Reviewerly/exHarmony huggingface dataset:
Checking Refactoring Detection Results Using Code Changes Encoding for Improved Accuracy

For example during software maintenance, it is often important to know the reason for a code change and therefore tools are researched to automatically detect changes due to refactorings. The tool RefDiff can achieve this supporting multiple programming languages. It provides a good precision, but at the cost of a large number of false negative results due to the necessary use of a high threshold in refactoring candidate selection. We have created a result checker that improves the overall performance of RefDiff by including more candidates and reducing false positives from RefDiff detection results afterwards. The checker encodes the textual differences (so-called diffs) corresponding to the results and uses machine learning to predict the contained refactoring type. The main contribution of this paper is the approach for extracting the diffs from the detection results and encoding them as image data for machine learning processing, as well as the training of the machine learning algorithm. We have shown that lowering the candidate threshold in conjunction with the checker improves not only the recall of RefDiff, also the precision is increased. Our approach improves the RefDiff detection results to 99.5% precision and 95.2% recall.
Credit Card Fraud Detection using Bagging and Boosting Algorithm

In the era of continuously evolving technology and increasing number of credit card holders it has become necessary to find techniques to detect fraud transactions. This problem can be tackled with data science and machine learning. This project aims to build a model that can efficiently classify fraud and non-fraud transactions. After training the sample data set by the model, this model can be further used to detect whether new transactions are fraud or not. Our objective here is to detect fraud transactions with maximum accuracy and to reduce the incorrect classification of transactions. Fraud detection is a classification problem in machine learning. We have focused on analyzing and preprocessing the data set. Further the data is trained using classification algorithms like Random Forest, which is a Bagging based algorithm and Adaboost and XGBoost which are Boosting based algorithms.
Face Recognition for Attendance System using CNN based Liveliness Detection

The high-speed development of the technology behind facial recognition systems has enabled its usage in the application to record attendance without human interference. The advantage of the software lies in its security, authentication, and identification features. The paper proposes one such attendance system for educators. The five phases of the attendance system are database creation of students, face detection, liveliness detection, face recognition, and attendance marking. The database is created by the images of the students in class. One of its distinguishing features is the liveliness detection algorithm, which solves the proxy attendance problem. Liveliness detection is performed using blinking detection and CNN classification between real and spoof images. The accuracy obtained from the system is 93%. Further, face recognition is performed using FaceNet. A web-based application is developed to make attendance marking more convenient for educators.
Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.
Soceng Warriors: Game-Based Learning to Increase Security Awareness Against Social Engineering Attacks

Most businesses rely on technology, such as the internet and smartphones. A significant amount of money is spent on security tools, software, and hardware, but a naive employer can provide an attacker with all the information without accessing the system. Social engineering is about utilizing the weakest aspect of any institution or organization: the human element. Humans are more accessible to compromise than computer systems and networks. Most people are raised to be polite and helpful, causing them to trust others by nature. Improving cybersecurity technologies and infrastructure alone will not suffice to combat social engineering attacks. An education approach is one strategy. This paper suggests ‘Soceng Warriors,’ an educational game designed to raise security awareness of social engineering attacks. This game was tested by 30 responders aged 14 to 25 years. Each responder completed the pre-test and post-test questionnaires. The test is used to determine the extent to which people are aware of social engineering attacks. According to the results of the paired T-test, Soceng Warriors can raise security awareness against social engineering assaults.
PVCLS-SI: Isogeny-based Certificateless Signature Scheme

Certificateless signatures suffer from neither cer-tificate management problems in public key infrastructure nor key escrow problems in identity-based signatures. Isogeny-based cryptography is a promising approach for post-quantum cryp-tography. In this paper, we present a certificateless signature scheme, PVCLS-SI, based on isogenies between supersingular elliptic curves. We prove strictly that our scheme is unforgeable against chosen message attacks. Meanwhile, verifiers can effi-ciently authenticate the identity of the signer by using piecewise verifiable proofs. Particularly, the implementation result shows that our scheme is efficient and the key and signature sizes remain compact, which are 2.4 KB in total.
Research Trends in Agile Software Development

Agile software development (ASD) is a popular research area that attracts the attention of the software development industry as well. Many studies have been conducted to explore the issues related to the ASD domain. Research is still very vigorous in this domain as there is continuous interest from companies to adopt ASD in their software development processes. Moreover, with the remote work setting that the pandemic forces, software companies search for new methods and approaches to manage their projects effectively and successfully. Hence, this study explores the research conducted in the last two decades in the ASD domain through text mining to reveal the trending topics that get attention from scholars. According to the results of this study, eight topics are identified for the research in ASD domain. Among those, topics of Team Communication, Agile Development, and Software Development Process are the most popular, the most funded, and the most cited topics respectively.
Evaluation of Campus Event Management Information System Using System Usability Scale Method

Within a year, universities as educational institutions can organize tens or hundreds of activities or events, such as seminars, workshops, exhibitions, cultural festivals, arts and sports and others. A web-based Campus Event Management Information System was developed and implemented to provide better managerial services in managing campus activities for both internal and external parties. This research is a continuation of previous research and focus on the evaluation of usability system to confirm whether users could approve Campus Event Management Information System using System Usability Scale (SUS). The average value of SUS is 70.80, indicating that the system is classified OK/fair based on the adjective ratings users. The system has not fully accepted and needs improvement in GUI interface since some users have difficulty in navigating the systems.
RF Fingerprint Recognition Method Based on DBN-SVM

Radio frequency fingerprint identification technology is an emerging technology to ensure the security of wireless network. Aiming at the classification of multiple transmitters with highly similar structures and emitting the same RF signal, this paper proposes a radio frequency fingerprinting method based on deep belief network and support vector machine (DBN-SVM) model. First, the signals of different devices are preprocessed, and then the DBN model optimized by Quantum Particle Swarm Optimization (QPSO) is used for feature extraction. Finally, the features extracted by the DBN are put into the hierarchical SVM (H-SVM) for classification to obtain the classification results. The experimental results show that the accuracy of the RF fingerprinting algorithm based on DBN-SVM reaches 92.53 % in complex electromagnetic environment and real channel conditions.
A Facial Sentiment Recognition System based on Smart Edge Device

How to establish a facial sentiment recognition system based on Computer Vision is a hot research issue of affective computing but still remains an open problem. This paper first introduced the architecture of our facial sentiment recognition system and the control flow in detail. We then used a self-built dataset containing more than 6000 facial expression images to train the convolutional neural network model, which is the core classifier of our sentiment recognition system. The hardware in the study adopts Yahboom K210-Deve1oper-Kit which is a low-power, low-computing power, low-cost development board, in the meanwhile programs and our six-layer neural network model are installed on the development board. Experimental results show that on edge devices with limited computational resources, our system is able to recognise four facial expressions (i.e. smiling, neutral, angry and surprised) with over 80% accuracy.
Hybrid Transformer and Convolution for Medical Image Segmentation

Using medical image segmentation algorithm to automatically label diseased organs or tissues can effectively help doctors to diagnose and treat various diseases. Most segmentation models are designed based on convolution. However, due to the inherent limitations of convolution operation, its receptive field is usually limited and cannot capture the global information. With transformer, it can capture long distance dependencies, but the lack of local detail can lead to limited predictive capability. Therefore, aiming at the above problems, we propose a segmentation method based on hybrid transformer and convolution. It effectively merges the classic convolution layer with the transformer layer to help guide accurate segmentation. Specifically, a lightweight convolutional layer is first used to extract low-level features and reduce the amount of data. This is followed by a mix of transformer blocks and convolution blocks to aid in the extraction of high-level information. This hybrid convolution layer and transformer layer help to improve the generalization and robustness of the learned representation. In the decoding part, the feature maps of adjacent dimensions are up-sampled into higher resolution feature maps and sent to transformer layer for decoding. The cross-fusion attention mechanism is used to adaptively screen the valid features from the encoded part by reducing the semantic gap between the feature maps of the encoder and decoder subnets. We have done full experiments on Synapse dataset and proved that the proposed method has great competitiveness and advantages compared with other segmentation methods.
A Heterogeneous Learning Resources Recommendation Algorithm Combination of Content-Based and Keyword Vectors Weight

At present, there are a variety of learning resources which can be applied to personalized recommendations in the field of online education. While being stored, these learning resources should be described in text. Therefore, the use of content-based recommendation algorithms in the resource recommendation field can achieve good recommendation results and can solve the cold start problem of collaborative filtering. However, because traditional content-based recommendation algorithms only consider the statistical characteristics of text when modeling items, it has certain semantic analysis defects and cannot handle unstructured data such as audio and video. In this paper, first, aiming at the problem that there are many types of learning resources and the structure are quite different, the resource standardization metadata structure and heterogeneous resource mapping rules are defined, then build a standardized modeling framework for heterogeneous resources and realize the standardization of heterogeneous resources. Subsequently, the problem of semantic analysis flaws in traditional content-based recommendation algorithms can be solved by combining the traditional content-based recommendation algorithm and the Word2Vec word vector model with semantic analysis advantages, the statistical features and semantic features of the resource text are comprehensively extracted to perform resource feature modeling to solve the problem. The traditional semantic analysis defect problem based on content recommendation algorithm is solved, and the similarity calculation method of traditional algorithm is improved to further improve the recommendation effect of recommendation algorithm.
Autonomous Museum Tour Guide Robot With Object Detection Using Tensorflow Learning Machine

This device focuses on making tools as a substitute for human resources in historical tourism by creating the Autonomous Museum Tour Guide Robot. This robot is made to help tourists in the Museum walk around in a coherent way throughout the museum area and explain comprehensive information and experiences related to the Museum and the existing collections. The tool’s implementation is controlled by the deep learning Convolutional Neural Network method using the Tensor flow framework to recognize and classify the detected objects. The robot that will be created is a 3-wheeled robot with one camera as a sensor to detect objects around the robot. The robot is equipped with an audio speaker to provide object detection information. The robot detected six objects at the Sumenep Palace Museum, which were integrated into a robot with a 100% success percentage in the 5th epoch with the 175th iteration. The time required was 117.11 error of 0.393. Weaknesses in this study are the need for control throughout the tour guide robot so that it runs more stable and the use of a camera with a higher resolution, but when run on the system, it does not affect system performance.
Fusion based Underwater Image Enhancement and Detail Preserving

Underwater images suffer degradation of image quality and visibility due to color retention and contrast under non-uniform illumination of light penetrating in the underwater medium. In this paper, we propose a frequency domain model-free method comprising four phases to improve underwater image quality. The first phase includes homomorphic filtering with a high emphasis filter to obtain the edge region by enhancing the contrast of the RGB image. The second phase comprises a discrete wavelet transform (DWT) decomposition of the homomorphic filtered output image from the first phase, and wavelet denoising to obtain the background region by enhancing low-frequency components of each RGB channel. DWT improves the further edge region and enhances the background region. To overcome color distortion and low contrast problems, the underwater image is reconstructed using Inverse DWT and color correction. The third phase is wavelet-based image fusion with the mean approximation of low and high-frequency components. The fourth phase includes a guide filter applied to the fusion output image to preserve the image edges. Further, the unsharp masking filter amplifies the high-frequency components to enhance the edges. The proposed method experimental results effectively improve in terms of low contrast, color retention and better visibility.
Underwater Image Enhancement Using Color Constancy Via Homomorphic Filtering and Depth Estimation

Underwater image visibility depends on the light scattering and absorption, and the illumination variations degrade the image details with color distortion, low contrast, blurring, non-uniform illumination etc. An image enhancement framework is proposed to address the degradation problems in underwater images. This framework comprises three steps: The first step is to process an input RGB image via white patch retinex (WPR) based gamma correction for illumination compensation and chromatic adaptation technique (CAT) in adjusting the color in RGB image based on estimated scene illuminant by WPR; Second step is to perform image enhancement via homomorphic filtering by considering only the high-frequency components with high frequency emphasis filter (HFEF), and finally dark channel prior (DCP) method is adapted to obtain the visually enhanced image by preserving the image details. The proposed method is tested on a standard underwater image dataset, and observed that qualitative and quantitative evaluations are better with visibility and color restoration with reduced artifacts and halo effects.
Image Dehazing Based on Multi-scale Retinex and Guided Filtering

Haze removing from single image is a challenging task in the field of computer vision. Aiming at the problems of low contrast, details loss and serious color distortion, this paper proposed an effective dehazing method. Firstly, the original image is decomposed into reflective image channel and illumination image channel by multi-scale retinex theory; Secondly, the traditional histogram equalization and guided filter are used in the reflective image channel to enhance contrast and compensate details from guided image; Then, 2-D gamma transform in the illumination image channel for light compensation; Finally, white balance on composite image to solve the problem of color distortion. Experimental results demonstrate that the proposed algorithm can efficiently enhance image contrast, as well as improving color visual effects and details of images.
Deep Learning Super Resolution of Sea Surface Temperature on South China Sea

Surface temperature is one of the key observations to analyse the greenhouse effect on the Earth. The surface of the ocean can be captured using satellite sensors and transmitted to a meteorological center for real-time analysis. The use of the deep learning paradigm in super resolution has its potential in geoscience applications to increase the data transmission latency and enhance low-quality observation from remote sensing data. In this paper, the deployment of Generative Adversarial Network (GAN) architecture is studied to apply resolution reconstruction using the South China Sea sea surface temperature data. In addition, the development of spectral normalization is added to the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) architecture to improve the training mechanism of generator and discriminator. This improved ESRGAN is compared with its super resolution performance against peak signal-to-noise ratio and structural similarity index evaluation metrics. The experiment shows that the low resolution of South China Sea data can be inferred to obtain a higher resolution with a more realistic resolution as compared to the conventional upsampling approaches.
Path Planning for Mobile Robot Based on Deep Reinforcement Learning and Fuzzy Control

Aiming at the problems of slow training speed and poor generalization ability of deep reinforcement learning model in path planning, this paper proposes a multi-controller model of Dueling DQN combined with fuzzy control(DDFC). At the beginning of training, fuzzy control is used to provide a large number of positive samples for the Dueling DQN model, so as to improve the training efficiency of the model while ensuring that the mobile robot has certain obstacle avoidance ability at the beginning of training. The negative feedback shaping reward function and state space are designed to alleviate the problem of sparse reward. Aiming at the fact that the membership function of traditional fuzzy control can’t deal with difference situations in the process of moving, an improved membership function is designed, which can change according to the change of the situation. The simulation results show that the improved model can make the mobile robot avoid obstacles effectively and improve the rate of convergence. It also has good performance in different scenes and improves the generalization ability of the model.
A preliminary evaluation on the relationship among architectural and test smells

Software maintenance is the software life cycle's longest and most challenging phase. Bad architectural decisions or sub-optimal solutions might lead to architectural erosion, i.e., the process that causes the system's architecture to deviate from its original design. The so-called architectural smells are the most common signs of architectural erosion. Architectural smells might affect several quality aspects of a software system, including testability. When a system is not prone to testing, sub-optimal solutions may be introduced in the test code, a.k.a. test smells. This paper explores the possible relations between architectural and test smells. By mining 798 releases of 40 open-source Java systems, we studied the correlation between class-level architectural and test smells. In particular, Eager Test and Assertion Roulette smells often occur in conjunction with Cyclically-dependent Modularization, Deficient Encapsulation, and Insufficient Encapsulation architectural smells.
Classification and Ranking of Delta Static Analysis Alarms

Static analysis tools help to detect common pro-gramming errors but generate a large number of false positives. Moreover, when applied to evolving software systems, around 95 % of alarms generated on a version are repeated, i.e., they have also been generated on the previous version. Version-aware static analysis techniques (VSATs) have been proposed to suppress the repeated alarms that are not impacted by the code changes between the two versions. The alarms reported by VSATs after the suppression, called delta alarms, still constitute 63% of the tool-generated alarms. We observe that delta alarms can be further postprocessed using their corresponding code changes: the code changes due to which VSATs identify them as delta alarms. However, none of the existing VSATs or alarms postprocessing techniques postprocesses delta alarms using the corresponding code changes. Based on this observation, we use the code changes to classify delta alarms into six classes that have different priorities assigned to them. The assignment of priorities is based on the type of code changes and their likelihood of actually impacting the delta alarms. The ranking of alarms, obtained by prioritizing the classes, can help suppress alarms that are ranked lower, when resources to inspect all the tool-generated alarms are limited. We performed an empirical evaluation using 9789 alarms generated on 59 versions of seven open source C applications. The evaluation results indicate that the proposed classification and ranking of delta alarms help to identify, on average, 53 % of delta alarms as more likely to be false positives than the others.