Reviewerly/exHarmony huggingface dataset:
Weighted semantic plagiarism detection approach based on AHP decision model

The increasing rate of academic plagiarism is a social problem that engages institutions and publishers. Plagiarists try to mislead the plagiarism detection system using synonyms and inverted word order. Numerous algorithms tried to overcome these problems using structural and semantic detection. However, most of them focus on overcoming some challenges. Moreover, all of them consider the same significant degree for all terms of the documents. On the other hand, the time complexity is an essential parameter that must be considered. This paper presents an effective way to detect structural and semantic similarity degrees among two papers only using some part of the paper's content instead of all content, decreasing the time complexity. The similarity is calculated using a set of impressive terms and various combinations to augment plagiarism detection ability even if the word order is changed. Different weight is assigned to the word according to its position in various sections of the paper. Finally, an AHP (Analytical Hierarchy Process) model uses to calculate a weighted similarity. The results indicated that the proposed approach has more ability to detect semantic academic plagiarism, and the runtime is reduced compared to similar ones.
Using decision trees to learn ontology taxonomies from relational databases

Relational databases are widely used; they are at the backend of the majority of information systems. However, these databases are semantically poor. To solve this issue, it is necessary to build ontologies. In this paper, we propose an automatic approach to learn ontologies from relational databases by using classification techniques, more specifically decision trees. Finally, we evaluate our approach by conducting tests and comparing our results with results from previous works. The results were satisfactory in terms of extracting taxonomies from relational databases.
Multi-views Action Recognition on 3D ResNet-LSTM Framework

In order to solve the problem of low accuracy rate of individual action recognition due to different camera views. In this paper, we propose a deep neural network framework based on 3D ResNet-LSTM for the recognition of individual actions of multiple views. The IXMAS dataset was chosen for the experiment, which was created from videos of 12 actions taken from 5 views. This dataset is preprocessed into a 5D tensor dataset and then used as the input to 3D ResNet. 3D ResNet can acquire both the appearance information of the image and the timing information between sequence frames. LSTM solves the problems of vanishing gradient and exploding gradient problems in the long time sequence training process. The two networks are connected in series through the reshape function, and the accuracy of image sequence recognition is significantly improved compared to other deep neural networks. Use the 3D ResNet-LSTM framework to train the 5D tensor dataset. Among the five views, the front view and the right oblique upper view had better recognition, with accuracy rates of 93.7% and 92.3%, respectively, and the overhead view had the lowest accuracy rate. By analyzing the accuracy of the five views, we fuse the data of the image sequences from the front-front view and the right oblique top view and produces a 5D tensor dataset, which finally yields an accuracy of 94.9%, which is higher than the accuracy of the individual views.
Learning media development based on CNC simulator as the digital tool to support the CNC practice learning during COVID-19 new normal

Abstract Covid-19 pandemic changed human’s lifestyle in various areas. Indonesia Government enacted many rules to avoid Covid-19 transmission. The Ministry of Education and Culture issued a policy through a circular regarding the arrangement of learning activities from home during the Covid-19 emergency. Due to the policy, classical in-class learning method changed into virtual through online classes. The CNC simulation makes it possible for students to visualize model and concept into realization. The CNC Simulator is a computer program that could simulate the setting and operation of the CNC machine. The CNC simulator can be used as a digital tool to support the CNC practice learning activity, which was hindered by social activities restrictions during the COVID-19 pandemic. This research used the ADDIE development model, according to Lee and Owens. The stages were: (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. Based on the evaluation, it could be concluded that all five indicators fell into the category of proper to use without revision because on average, they had above 80% score. The final score was 84.25%; thus, learning media based on CNC simulator was proper to sue in the learning activity.
Development of Pocket Book Based on Science Literacy

Abstract The purpose of this study was to determine the feasibility of science literacy based pocket books for fifth grade elementary school students. This research method refers to the Borg band Gall theory, using ten stages of research, namely information gathering, research planning, initial product development, field testing, revision, product testing, revision of field product testing results, validation, final improvement, and implementation. The data collection techniques used were observation, interview and questionnaire techniques. Sources of data in this study were fifth grade students and teachers in Rembang Regency. The design of the pocket book development is based on scientific literacy developed by the researcher, consisting of 3 parts, namely introduction, content, and closing. In the content section, the material integrates 4 aspects of scientific literacy. Product feasibility is seen from the validation results of material experts and linguists. From the material expert validation process obtained an average result of 3.33 with a percentage of 83% and very good category. The validation of linguists obtained results of 85% with an average of 3.40 and the category was very good. Based on the validation results, a scientific literacy-based pocket book is suitable for use as a companion book in learning.
Requirements for fault-tolerant quantum computation with cavity-QED-based atom-atom gates mediated by a photon with a finite pulse length

We analyze the requirements for fault-tolerant quantum computation with atom-atom gates based on cavity quantum electrodynamics (cQED) mediated by a photon with a finite pulse length. For short photon pulses, the distorted shape of the reflected pulses from the cQED system is a serious error source. We optimize the cQED system parameters to minimize the infidelity due to the shape distortion and the photon losses in a well-balanced manner for the fault-tolerant scheme using probabilistic gates [H. Goto and K. Ichimura, Phys. Rev. A 80, 040303(R) (2009)]. Our optimization greatly relaxes the requirements for fault-tolerant quantum computation in some parameter regions, compared with the conventional optimization method where only the photon loss is minimized without considering the shape distortion [H. Goto and K. Ichimura, Phys. Rev. A 82, 032311 (2010)]. Finally, we show that reducing the cavity length is an effective way to reduce the errors of this type of gate in the case of short photon pulses.
QKD-Enhanced Cybersecurity Protocols

Security of QKD is guaranteed by the quantum mechanics laws rather than unproven assumptions employed in computational cryptography.Unfortunately, the secretkey rates are way too low and transmission distances are limited.The post-quantum cryptography (PQC) is proposed as an alternative to QKD.However, the PQC protocols are based on conjecture that there are no polynomial time algorithms to break the PQC protocols.To overcome key challenges of both post-quantum cryptography and QKD, we propose to use the QKD only in initialization stage to set-up corresponding cybersecurity protocols.The proposed concept is applied to both computational security and PQC protocols.The proposed QKD-enhanced cybersecurity protocols are tolerant to attacks initiated by quantum computers.
CORG: A Component-Oriented Synthetic Textual Requirements Generator

The majority of requirements formalisation techniques operate on textual requirements as input. To establish and verify the reliability and coverage of such techniques, a large set of textual requirements with diverse structures and formats is required. However, such techniques are typically evaluated on only a few manually curated requirements that do not provide enough coverage of the targeted structures. Motivated by this problem, we introduce a Component-oriented synthetic textual requirements generator (CORG) that can generate large numbers of synthesised diverse-structure textual requirements, along with key components breakdowns. CORG utilises a controlled random-selection (CRS) strategy throughout the backtracking-based generation. We evaluate the coverage, diversity, performance and correctness of CORG. The evaluation results show that CORG can generate comprehensive diverse-structure combinations in reasonable time without being affected by the size of the produced requirements.
The Statistics of Circular Optimal Transport

Empirical optimal transport (OT) plans and distances provide effective tools to compare and statistically match probability measures defined on a given ground space. Fundamental to this are distributional limit laws, and we derive a central limit theorem for the empirical OT distance of circular data. Our limit results require only mild assumptions in general and include prominent examples such as the von Mises or wrapped Cauchy family. Most notably, no assumptions are required when data are sampled from the probability measure to be compared with, which is in strict contrast to the real line. A bootstrap principle follows immediately as our proof relies on Hadamard differentiability of the OT functional. This paves the way for a variety of statistical inference tasks and is exemplified for asymptotic OT-based goodness of fit testing for circular distributions. We discuss numerical implementation, consistency and investigate its statistical power. For testing uniformity, it turns out that this approach performs particularly well for unimodal alternatives and is almost as powerful as Rayleigh’s test, the most powerful invariant test for von Mises alternatives. For regimes with many modes, the circular OT test is less powerful which is explained by the shape of the corresponding transport plan.
Seam Carving based visible watermarking robust to removal attacks

Visible watermarking offers direct copyright protection to the naked eye using visible pattern superimposed in the host image. However, due to their exposure, the watermark pattern becomes an easy target to remove by malicious entities that generates copyright-free images. Until now, in the scientific literature several semi-automatic visible watermark removal attacks have been proposed, showing enough visual quality of the recovered host image. In this paper, we propose a watermark design based on modified Seam Carving technique to provide robustness against removal attacks including inpainting-based attacks, Independent Component Analysis (ICA)-based attacks, and image matting-based attacks. Robustness against the attacks is measured using common image quality assessments and a watermark visibility assessment based on the Human Visual System (HVS). Experimental results show robustness of the proposed method against above mentioned watermark removal attacks under the most severe situation, in which adversary has the logotype used as a watermark pattern because it is public in Internet. The proposed method can be applied to any visible watermarking algorithms to make them robust against illegal removal attempts, without sacrificing their original functionality, such as reversibility for authorized users.
A Two-Phase Splitting Approach for the Removal of Gaussian-Impulse Noise from Hyperspectral Images

In this paper, we design a framework for denoising hyperspectral images (HSI) using Maximum a posteriori (MAP) criterion with emphasis on Gaussian-impulse noise which is the characteristic of HSI data. We derive fidelity terms with respect to Gaussian-Laplacian distribution to collectively remove mixed Gaussian noise and sparse high intensity impulse noise. We split the degradation model into two parts to facilitate removal of residual noise encountered, while separately handling the two noise cases. Behaviour of this residual noise, often rendered as artefacts in the final results, is handled by proper tuning of hyper-parameters in our objective function. Experimental results on synthetic data are conducted in the noise range of 20 dB to 5 dB for Gaussian noise and $$0.5\%$$ to $$20\%$$ for impulse noise. Quantitative and qualitative denoising results on synthetic and real HSI data illustrate the effectiveness of our method against the state-of-the-art techniques.
Performance Evaluation Based on Classification of Web Log Data: A Machine Learning Approach

The present study contributes to the understanding of artificial intelligence by assessing the importance of data in the generation of meaningful information and its prompt retrieval. Web mining is studied with the effective use of the WEKA tool. Web mining mainly deals with the extraction of knowledge flow and data present on the Web. Web mining can be categorized into three different categories Web structure mining, Web content mining, and Web usage mining. The present study emphasizes on Web usage mining that follows data processing under the three levels such as preprocessing, pattern discovery, and pattern analysis. The study findings reveal various algorithms can be adopted for data feeding and classification of data over some preprocessed method and comprised of the results. The findings of the study will be helpful for the users in acclimatizing with vivid algorithms that are associated with the Web data mining process which further gives them the sumptuous knowledge on how a dataset can be managed on Web usage according to their need of hours for generalizing the valid results.
Implementation of Internet of Thing Technology for Infrared Therapy Device Design

Abstract This study aims to develop an infrared therapy device by embedding the internet of things technology in the research area of therapy devices, especially infrared therapy. Low back pain is a disease that attacks the back muscles that can cause disability and discomfort in activities. This system was developed with an automatic system located in the automation of the lights off and the LM35 temperature sensor, which records the temperature if you feel hot and the timer setting that appears on the LCD and the android interface. This system is based on the internet of things to be monitored from anywhere, anytime. This study consists of an LED lamp, cloud system, and ATmega 328 microcontroller. The study and literature review are carried out following the device development method that embeds the internet of things technology systems in infrared. This study’s result is a prototype design of an infrared therapy device with the internet of things technology that can be used to treat people with low back pain.
Spatio-Temporal models with intervention effect for modelling the impact of Covid-19 on the tourism sector in Indonesia

Abstract Coronavirus is a virus that attacks the respiratory system, this disease due to viral infection is called Covid-19. Since it was found at December 2019 in China, the Covid-19 outbreak has spreads to several countries, one of them is Indonesia. This causes the tourism sector in Indonesia to decline drastically. Therefore, this study aims to analyze the impact of Covid-19 outbreak on the number of foreign tourist arrivals to Indonesia especially in Jakarta, Bali, and Surabaya. This data is ordered by times from several different locations and has a relationship with each other or is called spatio-temporal data. Furthermore, the data will be added the intervention variable is time where the covid-19 outbreak found in December 2019. The data will be analyzed using a spatio-temporal models. The result shows that seasonal GSTARX-GLS models tend to give more accurate forecast than VARX and seasonal GSTARX-OLS models.
Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features

Unstructured data has become a challenge for traditional machine learning in the industry. Moreover, enterprise data analytics have contemplated machine learning seeks to understand the complex problem by leaning on one of the probabilistic learning. The present work proposes a new technique for extracting high dimensional features from faulty mechanical parts in rotatory machines, used in CNC and lathe machines. In essence, this technique uses vibration signals from faulty bearings and gearboxes to classify faulty from healthy conditions in different rotor speeds. The vibration sensor (e.g., accelerometer) at the CNC Z Outer Linear Guide’s surface, close to the tailstock, detects the vibration. These vibrations, emitted from the spindle, sub-spindle, and structure, are utilized to generate a unique/bespoke baseline for the machines. In consideration, i.e., one which describes the normal/expected variations in the profile that corresponds to ‘healthy condition’ for the range of spindle speed experienced by the CNC controller. Furthermore, statistical features are extracted using batch processing principles to ensure that sufficient information is captured from the time series data. Finally, a generalized anomaly detection algorithm is proposed in this paper where compressed features (low dimension) are automatically engineered, and anomalies are detected based on a simple probabilistic model.
Reconstructing Recognizable 3D Face Shapes based on 3D Morphable Models

Abstract Many recent works have reconstructed distinctive 3D face shapes by aggregating shape parameters of the same identity and separating those of different people based on parametric models (e.g. 3D morphable models (3DMMs)). However, despite the high accuracy in the face recognition task using these shape parameters, the visual discrimination of face shapes reconstructed from those parameters remains unsatisfactory. Previous works have not answered the following research question: Do discriminative shape parameters guarantee visual discrimination in represented 3D face shapes? This paper analyses the relationship between shape parameters and reconstructed shape geometry, and proposes a novel shape identity‐aware regularization (SIR) loss for shape parameters, aiming at increasing discriminability in both the shape parameter and shape geometry domains. Moreover, to cope with the lack of training data containing both landmark and identity annotations, we propose a network structure and an associated training strategy to leverage mixed data containing either identity or landmark labels. In addition, since face recognition accuracy does not mean the recognizability of reconstructed face shapes from the shape parameters, we propose the SIR metric to measure the discriminability of face shapes. We compare our method with existing methods in terms of the reconstruction error, visual discriminability, and face recognition accuracy of the shape parameters and SIR metric. Experimental results show that our method outperforms the state‐of‐the‐art methods. The code will be released at https://github.com/a686432/SIR .
Ontology-based decision-making

A knowledge-based system is a kind of decision support system with specialized problem-solving expertise. The expertise consists of knowledge about a particular domain, understanding symptoms, problems, and solutions within that domain. These systems can essentially suggest or recommend actions. We can say that a person–machine team processes knowledge to accomplish a task. In this context, ontologies emerge as a powerful tool to formally representing such a domain of knowledge, allowing the machine to comprehend it and therefore assist us. This chapter presents the benefits of using ontologies in decision-making and how to achieve them. An actual problem-solving Web Ontology Language ontology, namely Issue-Procedure Ontology, and a corresponding fictitious, but with real-world challenges, health care case study are introduced.
RETRACTED: A Comparative Analysis of Pre-Processing Time in Summary of Hindi Language using Stanza and Spacy

Abstract Text summarization is a method or say way for converting large texts into smaller ones by keeping all important points of the larger text as it is in the smaller ones and giving the output in the modified form called summary of original text document. This task is very difficult for humans as it required large amount of rigorous analysis of the document. In this proposed paper we are comparing pre-processing time for two tools of natural language processing one is STANZA and the other is SPACY both are based on modern technologies and are examine for HINDI language processing. In this paper we are preforming a comparative study of both the tools on the bases of their pre-processing time for processing HINDI language. Now a days, a lot of text summarizer tools are present in the environment and we are keen to use them but here the point of this paper comes into light that how we know which Summarizer is fast enough to get us the same accuracy.
Deep Optimisation: Multi-scale Evolution by Inducing and Searching in Deep Representations

The ability of evolutionary processes to innovate and scale up over long periods of time, observed in nature, remains a central mystery in evolutionary biology, and a challenge for algorithm designers to emulate and explain in evolutionary computation (EC). The Major Transitions in Evolution is a compelling theory that explains evolvability through a multi-scale process whereby individuality (and hence selection and variation) is continually revised by the formation of associations between formerly independent entities, a process still not fully explored in EC. Deep Optimisation (DO) is a new type of model-building optimization algorithm (MBOA) that exploits deep learning methods to enable multi-scale optimization. DO uses an autoencoder model to induce a multi-level representation of solutions, capturing the relationships between the lower-level units that contribute to the quality of a solution. Variation and selection are then performed within the induced representations, causing model-informed changes to multiple solution variables simultaneously. Here, we first show that DO has impressive performance compared with other leading MBOAs (and other rival methods) on multiple knapsack problems, a standard combinatorial optimization problem of general interest. Going deeper, we then carry out a detailed investigation to understand the differences between DO and other MBOAs, identifying key problem characteristics where other MBOAs are afflicted by exponential running times, and DO is not. This study serves to concretize our understanding of the Major Transitions theory, and why that leads to evolvability, and also provides a strong motivation for further investigation of deep learning methods in optimization.