Reviewerly/exHarmony huggingface dataset:
Reducing Syntactic Complexity for Information Extraction from Japanese Requirement Specifications

In software development, ambiguities in requirements described in natural language (NL) prevent the application of formal approaches, posing a difficulty that has heretofore been avoided in two main ways: discovery based on formal specifications generated from NL requirements, and the creation of non-ambiguous NL requirements. In the former, NL is more expressive and does not rely on the user‘s expertise, but instead makes the automatic generation of formal specifications difficult. The latter facilitates the automatic generation of formal specifications and has the advantage of reduced syntactic complexity, but in exchange for reduced expressiveness of NL. In this paper, we take an approach that allows users to describe highly expressive NL requirements and reduces syntactic complexity to support the automatic generation of formal specifications from NL requirements. We also propose an information extraction method using syntactic patterns of low syntactic complexity. Applying our method to practical requirement sentences reveals that it is effective in reducing the complexity of information extraction rules. We expect that our method can support the automatic generation of formal specifications from NL requirements without compromising the expressive power of the language.
Toward a Better Alignment Between the Research and Practice of Code Search Engines

When studying the research literature, one comes to the impression of code search engines as an essential software development tool that developers use regularly to accomplish their daily tasks. Driven by this impression, researchers primarily focus on improving the performance of code search. Nevertheless, as we argue in this paper, this impression is mostly unfounded. As a result, developers and researchers hold dissimilar perspectives on what code search engines are and their most important characteristics, with developers’ perspectives and the state of the art often diverging widely.This paper aims at reconciling these divergent perspectives by drawing a comprehensive picture of code search engines, as reflected in developers’ experiences and perspectives as well as the state of the art. To that end, we first survey more than 100 software developers to ascertain their usages of and preferences for code search engines. We then review the state of the art on this topic by analyzing academic papers, industry releases, and open-source projects. Finally, we juxtapose the results of our two investigations to synthesize a call-for-action for researchers and industry practitioners to better meet the demands of software developers when it comes to searching for code. Our findings can be used to better align the state of the art and practice of code search engines, leading to wider adoption and more effective use of this powerful software development tool.
Retrieve-Guided Commit Message Generation with Semantic Similarity And Disparity

High quality commit messages are important for program understanding and maintenance, which describes the content of code changes. Neural-based methods are most popular ways to generate commit messages, but they neglect retrieval results including retrieval diffs and retrieval messages. The existing combination models of neural-based methods and retrievalbased methods have two major limitations: a) Only use retrieval diffs but ignore retrieval messages. b) Seldom consider the similarity and disparity between the retrieval results and given diff. To address the above two issues, we propose a retrieveguided method named ReGenSD to generate commit messages, which consists of three steps. Firstly, we apply a similaritybased IR technique to get retrieval diff and retrieval messages. Secondly, we introduce a selective mechanism to decide whether to use retrieve-guided model based on lexical similarity between retrieval diff and given diff. Lastly, for retrieve-guided model, we design a novel seq2seq network with Bi-LSTM that takes given diff, retrieval diff and retrieval message as input. We introduce a relation gate in encoder to leverage retrieval message adaptively based on semantic similarity, and a difference vector in decoder to refine the utilization of retrieval message based on semantic disparity. Experimental results on an open source dataset demonstrate that retrieval messages guidance can facilitate commit message generation task. Besides, ablation experiments prove the effectiveness of our proposed mechanisms on adjusting the use of retrieval results.
NOW - LBP GLCM Feature as the Texture Descriptor of the Camera Fingerprint in Image Forensics

Digital Image Forensics (DIF) aims to ensure the authenticity of a digital image and also it identifies the origin of the image and detects the forgeries/ tampering in an image. This paper proposes a texture descriptor of the camera fingerprint, for Source Camera Identification. Gray Level Co-occurance Matrix (GLCM) of Non - Overlapping Window Local Binary Pattern(NOW-LBP GLCM) feature of the noise residual of an image is identified and used as the texture feature of the camera fingerprint to detect the source camera/origin of the image. The experimental results shows that this feature can be used to distinguish 4 source cameras with a detection accuracy of 99.61% and 98.41% respectively for two different sets of cameras.
LGCM: A Communication-Efficient Scheme for Federated Learning in Edge Devices

Abstract-For a couple of recent years, fields such as Computer Vision, NLP, and Speech Recognition have been revolutionized by Machine Learning (ML). This success can be attributed to Big Data. The Process of Collecting this large amount of data has often been done in privacy-invasive ways. Traditional ML approaches require collecting the training data in a common store to train a model and deploy it. Federated Learning (FL), is a recent technique that allows machine learning models to be trained without needing to submit collected data to the server. Instead of sharing the data with the client, the model parameters will be sent to the clients to collaboratively train the model. The data used to train the global model never leaves the devices, strictly enforcing privacy. Only the clients and the server share model learning parameters such as Weights, Biases. While this approach respects privacy and is flexible, it comes at a cost of additional challenges like communication cost and model bias due to data heterogeneity that needs to be tackled. To improve FL’s efficiency in terms of communication overhead and accuracy, In this work, we propose a scheme called Learned Gradient Compression Method (LGCM). In this scheme accuracy declination which causes by non-iid(non-independent and identically distributed) data and communication overhead is improved. To improve the accuracy declination caused by non-iid data, clients with a lower level of non-iid data have been selected for training the ML Model. To improve communication efficiency we employ compression of weight updates by using Autoencoder(AE). The proposed scheme has been applied in a typical ML task to study the performance and effectiveness of the scheme and has been compared with baseline algorithms. We have used a CNN model to classify handwritten digits using the MNIST dataset. The achieved accuracy of 96% outperforms the baseline FedAvg algorithm. the Compression The ratio achieved using AE is approx. 5000x.
Exploration of Bonafide and Spoofed Audio Classification Using Machine Learning Models

Recently, it has been shown that most automatic speaker verification systems (ASV) are vulnerable to various spoofing attacks. These spoofing attacks can be divided into three major categories: those deriving from text to speech (TTS), voice conversion (VC), and replay spoofing attacks. In the last couple of years, many researchers have proposed many algorithms to detect these spoofing attacks in ASV systems and have made considerable improvements. This paper presents a novel bonafide and spoofed audio detection approach using machine learning and deep learning classifier models with resampling and data augmentation techniques. This paper uses seven machine learning classifier models with different entropy features and three deep learning classifier models with various acoustic features. The performance of these classifier models is analyzed using metrics like precision, recall, accuracy, equal error rate, loss, and confusion metrics. State-of-the-art authors used equal error rate as their performance measure metric; our primary focus is on equal error rate in this paper. And we observed that in our proposed methodology, the deep learning classifier models showed considerable performance improvement. The training phase shows approximate 100% accuracy with a loss of 0.0090 and an equal error rate of 0.06%. In the evaluation phase, it offers approximate 98% accuracy with a loss of 0.0906 and an equal error rate of 1.72%.
Voting Ensemble SVM Model for Deep CNN Based Breast Histopathology Classification

Breast cancer is the second most common cause of cancer-related mortality among women. Histopathology image analysis forms a powerful tool for analyzing the tumor type. A convolutional neural network (CNN) is used to extract features from histopathology images, and the test images are classified using support vector machine classifiers. A voting ensemble technique that combines the estimated class probability scores from individual support vector machine (SVM) models is applied to increase the final accuracy. Each different model is evaluated using 10-fold cross validation. The average performance of each algorithm from random trial testing in a publicly available histopathology dataset using our method provides a maximum patch level accuracy of 98.36% with specificity 98.73% and sensitivity of 98.21% which are comparable to state-of-the-art.
Manifold Learning-Based Feature Transformation for Object Recognition

Recognizing objects is an important task for any computer vision application. Therefore many research works are carried out to make machines understand the objects present in a picture. This paper presents a novel approach to reduce the domain difference of source and target domain using manifold feature learning while preserving the locality and discriminative information of the source data. The method balances the distributional shifts of the domains using MMD metric and the target domain data is maximally varied inorder to avert projecting the data onto trivial dimensions. The manifold feature learning aligns the source and target subspaces by constructing a geodesic flow between the datapoints and integrates the subspaces along the flow. A thorough experimental analysis was performed on the method using benchmark datasets like Office-Caltech 10 using SURF, Decaf <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> and NASNETLarge feature descriptors. Various existing methods has not properly exploited the feature aligning techniques for better structuring of the datapoints so that the data can be well positioned in the subspaces. Experimental results shows that the our proposed framework outperforms the traditional domain adaptation approaches on this dataset. Office-Caltech dataset achieved an accuracy of 88.58% for Decaf <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> features and 48.21 %for SURF features and 97.24 % for NASNETLarge features respectively.
Development of a ROS based waste collection robot for campus use

Keeping the environment clean is a difficult task. Lots of manpower and man hours are required to keep our surrounding clean. The developments in robotics can be utilized to automate many tasks including waste management. In this paper a mobile robot which is capable of navigating autonomously using GPS navigation is designed and simulated. A manipulator is also attached to the mobile robot to manipulate the waste collection bins. This project is entirely developed in Robot Operating System(ROS) with Noetic distribution. Modelling of the robot is done using Autodesk Fusion 360 software. Simulation is done in Gazebo environment with necessary sensor plugins.
A Deep Learning Approach to Facial Expression Recognition in the Presence of Masked Occlusion

Detecting facial expressions is a vital aspect of interpersonal communication. Automatic facial emotion recognition (FER) systems for detecting and analyzing human behavior have been a subject of study for the past decade, and have played key roles in healthcare, crime detection, and other use cases. With the worldwide spread of the COVID-19 pandemic, wearing face masks while interacting in public spaces has become recommended behavior to protect against infection. Therefore, improving existing FER systems to tackle mask occlusion is an extremely important task. In this paper, we analyze how well existing CNN models for FER fare with masked occlusion and present deep CNN architectures to solve this task. We also test some methods to reduce model overfitting, such as data augmentation and dataset balancing. The main metric used to compare the models is accuracy, and the dataset used here is FER2013. Images from FER2013 were covered by masks using a certain transformation, resulting in a new dataset, MFER2013. From our evaluation and experimentation, we found that existing models need to be modified before they can achieve good accuracy on masked datasets. By improving the architecture of the base CNN, we were able to achieve a significantly improved accuracy.
Improving Performance of NeMo ASR system for Indian Accent English

While many speech recognition applications are quite commonplace now a days, applicability of the recognition techniques to accented languages is still a major challenge. Here, in this paper we have fine-tuned a state-of-the-art convolutional ASR system on regional accented Indian English to improve it’s performance for accented English. We used NVIDIA NeMo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> as our base framework and used QuartzNet 1D Convolutional model to explore their ability to understand language specific phonology. We also fine-tuned the convolutional network on regional accented Indian English. The tests were carried out on a dataset containing male and female English speech samples collected over 13 different regional accents. The experiments were bench-marked against the output generated by well known speech services and the effect of the convolution was found to be positive.
High-degree Cubature Quadrature Kalman Filter with Fractional Delayed Measurement

Gaussian filtering is a popular Bayesian approximation filtering method for nonlinear systems. In general, the Gaussian filters are designed for non-delayed measurements; however, the measurements are often delayed in practice. This paper considers that the delay is fractional multiple of sampling interval. Subsequently, it extends a significantly accurate filter, named higher degree cubature quadrature Kalman filter (HDCQKF), for handling the fractional delay problems. The simulation results conclude an improved accuracy of the HDCQKF-based delayed filtering algorithm in comparison to the popularly known cubature Kalman filter (CKF)-based delayed filtering algorithm.
RMSOD: A RetinaNet based Mixed Supervised Object Detection Framework

Mixed supervised object detection (MSOD) exploits a source dataset with full instance-level annotations and a target dataset with few instance-level annotations to detect new categories in the target dataset. In this work, we propose a RetinaNet based MSOD by employing a three-stage training pipeline, where the detector is trained on a source dataset with complete bounding box information, a target dataset with image-level annotation and on few images from target dataset with instance-level annotation. The proposed RMSOD framework effectively makes use of the readily available image-level annotation of the target dataset, but do not rely on any kind of relationship between object categories in source and target datasets unlike existing MSOD frameworks. The mAP results on PASCAL VOC 2007 test dataset shows the efficacy of the proposed MSOD configuration over existing few-shot object detection algorithms based on weakly-supervised, semi-supervised and mixed-supervised learning. Furthermore, by employing RetinaNet as baseline detector we ensure the deployability of the proposed detector for real-time implementation.
A Novel Segmentation Algorithm For Handwritten Document Using Contour Point Extraction

In the present world, the financial system, postal service, and insurance firms all write a lot of data on paper. The Optical Character Recognition system is used to recognize the text from these physical paper documents. The most important phase in any OCR system is the segmentation process. The proposed system comes under the area of Image processing. The domain where segmentation for handwritten documents is used is Pattern recognition. One of the main issues with handwriting recognition has been the division of words into individual letters. This project aims to develop an automatic segmentation algorithm to segment characters from handwritten documents and address all the challenges. To achieve better results, different preprocessing techniques and a histogram projection method are used. A Vertical histogram projection is proposed to segment the words from the input image. The lines are segmented by extracting the row-wise existence of consecutive black pixels. The IAM dataset was used to evaluate the effectiveness of the suggested technique. In the proposed method, the accuracy of line, word, and character level segmentation is 99%, 95%, and 85% respectively. A Contour Point Extraction method is used for character segmentation which gives higher accuracy than the existing methods. The segmented characters can be further used to train a Machine Learning algorithm for character recognition.
Three - Operand Binary Addition Using Parallel Prefix Adders

The field of cryptography has seen remarkable progress with the introduction of three operand binary adders in the applications of pseudo-random bit generators (PRBG). This is especially true in the case of Modified dual-CLCG (MDCLCG) being it the safest and most random PRBG method. Parallel prefix adders which are extensively used for fast binary addition can be extended towards the use in three operand binary adders hence making them much more efficient. In this paper, all the basic six topologies of Parallel Prefix Adders – Han Carlson, Kogg – Stone, Brent – Kung, Sklansky, Ladner - Fischer and Knowles adders are used within the four stage architecture of a multi - bit three operand adder. All the adder designs with the word sizes of 16, 32 and 64-bit have been simulated and implemented on Xilinx Vivado Design Suite 2020.2 using the programming language Verilog HDL The architecture of these three operand adders are prototyped on the commercially available Basys 3 Artix-7 FPGA Board for the on-chip functional verification. Further, all these designs are synthesized using Cadence tool with GPDK 45nm technology library for the purpose of comparing various adders with respect to performance parameters – area, delay and power for the different word sizes.
State Estimation of Van-der Pol Oscillator from Noisy Sensor measurement

The Van-der Pol oscillator is used to analyze realistic oscillations in a variety of real-world systems. Among the most famous self-oscillating systems is the Van-der Pol oscillator. Its mathematical model resembles many complicated systems in nature. An approach based on Bayesian theory is presented in this paper for estimating unknown parameters from noisy sensor data. The Gaussian filtering strategy is the most popular among Bayesian approaches. The intractable integrals encountered during the estimation process are approximated numerically, which is a major issue with Gaussian filtering. A well-known Gaussian filter, the cubature Kalman filter (CKF), is used in this work to estimate unknown parameters. An intractable integral’s numerical approximation is achieved by employing a spherical radial rule of third-degree. Its estimation accuracy is demonstrated on a nonlinear oscillator by the root mean square error (RMSE).
Designing a Trustworthy and Secured House Rental System using Blockchain and Smart Contracts

The traditional process of renting the house has several issues such as data security, immutability, less trust and high cost due to the involvement of third party, fraudulent agreement, payment delay and ambiguous contracts. To address these challenges, a blockchain with smart contracts can be an effective solution. This paper leverages the vital features of blockchain and smart contract for designing a trustworthy and secured house rental system. The proposed system involves off-chain and on-chain transactions on hyperledger blockchain. Off-chain transaction includes the rental contract creation between tenant and landlord based on their mutual agreement. On-chain transactions include the deposit and rent payment, digital key generation and contract dissolution, by considering the agreed terms and conditions in the contract. The functional and performance analysis of the proposed system is carried out by applying the different test cases. The proposed system fulfills the requirements of house rental process with high throughput (>92 tps) and affordable latency (<0.7 seconds).
Adaptive color sensing mechanism for smart illumination and camouflage

This paper presents a functional mechanism to facilitate artificial lighting, demonstrating ambient illumination by controlling the color and brightness of lighting elements and the concept of effective camouflage by matching the display color with the dominant color of its surroundings. As the core principle behind both notions lies in the controllability of lighting elements, there exists a need to develop a standard technique with which both of the above conditions are met with the minimum usage of hardware components. The proposed model features an embedded system application which is interfaced with a color sensor module to capture the color of its surroundings and reciprocate the same to illuminating material, thereby realizing the desired objective. Depending on this material, one can use the model mentioned above for ambient lighting in electronic devices and home automation, as well as in military applications for infiltration into combat environments.
Robust Positioning and Grubbs Outlier Test for Navigation in GPS Spoofing Environment

Global positioning system (GPS) is favored to provide the position, velocity, and time (PVT) details across the globe. This paper proposes an epoch-by-epoch robust positioning algorithm followed by the Grubbs outlier test to address the GPS spoofing problem. We propose to accept both authentic and spoofed GPS signals to compute the robust positions. The robust positioning considers all possible combinations of measurements and generates several position estimates, which contain actual position, spoof position, and biased positions. In this case, the positions evolved due to spoof pseudorange measurements must be removed. Hence, we model eliminating spoof locations as an outlier problem and is addressed using Grubbs outlier test. The median of the processed data after the Grubbs test is the positional information at that epoch. Moreover, this problem is also extended to the Kalman filter’s (KF) framework to address the time-varying kinematics of the target. Simulations are carried out for various numbers of actual and spoofed pseudorange measurements. In order to illustrate the robustness of the proposed technique, position root mean square error (PRMSE) is taken as a metric.