Miracle-dz/UniFAR-FTUs huggingface dataset:
Explaining Deep Neural Networks for the Prediction of Translation Rates

A recent convolutional neural network model accurately quantifies the relationship between massively parallel synthetic 5’ untranslated regions (5’UTRs) and translation levels, but the underlying sequence determinants remain elusive. Applying model interpretation, we extract representations of regulatory logic, revealing a complex interplay of regulatory sequence elements. Guided by insights from model interpretation, we adapt the model by human reporter data to obtain superior performance, which will promote applications in synthetic biology and precision medicine.
The deformation of a drop in a general time-dependent fluid flow

A theoretical method is given for the determination of the shape of a fluid drop in steady and unsteady flows by making an expansion in terms of the drop deformation. Effects of fluid viscosity and interfacial tension are taken into account. Examples given include the determination of the shape of a drop in shear and in hyperbolic flow when each is started impulsively from rest.
Graph representations of iEEG data for seizure detection with graph neural networks

Epilepsy is a neurological disorder that affects over 50 million individuals worldwide. Today, the gold-standard treatment for those who are drug resistant, meaning that symptoms cannot be controlled with medication, is to surgically remove the seizure onset zone (SOZ), the area of the brain believed to cause seizures: the main symptom of epilepsy. Unfortunately, around 50% of drug resistant patients are not resective candidates, which can be attributed in part to poor SOZ localization. SOZ localization is a complex and lengthy procedure, requiring visual inspection and manual processing by human experts that first need to localize and isolate seizure events. The intracranial electroencephalography (iEEG) is a tool that records electrophysiological activity of the inner brain at different regions and depths, and provides critical information on the SOZ. However, iEEG data processing methodologies are not standardized, and practice and resources vary across hospitals and clinics. To assist human experts with systematic processing of iEEG data, we propose a data processing pipeline that generates graph representations of iEEG data. We evaluate 9 different graph representations of publicly available iEEG data from 25 patients with epilepsy with a graph neural network model trained to detect seizures. Our results suggest that graph representations of iEEG data that leverage electrode and functional connectivity features are powerful data structures to analyze and interpret iEEG data in the context of epilepsy. We anticipate that our data pipeline that provides a systematic processing of neural data with graphs can integrate other data modalities like neuroimaging data. Moreover, methods used in the data pipeline have potentials to apply to other neurological disorders such as Parkinson’s disease or major depression disorder.
Orthonormal wavelet decomposition of turbulent flows: intermittency and coherent structures

Experimental data obtained in various turbulent flows are analysed by means of orthogonal wavelet transforms. Several configurations are analysed: homogeneous grid turbulence at low and very low Reλ, and fully developed jet turbulence at moderate and high Reλ. It is shown by means of the wavelet decomposition in combination with the form of scaling named extended self-similarity that some statistical properties of fully developed turbulence may be extended to low-Reλ flows. Indeed, universal properties related to intermittency are observed down to Reλ≃10. Furthermore, the use of a new conditional averaging technique of velocity signals, based on the wavelet transform, permits the identification of the time signatures of coherent structures which may or may not be responsible for intermittency depending on the scale of the structure itself. It is shown that in grid turbulence, intermittency at the smallest scales is related to structures with small characteristic size and with a shape that may be related to the passage of vortex tubes. In jet turbulence, the longitudinal velocity component reveals that intermittency may be induced by structures with a size of the order of the integral length. This effect is interpreted as the signature of the characteristic jet mixing layer structures. The structures identified on the transverse velocity component of the jet case turn out on the other hand not to be affected by the mixing layer and the corresponding shape is again correlated with the signature of vortex tubes.
Advantages of XLM-R Model for Urdu Sentiment Multi-Classification

Sentiment Multi-Classification detection has gained much attention in recent years. The multi-label sentiment detection task refers to additional comments based on social media or shopping platforms, which usually contain different personal solid emotions. How to classify these reviews into multiple sentiment types using efficient methods such as machine learning is the main content of this type of task. We describe our XLM-R based method for tracking emotion detection task at FIRE 2022 in this paper. The system uses the XLM-R pretrained model to extract semantic features from Urdu text. After using dynamic learning rate-based tuning, we found that the model is more stable in performance and has a higher score on the test set. In the final result, our system achieved a Micro F1 score of 0.759 and a Macro F1 score of 0.687 in this task and won the first rank in the FIRE2022 track emotion detection task.
Review of Proof Techniques and Probability

Acknowledgment: This document has been adapted from a similar review session for CS224W at Standard with substantial modifications by Yikun Zhang at Winter 2023 for CSE547 / STAT548 at UW. Some good references about writing proofs and basic probability theory include • Greg Baker, “Introduction to Proofs”: https://www.cs. sfu.ca/~ggbaker/zju/math/proof. html. • CS103 Winter 2016 at Stanford, “Guide to Proofs”: http://stanford.io/2dexnf9. • Eugenia Cheng, “How to write proofs: a quick guide”: https://deopurkar.github. io/ teaching/algebra1/cheng.pdf. • “Quick tour to Basic Probability Theory”: http://snap.stanford. edu/class/cs224w-2015/ recitation/prob_tutorial. pdf.
Maintenance Effort Estimation for Open Source Software: Current Trends

Software maintenance of Open Source Software (OSS) has gained more attention in recent years and facilitated by the help of the Internet. Since volunteers in OSS do not record the effort of their contribution in maintenance tasks, researchers have to indirectly estimate the maintenance effort of such software. A review of the published OSS-MEE models has been performed using a set of 65 selected studies in a Systematic Mapping Study (SMS). This study analyses, discusses the state of the art about O-MEE and identifies trends through five additional Mapping Questions (MQs). In summary, various maintenance effort estimation (MEE) models were developed for OSS or industrial software. Researchers have mostly expressed the maintenance effort in terms of bug fixing, bug resolution time and severity in conjunction with bug report attributes. Regression Analysis and Bayesian Networks were most used estimation techniques, Recall, Precision, R² and F-measure evaluation criteria in addition to k-fold cross validation method. Most of the models were implemented using WEKA, R software and MATLAB. More than half of the selected studies lacked of any validity analysis of their results. Trends are also discussed to identify a set of implications for researchers.
Nest-building using place cells for spatial navigation in an artificial neural network

An animal behavior problem is presented in the form of a nest-building task that involves two cooperating birds, a male and female. The female builds a nest into which she lays an egg. The male’s job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.
Short-Range Navigation Radio System Simulator

The work is devoted to the description of the key aspects of the development and use of a computer simulator of a short-range radio engineering system. The simulator allows to simulate a wide range of situations of interaction between the beacon and the aircraft on the ground. So, it is possible to link the aircraft and the beacon on a real map of the area at the required scale. Set and change their parameters at the required time. Build separate aircraft flight routes. It is possible to create and use entire flight scenarios. The system has worked out not only algorithms and functionality, but also the interface and visualization system of the simulator. This allows to effectively use the simulator for training purposes. Moreover, the simulator works in real time. And it is fully integrated, that is, it adequately takes into account all the main factors of influence that must be taken into account in the subject area to describe the interaction between the aircraft and the beacon. The simulator was highly appreciated by experts in the subject area.
A Sequential DNN for Sentiment Analysis of Dravidian Code-Mixed Language Comments on YouTube

A method for determining if a block of text is positive, neutral, or negative is sentiment analysis. As code-mixed material in many native languages is becoming increasingly widespread, there is also an increasing need for intense research in order to produce satisfactory results. This research paper aims to classify the sentiments from a data set of comments/posts into pre-defined classes belonging to the code-mixed text in Tamil, Malayalam, and Kannada by utilizing the Sequential Deep Learning model on the code-mixed data set. The sequential model achieved an f1-score of 0.20 for Tamil-English, 0.48 for Malayalam-English, and 0.47 for Kannada-English data sets. The results were submitted to the competition ‘Shared Task on Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages’ organized by DravidianLangTech.
A Robust Drift Detection Algorithm with High Accuracy and Low False Positives Rate

The number of decision-making processes that rely on machine learning models to operate has been increasing in recent years. Safety of those systems is compromised when models deviate from their expected behavior. One root cause is a shift in the underlying data distribution, known as concept drift. A direct consequence of concept drift is a rapid drop in model’s predictive power. Accurate detection of drift is essential as false alarms lead to unnecessary down time and undermine confidence in the drift detection model. This paper introduces Real-Drift Detector (RDD), a drift detector that is not triggered by virtual drift. RDD does not need use class labels during the inference phase to operate. Our detector outperformed the state of the art in an extensive benchmark on a large panel of well-known datasets used in drift detection.
AAOUJ-10-2021-0124_proof 84..100

Purpose – The purpose of this paper is to identify the factors affecting learners’ perception of e-learning during the Coronavirus-2019 (Covid-19) pandemic. Furthermore, it investigates mechanisms that enhance students’ engagement in e-learning, especially under the constraints created by the pandemic. Design/methodology/approach – Quantitative research approach was used, and data were collected using a structured questionnaire. The sample consisted of 163 undergraduates registered at the Faculty of Management Studies of the Open University of Sri Lanka (OUSL). The structured questionnaire was designed taking into consideration learners attached to different regional centers and study centers of the OUSL. Findings – As per the findings, performance expectancy, social influence, effort expectancy and service quality are the factors which have significant effects on students’ “intention to use” e-learning as a method of pursuing education. Furthermore, it was observed that 65.6% of the students had access to technology through mobile phones, while 53.4% of the students had engaged in e-learning for the first time. This wasmainly due to restrictions that were imposed during the Covid-19 pandemic. Practical implications – The study will help in formulating policies and introducing procedures in relation to online teaching-learning models to be used by both teachers and learners, especially in similar pandemic situations in the future. Originality/value –This study will assist to determine the effectiveness of the e-learning system used by the OUSL. The findings highlighted the importance of improving information technology (IT) facilities available at all the regional and study centers across Sri Lanka.
Multi-Lingual Contextual Hate Speech Detection Using Transformer-Based Ensembles

We present three different transformer-based approaches to contextual hate speech detection, submitted respectively to the three tasks of the HASOC 2022 competition [1]. To deal with the scarce dataset we use an ensemble of cross-validation trained models (CV ensemble) that makes use of all available data while still having validation sets to apply early stopping. We try different methods such as ensemble of ensembles, cross validation trained ensembles and embedding isotropy optimization. Furthermore, we compare different base models for the ensembles as well as different solution proposals to the tasks. Our team, hate-busters , ranked 3rd in task 1, 5th in task 2, 3rd in task 3A, 1st in task 3B and 4th in task 3C.
Evaluating Usefulness of C Comments using SVM and Naïve Bayes Classifier

Comments are very useful to the flow of code development. With the increasing use of code in commonplace life, commenting the codes becomes a hassle for rookie coders, and often they do not even think commenting as a part of the development process. This in general causes the quality of comments to degrade, and a considerable amount of useless comments are found in such codes. In these experiments, the usefulness of C comments are evaluated using Support Vector Machine (SVM) and Naïve Bayes Classifier. The results of the experiments create a baseline for better results that can be found in the future through more research. Based on these findings, more complex and intricate machine learning models can be created that can improve the accuracy achieved in performing said task.
Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier

Heart updated failure is a very serious medical issue nowadays. It causes a lot of deaths all over the world. The bad lifestyle, bad eating habits, unusual food timings are some of the factors responsible for this disease. Artificial intelligence and machine learning is a technology which is used by many researchers for prediction of diseases. Machine Learning (ML) algorithms provide some models which are first trained on a training data and then can be used to test the input data. These models are very helpful in prediction of heart disease. In this work XGBoost, Logistic Regression and Support Vector Machine ML models are used to predict heart disease. Cross validation method is used in this work which improved the prediction accuracy of all the three models. Outcoming results ensure that the XGBoost classifier is the best ML model for heart disease prediction as compared to Logistic Regression and Support vector Machine.
Research on Risk Model of ADSS optical fiber cable for Power Communication Based on Artificial Intelligence

The safe operation of power communication systems is crucial to achieving high efficiency, intelligence and security in power production. ADSS optical fiber cables are widely used in power networks as a fast and economical transmission channel and undertake important transmission tasks. Therefore, it is essential to detect and handle any appearance damage in a timely manner. This paper trains and builds the ADSS optical fiber cable risk model of electric power communication in four forms: high-risk, medium-risk, low-risk and normal. The optimized OpenCV image algorithm is used to output the initial judgment result with the field picture data. The final output can be consulted by operation and maintenance personnel, which improves the efficiency of risk identification and investigation for ADSS optical fiber cable by communication professionals.
Predicting Next Quarter Nifty 50 Price using Genetic Algorithm and Support Vector Regression

The stock market is difficult to predict due to uncertainty and complexity. Still, several methods are utilized to predict stock prices, such as fundamental analysis, technical analysis, and statistical and machine learning methods. Most of the studies, which use daily historical price, predicts next-day price. However, next-day price is not so useful for investors or short-term traders. In this study, genetic algorithm optimized support vector regression (GA-S VR) predicts next quarter's Nifty 50 index price using daily historical dose price. Further, the study compares the GA-SVR prediction with Support Vector Regression (SVR) based on Root Mean Square Error (RMSE). The result shows that GA-S VR improves the prediction of S VR.
A Grad-CAM-based Knowledge Distillation Method for the Detection of Tuberculosis

Automatic screening for tuberculosis (TB) from X-ray images using artificial intelligence techniques has attracted the attention of researchers in the fields of computing and medicine. However, existing models are computationally intensive and require high computer hardware, which limits the use of people in areas where medical resources are scarce. Another problem with the existing model is poor interpretability. The model only provides the final result and lacks intuitive information about the location of the lesion. To solve these problems, this paper proposes a Grad-CAM-based knowledge distillation method for the detection of TB. Firstly, this study used Unet to extract the lung region, avoiding the influence of regions outside the lung on the detection results. Subsequently, five models (Densenet121, Inception V3, Resnet18, Mobilenet V3, VGG16) are applied to TB detection, and the attention maps of each model are visualized using Grad-CAM. These attention maps are applied to knowledge distillation to finally obtain a lightweight interpre table TB detection model. This model achieves 91.2% and 85.7% accuracy on Shenzhen and Montgomery datasets, which verifies the effec-tiveness of the model.
Earlier Stage Identification of Bone Cancer with Regularized ELM

A major focus of current research in the field of image processing is the application of such methods to the field of medical imaging. While dealing with biological issues like fractures, canoers, ulcers, etc., image processing facilitated pinpointing the precise cause and tailoring a remedy. In the field of tumor identification, medical imaging has set a new standard by overcoming a number of challenges. Medical imaging is the practice of generating images of the human body for diagnostic or exploratory purposes. Because of its high image quality, MRI is the method of choice for detecting tumors. This research study proposes the integration of RLM to detect tumors and presents an automatic bone cancer detection system to assist oncologists in making early diagnosis of bone malignancies, which in turn allows patients to receive treatment as soon as possible. This research work also proposes to detect bone tumors by using a combination of the RELM based M3 filtering, Canny Edge segmentation, and the Enhanced Harris corner approach. When compared to other models like CNN, ELM, and RNN, the suggested technique achieves an accuracy of around 97.55%.
5G Positioning Reference Signal Configuration for Integrated Terrestrial/Non-Terrestrial Network Scenario

The Fifth Generation (5G) New Radio offers a new Positioning, Navigation, and Timing (PNT) service with larger signal bandwidth and higher frequency carriers than previous generations, delivering more accurate measurements. This allows other vertical industries to benefit from this feature, opening up new possibilities. Furthermore, the 5G network includes Non-Terrestrial Network (NTN) elements such as Unmanned Aerial Vehicle (UAV), High-Altitude Platform Systems (HAPS), and satellites, which are gaining significant attention from the industry to allow for global communication. The future 6G aims to create a single network entity with multiple connectivity layers for all devices in all scenarios. Therefore, when combining both aspects of the 5G networks, the PNT service, and the NTN, there are several benefits such as: an independent and complete communication and navigation system under a single network, higher accuracy on the PNT solution than previous generation, global coverage for join navigation and communication, higher resilience on the positioning estimation, or new services offered. However, this is not free of challenges, as it is expected to achieve an accuracy, at least, similar to Global Navigation Satellite System (GNSS). One of the challenges is the multiplexing of the data and positioning service using a single infrastructure such a satellite. This paper has the purpose of analysing the effect in the accuracy of a delay estimator when a satellite constellation send a Positioning Reference Signal (PRS). Assuming that all satellites share the same frequency carrier and are synchronised between them. This 5G PRS main characteristic is its flexibility in terms of resource usage such as bandwidth, resource element density, symbols periodicity, a muting scheme, etc. This flexibility will be exploited in this paper to get a UE capable to estimate the Downlink Observed Time Difference of Arrival (DL-OTDoA) of the signal. Two challenges are present in this work, both are related to the characteristics of the RF channel between the Next Generation Base Station (gNB) and the User Equipment (UE): the first one is how the UE will cope with the high Doppler shift due to the high speed of the Low Earth Orbit (LEO) gNB increasing the Inter-Carrier Interference (ICI); and the second challenge is the effect of variable delay between OFDM symbols in the same slot and transmitter, increasing the effect of Intersymbol Interference (ISI). The contribution of the authors on this paper is the analysis of different PRS configuration that keeps a low interfere level between the moving gNBs. The result of this research highlight the impact that the length in number of subcarriers and number of OFDM symbol has in the accuracy of the delay estimation. It shows a trade-off in the constellation design, as a higher number of satellites in visibility also increase the ICI and ISI.
State Estimation for Equality and Inequality-Constrained Systems

In control theory estimation of the states is the problem of central importance. To estimate the unobserved states an observer is needed. Here kalman filter is used for this work for its ease of implementation and estimation accuracy. But if the states satisfy some known relation among them, then some modification is required in kalman filter, to include this information which is the central topic of interest in this thesis. This additional information can be in either form of equality and inequality constraints. This thesis claims a comparative study among unconstrained kalman filter, model reduction approach, perfect measurement approach, MHE, constrained projection approach and restricted optimal kalman gain approach for linear equality and inequality constrained system & CEKF(constrained extended kalman filter) and MHE for non-linear filtering with constraints. The discussion has been done about the optimal method both for linear and non-linear systems by using simulation of different systems.