Reviewerly/exHarmony huggingface dataset:
Sustenance of Indian Moored Buoy Network During COVID-19 Pandemic – A Saga of Perseverance

The moored buoy network in the Indian Ocean revolutionized the observational programs with systematic time-series measurement of in situ data sets from remote marine locations. The real-time meteorological and oceanographic data sets significantly improved the weather forecast and warning services particularly during extreme events since its inception in 1997. The sustenance of the network requires persistent efforts to overcome the multitude of challenges such as vandalism, biofouling, rough weather, corrosion, ship time availability, and telemetry issues, among others. Besides these, the COVID-19 pandemic constrained the normal functioning of activities, mainly by delaying the maintenance of the network that resulted in losing a few expensive buoy system components and precious data sets. However, the improvements in the buoy system, in-house developed data acquisition system, and efforts in ensuring the quality of measurements together with “best practice methods” enabled 73% of the buoy network to be functional even when the cruises were reduced to 33% during the COVID-19 lockdown in 2020. The moored buoys equipped with an Indian buoy data acquisition system triggered high-frequency transmission during the Super cyclone Amphan in May 2020, which greatly helped the cyclone early warning services during the COVID-19 pandemic. The COVID-19 lockdown points toward the reliability and enhanced utility of moored buoy observations particularly when other modes of measurements are limited and necessitates more such platforms to better predict the weather systems. The present study analyzed the enhancement of the buoy program and improvisation of the buoy system that extended the life beyond the stipulated duration and enabled the high-frequency data transmission during cyclones amid the COVID-19 lockdown. The recommendations to better manage the remote platforms specifically in the event of a pandemic based on the operational experience of more than two decades were also presented.
Student Behavior Analysis and Research Model Based on Clustering Technology

Now, entering the 21st century, with the continuous improvement of my country’s higher education level, the enrollment rate of all colleges and universities across the country is increasing year by year. Faced with the information management of a large number of students, the workload and work pressure of consultants at various universities have doubled. The rapid and effective development of modern computer software and hardware has also initiated and effectively developed the informatization process of universities. The student management system is the core and foundation of the entire school education management system. This study mainly introduces the application of student behavior analysis and research models based on clustering technology. This paper uses the application research of student behavior analysis and research model based on clustering technology, uses clustering technology to analyze student behavior, and reasonably analyzes the feasibility of KMEANS algorithm and campus data mining. The cluster analysis algorithm is used to divide students into different groups according to the characteristics of the students, and then, data analysis and data association rules’ mining are performed on each group of students. At the same time, the decision tree algorithm is used to predict the future of students based on the historical data of the students and the current data of the students. The development status of the school helps the school to understand the situation of the students in real time, make predictions and warnings for possible situations, provide personalized applications for teachers and students, and provide decision-making support for the management. It can be seen from the experimental analysis that the application of student behavior analysis and research models based on clustering technology has increased the efficiency of student education by 17%. The limitations of student behavior analysis and research on clustering technology provide good applications for the KMEANS algorithm. Analysis, discussion, and summary of the methods and approaches are obtained to enrich the academic research results.
Isomorphic mapping in fictional interpretation

This chapter investigates how readers/hearers come to assign a subjective interpretation to a fictional text. It argues that fictional texts use a speech act operator that involves an inference from the text worlds to the reader’s reality (Bauer and Beck 2014). The inference is based on a mapping from concepts in the text to real objects, properties, and relations which has isomorphic features, i.e., it is a structure-preserving mapping. We illustrate the workings of our semantic framework by applying it to two poems by Emily Dickinson and offer some generalizations regarding plausible vs. implausible mappings and what governs their choice.
Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario

The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method.
Adaptive Conformal Inference Under Distribution Shift

We develop methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion. Our framework builds on ideas from conformal inference to provide a general wrapper that can be combined with any black box method that produces point predictions of the unseen label or estimated quantiles of its distribution. While previous conformal inference methods rely on the assumption that the data points are exchangeable, our adaptive approach provably achieves the desired long-term coverage frequency irrespective of the true data generating process. We accomplish this by modelling the distribution shift as a learning problem in a single parameter whose optimal value is varying over time and must be continuously re-estimated. We test our method, adaptive conformal inference, on two real world datasets and find that its predictions are robust to visible and significant distribution shifts.
Czert – Czech BERT-like Model for Language Representation

This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures.We pre-train our models on more than 340K of sentences, which is 50 times more than multilingual models that include Czech data.We outperform the multilingual models on 9 out of 11 datasets.In addition, we establish the new state-of-the-art results on nine datasets.At the end, we discuss properties of monolingual and multilingual models based upon our results.We publish all the pretrained and fine-tuned models freely for the research community.
Distribution-invariant differential privacy

Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy protection and statistical accuracy. In this work, we mitigate this trade-off by developing a distribution-invariant privatization (DIP) method to reconcile both high statistical accuracy and strict differential privacy. As a result, any downstream statistical or machine learning task yields essentially the same conclusion as if one used the original data. Numerically, under the same strictness of privacy protection, DIP achieves superior statistical accuracy in in a wide range of simulation studies and real-world benchmarks.
Side-Channel Analysis of CRYSTALS-Kyber and A Novel Low-Cost Countermeasure

In this paper, we propose a vertical side-channel leakage detection on the decryption function of the third round implementation of CPA-secure public-key encryption scheme underlying CRYSTALS-Kyber, a lattice-based key encapsulation mechanism, which is a candidate to the NIST Post-Quantum Cryptography standardization project. Using a leakage assessment metric, we show that the side-channel information can be efficiently used to pinpoint operations leaking the secret variable and how masking countermeasures can be applied. We detect leakages in the polynomial multiplication between the secret key and the ciphertext. We propose and evaluate two different masking countermeasures, based on additive and multiplicative masking. To the best of our knowledge, the multiplicative masking has not been proposed before as a countermeasure to CRYSTALS-Kyber vulnerabilities. We demonstrate their efficiency and discuss their impact in terms of performance. Our work is beneficial to assess and enhance the security of Post-Quantum Cryptography against advanced vertical side-channel analysis.
Machine Learning Based Platform and Recommendation System for Food Ordering Services within Premises

Normally, the long queues and crowd can be seen at the canteens/hotels whenever there is a lunch break in an organization or within a campus. This paper proposes a solution for eliminating the queue system and introduces the facility to remotely place food orders. Further, this paper proposes a real-time food recommendation system to suggest the dishes to users based on their past orders. The solution has been implemented through a mobile application built using Flutter. The mobile application has been empowered with a machine learning model for recommending the items that the user might like. The proposed method has been tested with 2 vendors catering to 60 customers successfully. The accuracy of the recommendation has been satisfactory.
SVM-Based Switching Filter Hardware Design for Mixed Noise Reduction in Digital Images Using High-Level Synthesis Tools

Impulse and Gaussian are the two most common types of noise that affect digital images due to imperfections in the imaging process, compression, storage and communication. The conventional filtering approaches, however, reduce the image quality in terms of sharpness and resolution while suppressing the effects of noise. In this work, a machine learning-based filtering structure has been proposed preserves the image quality while effectively removing the noise. Specifically, a support vector machine classifier is employed to detect the type of noise affecting each pixel to select an appropriate filter. The choice of filters includes Median and Bilateral filters of different kernel sizes. The classifier is trained using example images with known noise parameters. The proposed filtering structure has been shown to perform better than the conventional approaches in terms of image quality metrics. Moreover, the design has been implemented as a hardware accelerator on an FPGA device using high-level synthesis tools.
A Poisson-Gaussian Noise Limited Quantum Iterative Multi-User System With Non-Ideal Photon-Counting Receiver

Coherent state quantum communication is attracting extensive attention for its great value in terrestrial and satellite information networks. This paper proposes a new non-orthogonal quantum multi-user (MU) iterative detection scheme. At the receiving end, we develop a composite quantum receiver with multi-stage measurement instrument and soft iterative MU detector. Furthermore, based on a three-dimensional factor graph, a nonlinear Poisson-Gaussian noise limited coherent state iterative MU parallel interference cancellation (PIC) algorithm is derived. Our results demonstrate that this system can well support MU communication and achieve good performance. Quantitatively, for the 8 users scenario, the BER of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-6}$</tex-math></inline-formula> can be obtained when the photon number is 19. Further numerical results show that the system we designed exhibits superior robustness against imperfections (e.g., quantum mode mismatch and quantum thermal noise).
Emotionalism Within People-Oriented Software Design

In designing most software applications, much effort is placed upon the functional goals, making a software system useful. However, the failure to consider emotional goals, which make a software system pleasurable to use, can result in disappointment and system rejection even if utilitarian goals are well implemented. Although several studies have emphasised the importance of people’s emotional goals in developing software, there is little advice on how to address these goals in the software system development process. This paper bridges the gap between emotional goals elicitation and the software system design process by proposing a novel technique entitled the Emotional Goal Systematic Analysis Technique (EG-SAT) to systematically analyse people’s emotional goals in cooperation with functional and quality goals. EG-SAT allows in-depth analysis of emotional goals to build a software system and provides a visual notation for representing the analysis, facilitating communication and documentation. EG-SAT provides traceability of emotional goals in system design by connecting the emotional goals to functional and quality goals. To demonstrate the method in use, a two-part evaluation is conducted. First, EG-SAT is used to analyse the emotional goals of potential users of a mobile learning application that provides information about low carbon living for tradespeople and professionals in the building industry in Australia. The results of using EG-SAT in this case study are compared with a professionally developed baseline. Second, we ran a semi-controlled experiment in which 12 participants were asked to apply EG-SAT and another technique to our case study. The outcomes show that EG-SAT helped participants analyse emotional goals and gain valuable insights about the functional and non-functional goals for addressing people’s emotional goals. The key novelty of the EG-SAT is in proposing an easy to learn and easy to use technique that helps system analysts gain insights on how to address people’s emotional goals. Furthermore, the EG-SAT enables system analysts to convert emotional goals to traditional functional and non-functional goals that existing software engineering methodologies can analyse without demanding excessive effort.
Multi Relational and Social Influence Model for Predicting Student Performance in Intelligent Tutoring Systems ITS

Recent studies have shown that Matrix Factorization (MF) method, deriving from recommendation systems, can predict student performance as part of Intelligent Tutoring Systems (ITS). In order to improve the accuracy of this method, we hypothesize that taking into account the mutual influence effect in the relations of student groups would be a major asset. This criterion, coupled with those of the different relationships between the students, the tasks and the skills, would thus be essential elements for a better performance prediction in order to make personalized recommendations in the ITS. This paper proposes an approach for Predicting Student Performance (PSP) that integrates not only friendship relationships such as workgroup relationships, but also mutual influence values into the Weighted Multi-Relational Matrix Factorization method. By applying the Root Mean Squared Error (RMSE) metric to our model, experimental results from KDD Challenge 2010 database show that this approach allows to refine student performance prediction accuracy.
RANCANG BANGUN SISTEM INFORMASI E-MARKETING PADA PT PULAU CAHAYA TERANG

Current technological developments are increasing, one of which is the development of the internet that is very fast, many companies are starting to offer their products through the internet to maximize their profits, so that competition between companies is getting tighter. PT Pulau Cahaya Terang is one company that manufactures and sells various types of spring beds and equipment in the city of Batam. The sales system is by the way consumers come directly to the company, contact by telephone, or the company offers products directly through salesmen, the system Used is less effective now, becaUse consumers find it difficult to get the latest information and making an order quickly. For this reason, it is necessary to create a web-based sales information system at PT Pulau Cahaya Terang so that consumers can easily access product information and can also directly order and pay for products through the website. The method Used is the SDLC Waterfall model which consists of needs analysis, design, programming code, testing, implementation and maintenance. The results of this research are a website-based sales information system that makes it easier for consumers to obtain information and order products, and save employee costs and time
Finding Local Explanations Through Masking Models

Among the XAI (eXplainable Artificial Intelligence) techniques, local explanations are witnessing increasing interest due to the user need to trust specific black-box decisions. In this work we explore a novel local explanation approach appliable to any kind of classifier based on generating masking models. The idea underlying the method is to learn a transformation of the input leading to a novel instance able to confuse the black-box and simultaneously minimizing dissimilarity with the instance to explain. The transformed instance then highlights the parts of the input that need to be (de-)emphasized and acts as an explanation for the local decision. We clarify differences with existing local explanation methods and experiment our approach on different image classification scenarios, pointing out advantages and peculiarities of the proposal.
End-to-End Deep Learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images

Metastatic breast cancer is one of the attributed leading causes of women deaths worldwide. Accurate diagnosis to the spread of breast cancer to axillary lymph nodes (ALNs) is done by breast pathologist, utilizing the microscope to inspect and then providing the biopsy report. Because such a diagnosis process requires special expertise, there is a need for artificial intelligence-based tools to assist breast pathologists to automatically detect breast cancer metastases. This study aims to detect breast cancer metastasized to ALN with end-to-end deep learning (DL). Also, we utilize several DL architectures, including DenseNet121, ResNet50, VGG16, Xception as well as a customized lightweight convolutional neural network. We evaluate the DL models on NVIDIA GeForce RTX 2080Ti GPU using 114 processed microscopic images pertaining to ALN metastases in breast cancer patients. Of note, we included in the evaluation several machine learning (ML) algorithms coupled with histogram features, extracted from the same studied images. These ML include Random Forest, AdaBoost and SVM. Compared to all models employed in this study, experimental results show that DenseNet121 generates the highest performance results based on measures used in class-imbalance problems.
Trajectory planning for automated buses in parking areas

Automated Vehicles (AVs) are complex, and they require solutions in different areas such as perception, communications, decision, and control. In urban environments, path planning for parking maneuvers is a relevant feature. There is a good amount of research on this topic considering passenger cars. However, there are some gaps considering automated buses. This work proposes a path planning approach for parking an automated bus that targets the fewer possible trajectories to complete the parking maneuver. The algorithm considers the vehicle geometry, its kinematics, and the blockage or availability of the surrounding parking spaces, to create a set of trajectories to complete the parking maneuver. The approach was tested in simulation environments considering an electric Gulliver minibus in the framework of the European project SHOW.
Automating Computer Science Ontology Extension With Classification Techniques

In information technology, an ontology is a knowledge structure consisting of the definitions and relations of information within one or even multiple domains. This semantically represented information is helpful for tasks such as document classification and item recommendation in recommender systems. However, as big data prevails, manually extending existing ontologies with up-to-date terminologies becomes challenging due to the tedious and time-consuming process and the expensive cost of expert manual labor. This study aims to achieve a fully automatic ontology extension.We propose a novel "Direct" approach for extending an existing Computer Science Ontology (CSO). This approach consists of two steps: initially extending the CSO with new topics and using this extended graph to obtain the new topic’s node embeddings as inputs for training classifiers. However, this initial extension still contains many noisy links; therefore, the classifier later acts as a filter and a link predictor. We experiment with various traditional machine learning and recent deep learning models and then compare them using our Direct approach. We also propose two evaluation procedures to decide the best-performing model and approach: the novel Wikipedia-based F1w score and the total number of resulting links. Furthermore, manual evaluation by four human experts is conducted to conclude the reliability of our proposed approach and evaluation procedure. This study concludes that the Direct approach’s Gaussian Naive Bayes model produces the most valid and reliable links, and we, therefore, use it to further extend the CSO with hundreds of new CS topics and links.