Reviewerly/exHarmony huggingface dataset:
Pathways to Leverage Transcompiler based Data Augmentation for Cross-Language Clone Detection

Software clones are often introduced when developers reuse code fragments to implement similar functionalities in the same or different software systems resulting in duplicated fragments or code clones in those systems. Due to the adverse effect of clones on software maintenance, a great many tools and techniques and techniques have appeared in the literature to detect clones. Many high-performing clone detection tools today are based on deep learning techniques and are mostly used for detecting clones written in the same programming language, whereas clone detection tools for detecting cross-language clones are also emerging rapidly. The popularity of deep learning-based clone detection tools creates an opportunity to investigate how known strategies that boost the performances of deep learning models could be further leveraged to improve the clone detection tools. In this paper, we investigate such a strategy, data augmentation, which has not yet been explored for cross-language clone detection as opposed to single language clone detection. We show how the existing knowledge on transcompilers (source-to-source translators) can be used for data augmentation to boost the performance of cross-language clone detection models, as well as to adapt single-language clone detection models to create cross-language clone detection pipelines. To demonstrate the performance boost for cross-language clone detection through data augmentation, we exploit Transcoder, which is a pre-trained source-to-source translator. To show how to extend single-language models for cross-language clone detection, we extend a popular single-language model, Graph Matching Network (GMN), in a combination with the transcompilers and code parsers (srcML). We evaluated our models on popular benchmark datasets. Our experimental results showed improvements in F1 scores (sometimes up to 3%) for the cutting-edge cross-language clone detection models. Even when extending GMN for cross-language clone detection, the models built leveraging data augmentation outperformed the baseline with scores of 0.90, 0.92, and 0.91 for precision, recall, and F1 score, respectively.
Natural and Imperceptible Backdoor Attack against Deep Neural Networks

Over the past few years, deep learning has demonstrated impressive performance across a wide range of applications. As the same time, researchers have become increasingly concerned with ensuring the security of deep learning models. Specifically, backdoor attacks have emerged as a significant threat to Deep Learning Model, whereby attackers control the predictions of model by implanting a concealed backdoor into the model. Under this scenario, the backdoored model will appear to make normal predictions on clean images, but will exhibit abnormal behaviors when the trigger is presented. Many existing backdoor attacks utilize a fixed pattern as the trigger, which can be easily detected by defense methods or even humans. Furthermore, existing backdoor attack methods are rarely targeted specifically at the Vision Transformer (ViT) model. Therefore, in this paper, we propose a novel natural backdoor attack method. We exploit natural phenomena to carry out a backdoor attack called the fog backdoor attack, which can utilize fog in the natural world as a trigger to be seamlessly integrated into clean images without being perceived by humans. The generated fog will diffuse over the image, creating a natural-looking effect. In contrast to the fixed and limited triggers produced by other methods, our triggers are more natural and imperceptible. Experimental results demonstrate the effectiveness and robustness of the proposed backdoor attack on different models. Specifically, the attack success rate of the proposed backdoor attack is 98.85% on VGG-16 model, 99.44% on ResNet-18 model and 9S.56% on ViT model, respectively. Furthermore, the proposed attack does not compromise the clean accuracy of the model.
ChameleonIDE: Untangling Type Errors Through Interactive Visualization and Exploration

Dynamically typed programming languages are popular in education and the software industry. While presenting a low barrier to entry, they suffer from runtime type errors and longer-term problems in code quality and maintainability. Statically typed languages, while showing strength in these aspects, lack in learnability and ease of use. In particular, fixing type errors poses challenges to both novice users and experts. Further, compiler type error messages are presented in a static way that is biased toward the first occurrence of the error in the program code. To help users resolve such type errors we introduce ChameleonIDE, a type debugging tool that presents type errors to the user in an unbiased way, allowing them to explore the full context of where the errors could occur. Programmers can interactively verify the steps of reasoning against their intention. Through three studies involving actual programmers, we showed that ChameleonIDE is more effective in fixing type errors than traditional text-based error messages. This difference is more significant in harder tasks. Further, programmers actively using ChameleonIDE’s interactive features are shown to be more efficient in fixing type errors than passively reading the type error output.
Modified Huffman based Text Compression Scheme for VLF Communication System

Ships and submarines, being the important and critical assets of Naval Forces, it becomes essential to convey tactical information to them during operation under hostile environment. Principally, Very Low Frequency (VLF) band is used across the world for communication with Naval vessels. The VLF communication system is used to broadcast text messages to ships and submerged submarines using land based high power transmitters. With accelerating developments in communication and changing global scenario, it is very challenging to increase data rate under low bandwidth constraint. To enhance the capacity of VLF system, lossless data compression with reference to text messages is required. There is enough work done on text compression as English dictionary text is said to have entropy of the order of 2.5 bits per character. Compression to this performance is achievable by use of higher order statistical coding schemes. However, the performance of these schemes is highly dependent on the input message size, making them practically not suitable for low data rate which requires small packet based communication to reduce latency. Additionally, a single bit error in the receive sequence produces unlimited character errors in adaptive data compression decoding schemes. Huffman scheme on the other hand has no such limitations. In this paper, modified Huffman based text compression scheme for low data rate and small packet based communication has been presented.
Progressive Image Transmission using Wavelet Transform over VLF Communication

Ships and submarines operating in deep water are critical security assets of naval forces across the world and it is very essential to convey tactical information to them during operation under hostile environment. Principally, Very Low Frequency (VLF) band is used across the world for ship and submarine communication. In this scenario of low bandwidth availability in VLF, image transmission is a challenging task. An optimal solution can be Progressive Image Transmission (PIT) that provides a convenient user interface when images are transmitted slowly. Out of various schemes available for PIT, wavelet transform is a widely used scheme. In this paper, Progressive Image Transmission of grayscale images using wavelet transform over VLF communication system is analyzed. The standalone executable GUI used for PIT is a state of the art design in the field of VLF communication. The scheme with GUI has also been tested in laboratory on a low bit rate VLF communication system.
Slicito: Using Computational Notebooks for Program Comprehension

Although integrated development environments provide developers with code structure analysis tools, program comprehension tasks still require significant manual effort. A promising direction to solve this problem is Moldable Development, a way of programming which encourages developers to build custom program visualization tools during software development process. To foster this practice within the .NET development community, we provide a tool called SLICITO, capable of analyzing and visualizing a C# program structure in a highly configurable way. Since SLICITO is implemented as an extension to computational notebooks, it takes advantage of their interactivity and visualization principles used for data analysis, and applies them in the field of program comprehension. In contrast to similar tools for C#, SLICITO is more flexible and provides more detailed information in code inspection. Its usage is shown in a video located at https://www.slicito.com/icpc video.mp4.
A Generative model to represent real time classical data as quantum state

Existence of quantum systems in superposition states gives an exponential advantage over classical system in both time and space. In this paper we will look at reconstructing real time classical data as a pure quantum state. Each classical data sample eg. each pixel location in an images is indexed by a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{n}$</tex> bit string representing one of the basis superposition states. The amplitude of the state which represents probability of measuring the state can be encoded as the normalized value of the sample at that location. A parameterized circuit[2] is used to prepare the quantum state as superposition of basis states from initial ground state. Each basis state corresponds to a sample classical data (eg. a pixel in case of an image). Quantum amplitude estimation based on Grovers algorithm[15] is used to estimate the amplitudes of each of these states. The parameters of the circuit is adjusted to minimize a loss function using classical gradient descent algorithm. The normalized amplitude at the output of our circuit is multiplied by a normalization factor that can also be tuned to represent the original data sample. This enables us to use the original real time data in our loss function computation. Section 1 of the paper gives a brief introduction. Section II covers the related works. Section III talks about amplitude encoding. Section IV describes quantum state preparation using supervised Learning. Section V describes implementation of the algorithms and finally Section VI gives the final conclusion of the research paper along with a result from the algorithm.
Continuance Intention Factor of Online Learning Management System in Case on Faculty of Computer Science at Brawijaya University in Indonesia

The outbreak of the COVID-19 pandemic changed the model of the learning process.Online learning became one of the best solutions for many educational institutions, including the University, especially the Faculty of Computer Science, Brawijaya University.The continuance intention factor for using an online learning management system is important to ensure learning sustainability.To understand students' continuance intention this study proposes self determination model as a research model to find the factors affecting students' continuance intention toward online learning management systems.The proposed model is useful for investigating continuance intention factors.The study finds that intrinsic regulation, external regulation, identified regulation, and introjected regulation significantly positive impact on continuance intention in learning management systems.Finally, this study provides suggestions for the Faculty to improve the continuance intention of the student in using an online learning management system.
Capstone Class of "Mechatronics Innovation Project" as STEM Educational Curriculum for Teacher Training Course

We have a set of STEM oriented special curriculum with name of "Mechatronics" in our teacher training course of "Program in Technology and Information Education, in School of Education, Hiroshima University".The mechatronics classes start from first grade, and they are eventually integrated in a capstone class of "Mechatronics Innovation Project" in third grade.We set a different theme in each year, in which the mechanical, electronic and programming elements are integrated, and students are going to solve it by making automatic "mechatronics" machines.The theme in 2021 was wood climbing robots.
Accelerating Distributed GNN Training by Codes

Emerging graph neural network (GNN) has recently attracted much attention and has been used extensively in many real-world applications thanks to its powerful expression ability of unstructured data. The real-world graph datasets are very large-scale, which can contain up to billions of nodes and tens of billions of edges. It usually requires distributed system to train GNN on such huge datasets. As a result, the data communication overheads between machines become the bottleneck of GNN computation. Our profiling results show that getting attributes from remote machines during sampling phase in GNN occupies <inline-formula><tex-math notation="LaTeX">$&gt; $</tex-math></inline-formula> 75% of the time of the training process. To address this issue, in this article, we propose Coded Neighbor Sampling (CNS) framework, which introduces codes technique to reduce the communication overheads of GNN. In the proposed CNS framework, the codes technique is coupled with GNN sampling method to exploit the data excess among different machines caused by unstructured nature of graph data. An analytical performance model is built for the proposed CNS framework, whose results are corroborated by the simulation and validate the benefit of the proposed CNS framework over both conventional GNN training method and conventional codes technique. Performance metrics, such as communication overheads, runtime, and throughput, of the proposed CNS framework are evaluated on a distributed GNN training simulation system implemented on MPI4py platform. The results show that, on average, the proposed CNS framework can save communication overhead by 40.6%, 35.5%, and 16.5%, reduce the runtime by 12.1%, 17.0%, and 10.0%, and improve the throughput by 16.2%, 24.4%, and 11.2%, respectively, when training GNN models with Cora, PubMed, and Large Taobao.
How to Progressively Build Thai Spelling Correction Systems?

Neural-based sequence-to-sequence methods (Seq2Seq) have proven to be highly effective for Context-sensitive Thai spelling correction. However, they also inherit the drawbacks of Seq2Seq, such as a fixed vocabulary and large data requirements. However, dictionary-based methods and their typical applications are insufficiently robust to produce corrections with reduced error rates. These drawbacks inhibit the application of these methods in a broader range of use cases. In this paper, we provide a practical guide on how to build correction systems progressively and efficiently with three main contributions. First, we present a process for efficiently and progressively producing training data for both neural-based and dictionary-based methods. Our annotation process enables existing methods to be trained with only two percent of the data hand annotated. Second, we propose the Extendable Neural Contextual Corrector (XNCC), a novel text correction approach that decouples the dictionary from the neural model. This enables the dictionary to be extended post-training. Finally, we compare text correction systems with various configurations to demonstrate how these systems can be effectively used to produce corrections. Our experiments show that (1) minor changes to dictionary-based methods can significantly improve correction performance, (2) neural-based correction systems can be trained using a fraction of the data, and (3) XNCC can have the dictionary extended to generalize to new data without re-training. Lastly, we provide recommendations for progressively building text correction systems at multiple levels of implementation effort based on our findings.
Deep Unsupervised Anomaly Detection in High-Frequency Markets

Inspired by recent advances in the deep learning literature, this article introduces a novel hybrid anomaly detection framework specifically designed for limit order book (LOB) data. A modified Transformer autoencoder architecture is proposed to learn rich temporal LOB subsequence representations, which eases the separability of normal and fraudulent time series. A dissimilarity function is then learned in the representation space to characterize normal LOB behavior, enabling the detection of any anomalous subsequences out-of-sample. We also develop a complete trade–based manipulation simulation methodology able to generate a variety of scenarios derived from actual trade–based fraud cases. The complete framework is tested on LOB data of five NASDAQ stocks in which we randomly insert synthetic quote stuffing, layering, and pump-and-dump manipulations. We show that the proposed asset–independent approach achieves new state-of-the-art fraud detection performance, without requiring any prior knowledge of manipulation patterns.
Textural Detail Preservation Network for Video Frame Interpolation

The subjective image quality of the Video Frame Interpolation (VFI) result depends on whether image features such as edges, textures and blobs are preserved. With the development of deep learning, various algorithms have been proposed and the objective results of VFI have significantly improved. Moreover, perceptual loss has been used in a method that enhances subjective quality by preserving the features of the image, and as a result, the subjective quality is improved. Despite the quality enhancements achieved in VFI, no analysis has been performed to preserve specific features in the interpolated frames. Therefore, we conducted an analysis to preserve textural detail, such as film grain noise, which can represent the texture of an image, and weak textures, such as droplets or particles. Based on our analysis, we identify the importance of synthesis networks in textural detail preservation and propose an enhanced synthesis network, the Textural Detail Preservation Network (TDPNet). Furthermore, based on our analysis, we propose a Perceptual Training Method (PTM) to address the issue of degraded Peak Signal-to-Noise Ratio (PSNR) when simply applying perceptual loss and to preserve more textural detail. We also propose a Multi-scale Resolution Training Method (MRTM) to address the issue of poor performance when testing datasets with a resolution different from that of the training dataset. The experimental results of the proposed network was outperformed in LPIPS and DISTS on the Vimeo90K, HD, SNU-FILM and UVG datasets compared with the state-of-the-art VFI algorithms, and the subjective results were also outperformed. Furthermore, applying PTM improved PSNR results by an average of 0.293dB compared to simply applying perceptual loss.
Developing a More Reliable Framework for Extracting Traffic Data From a UAV Video

Unmanned aerial vehicles (UAVs) have been used extensively in traffic data collection owing to their flexibility, stability, and ease of operation. However, vehicle detection methods using horizontal detectors are less efficient and accurate for UAV vehicle detection, whereas rotated detectors suffer the discontinuous boundaries problem. Therefore, we proposed a traffic data extraction framework based on YOLOv5-OBB object detection and DeepSORT-OBB tracking algorithms to extract highly accurate traffic data from UAV videos. The framework was tested using aerial videos recorded by a UAV-mounted high-definition camera. Field experiments were conducted to collect reference data from an onboard high-precision sensor for use in evaluating the precision of the extracted traffic data. The traffic data, including trajectory, yaw angle, speed, and heading of vehicles, were extracted from UAV videos captured in different traffic scenes. The overall extraction accuracy reached 98.5%, indicating the reliability of the proposed framework in extracting highly accurate traffic data.
Anomaly Detection Methods in Autonomous Robotic Missions

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on “anomaly” and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulates their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly and their detection methods could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.
Information Seeking Behaviour: Important Tools for Data Collection during Research Work

This study is information seeking behavior of students of Library and Information Science during their research work. Mainly students of M.LISc and M.Phil have been taken as respondent to carry out research work and from where the important tools took for research purpose has easily been found out. The paper displayed the online freely available sources have been taken by most of the students continuing their research work.
DAC: Disentanglement-and-Calibration Module for Cross-Domain Few-Shot Classification

Cross-domain few-shot classification (CD-FSC) aims to develop few-shot classification models trained on seen domains but tested on unseen domains. However, the cross-domain setup poses a challenge in the form of domain shift between the training and testing domains. Previous research has demonstrated that the encoder can disentangle features into domain-shared and domain-specific features. However, poorly estimated domain-specific features can lead to inadequate generalization on the unseen domain. This paper proposes a disentanglement-and-calibration module (DAC) to address this issue. The disentanglement component separates the features into domain-shared and domain-specific features, while the calibration component corrects the domain-specific features. We demonstrate that the DAC module can significantly enhance the generalization capability of several baseline methods. Furthermore, we show that MatchingNet with the DAC module outperforms existing state-of-the-art methods by 10%-11% when trained on mini-ImageNet, CUB-200, Cars196, Places365 and tested on Plantae dataset.