Reviewerly/exHarmony huggingface dataset:
Implementation of Machine Learning Techniques for Analyzing Crime and Dark Web Data

The dark web is a virtually untraceable hidden layer of the internet that is frequently used to store and access secret data. However, a number of situations have been documented in which this platform has been used to covertly undertake illicit and unlawful operations. Traditional crime-solving procedures are inadequate to meet the demands of the current crime environment. Machine learning can be used to detect criminal patterns. Past crime records, social media sentiment analysis, meteorological data, and other sources of data can be used to feed this machine learning technique. Using machine learning, there are five phases to predicting crime. These are data gathering, data classification, pattern recognition, event prediction, and visualization. Using crime prediction technologies, law enforcement agencies can make better use of their limited resources. In this chapter, the authors show the importance of learning the principles of various policies on the dark web and cyber crimes, guiding new researchers through cutting-edge methodologies.
KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN FAKTOR PENYEBARAN COVID-19 MENGGUNAKAN MODEL-BASED CLUSTERING t-MULTIVARIAT

The spread of Covid-19 had a significant impact in all sectors. Enforcement policies from the government that are appropriate with the conditions for the spread of the virus that are needed to prevent a bigger impact. Clusteritation by province based on data on the spread of Covid-19 is important for the government to set appropriate policies in order to prevent the spread of Covid-19. The data used include data on population density, testing rate, proportion of population 50 years and over, and proportion of population diligently hand-washing in each province. The data factors for the spread of Covid-19 tend to overlap and there are outliers in the data which causes the data not normally distributed. In this study, Model-Based Clustering t-multivariate was used for data clustering. The results show that using Integrated Completed Likelihood, two groups of optimal cluster were obtained. The second cluster has a higher risk of spreading Covid-19 than the first cluster. Keywords : Covid-19, Clustering, Model-Based Clustering t-Multivariat
The Evolution of Library and Information Science Education Promotion: A Comparative Analysis of LIS Program Websites between 1999 and 2019

This study compares library and information studies (LIS) departmental websites from 1999, 2009, and 2019 to explore the evolution of program promotion practices. Websites were analyzed using the 7Ps Marketing Mix model to explore how programs were marketed across this timeframe. Similar promotional messages were used across the years, but a key difference over time was that the 1999 sites promoted the program whereas the 2019 sites promoted people (faculty and students). Three key findings concerning changes in how LIS program websites evolved between 1999 and 2019 to serve program public relations goals were that (1) websites and social media have become key mechanisms for relationship building and promotion; (2) websites have evolved from sharing program details to showcasing faculty and student success stories as the core element of program promotion; and (3) the development of social media and dynamic websites have made these websites more interactive and content rich. An understanding of changes in LIS program websites over time can assist LIS program administrators in developing current and future program promotions activities.
A cybersecurity competition to support the autonomous, collaborative, and personalized learning in computer engineering

Cybersecurity is one of the most promising areas for future graduated students to develop their work life. However, the weight of cybersecurity is still low in most computer engineering programs, even though computer engineers are the most suitable professionals for cybersecurity activities. We claim the implementation of cybersecurity competitions, where students participate in teams between four and six people, improving the acquisition of cybersecurity competencies through the promotion of the autonomous, collaborative, and personalized learning. In order to validate the hypothesis, an experience was conducted during the second term of 2020/2021 course in Universidad Polit&#x00E9;cnica de Madrid. Experience was conducted in the computer engineering degree, in the context of the subject &#x201C;Cybersecurity in Systems and Networks&#x201D;. Two groups were defined: the control group following a traditional learning methodology, and a pilot group where learning was based on a cybersecurity competition, including short questionnaires, laboratory practices and puzzles. The results obtained validate the hypothesis and answer all research questions in a positive way. All learning indicators improve when students work on cybersecurity competencies through an educational competition.
Supporting Secure Multi-party Computation for R Framework

The trade-off between sharing and privacy is an important issue in data analysis. Secure multi-party computation (MPC) involves all parties jointly performing a computation without revealing their own data. R is a popular domain-specific programming language for data analysis. This paper develops an MPC-enabled R framework that allows data from different data owners to be analyzed and computed without revealing the data themselves. A programming model is proposed to support MPC on an R framework. A third-party library, ABY, is used to provide the ability for MPC. The interface between C/C++ and R is studied in order to integrate the ABY library. In addition, an ABY wrapper is developed to alleviate programmers’ workloads. An experimental application demonstrates that the proposed programming model can correctly complete secure computation for the tested benchmark programs.
Aspect based sentence segregated dataset of hybrid car's consumers online reviews

Dataset presented in this paper is obtained from the top online automobile selling and purchasing websites. A total of 1000 reviews related to hybrid cars in the form of text reviews are extracted with the help of the Web Scraper tool. The dataset presents the customers sentiments in the form of reviews related to hybrid cars. Various aspects are taken into consideration while annotating the reviews such as driving, performance, comfort, safety features, interior, exterior and accessories. The annotation of data is done at three levels by three annotators i.e., (1) overall polarity of a review, (2) segregation of the sentence term in which aspect is discussed, (3) polarity of the discussed aspect. Cohen's Kappa score of 0.90 was achieved among the authors while annotating the reviews. Dataset can be used for sentiment analysis, information retrieving, lexicon analysis, and grammatical and morphological analysis.
Towards Lightweight Authorisation of IoT-Oriented Smart-Farms using a Self-Healing Consensus Mechanism

This paper proposes a novel consensus mechanism towards access control in networks populated by constrained devices. The research explores existing mechanisms in the distributed technology blockchain, towards limiting scalability of data cumulation for microcontrollers, specifically the ESP32. The work utilises security functions native to the featured ESP32, for a more granular approach than binary authorisation; security solutions such as the de facto standard TLS adopts an &#x2018;in or out&#x2019; approach, and this can invite numerous problems. The design proposes a hybrid design to produce a robust solution that suffers from neither the vulnerabilities of centralisation or data aggregation of distribution. The decentralised proposal is then tested using formal verification; model-checking using the AVISPA and SPAN tools. The research concludes a safe design.
Real-Time Anomaly Detection for Smart and Safe City Using Spatiotemporal Deep Learning

A smart city ensures the safety of its citizens by the reduction of crime and terror threats. Despite intensive efforts to prevent and control anomalous human activities, they still pose a major risk and challenge to the society. This paper presents an automatic recognition of unusual human behavior captured by a CCTV camera in public areas, using spatio-temporal 3D convolutional neural networks. The weakly labeled benchmark dataset has been properly annotated to remove noise for accurately localizing anomalies within videos. This human-related dataset with real crime scenes is then compared to other state-of-the-art techniques such as Pseudo 3D and ResNet 3D. Our experimental results on the newly developed dataset outperforms most competing models in terms of area under the curve (AUC), obtaining 97.39% AUC.
Allocation of Resources for Cloud Survivability in Smart Manufacturing

With the development of virtualization technology, cloud computing has emerged as a powerful and flexible platform for various services such as online trading. However, there are concerns about the survivability of cloud services in smart manufacturing. Most existing solutions provide a standby Virtual Machine (VM) for each running VM. However, this often leads to huge resource waste because VMs do not always run at full capacity. To reduce resource waste, we propose a smart survivability framework to efficiently allocate resources to standby VMs. Our framework contains two novel aspects: (1) a prediction mechanism to predict the resource utilization of each VM in order to reduce the number of standby VMs; and (2) a nested virtualization technology to refine the granularity of standby VMs. We will use an open-source cloud simulation platform named cloudsim, with real-world data, to verify the feasibility of the proposed framework and evaluate its performance. The proposed Smart Survivable Usable Virtual Machine (SSUVM) will predict resource utilization of VMs on Rack1 periodically. When errors happen in VMs, the framework will allocate standby resources according to the predicted result. The SSUVM will receive the latest running status of the failed VM and its mirror image to recover the VM's work.
Bearing-only tracking using range-parameterized shifted Rayleigh filter

In this paper, we modified the shifted Rayleigh filter (SRF) to improve its performance of tracking with bearing-only measurement. The proposed method uses multiple parallel SRF with different initial positions and it is named as range parameterized shifted Rayleigh filter (RP-SRF). The RP-SRF is applied to an underwater target motion analysis (TMA) where the target moves in a nearly straight line motion with constant velocity, and the observer maneuvers to track the target. The performance of the proposed estimator is compared with the SRF, cubature Kalman filter (CKF), and RP-CKF in terms of root mean square error (RMSE), percentage of track loss, and computational time. Simulation results indicate that the RP-SRF is more accurate than the CKF, SRF, and RP-CKF and would be ahead of its nearest competitor for underwater bearing-only tracking.
Heat Treatment Management of Radio Frequency Integrated Circuit IC Based on Deep Learning

Integrated circuit (IC) is a microchip that consists of several electronic components which works as a single unit. Now, aiming at the problem of how to effectively dissipate the heat of the radio frequency integrated circuit in the work, in order to effectively manage the heat dissipation of the chip and to improve the working life of the IC chip, this paper uses the algorithm of convolutional neural network to analyze the IC chip based on deep learning. Identify and manage heat dissipation. The experiments show that the algorithm can effectively identify heat treatment, further improve the working time limit of IC circuits, and provide a reference for the research of IC heat treatment management.
Implementation of a Meeting Sign in Method Through Image Recognition

This system utilizes image recognition technology to study the problems of other people's check-in in the current check-in mode, poor data feedback, and low efficiency of manual management. A complete set of mobile conference sign-based based on image recognition technology is designed and implemented. The management system can greatly improve the efficiency of the conference check-in, save the time of the conference check-in, has strong usability and operability, and greatly saves the conference time occupied by the conference sign-on, and has high efficiency and practicability.
Analysis and Prediction of Subway Tunnel Surface Subsidence Based on Internet of Things Monitoring and BP Neural Network

With the acceleration of the urban development process and the rapid growth of China's population, the subway has become the first choice for people to travel, and the urban underground space has been continuously improved. The subway construction has become the focus of urban underground space development in the 21st century. During the construction of subway tunnels, the problem of surface settlement will inevitably be caused, and the problem of surface settlement will have a certain safety impact on the safe use of surface buildings. The impact of surface construction is predicted, so as to select the best construction technology and avoid the problem of surface subsidence to the greatest extent. On the basis of analyzing the principle of surface subsidence, this paper studies the optimal control strategy and process of subsidence in subway tunnel engineering. The research results of the article show the following. (1) The two sections of the pebble soil layer have basically the same subsidence trend. Among them, the first section has a larger settlement amplitude and both sides are steeper. The second section is mainly cobble clay soil. The pebble layer has good mechanical properties. If it can be well filled, its stability will be improved. The comparative analysis of the two sections shows that with the increase of the soil cover thickness, the maximum subsidence at the surface gradually decreases. The reason is that when the stratum loss is the same, the greater the soil cover thickness, the greater the settlement width. Sections 2 and 3 of a single silty clay have relatively close settlement laws, and the settlement changes on both sides of the tunnel are similar. (2) The surface subsidence caused by the excavation of the side hole accounts for more than 50% of the total surface subsidence, and the width of the settlement tank after the excavation of the side hole is increased by 8-10 meters compared with the excavation of the middle hole. (3) The prediction error of the BP neural network model proposed in this paper is the lowest among the four models, whether it is the prediction of the cumulative maximum surface subsidence or the location of the cumulative maximum surface subsidence, and the average relative error of the cumulative maximum surface subsidence is 3.27%, the root mean square error is 3.87, the average relative error of the location of the cumulative maximum surface subsidence is 7.96%, and the root mean square error is 21.06. In the prediction process of the cumulative maximum surface subsidence, the prediction error value of the Elman neural network is relatively large, and the GRNN generalized neural network and RBF neural network have no significant changes; in the process of predicting the position where the cumulative maximum surface subsidence occurs, the prediction error value of RBF neural network is maximum.
Multiframe track-before-detect method based on velocity filtering in mixed coordinates

In this paper, a velocity filtering based track-before-detect algorithm in mixed coordinates is presented to address the problem of integration loss caused by inaccurate motion model in polar coordinate sensors. Since the motion of a constant velocity (CV) target is better modeled in Cartesian coordinates, the search of measurements for integration in polar sensor coordinates is carried out according to the CV model in Cartesian coordinates instead of an approximate model in polar sensor coordinates. The position of each cell is converted into Cartesian coordinates and predicted according to an assumed velocity. Then, the predicted Cartesian position is converted back to polar sensor coordinates for multiframe accumulation. The use of the correct model improves integration effectiveness and consequently improves algorithm performance. To handle the weak target with unknown velocity, a velocity filter bank in mixed coordinates is presented. The influence of velocity mismatch on the performance of filter bank is analyzed, and an efficient strategy for filter bank design is proposed. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
An optimal model for domain specific named entity linking with heterogeneous information networks

In this web era, entity linking plays a major role. In the web the information’s are associated with different kinds of data and objects. Heterogeneous information networks (HIN) involved multi composed interlinked interconnected objects with various types of connections which is more prominent in this real world. Most of the research work focused towards processing homogeneous networks as well as linking entities with Wikipedia as knowledge base. In this paper we proposed a probabilistic based domain specific entity linking system that will link named entity mentions detected from unstructured web text corpus with corresponding entity in the existing domain specific Heterogeneous information networks as knowledge base. This work is most challenging due to entity name ambiguity as well as knowledge in the network that are limited one. The proposed model framework presents a model that will link named entity from unstructured web text with domain specific Heterogeneous information network mainly focuses on to learn the weight of meta path. The experiments are done over real world dataset such as DBLP and IMDB dataset.
Blockchain‐based deduplication with arbitration and incentives

Cloud storage is an ideal platform to accommodate massive data. However, with the increasing number of various devices and improved processing power, the amount of generated data is becoming gigantic. Therefore, this calls for a cost-effective way to outsource massively generated data to a remote server. Cloud service providers utilise deduplication technique which deduplicates redundant data by aborting identical uploading requests and deleting redundant files. However, current deduplication mechanisms mainly focus on the storage saving of the server, and ignore the sustainable and long-term financial interests of servers and users. This is not helpful to expand outsourcing and deduplication services. Blockchain is an ideal solution to achieve an economical and incentive-driven deduplication system. Though some current research studiess have integrated deduplication with blockchain, they did not utilise blockchain as a financial tool. Meanwhile, it lacks an arbitration mechanism to settle disputes between the server and the user, especially in a Bitcoin payment where the payment is not confirmed immediately and a dispute may occur. This creates a burden to achieve fair and transparent incentive-based deduplication service. In this work, we construct a deduplication system with financial incentives for the server and the user based on Bitcoin. The data owner will pay money via Bitcoin to the server for outsourcing the file, but this fee can be compensated by charging deduplication users with some fees to acquire the deduplication service. The server and the user can receive revenues using deduplication service. Disputes on the fair distribution of incentives can be settled by our arbitration protocol with chameleon hashes as arbitration tags. We give concrete construction and security requirements for our proposed B D A I $\mathsf{B}\mathsf{D}\mathsf{A}\mathsf{I}$ . The security analysis shows that our B D A I $\mathsf{B}\mathsf{D}\mathsf{A}\mathsf{I}$ is theoretically secure. The performance evaluation shows that our proposed B D A I $\mathsf{B}\mathsf{D}\mathsf{A}\mathsf{I}$ is acceptably efficient for the deduplication. Meanwhile, we evaluate and conclude that 1% of outsourcing fee (or less) is a reasonable and preferable price for each deduplication user to pay as compensation for data owner.
Modeling and multi-objective optimization of biogas high pressure water scrubbing

For the problem of modeling and optimization of biogas high pressure water scrubbing (HPWS) process, this paper proposes an adaptive online sequential extreme learning machine (AOS-ELM) modeling method and a novel multiobjective differential evolutionary (NMODE) optimization method. AOS -ELM established the adaptive models successfully for the product gas quality and equipment energy consumption. Compared with other four algorithms mentioned in this paper, the models obtained by our proposed AOS-ELM modeling method are of higher prediction accuracy. Furthermore, considering that the two goals of product gas quality and energy consumption are contradictory, the proposed NMODE outperforms the other two methods (NSGA-II and MODE) in terms of Pareto diversity and distribution, which means that our overall achievements are helpful and can provide enterprise decision makers with better operating conditions candidate sets in the HPWS process.
A Study on the Effectiveness of Task Representation Education in the Writing of Foreign University Students

The purpose of this study is to educate foreign university students on the practice of task representation and to examine its effects on their level of writing. The writing of foreign university students is greatly influenced by task representation. Therefore, providing more education on task representation will improve the quality of the texts submitted by students. First, the writing scores of foreign university students were evaluated. Group A was provided corrective feedback on writing without being given any training for task representation, while groups B and C were educated in this area. However, group B did not receive corrective feedback, but group C did. As a result of the experiment, there was a significant improvement found in the texts for groups B and C, but not in group A. In addition, the proportion of texts suitable for academic writing was higher in the former two groups. Therefore, we can conclude that education regarding task representation has a positive effect on improving the writing score of foreign university students.
Design and verification of small solid rockets navigation algorithm test platform

Abstract Aiming at the navigation system of small solid rockets, a navigation algorithm test platform with real time simulator and flight control computer is designed. The inertial navigation algorithm is taken as an example to test the effectiveness of the platform. The flight dynamic model of a small solid rocket is established in a real time simulator, and the test is carried out in the working environment of a small test rocket to simplify the model. The acceleration and angular velocity data generated by the simulation is sent to the onboard computer to calculate the attitude of the rocket in real time, which verifies the correctness of the data communication link, real time solution and navigation algorithm between the onboard computer and the real time simulator. The test results show that the test platform is easy to use and can test the rocket navigation algorithm quickly and effectively.