Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping Basal Melt Under the Shackleton Ice Shelf, East Antarctica, From CryoSat-2 Radar Altimetry

Ice shelvesbuttress the fast-flowing glaciers draining ice sheets, so changes in the ice shelves may alter the buttressing effect and the discharge of grounded ice, therefore influencing sea level. Basal melting of the ice shelves would reduce their ability to restrain the discharge of grounded ice into the ocean. However, estimating the basal melt rate is challenging due to the large uncertainty in the calculation. In this study, we use a Lagrangian framework to improve the basal melt rate derivation and apply the method to the Shackleton ice shelf as a case study. We use the CryoSat-2 data to characterize the spatial distribution patterns of ice shelf surface elevation changes between 2010 and 2018. Combining these results with the ice surface velocity and output from the regional climate model, we obtain a map of the basal melt rate and calculate the total and average basal mass balance. The total basal meltwater production for the Shackleton ice shelf is 54.6 ± 7.2 Gt/yr. The highest melt rates, which exceed 50 m/yr, are found close to the grounding line and the main trunk of Denman glacier. Based on the analysis, we show that the Lagrangian method can provide more spatially coherent patterns of ice shelf surface elevation changes and reduce the uncertainty in basal melt rate calculation.
Exploiting UML Diagrams for Test Case Generation: A Review

Testing is an integral part of life cycle of software development/ It aims to create a highly uncompromising framework. The program tests are conducted to verify the complex actions of the programme in a small range of test cases chosen from the execution domain. The unified modelling language (UML), which attracts considerable attention from the researchers and professionals, has emerged as an important standard for modelling software systems. In this paper, we will look at how to simplify test cases using UML diagrams including actvity, state, and sequence diagram.
A Performance Evaluation of Machine Learning Algorithms for Emotion Recognition through Speech

This paper aims to recognize the human expressive states from their voice samples. It intends to extract a few reliable features and combine them intelligently for the said task for effective recognition. Initially, it extracts a few sub-band spectral properties from voice samples containing emotional information. Further, the pitch and its standard deviation along with the log-energy features have been extracted to develop an efficient combinational model. The chosen features are complementary, hence expected to increase the available emotional information. To validate the combinational framework, several Machine Learning Algorithms (MLAs) have been simulated and compared. Among the classifiers, the Random Forest (RF) has outperformed all others in terms of classification accuracy whereas the Decision Tree remains computationally least expensive.
3-D Imaging Using Millimeter-Wave 5G Signal Reflections

Emerging 5G millimeter-wave (mm-wave) networks use electronic beamforming and beamsteering and support signal bandwidths on the order of hundreds of MHz. Given these characteristics, opportunities exist to develop 3-D sensing applications that leverage 5G mm-wave communications infrastructure. In this context, this work introduces a signal processing pipeline that can: 1) accurately extract the Time of Flight (ToF) of reflected orthogonal frequency division multiplexing (OFDM) communications signals and 2) enhance range resolution by coherently aggregating the reflection information from separate frequency bands. In combination with precise beamsteering, the proposed signal processing techniques enable high-resolution 3-D radar imaging without affecting communications protocols. An experimental system demonstrating this concept has been implemented and is described. This system consists of two software-defined phased array radios (SDPARs), one configured as a prototype 5G base station TX, and one as an auxiliary prototype 5G RX. Each SDPAR primarily consists of a Si-based 28-GHz, 64-element, phased array transceiver module and software-defined radio. Simulation and benchmark results show that our coherent bandwidth stitching enables accurate OFDM-based ranging with 15-cm resolution. Measurement results show 3-D radar images of indoor scenes with 2° angular and 15-cm ranging resolution, created by processing reflected 5G-like communication waveforms at 28 GHz. The produced 3-D radar images effectively depict the location of objects in the scene, and these locations are in close agreement with the ground truth.
Research on Simplified Mesh Deformation Based on Differentiable Computation

In this paper, we propose a simplified mesh deformation method based on the differentiable calculation and uses the Pytorch3D library of deep learning. There are four stages, simplification, deformation, subdivision, re-deformation in this method. The simplification stage transforms the original target mesh into a simple mesh. The deformation stage uses the Pytorch3D tool to predict the simple mesh in the simplification result. The subdivision stage subdivides the resulting mesh of deformation, and the re-deformation stage uses the subdivision stage result mesh as the source mesh to predict the original target mesh. Our experiment shows that the number of iterations is similar or less in terms of shape and local features after simplifying the predicted target mesh. Our method is superior to the direct mesh deformation method in terms of mesh deformation speed and local mesh characteristics of deformation and has a better deformation effect.
Peak-Current-Ratio-Enhanced Compact Symmetrical Doherty Amplifier Design by Using Active Harmonic Control

This article proposes a new and compact symmetrical Doherty power amplifier (DPA) configuration that provides extended output-back-off (OBO) and enhanced load modulation. The concept of active harmonic control (AHC) is introduced to remedy the reduced-conduction-angle operation of the class-C auxiliary device, providing enhanced auxiliary current and power capability. Semianalytical study based on generalized load-modulation theory and large-signal simulation reveal that an optimal AHC can offer increased peak current ratio, improved load modulation, boosted output power, and power utilization factor (PUF). Besides, a new output combiner, with the inclusion of parasitic effect, is devised for ultracompactness and simplicity. For verification, a 11-dB-OBO 2-GHz symmetrical DPA based on GaN HEMT transistors is designed, fabricated, and measured. Excellent performance with drain efficiency of 74% (52%) is obtained at saturation (back-off of 11 dB). Moreover, a saturation power of 42.98 dBm is achieved, corresponding to an uplifted PUF of 1.25 (compared with conventional symmetrical DPA design). Under 20-MHz modulated signal (peak-to-average power ratio of 11 dB) excitation, the prototype exhibits average efficiency of 53.2% at an average output power of 32 dBm. The adjacent channel leakage power ratio (ACLR) is found to be −27.5 and −50.9 dBc before and after digital predistortion, respectively.
A Data-Driven Dynamic Stochastic Programming Framework for Ride-Sharing Rebalancing Problem under Demand Uncertainty

To make a reasonable decision on ride-sharing platform utilizing time-series historical data, we propose a deep learning-based stochastic programming framework for open driver guidance and rebalancing to further reduce the rider average wait time. The approach proactively guides open drivers to the designated regions by integrating an innovative deep neural network named LSTM-MDN with a two-stage stochastic programming model, which is capable of yielding high quality guidance solutions by leveraging the rider demand information predicted by LSTM-MDN from time-series historical data. To validate the performance of the proposed framework, we conduct a group of numerical experiments based on the New York taxi trip data sets. The results show that our proposed framework is capable of reducing driver rebalancing distance significantly, which implies that the riders' wait time can be decreased effectively. Most importantly, it turns out that by average, riders' wait time with guidance using our approach is 97% lower than the myopic batch matching algorithm without guidance.
The Client-side System for Management of the Smart Home Climate Control System

The paper considers the development of a client-side indoor climate control system. The system consists of applications that contact the user, as well as a web server for communication between applications and devices, which purpose is managing the microclimate in the room. The mobile app controls the microcontroller using a unique scripting system developed by our project team. To identify the user and their smart home device, Google Mail authentication is added to the app. There are some features and fundamental things used in developing an WEB application. Moreover, in this paper are also shown the differences between WEB app and other parts of the project such as the scenarios creation process, statistics window and so on.
A New Current Source Converter Using AC-Type Flying-Capacitor Technique

Entering the new millennium, current source converters (CSCs) have drawn increasing attention not only for high-power but also some emerging low-power applications which prefer the inherent voltage boosting nature and low dv/dt on ac-side. However, the dc-link quality of CSC has not been well improved and is rarely discussed in literature. To fill this gap, a new type of CSC topology is proposed in this article through the isomorphic theory. It utilizes the ac-type flying capacitors in each phase leg and provides alternative commutation loops, which can reduce the dc-side dv/dt and improve the common-mode voltage simultaneously. Besides, this technique can increase the withstand voltage of the converter without series connections of switching devices. Different from the voltage-source converter type, the flying capacitor voltage in CSC should be maintained as ac voltage, thus, active voltage regulation is necessary. To deal with such a challenge, carrier-based pulsewidth modulation with flying capacitor voltage regulation is also developed in this work. Compared with the traditional device-series-connected CSC topologies, the numbers of active switches and the device blocking voltage can remain the same, while the ac-type flying capacitors are added in the proposed topology to gain all the above merits. Simulation and experiment results are obtained to verify the advantages of this new type of CSC topology.
Prediction of cardiac rhythm based on heart rate variability features in sinus tachycardia conditions

The variability observed in cardiac rhythm measurements makes classification and prediction of Sinus Tachycardia (ST) ECG signals challenging. The study aims to predict ST volunteer's heart rate by designing and developing a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN) model. The Heart Rate Variability (HRV) features comprising the time and frequency domain were considered as the input feature set for the designed and developed LSTM-RNN model. The proposed LSTM-RNN model works on the principle of multivariate time series forecasting. The LSTM-RNN model's training accuracy is 96.72%, and the validation accuracy is 85.21%. The performance of the LSTM model works well for predicting time series of unknown duration. The time gap in the input time series data does not affect the LSTM model, giving an advantage over other time series prediction models
Music Social Tags Representation in Dimensional Emotion Models

Much research has sought to recognize and retrieve music on the basis of emotion labels. These labels are usually obtained from either subjective test or social tags. Researchers use social tags usually either by grouping tags to emotion categories or clusters directly, or by mapping tags to dimensional quadrants simply. Few research work have undertaken semantic analysis on social tags for projecting them into a dimensional emotion space, especially based on recent neural word embedding techniques using large-scale datasets. In this paper, we propose an effective solution to analyse music tag information and represent them in a 2-dimension emotion plane without limiting the corpus to contain only emotion terms. In our solution, we apply neural word embedding methods for tag representation, including Skip-gram, Continuous Bag-Of-Words (CBOW) and Global Vectors (GloVe). In our experiment, we compare these methods with traditional Latent Semantic Analysis (LSA) model based on Procrustes Analysis evaluation metrics. The results shows that neural tag embedding methods outperform LSA and represent tags with high approximations with classic circumplex emotion definitions.
Effect of Image Augmentation on ECG Image Classification using Deep Learning

The advent of artificial intelligence has led to a better investigation of many complex research problems from a variety of domains. Recently deep learning approaches have emerged as cutting edge AI technologies and has been proved very effective in medical research. A large number of studies have recently used deep learning in the field of medical imaging for the detection and identification of various diseases including COVID19. In this work, we have investigated an ECG based approach for detection of COVID-19 and heart diseases using deep learning. Deep learning requires a lot of data and image augmentation is a way to enhance the size of data. In this study, we specifically examined the impact of augmenting ECG images for disease detection. Our study indicates that augmentation improves the detection accuracy to a certain extent and can adversely impact beyond that. Without augmentation, we achieved accuracy and F1 score of 0.818 and 0.776 which is reduced to 0.764 and 0.768 respectively, when multiple augmentation techniques are applied.
Word Suggestions for non-word Text Errors using Similarity Measure

Spelling errors are a common phenomenon in any text writing system. It is due to various factors like memorization, spelling construction and training, and word ambiguity of that language. With the advent of machines keyboard layout also one of the causes for spelling mistakes. Natural Language Processing (NLP) is a prominent research area in the territory of human language. A spelling checker is a key aspect of many applications such as the MT framework, information retrieval, Desktop applications, and Office automation framework, etc. There are various approaches to spelling checking like rule-based, data-driven, etc. With the commencement of machine learning, a new dimension was introduced as a similarity measure. In this paper, we proposed the cosine similarity measure which handles the nonword error in the Marathi language text. As cosine similarity measure is an important strategy of spelling checking by using this strategy, we enhanced the quality of suggestions for non-word errors. This algorithm also boosts the user’s proficiency in the situation when the users impotent to measure the accurate spelling by themselves. We have harvested about 9, 29, 663 unique words from various sources. The system results in suggestion generation accuracy of about 86.61%
An Analysis of Malware Detection and Control through Covid-19 Pandemic

Malware creators have been very opportunistic in creating novel malware by leveraging the vulnerabilities which can be either at system level or environmental level. Covid-19 pandemic was the next opportunity for them. Novel malwares were seen exploiting the new normal lifestyle during the Covid-19 pandemic. In this paper, we explore the malwares that were observed specially during the Covid-19 pandemic and then present an analysis of malware detection techniques with a focus on these Covid-19-themed malwares. This study aims to set a baseline for cyber security researchers exploring the malwares that surged during the Covid-19 pandemic and the malware detection techniques.
Real Time Heart Beat Monitoring Using Computer Vision

Among the different biological parameters, heart rate (HR) plays a very significant part in the disease diagnosis. It confirms the state of living of a person. Heart rate gives an idea about the person’s health and hence it should be observed. Nowadays, a non-contact based method is more comfortable, low cost, simple and easy to use. In this era, with an increase in automobiles, people who are engaged in driving are also on the surge. Such people are also victims of cardiac-related diseases. The proposed method demonstrates real-time non-contact heart rate monitoring systems that enable the detection of cardiac activity cycles without any cutaneous contact using machine learning. The methodology involves the extraction of heart beats signals from face images using independent component analysis. Neural network is employed to generate the BPM from the samples. As part of preparing the dataset, a heartbeat sensor is developed with a microcontroller. The algorithm is successfully tested with real time face images and the non-contact based detection method more conveniently helps to measure HR with more acceptable accuracy. It can apply to the drivers for the prevention of accidents or manual labourers who work in.
INS Fine Alignment With Low-Cost Gyroscopes: Adaptive Filters for Different Measurement Types

Inertial navigation system stationary fine alignment process is a critical step in reducing the initial errors of the attitude and sensor biases. While many studies had been made for tactical grade systems, less attention was given to low-cost sensors, which are a major player in today’s inertial sensors market. To fill this gap, a measurement strategy combining different INS aiding types is proposed, analyzed and compared using numerical simulations and field experiments. Additionally, an analytical linear observability analysis is made to support the numerical comparisons. Further, five types of adaptive Kalman filters with the proposed measurement strategy are compared to find the appropriate one to improve the alignment performance. The proposed measurement strategy can be used in other applications of stationary conditions such as land vehicles, robots or shoe-mounted inertial navigation systems.
A Model for Relational to NoSQL database Migration: Snapshot-Live Stream Db Migration Model

Data migration is simply a process of transforming data from a source database to target database. Companies are moving their database from one database (e.g., RDBMS) to another new one (e.g., NoSQL) because of many reason such as new database can handle more data, that could be fast, scalable etc. There are various tools and methods such as, mongify [1], mongoimport [2], sqoop [3] etc. These are available for database migration from RDBMS to NoSQL databases. NoSQL uses document model, graph model, key-value etc., as compared to RDBMS databases. NoSQL was primarily designed for storing and retrieving a large amount of data. In this paper, we have proposed a database migration model, which can migrate real-time as well as old data in a parallel way.The proposed model is implemented for MySQL (RDBMS) to MongoDB document database in Java. The prototype migrates database instance at a particular time (Snapshot of database) as well as live data in a parallel way. Finally, our experimental evaluation of snapshot and live data migration shows the higher performance as compared to other methods.
Scattering Transform of Averaged Data Augmentation for Ensemble Random Subspace Discriminant Classifiers in Audio Recognition

The paper presents an audio-based context recognition system using ensemble classifiers with wavelet feature extraction. The device-wise classification accuracy can be achieved with minimum computational time and improvement in classification accuracy compared to convolutional neural network (CNN) based technique via a subspace discriminant classifier. The proposed framework involves wavelet scattering feature extraction from the TAU2020 Mobile dataset via a modified data augmentation that improves the classification of unseen recording devices during training. The random sub-space discriminant classifier's device-wise classification performance on the extracted features is compared with other ensembles such as the bagged trees ensemble, boosted trees ensemble, and multiclass Naive Bayes classifier, and k-nearest neighbors. The evaluation results show around 72.6% accuracy and averaged 14.4% better than the ensemble methods. Compared to the highest performance of 76.5% in the DCASE2020 Challenge (Snapshot ensemble deep neural networks), the proposed approach offers a shorter computational time than CNN.
CLACTA: Comment-Level-Attention and Comment-Type-Awareness for Fake News Detection

There are many popular communication tools for news sharing in recent years. However, propagation of fake news becomes a serious issue concerning the public and government due to openness and rapidity of online communication. It is widely concerned how to automatically detect fake news as soon as possible. Nevertheless, most existing methods do not well utilize comments which contain rich semantic information or ignore their effect. Inspired by the revealing role of some comments to the original post, we propose the neural network model which consists of comment-level-attention (CLA) and comment-type-awareness (CTA) for fake news detection. In CLA, we devise the attention mechanism which considers semantic relation between the post and the comments. Based on attention weights we take the weighted sum of different comment representations for the sample as corresponding comment feature, which can capture key comment information. As similar to stance, we assume comments can gather into several different types naturally. Therefore, in CTA, we store comment type representations by the memory matrix which is learned in the training process of sample stream. Comment feature for the sample is aware of the memory matrix, and then corresponding comment type feature is obtained. We concatenate the above two auxiliary features and learned post feature to help detect fake news. Our validation experiments using the Weibo dataset and Pheme dataset demonstrate the effectiveness of the proposed model.
Novel Pythagorean Fuzzy Entropy with Application in MCDM to assess the Best Automotive Company

The fuzzy sets are quite proficient to handle inconsistency, vagueness, and improper information in many theoretical and practical problems in the multicriteria decision procedures. The Pythagorean fuzzy set (PFS) is significantly used in many complex problems in multicriteria decision making as compared to (IFS) intuitionistic fuzzy set. Moreover, The PFS are enabled to handle the fuzziness more precisely. In this context, this communication presents the novel entropy on PFS. The result of the new entropy measure has been validated for upon the ranking of the best automotive company-based technique for order preference by similarity to ideal solution (TOPSIS) model with (MCDM) multicriteria decision making. The comparative analysis of results with existing entropy measures defined on PFS concludes that novel entropy measure is more reliable, feasible, and precise.