Reviewerly/exHarmony huggingface dataset:
Toward the Analysis of Graph Neural Networks

Graph Neural Networks (GNNs) have recently emerged as an effective framework for representing and analyzing graph-structured data. GNNs have been applied to many real-world problems such as knowledge graph analysis, social networks recommendation, and even COVID-19 detection and vaccine development. However, unlike other deep neural networks such as Feedforward Neural Networks (FFNNs), few verification and property inference techniques exist for GNNs. This is potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs. This paper proposes GNN-Infer, an approach to analyze and infer properties of GNNs by extracting influential structures of the GNNs and then converting them into FFNNs. This allows us to leverage existing powerful FFNNs analyses to obtain results for the original GNNs. We discuss various designs of CNN-lnfer to ensure the scalability and accuracy of the conversions. We also illustrate CNN-Infer on a study case of node classification. We believe that CNN-Infer opens new research directions for understanding and analyzing GNNs. ACM Reference Format: Thanh-Dat Nguyen, Thanh Le-Cong, ThanhVu H. Nguyen, Xuan-Bach D. Le, and Quyet-Thang Huynh. 2022. Toward the Analysis of Graph Neural Networks. In New Ideas and Emerging Results (ICSE-NIER’22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512780
The Symptoms, Causes, and Repairs of Workarounds in Apache Issue Trackers

In issue tracker systems, a bug report can be resolved as workaround. Since the definition of workarounds is vague, many research questions on workarounds are still open. For example, what are the symptoms of bugs which are resolved as workarounds? Why does a bug report have to be resolved as workarounds? What are the repairs and impacts of workarounds? In this paper, we conduct an empirical study to explore the above research questions. In particular, we analyzed 221 real workarounds that were collected from 88 Apache projects. Our results lead to ten findings and our answers to all the above questions. Our findings are useful to understand workarounds and to improve software projects and issue trackers.
Hybrid Quantum-Classical Machine Learning for Lithography Hotspot Detection

In advanced semiconductor process technology, lithography hotspot detection has become an essential task in design for manufacturability. The ability to detect and repair lithography hotspots that can affect printability is critical to improving yield and productivity. Machine learning technology has become a powerful tool in a variety of applications, from finance to manufacturing and computer vision. The use of quantum systems to process classical data using machine learning algorithms has created an emerging field of research, namely quantum machine learning (QML). We explore the possibility of converting a novel machine learning model to a hybrid quantum-classical machine learning that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. In addition, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities. Then we compare their training performance to quantify the expected benefits. These results can be used to build a future roadmap to develop circuit-based hybrid quantum-classical machine learning for lithography hotspot detection.
Edge Detection Based Motion Tracking

Tracking and Detection of objects in group of video is very useful in computer vision. This has numerous application in human computer interaction, robotics, surveillance systems and other fields. All of these systems necessitate real-time processing, and finding a way that is both efficient and simple. This work presents robust and fast approach to identify and to track the moving objects. The majority of present methodology is centered on tracking the edge detection of mobility via the fix edges. Image capturing, background subtraction, and Canny edge detection can all be used to detect moving objects. The background subtraction technique, as used in my techniques, is based on directly subtracting two consecutive frames to derive the difference image. The difference image denotes the locations where a moving item was in frame N and where the object is now in frame N+1. The results reveal that, in addition to its efficiency, the suggested method is capable of overcoming problems such as variations in brightness and changes in background over a time.
Autonomous development of theoretical framework for intelligence automation system using decision tree algorithm

• The objective of the framework development is focused on analysis of autonomous behavior related to engineering application. • Decision tree algorithm is used for the development of proposed theoretical framework. A novel technique is proposed to arrange enormous information on likelihood which actually depends on the Numerical Model. Having adopted the Two-Fold Development Investigation Model, the non-linear differential conditions are presented using information measurements, characterization of stable and curved capacity. This becomes an ideal estimation technique to build the parameters such as test insights, likelihood, thickness, capacity of the dispersion of information and grouping. The present research also aims to get the information arrangement by Sigma Test, examines the likelihood rule, rejects the span, and demonstrates the solidness of the numerical model & the progressive union. Through the reenactment, information investigation, the available results of the proposed method show that the model precision rate is high, the normal mistake rate is low, and it also becomes acceptable in the context of assembly. Here, the ideal forecast of enormous data is being done by control input arrangement, the ideal target work, utilizing the mayhem factors, nonlinear arbitrary crossing of classification, focus crossing. It also sets up the topological connection between information focuses, and further develops the order calculation. Recreation results show that the proposed calculation can adequately work on the exactness of huge information order and decrease the misclassification rate.
ReInstancer: Automatically Refactoring for Instanceof Pattern Matching

Pattern matching for instanceof is widely used with the advantage of conditionally extracting components from objects and with the disadvantage of the compulsory usage of type castings. The feature of pattern matching has been periodically previewed in the latest released version of JDK to avoid redundant type castings and to optimize pattern matching in multi-branch statements. Although pattern matching has many benefits, manual refactoring for instanceof pattern matching is time-consuming and tedious. Furthermore, no existing work can provide suﬃcient support for such refactoring. To this end, this paper presents a refactoring tool ReInstancer that mitigates and simplifies type castings by converting a multi-branch statement with instanceof pattern matching into a switch statement or expression automatically. ReInstancer is evaluated on 7 real-world applications, with a total of 4404 instanceof expressions. The evaluation results demonstrate that ReInstancer can successfully remove 2060 type castings and refactor 141 multi-branch statements with a total of 1972 instanceof pattern matching, which improves the code quality.
Quarantine Perimeter Breach Monitoring System on Smartphone for COVID-19 Self Quarantine Patient

Quarantine place perimeter breach COVID-19 patients that required to do self-isolate and self-quarantine from public is usually occur when the patient is not following the SOP and regulations drawn by government. This will lead to possibly spread of the COVID-19 virus into the community. These persons need to be closely monitored their movement or location online in order to prevent further spread of COVID-19 to public within their surrounding area. In view of this situation, the research project proposed to develop Quarantine Perimeter Breach Monitoring System (QPBMS) that can detect and alert the authority when the COVID-19 patient commit the offence of perimeter breach from their isolation place. A smartphone GPS and HERE Tracker apps are used in the system for the detection and notification alert. The impact of this monitoring system will enable authority or health official to locate and closely monitored the COVID-19 adhered to their self-isolation period. With the accurate and stable tracing of the HERE tracker, the health officer law enforcer can be notified with a precise info about any quarantine period violation.
Convergence of APCARD and RFID for Attendance System

Attendance plays an important role in education. Lecturers can use it to track the academic progress of their students. Admin and non-academic staff can ensure that international students are attending classes, complying with the law when in another country. Hence, an RFID based attendance is to be made for APU students, where students are required to have a minimum 80% of attendance to pass their modules. This research aims to intend to develop an attendance-based system using the RFID chip embedded in every APIIT student’s APCard. This system helps in reducing human error, fake attendances, and the students’ attendance in classes can be generated for analytical purposes.
Evaluation Method of Students’ “Hierarchical” Education Management Effect Based on Data Mining

In order to improve the accuracy of students’ hierarchical education management effect evaluation, this paper proposes a method of students’ hierarchical education management effect evaluation based on data mining. Firstly, the effect of data mining technology in the evaluation of students’ “hierarchical” education management effect is analyzed, and the abnormal data of education management is eliminated by cosine determination calculation method to realize the preprocessing of students’ “hierarchical” education management data. Then, establish the index system for the evaluation of students’ “hierarchical” education management effect, design the association rules for the evaluation of students’ “hierarchical” education management effect according to the Apriori algorithm, calculate the evaluation index weight according to the analytic hierarchy process and the average method of expert opinions, and realize the consistency test of the evaluation index weight according to the judgment matrix. Finally, the effect evaluation of students’ “hierarchical” education management is realized through data mining. The experimental results show that the accuracy of this method is up to 98.8%, and the evaluation time is only 1.05 s, which shows that this method can effectively improve the evaluation effect of education management effect.
Applying Reconfiguration Cost and Control Pattern Modeling to Self-Adaptive Systems

Self-adaptive systems have become a popular research topic to overcome challenges of developing highly complex, interconnected, and heterogeneous systems and networks. These systems aim to autonomously adapt to a changing environment by adapting system behavior or composition to improve performance. Many self-adaptive systems are designed in a purely reactive way and without considering costs that may be incurred by performing adaptation. This thesis therefore aims to develop an approach for proactive self-adaptive systems and evaluate the impact of reconfiguration cost and proactive adaptation in an edge computing system. Additionally the autonomous adaptation of different control patterns for centralized or decentralized control will be explored and evaluated. This thesis proposes to extend feature models, as used in dynamic software product lines, with modeling for reconfiguration cost and of uncertainty in the system’s environment.
RM2Doc: A Tool for Automatic Generation of Requirements Documents from Requirements Models

Automatic generation of requirements documents is an essential feature of the model-driven CASE tools such as UML and SysML designers. However, the quality of the generated documents from the current tools highly depends on the attached descriptions of models but not the quality of the model itself. Besides, if the stockholders ask to generate ISO/IEC/IEEE 29148-2018 conformed documents, extra templates are required. In this paper, we propose a CASE tool named RM2Doc, which can automatically generate ISO/IEC/IEEE 29148-2018 conformed requirements documents from UML models without any templates. In addition, the flow description can be generated from a use case without additional information. Moreover, it can automatically generate the semantic description of system operations only based on the formal expression of OCL. We have conducted four case studies with over 50 use cases. Overall, the result is satisfactory. The 95% requirements documents can be generated from the requirements model without any human interactions in 1 second. The proposed tools can be further developed for the industry of software engineering.The tool can be downloaded at http://rm2pt.com/rm2doc, and a demo video casting its features is at https://youtu.be/4z0Z5mrLfBc
Wheelchair Operation Using Image Analysis

A wheelchair plays a very important role in the life of a physically disabled person. Usually, a wheelchair is operated with the help of another person. There have been advancements in this field as there are wheelchairs that can be controlled using a remote control, voice control, and head gestures. This project deals with making an automatic wheelchair that can be controlled using hand gestures. The system input is taken via the camera. The camera records the hand gesture and based on the gesture the raspberry pi operates the wheelchair. The main components that will be using for the implementation of this project are raspberry pi, a webcam to take the input, a relay driver to drive the wheelchair, a keyboard, and a power supply. In this project, basic commands such as front, back, left, stop and rotate are implemented. The hand gestures for these inputs will be one, two, three, four, five, and fist.
Formation and Schema Analysis of Oil Painting Style Based on Texture and Color Texture Features under Few Shot

Texture has strong expressiveness in picture art, and color texture features play an important role in composition. Together with texture, they can convey the artistic connotation of portrait, especially in oil painting. Therefore, in order to make the picture form oil painting style and oil painting schema, we need to study the texture and color texture in combination with the previous oil painting art images. But now, there are few samples of good oil paintings, so it is difficult to study the texture and color texture in oil paintings. Therefore, in order to form a unique artistic style of modern oil painting and promote the development of modern oil painting art, this paper studies the texture and color texture characteristics in the environment of few oil painting works. This paper establishes a model through deep neural network to extract the image incentive and color texture of oil painting art works, which provides guidance for promoting the development of oil painting art. The experiments in this paper show that the depth neural network has high definition for the extraction of texture and color texture of small sample oil painting images, which can reach more than 85%. It has high guiding significance for the research and creation of oil painting art.
Static Test Flakiness Prediction

The problem of flakiness occurs when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the software engineering research community has been working toward defining approaches for detecting and addressing test flakiness, but most of these approaches suffer from scalability issues. Recently, this limitation has been targeted through machine learning solutions that could predict flaky tests using various features, both static and dynamic. Unfortunately, the proposed solutions involve features that could be costly to compute. In this paper, I perform a step forward and predict test flakiness only using statically computable metrics. I conducted an experiment on 18 Java projects coming from the FlakeFlagger dataset. First, I statistically assess the differences between flaky and non-flaky tests in terms of 25 static metrics in an individual and combined way. Then, I experimented with a machine learning approach that predicts flakiness based on the previously evaluated factors. The results show that static features can be used to characterize flaky tests: this is especially true for metrics and smells connected to source code complexity. In addition, this new static approach has performance comparable to the machine learning models already in the literature in terms of F-Measure.
Employment Recommendation System Using Predictive Analysis and Secured Through Blockchain Technology

Electronics engineering offers a wide variety of fields, specializations, and works in the industry that the graduates can pursue. They sometimes lead to job mismatches as their abilities and perspective are not align to the qualifications and skills that a certain job requires. In this project, an employment decision support system was developed to help the graduates foresee the most suited work positions when entering the industry as an electronics engineer through the evaluation of their licensure or board exam results; self-assessment results which includes their abilities, hobbies, and attitude towards work that are based on job descriptions; and academic grades results from first year to fifth year where the four main subjects of the Philippine electronics engineer licensure examination that is Mathematics, General Engineering and Applied Sciences, Electronics Engineering, and Electronics Systems and Technologies will serve as its classifications. The result is encrypted in Blockchain technology that will be used as a security feature to ensure the credibility and reliability of the whole evaluation.
Cyber Security Techniques Management

The internet is one of the most widespread and fastest-growing areas of technological development. In today's era, many organizations like business, distributed technologies, cloud computing, and next generation are periodically changing. Cyber security management designs a model to define an organization's policies for information security. The relationship between the goals of information security is as follows: The model is based on three rules, i.e., Confidentiality, Integrity, and Availability commonly known as the CIA triad. The variation of the data using a laptop or a personal computer is shown in Figure 9.1b The graph shows the percentage of PCs estimated to have spyware/adware and the percentage of PCs lacking data protection. Passwords are known as secret words or phrases used by different organizations and multiple sites to identify the users. Passwords are unfortunately an enormous security threat since they are unprotected against being broken or speculated by an individual or program.
Kernel triplet loss for image‐text retrieval

Abstract Triplet loss is widely used as the objective function in image‐text retrieval tasks. However, as all the triplets are treated equally, triplet loss has a bottleneck problem of slow convergence and other unsatisfactory performances. In this article, we propose solutions by appropriately weighting triplets according to the relative similarities among the training samples. Specifically, we present three weighting functions to assign an appropriate weight for the selected informative triplets to accelerate the convergence. We evaluate our approach on two widely used benchmark datasets: Flickr30k and MSCOCO, with results outperforming the previous methods, which demonstrates its superiority.
Reminisce: Blockchain Private Key Generation and Recovery Using Distinctive Pictures-Based Personal Memory

As a future game-changer in various industries, cryptocurrency is attracting people’s attention. Cryptocurrency is issued on blockchain and managed through a blockchain wallet application. The blockchain wallet manages user’s digital assets and authenticates a blockchain user by checking the possession of a user’s private key. The mnemonic code technique represents the most widely used method of generating and recovering a private key in blockchain wallet applications. However, the mnemonic code technique does not consider usability to generate and recover a user’s private key. In this study, we propose a novel approach for private key generation and recovery. Our approach is based on the idea that a user can hold long-term memory from distinctive pictures. The user can generate a private key by providing pictures and the location of the pictures. For recovering a private key, the user identifies the locations of the pictures that are used in the private key generation process. In this paper, we experiment with the security and usability of our approach and confirm that our proposed approach is sufficiently secure compared to the mnemonic code technique and accounts for usability.
Note-ing Hill: A Note-Making Application

This paper provides an insight into the approaches taken for the development of the application - ‘Noteing Hill’ that aims to optimize the task of taking down notes in a classroom and make it more efficient. This application involves the following components: speech-to-text transcription, image to text conversion and text summarization, collectively supported by a cloud back-end(AWS). For speech-to-text transcription AWS’ transcribe feature was employed while image-to-text conversion (more formally, OCR - optical character recognition) was performed with the help of the Tesseract engine and finally, for text summarization, the extractive approach of the Text rank algorithm was used.
Effective Usage of Cognitive Computing for Designing Smart Sensing Systems and Networks in Transportation

Thousands of people utilize public transit every day all across the world. People travel to new areas on a regular basis using public transportation, and they may feel entirely disoriented in a new environment at times. At this point, this chatbot is here to assist. A chat interface that communicates with humans. It is frequently referred to be one of the most promising technologies used for human-machine interaction. It’s a piece of software that employs deep learning methods and natural language processing (NLP) to conduct an online chat conversation via text/voice. In the form of a GUI, it offers direct communication with a conscious human agent. It is a software program that uses methods for deep learning and natural language processing (NLP) to conduct an online chat conversation via text/voice. In the form of a GUI, it offers direct communication with a conscious human agent. It analyses and extracts from the user’s inquiry the relevant database values. The cognitive Computing technique employed to these chatbots is in charge of effectively comprehending the user’s intents and avoiding any misunderstandings. The chatbots respond to the user’s query request with the most relevant response once the user’s intent is recognized. The user subsequently receives all of the info regarding the bus name as well as their figures, allowing them to comfortably go to their intended place. Proposed research makes use of much accessible Application Programming Interfaces (APIs), including the Dialog flow API for effective NLP combination with our TARS chatbot sensor.