based Virtual Prototypes (VPs) and illustrate an architecture for a non-intrusive integration into an existing VP. To the best of our knowledge, this approach is novel as it is the first for finding spatial memory safety violations which addresses challenges spe-cific to constrained devices. Namely, limited computing resources and utilization of custom hardware peripherals. We evaluate our approach by applying it to the IoT operating system RIOT where we discovered seven previously unknown spatial memory safety violations in the network stack of the operating system.
Toward Quality-Aware Transaction Validation in Blockchains

With blockchain-oriented software engineering, the quality of input data is often overlooked. Controlling the transaction payloads in the transaction validation phase may prevent the poor-data-quality issues that can affect the output of even correct smart contracts.
Formal Verification of Neural Network Controllers for Collision-Free Flight

We investigate a method for formally verifying the absence of adversarial examples in a neural network controller. Our approach applies to networks with piecewise affine activation units, which may be encoded symbolically as a piecewise affine mapping from inputs to outputs. The approach rests on characterizing and bounding a critical subset of the state space where controller action is required, partitioning this critical subset, and using satisfiability modulo theories (SMT) to prove nonexistence of safety counterexamples on each of the resulting partition elements. We demonstrate this approach on a simple collision avoidance neural network controller, trained with reinforcement learning to avoid collisions in a simplified simulated environment. After encoding the network weights in SMT, we formally verify safety of the neural network controller on a subset of the critical partition elements, and determine that the rest of the critical set partition elements are potentially unsafe. We further experimentally confirm the existence of actual adversarial collision scenarios in 90% of the identified potentially unsafe critical partition elements, indicating that our approach is reasonably tight.
A Web Information Extraction Framework with Adaptive and Failure Prediction Feature

The amount of information available on the internet today requires effective information extraction and processing to offer hyper-personalized user experiences. Inability to extract information by using traditional and machine learning techniques due to dynamic changes in website layout pose significant challenges to the technical community to keep up with such changes. The focus of existing machine learning-based information extraction framework is only on information extraction by using core extraction logic that is susceptible to website changes, thus missing out core features such as ability to handle proactive failure prediction and intelligent information extraction capabilities. The aim of this article is to build a robust and intelligent information extraction framework with the ability not only to proactively predict website failure but also automatically extract information using deep-learning techniques using You Only Look Once and Long Short-term Memory (LSTM) networks. The proactive detection using LSTM detects new location of the web page due to layout changes and enables automatic extraction of information of the new web page. A real-world case with retail website for intelligent information extraction and an offline experimentation environment is setup to demonstrate proactive failure prediction and automatic extraction resulting in high failure prediction, precision and recall of object detection and information extraction.
On the Performance and Effectiveness of Digital Contact Tracing in the Second Wave of COVID-19 in Italy

Contact tracing smartphone applications have been developed and used as a complement to manual contact tracing in the COVID-19 pandemic.The goal of these apps is to trace contacts between people and notify the mobile phone owners when one of their contacts tested positive.People who receive a notification should behave as exposed people, take a test and possibly isolate themselves till they receive the result.Unfortunately, identifying contacts based on distance is technically a daunting task: apps can be configured conservatively (a very small number of people is notified, limiting the effectiveness of the app) or they may be more tolerant and produce a high number of notifications but also of false positives.We review the data available from Immuni, the Italian app, which provides detailed figures on the notifications sent and the positive users, and we show that Immuni was configured to generate a very large amount of notifications.We estimate the testing resources that the health system would have needed if the app was downloaded by 100% of the adult population, and every notified person would require a test.In such conditions, Immuni would have generated a number of tests orders of magnitude higher than what available.We compare the performance of Immuni with the currently available literature on other apps and observe that contact tracing apps had a limited impact in the second wave of the COVID-19 pandemic.As contact tracing exposes citizens to privacy risks, we discuss some ways to reshape the goal of the apps to achieve a better trade-off between social benefit and risk.
Motion Recognition System with Dynamic Time Warping Method using Kinect Camera Sensor

Gymnastic in one of sport to improve healthy. To help people do the right motion, it is necessary to build a system that can recognize the motion. Gymnastic motion recognition system is the implementation system to help people recognize the right motion while doing gymnastic sport. In this paper, this system is using Kinect camera sensor to tracking the motion. And using Dynamic Time Warping algorithm to recognition the gesture for real-time implementation, the dynamic image (several frames) can be used as references data and the system can also recognition gesture. The recognition success rate was over 90%.
Explaining a Random Survival Forest by Extracting Prototype Rules

Tree-ensemble algorithms and specifically Random Survival Forests (RSF) have emerged as prominently powerful methods for survival data analysis. Tree-ensembles are very accurate, robust, resilient to overfitting, and can naturally handle missing values as well as categorical data. However, since they consist of multiple models, they are not as interpretable as single decision trees. In this work, we propose a method that learns to extract a limited number of representative rulesets from the ensemble providing explanations of the ensemble model’s outcome. We propose a local approach, focusing on explaining predictions for a specific sample, and is mainly divided into three parts; tree-filtering, low dimensional representation, and prototype ruleset extraction. Here, we employ RSF as the ensemble model but our approach is generalised to other settings as well. We conducted preliminary experiments on both binary classification using relevant data as well as time-to-event predictions in a survival analysis context. The obtained results demonstrate that our approach performs comparably well to the original Random (Survival) Forest that it explains, while based only on few trees from the whole forest.
Target Detection for RD Images of HFSWR Based on CNN-ELM Model

High-frequency surface wave radar (HFSWR) can effectively detect ship targets. However, ship target signals are often affected by strong clutter and complex interference. In this paper, we propose an HFSWR target detection algorithm based on a two-stage cascade detector combined with a convolutional neural network-extreme learning machine (CNN-ELM) model. In the first stage, an extremum detector (ED) is used to obtain suspicious target regions (STRs) in the range-Doppler (RD) spectrum image. In the second stage, a CNN-ELM model is employed. The features of the STRs are learned by a lightweight convolutional neural network (LW-CNN) and fast classification is performed by an extreme learning machine (ELM). The experiments show that the proposed algorithm can achieve better performance in the measured RD image than the traditional detection algorithms.
From Uni-relational to Multi-relational Graph Neural Networks

Graph Neural Networks (GNNs), which extend deep neural networks to graph-structured data, have attracted increasing attention. They have been proven to be powerful for numerous graph related tasks that cover a variety of research areas including natural language processing, information retrieval and knowledge graph completion (KGC). GNNs are primary designed for simple homogeneous and uni-relational graphs. Due to its great success in handling the graph data, considerable studies have been developed to extend GNNs to process complex multi-relational graphs such as the knowledge graph. My research first focuses on learning effective representation of uni-relational graph to facilitate some downstream applications such as graph classification and query understanding, and show the great capacity of GNNs to advance these tasks. Although the GNNs have demonstrated its significant effectiveness on the uni-relational graph in a large range of applications, we surprisingly found it may not be as crucial as previously believed in the knowledge graph completion task. It suggests careful attention to more suitable GNNs designs for KGC task.
Research on Human Eye Detection Enhancement Based on Homomorphic Filtering

This paper introduces the methods of human eye detection under normal light source and infrared light source respectively, and puts forward the research of human eye detection image enhancement. During image preprocessing, the color image is transformed into gray image, and then the gray level probability distribution of gray image histogram is transformed into uniform gray level probability through histogram equalization, so as to improve the contrast of gray image and the accuracy of human eye detection. Homomorphic filtering image processing method developed on the basis of Retinex theory is used for image enhancement of human eye detection. Comparing the image enhancement effects of the two methods, it is concluded that after homomorphic filtering is used to filter and enhance the human eye image, the human eye contrast effect of the image is relatively more obvious and the image is clearer.