Reviewerly/exHarmony huggingface dataset:
Automatically Generating Code Comment Using Heterogeneous Graph Neural Networks

Code summarization aims to generate readable summaries that describe the functionality of source code pieces. The main purpose of the code summarization is to help software developers understand the code and save their precious time. However, since programming languages are highly structured, it is challenging to generate high-quality code summaries. For this reason, this paper proposes a new approach named CCHG to automatically generate code comments. Compared to recent models that use additional information such as Abstract Syntax Trees as input, our proposed method only uses the most original code as input. We believe that programming languages are the same as natural languages. Each line of code is equivalent to a sentence, representing an independent meaning. Therefore, we split the entire code snippet into several sentence-level code. Coupled with token-level code, there are two types of code that need to be processed. So we propose heterogeneous graph networks to process the sentence-level and token-level code. Even though we do not introduce additional structural knowledge, the experimental results show that our model has a considerable performance, which indicates that our model can fully learn structural information and sequence information from code snippets.
Evaluating the Use of Semantics for Identifying Task-relevant Textual Information

The information a developer seeks to aid the completion of a task typically exists across a range of artifacts. For example, for a task that requires upgrading to a new version of an API component, a developer may seek information in the API&#x0027;s official documentation, check community discussions about the newer version, and so on. To aid developers in locating the portion of the text that might be useful in these artifacts, prior work has used syntactic properties of the text, and an artifact&#x0027;s meta-data, to automatically identify relevant text for particular kinds of artifacts. Although effective, these techniques rely on assumptions about an artifact&#x0027;s structure or content that prevent applying them across the different types of artifacts that a developer may come across in their daily work. In this paper, we investigate whether techniques building on approaches to interpret the meaning, or semantics, of the text help to overcome these limitations. Particularly, we introduce six semantic-based techniques and evaluate that they can identify up to 58&#x0025; of the text that developers deem relevant to Android development tasks. When compared to a state-of-the-art approach, we find that our techniques achieve comparable recall values, identifying 63&#x0025; of the small fraction of the task-relevant text of Stack Overflow artifacts, but without the need for artifact-specific information.
Identifying Relevant Changes for Incremental Verification of Evolving Software Systems

Modern software verification tools are moving towards incremental verification of program properties to ensure safety of evolving software systems. These tools analyze each and every change in the code. However, not every change in the program impacts verification outcome of program properties. Moreover, analyzing these irrelevant changes adds to cost of incremental verification. To address this, we are proposing a light-weight pre-analysis phase that identifies relevant changes with respect to program properties before applying any incremental verification technique. To identify such relevant changes, we present the Relevant Change Identification Technique (RCIT). RCIT uses a variant of the Strongly Live Variables (SLV) analysis to compute variables that are influencing the verification outcome of program properties. RCIT, then uses these variables to identify relevant changes. We evaluated RCIT on the changes made in five versions of open source applications with respect to two type of program properties - array index out of bound and zero division. RCIT identifies 59&#x0025; of the actual changes as irrelevant.
Phrase2Set: Phrase-to-Set Machine Translation and Its Software Engineering Applications

Machine translation has been applied to software engineering (SE) problems, e.g., software tagging, language mi-gration, bug localization, auto program repair, etc. However, ma-chine translation primarily supports only sequence-to-sequence transformations and falls short during the translation/transfor-mation from a phrase or sequence in the input to a set in the output. An example of such a task is tagging the input text in a software library tutorial or a forum entry text with a set of API elements that are relevant to the input. In this work, we propose Phrase2Set, a context-sensitive statistical machine translation model that learns to transform a phrase of a mixture of code and texts into a set of code or text tokens. We first design a token-to-token algorithm that computes the probabilities of mapping individual tokens from phrases to sets. We propose a Bayesian network-based statistical machine translation model that uses these probabilities to decide a trans-lation process that maximizes the joint translation probability. To achieve that, we consider the context of the tokens in the source side and that in the target side via their relative co-occurrence frequencies. We evaluate Phrase2Set in three SE applications: 1) tagging the fragments of texts in a tutorial with the relevant API elements, 2) tagging the StackOverflow entries with relevant API elements, 3) text-to-API translation. Our empirical results show that Phrase2Set achieves high accuracy and outperforms the state-of-the-art models in all three applications. We also provide the lessons learned and other potential applications.
An Exploratory Study on the Usage of Gherkin Features in Open-Source Projects

With behavior-driven development (BDD), domain experts describe system behavior and desired outcomes through natural language-like sentences, e.g., using the Gherkin language. BDD frameworks partially convert the content of Gherkin specifications into executable test code. Previous studies have reported several issues with the current BDD practice, for example long repetitive Gherkin specifications and slow-running test suites. Data tables and additional features were added to the Gherkin syntax to express compactly test inputs (e.g., provide different combinations of input values and desired outputs to run tests multiple times) and also to improve the readability of Gherkin files (henceforth called spec files). However, there is no empirical evidence about the actual usage of these Gherkin features. To fill this gap, we analyzed the content of 1,572 spec files extracted from 23 open-source projects. For each spec file, we collected a set of metrics modeling the structure and the usage of the different Gherkin features. We found that only a minority of the considered spec files (i.e., 590) used data tables that contain two rows, on average. We also used statistical tests to compare the contents of spec files with and without data tables and found significant differences between the two populations, especially for what concerns the number of lines of code (LoC). On the one hand, our results shed some light on the discrepancies between the recommendations for defining Gherkin specifications and their actual adoption in practice. On the other hand, our findings demonstrate that the adoption of additional features, such as data tables, might only partially help to reduce the length of Gherkin specifications.
Historical Consideration and Statistical Analysis of Educational Evaluations on Advanced Course of National Institute of Technology in Japan

The advanced course of National Institute of Technology in Japan was established in Kosen (Japanese colleges of technology) education, 30 years ago. This advanced course is two-year course where higher education based on Kosen education and accepts approximately 1,500 students each year. Graduates of this course have bachelor’s degrees from the National Institution for Academic Degrees and Quality Enhancement of Higher Education (NIAD-QE). This research aims historically consider the progress of this advanced course system and to analyze the quality assurance elements of this advanced course education system from external evaluations.
Person Re-identification Using Multi-branch Cooperative Network

Person Re-identification (Re-ID) is to match the images of the given person across multiple non-overlapping cameras. To solve the problems of occlusions and unconstrained poses, we propose a multi-branch cooperative network for person Re-ID. First, attention branch and multi-scale branch are designed respectively. In the attention branch, we design shade module, random erasing module and stepped module, and guide each module to learn discriminative features of different regions through the consistent activation penalty function. In the multi-scale branch, we design a global and a local module to learn deep features to improve the performance of person Re-ID. Finally, the two branches are cascaded to concatenate multi-branch features. Extensive experiments on Market-1501, DukeMTMC-reID and CUHK03 demonstrate that the proposed method outperforms the state-of-the-art methods.
Automatic Flythrough Authoring in VR

The presentation of architectural models in virtual reality (VR) enables architects to evaluate their designs and present them to stakeholders, improving fidelity of architectural communication in architectural design.Virtual walkthroughs are a powerful tool to convey architectural concepts and unique features of the planned building.However, creating walkthroughs is a rather tedious process involving manual setting of keypoints at points of interest and in between, which are then interpolated to create the trajectory.This often results in awkward camera poses, sharp turns, intersection with geometry and clipping problems, all of which must be resolved by tweaking or adding new keypoints.Furthermore, the process requires constantly switching between an inside view, for local refinements, and an outside overview, for more global adjustments.To address this challenge, we present an interactive design tool for walkthroughs in VR.Our tool automatically provides a set of ideal keypoints, based on a customizable set of objectives, and instantly generates a complete and smooth trajectory that passes through them.It allows the user to tweak positions and update the walkthrough in real time, in VR.In addition, it allows the designer to have both an inside view and an overview simultaneously.
A New Image Encryption Based on Two Chaotic Maps and Affine Transform

In the present era, the non-linear and dynamic phenomenon of chaos play a vital role in image encryption. In this paper, we proposes a new image encryption method based on cubic map and Hénon map. The cubic map is employed and combined with affine transformation in substitution phase. The key expansion technique is built up with the Hénon map. Finally, the encryption process incorporates the Nonlinear key mixing (Nmix) function which enables to propagate the bit-pattern from $$n^{th}$$ ciphered pixel to $$(n+1)^{th}$$ ciphered pixel. The performance of the scheme has been successfully verified by several well known statistical attacks, differential attacks, and brute force search. Moreover, in comparative studies the proposed scheme has confirmed its potential with respect to related existing schemes.
Learning in the Open: A Netnography of Twitter Chats for English Language Teachers

Increasingly precarious working conditions for English language teachers in Canada (Breshears, 2019) and faculty in higher education (Canadian Centre for Policy Alternatives, 2021), as well as a global lack of professional development (PD) funding opportunities (Rosell-Aguilar, 2018) have pushed teachers to seek alternative forms of professional development. Many teachers have turned to social media platforms to pursue PD. Social media sites such as Twitter present unique opportunities for teachers to engage in self-directed professional development that is accessible, convenient, and immediately relevant. This paper documents part of an exploratory netnographic study of English language teachers’ (ELT) self-directed teacher development in Twitter chats as ELTs explored issues related to sustainability, intersectionality, and representation. The study consisted of several stages of data collection, beginning with social network analysis, using the data analysis tool NodeXL, and the first stage informed subsequent stages of collection and/or analysis. This study’s purpose in examining self-directed ELT PD in digital spaces attempted to shift away from an anthropocentric view of PD. The results of the study revealed that teachers benefit from chats because of Twitter’s immediacy, which offers teachers a platform to engage on topics broader than the methods and strategies for teaching language. Additionally, teachers appreciate Twitter for the opportunities to connect with teachers globally. ELTs indicated a preference for Twitter as the first place to explore self-directed development, but they were not always aware of the impact Twitter had on their communication. This study in self-directed teacher PD draws attention to the immediacy of Twitter and its affordances for individuals who participate in PD that is unique to their context, “just in time”, and anonymous, while also revealing the challenges and opportunities of public professional development on social media platforms.
A Secure Image Watermarking Technique Using Chinese Remainder Theorem in Integer Wavelet Transformation Domain

In this research work, a secured watermarking method has been proposed using Chinese Remainder Theorem (CRT) in combination with Integer Wavelet Transform (IWT). The CRT is applied for embedding watermark information by modifying IWT coefficient. The scheme uses the SHA-512 hash algorithm which encrypts the original watermark bit before embedding. The secret informations are embedded into the IWT coefficients of the LH, HL and HH subbands as calculated by the CRT method. The combination of CRT and IWT are produce a high imperceptible image without compromising the robustness of the proposed method. The metrics like PSNR, SSIM are used to analyse the imperceptibility and NCC, BER are used to analyse the robustness of the scheme. After that, the outcomes are evaluated with a number of other relevant schemes in order to validate the superiority.
Research on Preliminary Lesson Considering Motivation in Standard Tasks of NOKAIDAI

In the standard task “Embedded system construction task training” of the department of production electronic information systems technology in NOKAIDAI, a series of “manufacturing” efforts such as “planning, design, production, evaluation, presentation” as the production theme “embedded system using communication technology” is carried out as a group work. However, there are some students who cannot work on their own initiative. Therefore, in order to solve this problem, we developed preliminary lessons for standard tasks including its teaching materials considering the intrinsic motivation, conducted the lessons, and verified the effects.
HPEMed: Heterogeneous Network Pair Embedding for Medical Diagnosis

Capturing a high-quality representation of clinical events in Electronic Health Record (EHR) is critical for upgrading public medical applications such as intelligent auxiliary diagnosis systems. However, the relationships among clinical events have different semantics and different contributions to disease diagnosis. This paper proposes a novel heterogeneous information network (HIN) based model named HPEMed for disease diagnosis tasks. HPEMed takes advantage of high-dimensional EHR data to model the nodes (with features) and edges of a graph. It exploits meta paths to higher-level semantic relations among EHR data and employs a pair-node embedding scheme that considers patient nodes with rich features and diagnosis nodes together, which achieves a more reasonable clinical event representation. The experimental results show that the performance of HPEMed in diagnosis tasks is better than that of some advanced baseline methods.
Novel Design of Dual Voice Coil Microspeaker with Reduced Back Volume

Microspeakers are typically used in portable devices to generate sound. As smartphones become increasingly compact, the space available for microspeakers also decrease, which reduces the back volume from <tex>$0.60\ \text{cm}^{3}$</tex> to <tex>$0.25\ \text{cm}^{3}$</tex>. This reduction results in an increase in the stiffness of the microspeaker, which affects the sound pressure level (SPL) at low frequencies. This paper proposes a dual-coil microspeaker design for SPL enhancement at low frequencies while maintaining the same size as a prototype microspeaker. The analysis method uses to solve the electromagnetic-mechanical-acoustic coupling system is verified experimentally in advance. The proposed dual-coil microspeaker is designed and analyzed using three-dimensional finite element methods. Samples are manufactured and measured. Compared to the prototype, the novel dual-coil microspeaker in serial connection presents a higher Lorentz force (+7.4 &#x0025;), resulting in an SPL improvement of 2.27 dB at low frequencies.
Real-Time and Accurate Monocular 3d Sensor Using the Reference Plane Calibration and an Optimized Sgm Based on Opencl Acceleration

Speckle projection profilometry (SPP), as a promising structured light projection technique, can achieve global unambiguous 3D measurement by projecting a single random speckle pattern. In addition, the projected speckle pattern is usually etched into the microstructure of highly integrated Vertical-Cavity Surface-Emitting Laser (VCSEL), which makes the hardware system compact enough to be mounted on mobile devices such as robots. However, since the stereo matching algorithm used in SPP involves high computational overhead, it usually runs in real-time on specially customized hardware platforms such as ASIC/FPGA, rather than general-purpose mobile platforms. In this paper, we propose a real-time and accurate 3D measurement method using a monocular 3D sensor based on the infrared speckle projection. Similar to Kinect v1, our 3D sensor mainly consists of an IR dot projector and one IR camera for projecting and capturing speckle images synchronously. Low-cost and high-quality speckle projection is achieved by customizing the projection pattern of VCSEL and using the beam copy function of Diffractive Optical Elements (DOE). Based on the 3D imaging principle of monocular 3D sensors, a reference plane calibration method is proposed to obtain a high-quality reference speckle image for improving the monocular matching accuracy. Then, benefited from the local memory mechanism and multiple operating synchronizations on the OpenCL environment, an optimized semi-global matching (SGM) algorithm using GPU is presented to achieve efficient and accurate depth reconstruction dynamically. Within the measurement range of 0.8m (length) × 0.5m (width) × 1m (depth), the proposed method can achieve real-time and single-shot 3D imaging with an accuracy of 1.277 mm at 75 FPS on GTX 1060 and 15 FPS on ARM Mail G52(mobile platform).
Cyber4Dev Security Culture Model for African Countries

Creating a good information security culture among employees within organizations is the cornerstone for a safe and robust cyberspace. Furthermore, a strong information security culture within organizations will assist in reducing the effects of human habits that lead to data breaches. This article seeks to conduct a scoping review of the scholarly literature on Cyber Resilience for Development (Cyber4Dev) security culture within the context of African countries. With limited scholarly articles available for Cyber4Dev, the review will focus on information security culture to adapt it to a Cyber4Dev security culture that organizations in Africa can replicate. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for the scoping review, this paper analysed 40 scholarly articles on information security culture to propose a Cyber4Dev security culture model for organizations applicable within an African context. Economic, social-culture and trust were identified as some of the factors to consider in an African context to promote an information security culture. Organisations can consider these factors as part of their information security programs. The model serves as reference for further research to explore the influence of the identified factors in an African context.
A Dual Image Based Data Hiding Scheme Based on Difference of Pixel Values in Integer Wavelet Transform Domain

In this paper, a dual image-based data hiding scheme has been proposed in Integer Wavelet Transform (IWT) domain. The proposed method can embed secret data utilizing the differences between the values of the adjacent pixels. Here, the cover image is converted to integer coefficients after applying IWT. The coefficients of the IWT subbands are divided into a number of non-overlapping blocks. The maximum difference of a particular coefficient from another coefficient inside a block is calculated. The number of secret bits to be embedded in a single coefficient inside a block depends on the amount of this maximum difference value. The decimal equivalent of the secret bits is split to embed in the dual images. The secret data is folded to minimize the modification of the original pixel in both of the dual images. Using the Integer Wavelet Transform (IWT) domain instead of spatial domain minimizes the distortion in pixel values in both the stego images. Moreover, different subbands of the IWT coefficients are used efficiently to solve the problems regarding the center folding strategy. The experimental results show the superiority of the proposed scheme over some existing schemes in terms of imperceptibility and robustness. The proposed scheme embeds secret bits depending on the characteristics of the image. It also maintains a minimum difference between the same pixel values in both the stego images and retains good visual quality of the stego images.
Premium Control With Reinforcement Learning

We consider a premium control problem in discrete time, inspired by [9] and [10], formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimension of the state space. Hence, to combat the curse of dimensionality we explore reinforcement learning techniques, using linear function approximation. We illustrate the appropriateness of the approximate optimal premium rule compared with the true optimal premium rule in a simplified setting, and further demonstrate that the approximate optimal premium rule outperforms benchmark rules in a more realistic setting where classical approaches fail.
Photonic Quantum Computing

This chapter delves into the domain of photonics quantum computing and its applications. Unlike classical computing, photonics quantum computing exploits photons for the basic model of computation, which is due to their low or negligible loss and the fact that they can operate at room temperature which is also very promising. The Photonic Quantum Computers aptly satisfy five out of seven Vincenzo’s conditions. Consequently, extensive research has been done for the physical realization of scalable Photonic Quantum Computers, because of their covenanting properties like scalability, and their robust nature towards fault-tolerance making it an interesting and nearly viable candidate as a quantum computer. Photonic Quantum Computers include a wide variety of applications from Quantum Machine Learning and Quantum Cryptography to Quantum Key Distribution.