Reviewerly/exHarmony huggingface dataset:
EPIPOLAR LINE-BASED LATERAL VIBRATION MEASUREMENT BY USING TWO CAMERAS

Abstract. Vibration measurement techniques can be categorized into contact-type and non-contact-type techniques. These types of techniques can add mass-loading to a lightweight structure resulting in the negative performance of a structure, because sensors, high contrast speckles or targets should be mounted on a structure. Moreover, non-contact-type vibration measurement techniques have only been tested to detect vibrations using a single camera. As the vibrations occurring at the opposite sides of a rotating structure in a region of interest (ROI) can be different from each other. For 3-dimensional (3D) vibration measurement, the same position in videos acquired from two cameras should be used. Because the videos acquired by two cameras placed perpendicular to the structure can be used to detect the vibrations in the x-direction as well as y-direction. In this study, an epipolar line-based corresponding point selection on a rotating cylindrical structure was performed, to extract the same ROIs from videos recorded by two cameras. A fundamental matrix was constructed by using the targets attached on the structure and in the background. The coordinates of the mid-pixel of the ROI in a video acquired by one camera was used to determine the epipolar line for the same ROI in the video acquired by another camera. Then an edge-based vibration measurement technique was applied to measure the vibration in the extracted ROIs. The results were used to reconstruct a 3D vibration signal. The 3D vibration measurement results can be used to effectively recognize the deformations resulting in the negative performance of a structure.
What is the Metaverse? An Immersive Cyberspace and Open Challenges

The Metaverse refers to a virtual-physical blended space in which multiple users can concurrently interact with a unified computer-generated environment and other users, which can be regarded as the next significant milestone of the current cyberspace. This article primarily discusses the development and challenges of the Metaverse. We first briefly describe the development of cyberspace and the necessity of technology enablers. Accordingly, our bottom-up approach highlights three critical technology enablers for the Metaverse: networks, systems, and users. Also, we highlight a number of indispensable issues, under technological and ecosystem perspectives, that build and sustain the Metaverse.
Geometric property of off resonance error robust composite pulse

The precision of quantum operations is affected by unavoidable systematic errors. A composite pulse (CP), which has been well investigated in nuclear magnetic resonance (NMR), is a technique that suppresses the influence of systematic errors by replacing a single operation with a sequence of operations. In one-qubit operations, there are two typical systematic errors, Pulse Length Error (PLE) and Off Resonance Error (ORE). Recently, it was found that PLE robust CPs have a clear geometric property. In this study, we show that ORE robust CPs also have a simple geometric property, which is associated with trajectories on the Bloch sphere of the corresponding operations. We discuss the geometric property of ORE robust CPs using two examples.
Visual Superordinate Abstraction for Robust Concept Learning

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure of exploring the intrinsic semantic hierarchy of visual concepts, e.g. \{red, blue,...\} $\in$ `color' subspace yet cube $\in$ `shape'. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces (i.e. visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view, and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and a superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5\% on reasoning with perturbations and 15.6\% on compositional generalization tests.
Busuu as Online Learning Platform on Vocabulary Learning Motivation and Vocabulary Achievement among Students

A robust online learning platform is critical for extending education beyond the classroom and facilitating interactive and individualized learning. Language learning has developed some patterns due to social interactions and independent learning experiences. Vocabulary motivation and achievement are critical components of students' English proficiency. Nonetheless, both variables are recognized as critical by lecturers and students, but receive less attention. The purpose of this study was to examine the effect of Busuu, an online learning platform, on the motivation and achievement of university students in Indonesia when it comes to vocabulary learning. The platform's use as an independent variable, whereas motivation for vocabulary learning and vocabulary achievement, were dependent variables. This study employed a quantitative methodology. Busuu facilitates the learning experience by keeping track of the number of words learned, the level of fluency, and the duration of the learning day. The researchers refer to fluency as the indicator in the study. The questionnaire and test were adapted and modified from previous research on motivation and achievement in vocabulary learning. There were 98 participants. The research discovered that the Busuu online learning platform's performance significantly contributes to the motivation and achievement of vocabulary learners. The study proposes a new model of English learning that incorporates online learning platforms in order to boost students' motivation and vocabulary achievement.
A Comparative Study of Multi-Label Classification for Document Labeling in Ethical Protocol Review

An ethical clearance document ensures that the research will protect the subject in accordance with existing ethical principles. The ethical clearance is issued by the Research Ethics Commission (KEP). KEP will conduct a review of the proposed ethical protocol based on the seven standards contained in a protocol. The review process is done manually by KEP. This process often creates bottlenecks in research due to the large number of protocols that must be reviewed, so that the process to get ethical clearance takes a long time. This can affect the setback in the schedule of the research process. Therefore, in this research, a comparative study was conducted on the problem of multi-label classification to automate the ethical protocol review process. Automation of the labeling process can increase the effectiveness of the review process because it can provide an overview to the reviewer regarding the label of a document before conducting a more in-depth review process. The experiment results show that the use of the traditional machine learning approach produces better performance than the deep learning approach. The machine learning method with the best results is Naïve Bayes+BoW with precision, recall, and F-score values of 0.76, 0.80, and 0.78, respectively.
Few‐Copy Entanglement Detection in the Presence of Noise

Abstract Although the realization of useful quantum computers poses significant challenges, swift progress in emerging quantum technologies is making this goal realistically approachable. In this context, one of the essential resources is quantum entanglement, which allows for quantum computations outperforming their classical counterparts. However, the task of entanglement detection is not always straightforward for several reasons. One of the main challenges is that standard methods rapidly become unfeasible when dealing with quantum states containing more than a few qubits. Typically, this is due to the fact that a vast number of measurements are needed on many copies of the state. Generally, it is not unusual to deal with a very limited number of state copies in experimental settings—in fact, this may be the case for many large quantum systems. In this article, an overview is provided of a probabilistic approach that enables high‐confidence genuine multipartite entanglement detection using an exceptionally low number of state copies. Additionally, a study is presented that shows that this protocol remains efficient also in the presence of noise, thus confirming the practicality of the method for near‐term quantum devices and its suitability for complex experimental settings.
Systematic Literature Review: Investigating Speaking Challenges Among ESL Learners During the Covid-19 Pandemic

Effective verbal communication in the English language poses many challenges for Malaysian students. As English is not their first language, the average Malaysian finds it difficult to attain the required speaking proficiency. COVID has only exacerbated this problem. Moving from a traditional classroom setting to an online one limits cohesive and effective verbal communication between the spreader and listener (s). Both students and teachers had to switch from traditional classroom instruction to online classroom instruction with various devices and technologies as their main means of communication. As a result, this study provides a systematic review of pertinent existing research into Investigating Speaking Challenges among English Language Learners (ESL) in Online Classrooms. We concentrated on 51 articles from the year 2006 to the year 2021, from a few databases. Google Scholar, ERIC, Microsoft Academic, and ResearchGate were referred to. The review begins with a search and scanning of suitable articles related to this study before selection. Most of these articles disclosed speaking challenges and provided positive solutions to overcome them during the COVID-19 pandemic. Both students and teachers dealt effectively with psychological issues such as shyness, lack of confidence, and anxiety, as well as technological aspects. Overall, this review gave some insights into the difficulties that ESL students have when they speak, as well as many ways to deal with them when learning online.
Building Software Applications Securely with DevSecOps: A Socio-Technical Perspective

While continuous real-time software delivery practices induced by agile software development approaches create new business opportunities for organizations, these practices also present new security challenges in the DevOps environment. DevSecOps attempts to incorporate advanced automated security practices for agility in the DevOps environment. Mainstream perspectives of DevSecOps tend to overlook the collaborative role played by social actors and their relations with technologies in securing software applications in organizations. The first perspective emphasises the use of technologies such as containers, microservices, cryptographic protocols and origin authentication to secure the continuous deployment pipeline. The other dominant perspective focuses almost exclusively on the social aspects such as organizational silos, culture, and team collaboration. Such one-sided perspectives neglect the socio-technical argument that secure software applications from continuous deployment emerges when developers, quality assurers, operators and security experts combine their collective expertise together with DevSecOps technologies. The article presents a socio-technical framework of DevSecOps based on a systematic literature review. The review focused primarily, but not exclusively, on the computing and information systems literature and identified 26 peer reviewed articles from 2016 to 2020 which met the quality criteria and contributed to the analysis. The authors used a critical appraisal checklist and member checking to assess the quality of the articles. The authors then used thematic analysis to develop a comprehensive framework for DevSecOps based on the insights from these articles and a socio-technical lens. The socio-technical framework can be used by practitioners to perform a more holistic analysis of their DevSecOps practices. It highlights the key social and technical themes that underpin the effectiveness of DevSecOps and how insights about these themes can be used by practitioners to improve the instrumental and humanistic goals of DevSecOps. An interdisciplinary approach is proposed to adequately address challenging socio-technical relationships in DevSecOps. Future research can empirically test the importance of the interplay between technology and human activities to improve the overall performance of DevSecOps and other domains in cyber warfare and security.&#x0D;
Real Time Sign Language Detection

Abstract: Communication is the method of sharing or exchanging information, ideas or feelings. To have a communication between two people, both of them need to have knowledge and understanding of a common language. But in the case of deaf and dumb people, the means they use for communicating is different from that of normal people. Deaf is not able to hear and dumb is not able to speak. They communicate using sign language among themselves and with normal people but normal people don’t take seriously the importance of sign languages. Not every-one has the knowledge and understanding of sign language which make the communication difficult between a normal person and a deaf and dumb person. For overcoming this barrier,a model can be build based on machine learning. A model can be trained to recognize different gestures of sign language and translate them into English language. This will help a lot of people in communicating with deaf and dumb people with ease. A real time ML based system is built for the real time sign language detection with TensorFlow object detection in this paper. The major purpose of this project is to build a system for the differently abled people to communicate with others easily and efficiently. Keywords: Object detection, sign language, Deep learning, SSD(Single-shot Detector), TensorFlow
Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network

Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved.
A Barrier Certificate-based Simplex Architecture with Application to Microgrids

We present Barrier Certificate-based Simplex (BC-Simplex), a new, provably correct design for runtime assurance of continuous dynamical systems. BC-Simplex is centered around the Simplex Control Architecture, which consists of a high-performance advanced controller which is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In BC-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, BC-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We consider a significant application of BC-Simplex to a microgrid featuring an advanced controller in the form of a neural network trained using reinforcement learning. The microgrid is modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that BC-Simplex can automatically derive switching conditions for complex systems, the switching conditions are not overly conservative, and BC-Simplex ensures safety even in the presence of adversarial attacks on the neural controller.
Enhancement in privacy preservation in cloud computing using apriori algorithm

Cloud &lt;span&gt;computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of data frequency. In this research an apriori algorithm is proposed to enhance the privacy of encrypted data. The proposed algorithm is integrated with elagmal cryptography and it does not require fake transactions. In this way, the proposed algorithm improves the data protection as well as query privacy and it hides data frequency. Result analysis shows that the proposed algorithm improves the privacy as compared to previously proposed association rule mining and the algorithm also shows 3% to 5% improvement in performance when compared to other existing algorithms. This performance analysis with varying number of the data and fake transactions shows that the proposed algorithm doesn’t require fake transactions, like data privacy association rule mining.&lt;/span&gt;
A Dynamic Convolution Kernel Generation Method Based on Regularized Pattern for Image Super-Resolution

Image super-resolution aims to reconstruct a high-resolution image from its low-resolution counterparts. Conventional image super-resolution approaches share the same spatial convolution kernel for the whole image in the upscaling modules, which neglect the specificity of content information in different positions of the image. In view of this, this paper proposes a regularized pattern method to represent spatially variant structural features in an image and further exploits a dynamic convolution kernel generation method to match the regularized pattern and improve image reconstruction performance. To be more specific, first, the proposed approach extracts features from low-resolution images using a self-organizing feature mapping network to construct regularized patterns (RP), which describe different contents at different locations. Second, the meta-learning mechanism based on the regularized pattern predicts the weights of the convolution kernels that match the regularized pattern for each different location; therefore, it generates different upscaling functions for images with different content. Extensive experiments are conducted using the benchmark datasets Set5, Set14, B100, Urban100, and Manga109 to demonstrate that the proposed approach outperforms the state-of-the-art super-resolution approaches in terms of both PSNR and SSIM performance.
Detecting Anomalies in Logs by Combining NLP features with Embedding or TF-IDF

&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Following image classification, the focus is now shifting to text categorization. Text classification has numerous real-world uses. Using categories to tag information or items to improve browsing or identify related stuff on your website. The practise of categorising text into ordered groupings is known as text classification, sometimes known as text tagging or text categorization. Text classifiers can automatically assess text and assign a set of pre-defined tags or categories depending on its content using Natural Language Processing (NLP). In this study, we will try to create a model that can detect abnormalities in a log data collection by combining NLP features with other methodologies. This is handled as a text categorization challenge. That is why we evaluate two of the most well-known techniques while also using extra features extracted from the data set. We shall contrast the bag of words technique with the embedding technique. As opposed to Bag of words, embedding tries to preserve the meaning of the sentence, which can aid with text classification.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Selection of best feature reduction method for module-based software defect prediction

Abstract In the Software development life cycle (SDLC) the prediction of software defects is one of the crucial parts. Recent years have witnessed various studies for predicting software defects, and most of them involve machine learning techniques. Before predicting the defects some pre-processing steps are required, such as feature selection, outlier removal, and feature scaling as it helps in improving accuracy and reducing the execution time(ET). The present investigation focuses on feature selection which is a dimensionality reduction technique. Further, we have also proposed a framework on module-based software defect prediction using feature selection techniques. These techniques are divided into three categories i.e filter methods, wrapper methods, and hybrid methods that combine two wrapper methods i.e. Sequential Forward Selection and Sequential Backward Selection that we have developed. Finally, classification has been performed by KNN, Logistic Regression, Decision Tree, and SVM using the above-mentioned feature selection techniques on eight publicly available Promise datasets and compared them with the existing state-of-the-art(SOTA) methods. The result shows that the hybrid method performs better in terms of accuracy by 4.2%, 3.9%, 3.8% on datasets pc4, jm1, kc2 respectively when applied along with machine learning, as compared to filter and wrapper methods.