Reviewerly/exHarmony huggingface dataset:
Project-Based Course in Electronic Engineering Education

In the teaching of electronic engineering, some practical projects need to be added in order to connect various courses together. To strengthen the connections between courses, the project-based teaching method proposed in this paper advocates linking the knowledge of different courses through the combination of theory and practice. With this as a guide, projects have been set up for students. One of them is about designing and making a managed ethernet switch. In the process of making and completing this project, the students' ability has been significantly improved, which fully proves the benefits of the teaching method.
Text, Content and Data Analysis of Journal Articles: The Field of International Relations

Term frequencies is the basic, if not the main, measure that springs out, during the process of mapping latent information in a text corpus. This paper addresses the issue of exploring a set of textual documents based on their metadata and term frequencies, by introducing the mixed use of text mining and data analysis methods for analyzing social science journal articles. In particular, this survey links the quantitative research of scientific discourse—through specific tools of data analysis—with research on the development of a scientific field, namely International Relations. Preliminary results on field-related published journal articles demonstrate the effectiveness of the proposed methodology.
Security Enhancement and Monitoring for Data Sensing Networks Using a Novel Asymmetric Mirror-Key Data Encryption Method

Today, the world is moving to fully enable Internet technologies and gadgets. Devices become smarter and intelligent, and the data exchanges between the devices become exponentially increased and more private data in being transmitted through the Internet. By the use of the Internet and other digital technology such as mobile gadgets, social media networks, the Internet of Things (IoT), and personalized services, enormous quantities of data are open and continuously gathering, processing, evaluating, and using massive data on different networks like the cloud through individuals and organizations, and the term Big data is named for such a massive data manipulation. Then, it becomes a big challenge to data scientists to secure the data which is transmitted through the various medium. Historically, data is secured using cryptographically, and number of techniques is globally accepted. This chapter proposes a semi-asymmetric mirror dual encryption approach. Here, the encrypted key is appended along with the data, but even the intruder collects the data or the key, then they cannot hack the key or the data because of the mirroring cryptographic technique. This method is safer because it does not transfer the actual data, or the actual key for encryption. The key generated through a mirroring algorithm at the receiver side only. So this technique is secured than other symmetric key encryption because it is neither transmitting the actual data nor the encryption actual key as in conventional methods. The key is generated through a noble algorithm at the receiver side only.
IoT Aided Smart Home Architecture for Anomaly Detection

Internet of Things (IoT), and Information and Communication Technology (ICT) enhance the cities and homes with intelligence using various types of devices that are embedded for sensing purposes. The IoT nodes can collect time-series information for a given purpose, like measuring temperature, air pollution and traffic congestion, motion tracking etc. and provide node behavior and environment interaction. Besides the normal ritual events that happen according the measured parameters, some unusual states, called anomalies, can be detected by using the same measurements. The anomalies that happen at homes, such as fire, elderly people fall etc. are life-critical and their early detection and/or prevention can save lives. In this chapter, we propose an Anomaly Detection System Architecture for smart homes. The fire detection/prediction and fall detection of elderly people are examined as anomalies from the usual, everyday activities. An experimental study, as proof of concept for the fire emergence case, is run by using LSTM neural network architecture. One of the positive features of the proposed methodology is that the faulty readings and false positive alarms on specific parameters are not enough to rise an anomaly detection if those readings are not supported by other parameters.
Multi-Scale Approach using Remote Sensing Techniques for Lithium Pegmatite Exploration: First Results

Raw-materials like lithium (Li) are crucial to the current global decarbonization, but Li-exploration presents some technical challenges. Therefore, new solutions for Li-exploration are needed. Consequently, the aim of this study is to present a unique multi-scale remote sensing approach for Li-pegmatite exploration integrated within the LIGHTS project, considering as study area the Bajoca mine (Portugal). Satellite data allowed the identification of the spectral signatures of Li-pegmatites at a district scale, while drone-borne hyperspectral measurements provided data at the target scale. Handheld spectroscopy and in situ hyperspectral scans of the mine walls were carried out to validate the satellite and drone data. Hyperspectral field and laboratory scans also aim to collect information at the mineral scale, to distinguish different lithological materials, and to identify the Li-rich areas. In the future, machine learning algorithms will deliver an automated integration of all acquired data.
Pseudo-label Selection for Deep Semi-supervised Learning

Deep semi-supervised learning is a hot research topic in recent years. The main challenges are the small sample learning and make full use of unlabeled data. Pseudo-labeling is a simple yet effective approach in semi-supervised learning. However, how to obtain high quality pseudo-labeled data is key issue. When pseudo-labeled data with low confidence level are used to train deep learning model will enlarge or spread classification errors. To overcome this issue, we propose a confidence-based pseudo-labeling method to choose high quality pseudo-labels with a novel uncertainty model. Specifically, we treat the uncertainty of pseudo-labels as a random variable and model proposed to estimate the confidence level of the pseudo-labels. The selected pseudo-labeled data are produced by minimizing the average risk criterion. Experimental results on the MNIST dataset show that the proposed method is able to improve the performance for deep semi-supervised learning.
Analysis of Training Data Sets in Artificial Neural Networks Applied to a Radio Frequency Problem

In this paper, a novel study has been carried out to understand the functionality of artificial neural network (ANN) algorithms on a radio frequency (RF) problem. Determining the dominant TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> resonant frequency of a rectangular microstrip patch located above dielectric substrate backed by a ground plane has been chosen as our RF problem. Extensive training has been carried out using two widely used ANN algorithms: radial basis function (RBF) and feed-forward backpropagation network (FFBNET). Later, a different input testing data set was generated and fed into the ANN algorithms to determine the resonant frequency. Comparative analysis indicated that the RBF is suitable for small data sets whereas the FFBNET is applicable to large data sets.
Distributed Internet voting architecture: A thin client approach to Internet voting

Principles required for secure electronic voting using the Internet are known and published. Although the Internet voting functionalities and technologies are well-defined, none of the existing state-sponsored Internet voting approaches in use incorporate a total Internet-based system approach that includes voter registration, the voting process, and vote counting. The distributed Internet voting architecture concept discussed in this article uses a novel thin client approach to Internet voting. The architecture uses existing technologies and knowledge to create a viable whole system approach to Internet voting. This article describes various aspects and processes necessary to support an integrated approach. The application programming interface software for many of the critical functions was developed in Python and functionality tested. A virtual network, including a cloud-based functionality, was created and used to evaluate the various conceptual aspects of the proposed architecture. This included the concepts associated with programming and accessing smart cards, capturing and saving fingerprint data, structuring virtual private networks using tunneling and Internet Protocol Security, encrypting ballots using asymmetric encryption, using symmetric encryption for secret cookies, thin client interaction, and creating hash functions to be used within a blockchain structure in a Merkle tree architecture. The systems’ primary user targets are individuals remotely located from their home voting precincts and senior citizens who have limited mobility and mostly reside in assisted living facilities. The research supports the contention that a cybersecure Internet voting system that significantly reduces the opportunity for mail-in voter fraud, helps to ensure privacy for the voter, including nonrepudiation, nonattribution, receipt freeness, and vote acknowledgment can be created using existing technology.
Towards Statistically Provable Geometric 3D Human Pose Recovery

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 15 November 2019Accepted: 14 December 2020Published online: 22 February 2021Keywords3D recovery, nonconvex optimization, sparse model, deep learning, human poseAMS Subject Headings68Q25, 68R10, 68U05Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
Arpilleras Parlantes: Designing Educational Material for the Creation of Interactive Textile Art Based on a Traditional Chilean Craft.

Electronic Textiles (eTextiles) and open source material has been entangled with "DIY" movements and hands on work to create new interactive interfaces. Studies have shown the potential these have to bring computational knowledge closer to new audiences. We address how eTextiles can approach a traditional textile art, specifically "Arpilleras" from Chile, to understand how each field contributes from a symbolic, material and technical perspective. Following participatory approach methods and STEAM guidelines to create an educational program, we observed how eTextiles can influence the creation of soft interactive interfaces that enhance the communicative character and cultural heritage of a craft, and the potential of their use in a pedagogical context by using a specially designed kit. Our study suggests that connecting technology with strong cultural identity craft, can help to reach new audiences, revitalize the traditional technique, and create new tools for expression and creativity.
Research on Face Recognition Technology Based on Deep Learning

The traditional face recognition technology requires a lot of computation time for human face feature extraction, and the recognition accuracy is limited by the extracted face features. Aiming at the above problems, this paper studies face recognition technology based on deep learning. For the image to be recognized, the HOG feature is used to detect the face target in the image. In order to avoid the influence of facial expression and posture, face alignment processing is performed. After extracting the face recognition features in the image, the neural network is used to realize face recognition. Through comparison experiments with traditional face recognition technology, it is verified that the recognition technology using deep learning improves the recognition efficiency by about 1.5 times and has better recognition adaptability.
Efficient Off-Chain Transaction to Avoid Inaccessible Coins in Cryptocurrencies

Bitcoin and other altcoin cryptocurrencies use the Elliptic-Curve cryptography to control the ownership of coins. A user has one or more private keys to sign a transaction and send coins to others. The user locks her private keys with a password and stores them on a piece of software or a hardware wallet to protect them. A challenge in cryptocurrencies is losing access to private keys by its user, resulting in inaccessible coins. These coins are assigned to addresses which access to their private keys is impossible. Today, about 20 percent of all possible bitcoins are inaccessible and lost forever. A promising solution is the off-chain recovery transaction that aggregates all available coins to send them to an address when the private key is not accessible. Unfortunately, this recovery transaction must be regenerated after all sends and receives, and it is time-consuming to generate on hardware wallets. In this paper, we propose a new mechanism called lean recovery transaction to tackle this problem. We make a change in wallet key management to generate the recovery transaction as less frequently as possible. In our design, the wallet generates a lean recovery transaction only when needed and provides better performance, especially for micropayment. We evaluate the regular recovery transaction on two real hardware wallets and implement our proposed mechanism on a hardware wallet. We achieve a %40 percentage of less processing time for generating payment transactions with few numbers of inputs. The performance difference becomes even more significant, with a larger number of inputs.
Analysing and Exploiting Complexity Information in On-Line Signature Verification

This paper proposes an in-depth analysis on how the complexity of signatures affects the performance in on-line signature verification. In signature verification there is a very wide range of signatures from some based on a simple flourish to very complex ones. In this work we consider three different complexity groups: low, medium and high. We carry out an analysis of performance evaluation for each complexity group for both random and skilled forgeries. Two verification systems are used for this analysis, a traditional one based on the popular DTW and a state-of-the-art one based on time aligned recurrent neural networks (TA-RNN) recently proposed. The experiments are carried out over the largest database available to date for on-line signature verification (DeepSignDB). Then, we propose several approaches in order to exploit the information related to the signature complexity with the final aim of improving the signature verification system performance. Our best proposed approach is based on training a system with a balanced number of subjects regarding their type of signature complexity.
Extrapolation of load spectra by optimized Kernel Density Estimators using Monte-Carlo-Simulation

In the present article a method based on Monte Carlo Simulation (MCS) is developed with which any spectrum extrapolation method can be statistically evaluated. Thereby, the duration of service life spectrum and the extrapolation factor can be specified arbitrarily. In addition to the evaluation of extrapolation methods, the simulation model allows to calculate statistically based safety factors for load assumption. The use of general, mostly conservative factors can thus be omitted. For the extrapolation of load spectra, a procedure with kernel density estimators using an adaptive bandwidth is developed. The new Kernel Density Estimation (KDE) method contains three parameters for controlling the adaptive bandwidth which are optimized using the simulation model. During optimization, mathematically describable spectra are used, which are completely described by the maximum amplitude of the spectrum, the spectrum range and the spectrum shape parameter.
A Context-aware Interest Drift Network for Session-based News Recommendations

Session-based news recommendation systems aim to provide users with personalized reading suggestions based on their short-term sessions. In the news domain, users' interests change rapidly and are easily affected by the environment and breaking events, and this in turn affects users' next click. However, most existing approaches only capture a single dynamic of users' interests and consider little or no external influences. In this paper, we propose a context-aware interest drift network (CaIDN), a deep context-rich session-based news recommendation framework, that contains environment, breaking news, and article content information. The key component of CaIDN is a bidirectional attention recurrent network that effectively catches the drift of users' reading interests from various aspects. Additionally, to alleviate the cold-start problems, we perform CNN with multi-height filters on textual content and additional news information to generate valid article content features. Extensive experimental results on two real-world news datasets demonstrate that CaIDN outperforms state-of-the-art session-based news recommendation methods.
Design of Medical Image Cryptosystem Triggered by Fusional Chaotic Map

Image is considered as important data in the medical field because usage of medical images for diagnosing the disease keeps on increasing in the modern digital medical field. These images need to be encrypted at the time of transmitting over an insecure network for maintaining integrity and confidentiality. This work aims to propose a medical image cryptosystem with less computational time based on the fusional model of the modified one-dimensional chaotic map. First, the proposed fusional chaotic model generates the chaotic sequence and then the generated chaotic series is used for encrypting the intensity of medical images. Simulations and security assessment are evaluated by applying statistical and differential attacks. Robustness against exhaustive and noise attack results indicates the strength of the developed cryptosystem. Comparison analysis illustrates that the proposed cipher has efficient and enhanced security than state of the art. Hence, the developed cryptosystem has absolutely opted of medical image applications.
Design Thinking for Online Learning

With the spread of COVID-19, nearly all education systems in the world pivoted to distance education in order to overcome the crisis. Because it is an unexpected crisis, most institutions had to employ emergency remote teaching systems. This decision brought so many problems to education industry. But now, it is a known fact that the virus is with us at least one more year, and moreover, new epidemics may occur. Therefore, this chapter aims to provide a guideline with a different approach to online learning for practitioners working in education industry. Thanks to this guideline, the practitioners can create well-designed online learning environments, and as a result, they will not need emergency remote teaching applications anymore.
Multiple-image encryption based on light-field imaging and gravity model

A new multiple-image encryption method based on a light-field imaging system and the gravity model is presented in this paper. An inverse process of light-field imaging was used for multiple-image encryption. The method first combined multiple images into one light-field image. It was then encrypted using the gravity model, which further improved the security. The structural parameters of the light-field imaging system and the different objective distances of each original image could be used as part of the secret keys. A refocusing reconstruction algorithm for the light-field image was used in the decryption process. Experiments and analyses have shown that the proposed scheme could easily be accessed and provides a large key space and high sensitivity in the key space. Furthermore, it is robust against occlusion attacks. Compared with the repeated use of gravity model alone, the proposed method offers enhanced statistical properties and efficiency benefits. The proposed encryption can be achieved by a computational way, which can eliminate the negative effects of hardware limitations and aberrations.
Progressive Slicing for Application Identification in Application-Specific Network Slicing

With the proliferation of traffic encryption, the need for application identification of given encrypted traffic using machine learning is growing for application-specific traffic management. However, there is not much research on the negative impact of classification delay and misclassification. In particular, an application generating bursty traffic such as video streaming causes congestion during the classification, which degrades QoE due to a considerable classification delay. In this paper, we propose a new mechanism called Progressive Slicing, which adaptively assigns the flow to multiple slices according to the state of the classifications. This new classification mechanism mitigates the classification delay by progressively isolating flows into the slices in the course of the classifications. In addition, multiple stages of classifications improve accuracy and enable more flexible application slicing based on the confidence scores. We evaluate the proposed method on multiple user scenarios on Android VMs. We show that this method improves search response time on Twitter by 35% compared to the slicing with a single classification even when the traffic of video applications coexists.
A hybridization of SKH and RKFCM clustering optimization algorithm for efficient moving object exploration

Abstract Object detection is a really crucial application of image processing. It is of essential value for object active surveillance and various other applications. Hence, the object detection has been extensively investigated. Refined Kernel fuzzy c-means system still carries a couple of downsides, for instance, to reduce the convergence level, obtaining stuck in the regional area minima and problem to initiation level of sensitiveness. To conquer the over problems, the following suggested strategy for stud krill herd Clustering Optimization Algorithm. This paper stands for an optimizing approach to global optimizing utilizing a unique variation of KH (Krill Herd). This approach is termed as Stud Krill Herd (SKH).The stud krill herd Clustering Optimization procedure utilizes to find the optimal centroid. At first, the background and foreground area partition is finished by hybridization of refined kernel fuzzy c means algorithm (RKFCM) with stud krill herd Clustering Optimization procedure. The suggested new strategy is scholarly and furthermore dynamic clustering system for dividing the moving object. This research study performs recommended a reliable object detection making use of hybridization of stud krill herd Clustering optimizing and RKFCM. Moving object tracing is done via the blob detection which happens under the tracing phase. The assessment phase has characteristic abstraction and categorization. High and appearance-based and quality based attributes are mined from fine-tuned frames whichever attended to classification. Considering that categorization we are developing usage of J48 (C4.5) i.e., decision tree based classification. The effectiveness of the advised method is analyzed through prior approaches k-NN and MLP in regard to accuracy, f-measure, ROC and recall.