Reviewerly/exHarmony huggingface dataset:
An Empirical Study on Multi-Source Cross-Project Defect Prediction Models

Multi-source cross-project defect prediction (MSCPDP) refers to transferring defect knowledge from multiple source projects to the target project. MSCPDP has drawn increasing attention of academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP) and some MSCPDP models have been proposed. However, to the best of our knowledge, there are no empirical studies to investigate the effect of different MSCPCP models on the performance of MSCPDP. To comprehensively investigate the performance of different MSCPDP models, we first conduct the literature research about MSCPDP studies, and then identify and compare 7 state-of-the-art MSCPDP models in terms of multiple performance measures including PD, PF, area under ROC curve (AUC), F1, precision, Matthews correlation coefficient (MCC), and Popt20% on 20 publicly available defect datasets. Furthermore, a robust multiple comparison method, i.e., the Scott-Knott effect-size difference (ESD) test, is used for statistical test. The experiment results show that 1) Burak’s Filter always performs best in terms of precision, AUC, MCC, Popt20% except for F1;2) MSCPDP models outperform the mean performance of SSCPDP models on most datasets; 3) the performance of MSCPDP models still needs to be further improved. We suggest software engineers use MSCPDP models but not SSCPDP models for CPDP and pay more attention to both the distribution difference of different datasets and the problems of sample similarity and weight when building MSCPDP models.
A checklist-based approach to assess the systematicity of the abstracts of reviews self-identifying as systematic reviews

Systematic reviews are crucial for various stakeholders since they allow them to make evidence-based decisions without being overwhelmed by a large volume of research. Systematic reviews are increasingly popular in the software engineering field. The abstract is one of the most important systematic review’s components since it usually reflects the content of the review. It may be the only part of the review that most of the readers will read when needing to form an opinion on a given topic. Besides, the content of an abstract is usually the main information readers use to decide if they want to access the full content of the review or not. Since an abstract usually summarizes a review, readers may therefore mostly rely on that abstract to judge the quality of the review as well as its methodological rigor. However, abstracts are sometimes poorly written and may therefore give a misleading and even harmful picture of the reviews’ contents. To assess abstracts, we propose a measure that allows quantifying the systematicity of reviews’ abstracts i.e., the extent to which these abstracts exhibit good reporting quality. Experiments on 151 reviews published in the software engineering (SE) field showed that these reviews’ abstracts exhibit a suboptimal systematicity.
Code Question Answering via Task-Adaptive Sequence-to-Sequence Pre-training

The development of a question answering (QA) system for code can greatly facilitate programs understanding for developers. Recently, pre-trained language models (PLMs) have shown promising results in the code QA task. However, directly applying PLMs to code QA often causes suboptimal performance due to the large discrepancy between pre-training and the downstream QA task. While code PLMs are pre-trained on largescale unlabeled code corpora, there is often a scarce availability of annotated QA pairs for fine-tuning. Existing code PLMs simply reuse the code representation part and require to train the QA part from scratch, which causes the model to overfit QA data. In this paper, we propose CodeMaster, a novel pre-training based approach for automatically answering code questions via task adaptation. CodeMaster employs CodeT5, a popular PLM for source code. In order to mitigate the gap between pretraining and QA, CodeMaster continually pre-trains CodeT5 on multiple self-supervised learning tasks such as partial comment completion and noun-phrase prediction. Experimental results on the CodeQA benchmark show that CodeMaster achieves state-of-the-art performance, and highlight the effectiveness of our approach.
The Language of Programming: On the Vocabulary of Names

Most of the text in a computer program is composed of the names of variables and functions. These names are selected by one developer, and need to be understood by others. This is similar to the role of words written in natural language. But there are several marked differences between the names in a program and the words in a book. First, names are frequently composed of multiple existing words, in an attempt to capture nuanced meanings and intents. Second, because of the use of multiple words, names can be rather long. Third, conventions may also allow names to be very short, and many single-letter names are used. But despite these differences, the general statistics of names are rather similar to the statistics of words. Like words, the distribution of names is close to a Zipf distribution. Also, popular names tend to be shorter than rarely used names. However, the underlying vocabulary if different. The composition of words leads to a more diverse vocabulary that can grow without bounds. But if we look at the individual words used in compound names, we find a rather limited vocabulary. These properties help explain the predictability of software, and how it can coincide with the large variability of names. It also suggests that it may be beneficial to model programs at the level of individual words rather than at the level of source code tokens.
SceneCommitter: a screen scenario review system using the GitHub and Figma WebAPI in the waterfall based software development

In software development, screen scenarios (screen diagrams with intuitive transitions) are often created to design the contents. However, there is a problem that it is difficult to review them in conjunction with version control tools such as GitHub. To solve this problem, we propose a system that converts project files created using the screen design system Figma into text format and automatically uploads them to GitHub.
Bytecode Obfuscation for Smart Contracts

Ethereum smart contracts face serious security problems, which not only cause huge economic losses, but also destroy the Ethereum credit system. To solve this problem, code obfuscation techniques are applied to smart contracts to improve their complexity and security. However, the current source code obfuscation methods have insufficient anti-decompilation ability. Therefore, we propose a novel bytecode obfuscation approach called BOSC based on four kinds of bytecode obfuscation techniques, which is directed at solidity. The experimental results show that, after the bytecode obfuscation, the failure rate of decompilation tools is over 99% and only a small amount of gas is consumed.
An additional approach to pre-trained code model with multilingual natural languages

Pre-trained language models have achieved many prominent results in natural language processing. Since software engineering widely includes many natural language documents, the application of pre-trained language models have received much attention in software engineering tasks. However, pretraining a large volume of source code requires a huge amount of computational resources and time. In this study, we propose an additional pre-training approach to a well-trained language model. Our initial results on mT5, multilingual T5 with an additional pretraining of Python code shows improved performance on multiple software engineering tasks including code generation, code summarisation, code repair, and error diagnosis.
Detection of Motorcyclists without Helmet from Traffic Video using Deep Learning Techniques

An increase in the vehicle density on the road can cause a rise in the number of accidents, where motorcyclists are more exposed to serious head injuries. Existing detection methods, however, have several disadvantages, including the inefficiency to follow particular motorcycles across multiple frames, and false detection of parked motorcycles in the traffic video. Additionally, variations in traffic density and traffic environments are restricted in the datasets that are used to develop existing approaches. In this paper, we propose a system to automatically detect motorcyclists those who are not wearing helmets from traffic videos using deep learning techniques. Motorcycles are detected and identified using a pre-trained YOLOv4 network and moving objects are detected using background subtraction followed by morphological operations. The detected motorcycles are tracked and top region of bounding boxes are given to a customized CNN classifier to detect motorcyclists with or without helmets. The effectiveness of the proposed approach is illustrated on HELMET dataset recorded in Myanmar.
A FAISS-based Search for Story Generation

Stories have the power to change human perspectives and have applications in game development and film making. An intelligent system can generate appropriate stories for a set of keywords. We aim to build a system capable of getting stories by providing keywords as input. The stories must have a relation with the input keyword. We experimented with the ROCStory dataset. The preprocessed data are encoded using a sentence transformer, called msmarco-distilbert-base-prod-v3. We relied on a search approach based on Facebook AI Similarity Search (FAISS) to generate appropriate stories. The output story has been converted to audio via pyttsx3. The performance of the proposed model is compared with that of the sentence transformer paraphrase-MiniLM-L6-v2 approach. We made a subjective evaluation. The results show that the proposed approach outperforms the baseline method.
Light-weight Recommendation System using Graph Neural Networks

With the explosion of online information, recommendation systems play an imperative role in alleviating the lack of access to necessary information. A key challenge in implementing an efficient Recommendation systems is basically learning the most effective way to recommend users and items. Recently, researchers are showing interest in graph representation of data and the underlying deep learning networks that learns the features from the graph more effectively, especially for Recommendation Tasks. In this paper, a novel light-weight recommendation using Graph Neural Network (GNN) for rating prediction in Recommendation Systems is proposed, due to the fact that the Graph Neural Networks (GNN) efficiently learns the graph feature representation for any task. The user-item bipartite graph serve as input to the proposed model, which follows a systematic approach to model the user-item interaction in item space and opinion-item interaction in user space in the form of latent factors. Two real-time standard publicly available datasets : Ciao, Epinions are used to study the performance of the model. The latent factors of the user and items with respect to item space and user space are jointly captured to predict the ratings. Being a regression problem, the Standard Evaluation metrics : Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are computed to understand the rating prediction errors of the proposed model. According to the experimental results, the proposed model performs extensively well with RMSE score of 1.0184 and MAE score of 0.7539 in Ciao Dataset and with RMSE score of 1.0341 and MAE score of 0.7499 in Epinions Dataset compared to other GNN-based and other state-of-the-art models.
A framework for IoT based on Blockchain and Edge Computing in Cyber Physical Systems

Today various technologies like artificial intelligence, edge computing, the internet of things, machine learning, blockchain platforms, and are all playing a very significant role as the internet evolves into a novel industrial science typology that is built on the foundation of smart systems. The combination of three technologies namely blockchain, internet of things (IoT), and edge computing has been an extremely essential aspect in creating revolutionary autonomous assistance and industrialized models with many anticipated qualities, such as self-verifying and execution, data integrity, and data dependability, along with secrecy. Critical concerns such as privacy, sustainability, and resource planning should be resolved in order to achieve efficient integration. In this study, edge computing is utilized to construct an Internet of Things (IoT) architecture that fulfills the confidentiality and scalability requirements necessary for integration, using peer-to-peer and blockchain technologies.
Behavioural Biometric Authentication using Keystroke Features with Machine Learning

Computers are depended on to store sensitive information and provide this information security from outsiders. Biometric authentication is very important in order to keep a system secure and keep information safe from outside attacks. Most of the biometric methods use expensive hardware and software in order to ensure this. Hence behavioural biometrics were proposed that perform authentication based on only behavioural characteristics of a user like gait, manner, way of typing. Keystroke dynamics offers great promise, emerging as one of the top ways for behavioural biometric authentication. Keystroke dynamics is extremely difficult to impersonate hence it is an extremely useful method for biometric authentication. In this paper, the best method for authentication using keystroke dynamics is discussed using the CMU Keystroke Dynamics Benchmark Data Set. Five Machine Learning Algorithms are compared to identify the best algorithm to be used to build the authentication system based on the lowest Equal Error Rate (EER).
Telugu Dialect Speech Dataset Creation and Recognition using Deep Learning Techniques

According to India’s 2011 demography, there seem to be approximately 8 crore Telugu communicators. Apart from that, the Telugu language has many dialects spread across the states Telangana and Andhra Pradesh. Telangana, Rayalaseema, and Coastal accents are the most common. The main concern is to understand the language irrespective of the dialects to have good communication near border areas of these states. Availability of data for analysis of Telugu speech dialects is of high scope for recognition. So, the creation of data is done for Telugu dialects with a total of 9 speakers, 3 speakers for each dialect. Once the data is created, analysis and recognition can help direct our needs. Classifying dialects cannot only solve this problem but also can act as a subset for solving bigger problems like machine translation, sentiment analysis, etc. We have used four RNN models viz. LSTM, GRU, BiLSTM & BiLSTM with attention layer for classification using speech data as input. Maximum test accuracy of 99.11% was obtained using the BiLSTM model with attention layer.
A New Error Correction Technique in Quantum Cryptography using Artificial Neural Networks

Information Security is of paramount importance in digital communication in today’s highly networked world. Conventional information security methods face many future threats, one being the likelihood of large scale Quantum Computation. Quantum Cryptography is coming to the fore in the field of information security. It is all about using quantum physics to perform the cryptographic tasks as well as to break cryptographic systems. There are recent many advancements in the quantum cryptography field like Quantum Coin Flipping, Quantum Bit Commitment, and Quantum Key Distribution (QKD) networks. In this paper, a new concept for error correction and key reconciliation method is presented that uses Artificial Neural Network (ANN) with the help of Tree Parity Machine (TPM). In our work, we have considered a few cases of TPMs synchronization process with specific values of Quantum Bit Error Rate(QBER) and have performed some comparison among those. The experimental results observed into this paper that prove the correctness of the proposed work.
Discrete Cosine Transform based Laplacian Phase Unwrapping for Phase Image application

Phase images can be disintegrated from magnetic resonance imaging (MRI) data using the arc tangent function. Since phase contains more details than magnitude image, therefore, exploits in numerous applications, including the medical field [1]. However, the raw phase image acquired has a phase value within the (−π,π] or in the 2π range. This causes an aliasing issue in reconstructing the original phase value. Hence, phase unwrapping (PU) is required to deal with the aforementioned problem. The laplacian phase unwrapping (LPU) technique is popular and easy to implement. For PU, the fast fourier transform (FFT) approach can be employed to accomplish an unwrapped solution of the phase equation in the fourier domain. However, because of the mirroring property and computational time complexity, LPU with FFT takes more computational time. On the contrary, DCT depends only on real values and its computational time complexity of order N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> N. Therefore, it yields a more accurate phase value and takes less time. The paper proposed DCT for MRI phase image application to avoid the demerits of FFT. Experimental results demonstrate that the aliasing or wrapped effect is mitigated more effectively with DCT than with other techniques.
Deep Learning-based 3D Object Detection Using LiDAR and Image Data Fusion

Robust environmental perception systems are key to implementing advanced driver assistance features in modern vehicles. To acquire knowledge of the environment, vehicles employ sensors like LiDAR, Camera and Radar for object detection and tracking. For autonomous driving applications (SAE J3016 Level 3 and above), LiDAR and camera-based systems are most common. Camera-based systems have difficulty in operating under low-visibility conditions while LiDAR-based systems do not perform well in detecting objects at farther distances due to sparse point clouds. This work presents an approach to improving the shortcomings of the LiDAR-based detection systems by upsampling sparse LiDAR point clouds and then fusing the camera data to it to improve the quality of the point clouds generated that can be then used for object detection and identification. The proposed technique has been validated on the KITTI vision benchmark dataset. Results indicate that the proposed system is able to outperform standalone LiDAR-based 3D Object detection.
Automatic Correction of Speech Recognized Mathematical Equations using Encoder-Decoder Attention Model

Automated correction of common and systematic errors caused by the ASR system is part of the post-processing of automatic speech recognition (ASR). The ASR system’s output is prone to grammatical, spelling and phonetic problems. The sentences which are the outputs from Automated Speech Recognition (ASR) models have been refined using error correction approaches to obtain a reduced word error rate (WER) and Character Error Rate (CER) than the initial ASR outputs. In this paper we propose a model that reduces the word error rate and character error rate of the speech recognized mathematical equations. The proposed model is the encoder-decoder with attention model.
Unsupervised Abstractive Text Summarization with Length Controlled Autoencoder

This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the use of an adversarial autoencoder model. The model encodes the input to a smaller latent vector and the decoder decodes this latent code to generate the higher dimensional output with some loss. Unlike variational autoencoders, AAE’s use discriminators to learn using adversarial loss. K-Means clustering and language models are used to get the final summary. This model has been tested with different datasets like the Amazon, Rotten Tomatoes and Yelp reviews dataset to essentially do an opinion summarization task and this is finally evaluated using ROGUE-1, ROGUE-2,ROGUE-L and BLEU scores. The same task is also conducted on a dataset in Hindi. We obtain a ROGUE-1 score of around 24% for Amazon, Yelp and CNN/Daily Mail dataset and a score of 12% for Rotten Tomatoes while the score obtained for the Hindi news articles dataset is only 8%.
Word-Level Speech Dataset Creation for Sourashtra and Recognition System Using Kaldi

For a nation such as India with more than 20 formally identified regional languages, it is essential to overcome language barriers to ensure smooth communication. Researchers have been working on the creation of intelligent engines capable of bridging the gap between different natural languages through machine perception. This paper proposed and describes an Automatic Speech Recognition (ASR) system for the language, Sourashtra which is built using the Kaldi toolkit. A custom speech dataset was created with the help of native Sourashtra speakers. Due to the absence of phoneme representations for this language currently, the Devanagari script was used for transliteration and language modelling, following the ILSL12 convention. With a total of 2000 word utterances, we achieve a word error rate (WER) of 5.5 and a CER of 0.2, using a GMM-HMM based acoustic model trained for monophones.
Conversion of Hand Gesture to Speech for Speech Impaired People

Computer vision has taken Human Computer Interaction to a very advanced level. Earlier, there were only a few input devices such as a keyboard, mouse, etc. But, due to the advancement in the field of computer vision, new input methods such as facial expression and hand gestures have been developed. In this paper, we have proposed an approach that converts hand gesture to text and speech. This hand gestures will be captured using webcam. The main aim of this paper is to assist speech impaired people to converse with outer world. We are not going to recognise a letter or a word but rather a complete sentence. Once we have given our desired message to the machine using hand gesture we will stop the process and then the text message will be converted to speech. The proposed method does not make use of any image classification algorithms hence the chances of getting an error is less.
Workload Characterization in HPC Environment for Auto-scaling of Resources – Preliminary Study

Machine learning and deep learning techniques have been shaping many sectors, one of them is High-Performance Computing. As predicting workloads assists decision-making and resource management, many statistical and machine learning techniques have been employed to predict workloads. This paper presents several machine learning and ensemble learning techniques along with a neural network approach to predict the run-time of a scientific application CP2K. It is a molecular dynamics simulation software. Its run-time was characterized under various resource allocation and algorithmic parameters. The data thus generated were used to train Random Forest, SVM, XGBoost, CatBoost, GradientBoost, and Neural Network models. The trained models help predict its run time under different resource allocation and algorithmic parameters thus enabling better resource allocation request by HPC users. Though all the models have shown promising results during both training and testing phases, the Random Forest has beaten all the models in terms of Mean Squared Error (MSE).