Reviewerly/exHarmony huggingface dataset:
De-noising and enhancement of MRI medical images using Gaussian filter and histogram equalization

Abstract Noise in Medical Images reduces the image contrast, resolution, and is observed as an obstacle in image understanding. MRI is used for the treatment of brain, ankle, foot, and prostate cancers. Normally, MR Images consist of Gaussian Noise, Poisson Noise, Speckle Noise, Salt and Pepper Noise, etc. In Medical Image Processing, removing noise and obtaining information from MR Images is a crucial and challenging task. An accurate image is very important for further diagnosis or post-processing tasks such as visualization, segmentation, etc. For removing different types of noise from MR images, different noise filtering algorithms are used. Selecting a particular filtering method is application dependent. Gaussian Filter is the best technique for de-noising of medical images efficiently. To reveal the detailed information hidden in a very small range of grey level or color level from a low contrast image, a powerful tool such as a contrast enhancement algorithm is used for image enhancement to increase the visual appearance or quality of the image. Before Medical Image Analysis, improving the contrast of Medical Images is necessary using contrast enhancement methods. Histogram Equalization (HE) is an important and widely used technique for enhancing the contrast of an image among the different contrast enhancement methods. This paper explains the method of de-noising and image contrast enhancement using Gaussian filter and HE and performed simulations using different databases such as IRMA, KAGGLE, and WANG, etc. and observed that Gaussian filter is used to reduce Gaussian noise efficiently for removal of Gaussian noise by smoothing inner distinct image region and also in Histogram Equalization, the histogram of the resultant image is flat.
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition

Stream fusion, also known as system combination, is a common technique in automatic speech recognition for traditional hybrid hidden Markov model approaches, yet mostly unexplored for modern deep neural network end-to-end model architectures. Here, we investigate various fusion techniques for the all-attention-based encoder-decoder architecture known as the transformer, striving to achieve optimal fusion by investigating different fusion levels in an example single-microphone setting with fusion of standard magnitude and phase features. We introduce a novel multi-encoder learning method that performs a weighted combination of two encoder-decoder multi-head attention outputs only during training. Employing then only the magnitude feature encoder in inference, we are able to show consistent improvement on Wall Street Journal (WSJ) with language model and on Librispeech, without increase in runtime or parameters. Combining two such multi-encoder trained models by a simple late fusion in inference, we achieve state-of-the-art performance for transformer-based models on WSJ with a significant WER reduction of 19% relative compared to the current benchmark approach.
Interactive Map Using Data Visualization and Machine Learning

This project aims to build a smart map, named VAWMap (Violence Against Women Map), that displays the intensity of various crimes against women across the states and districts of India in efforts to inspire action to combat this prevalent violence. The data set to be depicted on the map consists of the number of victims of rape, dowry deaths, kidnapping, cruelty by husbands, insult to modesty, and assault for each district of every state in India through the years between 2001-2013. Depicted states and their respective districts include Rajasthan, Punjab, and Haryana. Two algorithms, QR Decomposition with Column Pivoting and the Projected Clustering Algorithm, are leveraged to reveal prominent crimes and their locations-effectively yielding certain hot spot crime areas of North India. Two novel strategies to depict the intricate statistics obtained from these ML algorithms are proposed using a map-making platform, Mapbox. The result is two maps of India that display the intensity of the most prevalent crimes through a color gradient. The effectiveness, in terms of data reduction and visualization, between the two algorithms are compared and discussed.
SaPeer Approach for Training Requirements Analysts: An Application Tailored to a Low-resource Context

[Context and Motivation] Role-playing is a typical pedagogical strategy frequently applied in requirements engineering education and training (REET). The technique was proven to be successful for teaching different requirements engineering (RE) activities, and the SaPeer role-playing approach was recently proposed to train students in requirements elicitation interviews. SaPeer was shown to be effective and useful in the context of a high-resource RE module involving seven tutors, and a three-weeks individual assignment. [Question/Problem] RE lectures are frequently conducted as part of software engineering courses, or in short RE modules, and there is often limited time to teach RE in general, and interviews in particular. Therefore, SaPeer needs to be adapted to these constrained contexts, and adequately assessed. [Principal idea/Results] In this paper, we present the application of SaPeer to a low-resource context. We tailor the approach to a one-week group assignment, involving one tutor only, and we apply it to a class of 24 students. By comparing our results with the original study, we find that students struggle in similar areas, and especially in question omission and planning. A qualitative analysis of the feedback of the students shows the appreciation for the interview experience, and offers specific recommendations for improving the educational material. [Contribution] We contribute to the literature in REET with the first tailored application of SaPeer. Our study extends the scope of SaPeer and offers the possibility of adopting it in other constrained contexts.
Learn2Sing: Target Speaker Singing Voice Synthesis by Learning from a Singing Teacher

Singing voice synthesis has been paid rising attention with the rapid development of speech synthesis area. In general, a studio-level singing corpus is usually necessary to produce a natural singing voice from lyrics and music-related transcription. However, such a corpus is difficult to collect since it's hard for many of us to sing like a professional singer. In this paper, we propose an approach - Learn2Sing that only needs a singing teacher to generate the target speakers' singing voice without their singing voice data. In our approach, a teacher's singing corpus and speech from multiple target speakers are trained in a frame-level auto-regressive acoustic model where singing and speaking share the common speaker embedding and style tag embedding. Meanwhile, since there is no music-related transcription for the target speaker, we use log-scale fundamental frequency (LF0) as an auxiliary feature as the inputs of the acoustic model for building a unified input representation. In order to enable the target speaker to sing without singing reference audio in the inference stage, a duration model and an LF0 prediction model are also trained. Particularly, we employ domain adversarial training (DAT) in the acoustic model, which aims to enhance the singing performance of target speakers by disentangling style from acoustic features of singing and speaking data. Our experiments indicate that the proposed approach is capable of synthesizing singing voice for target speaker given only their speech samples.
The effect of using augmented reality based teaching material on students' academic success and opinions

The purpose of this research is to determine the effect of course material developed with augmented reality (AR) technology in teaching subjects related to coding on students' academic achievement, their level of permanence in learning and their opinions on the process. The mixed method was used in the study. In the quantitative dimension of the research, semi-experimental design with pretest-posttest control group, and in qualitative dimension phenomenology pattern were preferred. The quantitative data of the research were collected with the achievement test and the qualitative data were collected with a semi-structured interview form. The study group consists of 64 students attending a university in Computer Programming department in Turkey. There are 34 students in the experimental group and 30 students in the control group. The prepared achievement test was applied to the students as pretest, posttest and permanence test. After the application, interviews were made with 15 students selected from the experimental group. As a result of the research, it is seen that the academic achievement and permanence levels of students in the experimental group using AR course material for teaching coding education are higher than the students in the control group. According to the results obtained from the qualitative dimension of the research, it has been determined that AR technology provides many advantages such as efficiency, permanence, comprehensibility and convenience on learning; accordingly, it has been suggested that it should be expanded in different courses and fields.
A novel Siamese Attention Network for visual object tracking of autonomous vehicles

In the environment perception stage of autonomous driving, vehicles need to track its surrounding objects quickly and accurately to avoid dangerous behaviors. Therefore, visual object tracking has important practical application value in autonomous driving system. However, the performance of most hierarchical convolutional feature trackers are limited by ignoring the complex environment of autonomous driving. In this paper, a novel Siamese Attention Network to explore the rich spatial and channel information of objects was proposed. Because of the lack of important information between the channel and the spatial position, the tracking performance is reduced by the challenges of illumination change and deformation. The spatial attention block and channel attention block focus on the importance of different spatial positions and channels, respectively. The effective fusion of the two makes our tracker achieve the state-of-the art performance of 0.300 in the EAO criterion of 2017, which exceeds the baseline by 5.7%.
EMC: Earthquake Magnitudes Classification on Seismic Signals via Convolutional Recurrent Networks

We propose a novel framework for reliable automatic classification of earthquake magnitudes. Using deep learning methods, we aim to classify the earthquake magnitudes into different categories. The method is based on a convolutional recurrent neural network in which a new approach for feature extraction using Log-Mel spectrogram representations is applied for seismic signals. The neural network is able to classify earthquake magnitudes from minor to major. Stanford Earthquake Dataset (STEAD) is used to train and validate the proposed method. The evaluation results demonstrate the efficacy of the proposed method in a rigorous event independent scenario, which can reach a F-score of 67% depending upon the earthquake magnitude.
Transforming attitudes to science in rural India through activity based learning

K-12 science education in rural India is characterized by rote learning in teacher-centered classrooms with poor lab facilities. As a result, students often have poor attitudes toward science. To inculcate the joy of learning in rural students, we conducted three science camps in remote villages in North India. The camps emphasized hands-on learning using low-cost experiments from locally available materials. Videos, animations and simulations on mobile phones were used to supplement the physical experiments. The goal was not only to teach science, but to change the attitude of students towards science. This paper describes the results of daily assessments, pre, post, and six-month follow-up surveys. Findings include a significant improvement in attitude towards science, a particularly strong improvement for girls, and a positive effect of videos and simulations to learn abstract concepts. Also the academic marks of the students improved significantly after the camp in a longitudinal analysis. Additional insight was gained by clustering students by two simple dimensions: attitudes and learning styles. The change in attitude towards science was larger for students who prefer hands-on learning or concept learning.
Problems of Online Learning during Covid-19 Pandemic in EFL Classroom and the Solution

This literature study aims at analyzing online learning difficulties faced by EFL teachers and students, and students' parents during the Covid-19 pandemic and solutions to solve them. Research data sources are collected through textbooks in print or online form, articles from periodical journal websites, law regulations, and other sources which are relevant to the research problem. The data was analyzed qualitatively with an interactive model, covering data collection, data reduction, data presentation, and conclusions. The implementation of the online learning during the Covid-19 pandemic, especially in EFL learning, caused various problems for teachers, students, and parents, as indicated in the current study.
A Cryptocurrency Risk–Return Analysis for Bull and Bear Regimes

In this article, the authors develop a new analytical lens through which to examine the risk–return profiles of bitcoin, litecoin, ripple, and ethereum. Their focus is to understand better the price behavior of individual cryptocurrencies and their influence on one another. To achieve this, they segment each cryptocurrency’s time series of returns into disparate bull and bear regimes. They then examine the nature and extent of overlap between these regimes and whether they change over time. They also collect and plot several indicative distributed-denial-of-service attacks against the time series to investigate their possible impact on regime change episodes. Their findings shed light on previously unexplored systemic risk indicators within the cryptomarket as a whole and on the relationship between specific cryptocurrency pairs. These findings enhance the risk management toolkit for investors by revealing potential price behavior contagion patterns between cryptocurrencies pertinent to blended portfolio management. Furthermore, the authors’ approach serves as a blueprint for additional research into regime-type overlap within the cryptomarket. <b>TOPICS:</b>Currency, exchanges/markets/clearinghouses, financial crises and financial market history <b>Key Findings</b> ▪ Periods of overlapping regimes increase over time. The increase indicates a rise in cryptomarket systemic risk and an associated reduction in the diversification value of a blended portfolio of cryptocurrencies. ▪ Bitcoin exhibits the most favorable risk measures across both bull and bear regimes, including the lowest proclivity for extreme events during bear regimes. In contrast, ripple displays the overall riskiest profile across both regime types. ▪ Bitcoin’s regime type has the most meaningful impact on the risk–return profile of other cryptocurrencies, namely, litecoin and ripple. However, this relationship does not hold in reverse, a likely consequence of bitcoin’s market dominance and relative maturity.
Lightweight Asymmetric Dilation Network for Real-Time Semantic Segmentation

Semantic segmentation is a very important and challenging problem in computer vision.Many applications, such as automated driving and robotic navigation in urban road scenes, require accurate and efficient segmentation.Nowadays, system models are often designed with high speed but a large number of parameters, or they take up a lot of memory space with a very small speed, so they are not suitable for realtime semantic segmentation conditions.In order to solve this problem, we propose a more comprehensive model that has not only a faster speed, but also a smaller number of parameters and a higher accuracy which is termed as Lightweight Asymmetric Dilation Network (LADNet).Our model is based on our Lightweight Asymmetric Dilation Module (LAD Module) which provides a larger receptive field than all existing lightweight models to learn more information, while Lightweight Asymmetric Dilation-A (LAD-A) can better perceive spatial and semantic information, and Lightweight Asymmetric Dilation-B (LAD-B) can better perceive semantic information.Our Lightweight Downsampling Module (LDM) downsamples the feature map, it can greatly reduce model parameters.Finally, our Attention Enhancement Decoder (AED) to restore the feature map to the same size as the resolution of the original image, AED enables two attentional feature maps to simultaneously guide semantic information for better semantic segmentation of images.Our extensive experiments on the Cityscapes, CamVid, and NYUv2 test set show that our model is able to achieve the best balance in parameters, accuracy, and speed.
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning

Detection and localization of actions in videos is an important problem in practice. State-of-the-art video analytics systems are unable to efficiently and effectively answer such action queries because actions often involve a complex interaction between objects and are spread across a sequence of frames; detecting and localizing them requires computationally expensive deep neural networks. It is also important to consider the entire sequence of frames to answer the query effectively.
Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications

Stochastic approximation was introduced in 1951 to provide a new theoretical framework for root finding and optimization of a regression function in the then-nascent field of statistics. This review shows how it has evolved in response to other developments in statistics, notably time series and sequential analysis, and to applications in artificial intelligence, economics and engineering. Its resurgence in the big data era has led to new advances in both theory and applications of this microcosm of statistics and data science.
Extending Intel PML for hardware-assisted working set size estimation of VMs

Intel page modification logging (PML) is a hardware feature introduced in 2015 for tracking modified memory pages of virtual machines (VMs). Although initially designed to improve VMs checkpointing and live migration, we present in this paper how we can take advantage of this virtualization technology to efficiently estimate the working set size (WSS) of a VM. To this end, we first conduct a study of PML with the Xen hypervisor to investigate its performance impact on VMs and the accuracy of a WSS estimation system that relies on the current version of PML. Our three main findings are as follows. (1) PML reduces by up to 10.18% the time of both VM live migration and checkpointing. (2) PML slightly reduces the negative impact of live migration on application performance by up to 0.95%. (3) A WSS estimation system based on the current version of PML provides inaccurate results. Moreover, our experiments show that write-intensive applications are negatively impacted, with up to 34.9% of performance degradation, when using PML to estimate the WSS of a VM that runs these applications. Based on the aforementioned findings, we introduce page reference logging (PRL), an extended version of PML that allows both read and write memory accesses to be tracked without impacting user VMs, thus more suitable for WSS estimation. We propose a WSS estimation system that leverages PRL and show how it can be used in a data center exploiting memory overcommitment. We implement PRL and the underlying WSS estimation system under Gem5, a popular open-source computer architecture simulator. Evaluation results validate the accuracy of the WSS estimation system and show that PRL does not incur more performance degradation on user’s VMs.
Feature Selection Module for CNN Based Object Detector

In the field of computer vision, the detection of multiple objects with different scales within a single image is challenging. To target this problem, feature pyramids are a basic component commonly found in multi-scale object detectors. In the construction of standard feature pyramids, different semantic features are simply connected to rebuild a new feature map, regardless of whether these features have a positive effect to the output or not. In order to avoid introducing too many redundant features within the feature fusion stage, a new feature fusion module called the Feature Selection Module (FSM) was proposed in this paper, which can automatically detect the most representative features for the rebuilding of feature maps. The channel attention mechanism in FSM is able to process and score each channel, filtering out irrelevant features while focusing on features with high contribution. Moreover, FSM can be easily embedded within feature pyramids. Simply adding a small number of trainable parameters to the network can significantly improve the ability of feature extraction. We validated our FSM with the VOC 2007 object detection dataset, based on Yolo series detectors. Findings from the present study demonstrates that for a small computational cost, our method is able to consistently improve the performance of Yolo detectors.
Global Path Optimization of Humanoid NAO in Static Environment Using Prim’s Algorithm

This paper focuses on navigation of a humanoid robot cluttered with obstacles, avoiding collisions in static environment using Prim’s algorithm. Prim’s algorithm is a minimum spanning tree (MST) method with greedy approach which uses the concept of sets. It generates the MST by selecting least weights from the weighted graph and randomly forms disjoint sets with picking one least weight edge from the ones remaining for creating node incident to form the tree. Similar approach repeats for selecting all ‘n – 1’ edges to the tree which is the path direction to humanoid NAO. The developed algorithm is implemented in both simulation and experimental platforms to obtain the navigational results. The simulation and experimental navigational results confirm the efficiency of the path planning strategy as the percentage of deviations of navigational parameters is below 6%.