Reviewerly/exHarmony huggingface dataset:
Sketch-Guided Latent Diffusion Model for High-Fidelity Face Image Synthesis

Synthesizing facial images from monochromatic sketches is one of the most fundamental tasks in the field of image-to-image translation. However, it is still challenging to teach model high-dimensional face features, such as geometry and color, and to the characteristics of input sketches, which should be considered simultaneously. Existing methods often use sketches as indirect inputs (or as auxiliary inputs) to guide models, resulting in the loss of sketch features or in alterations to geometry information. In this paper, we introduce a Sketch-Guided Latent Diffusion Model (SGLDM), an LDM-based network architecture trained on the paired sketch–face dataset. We apply a Multi-Auto-Encoder (AE) to encode the different input sketches from the various regions of a face from the pixel space into a feature map in the latent space, enabling us to reduce the dimensions of the sketch input while preserving the geometry-related information of the local face details. We build a sketch-face paired dataset based on an existing method XDoG[30] xdog and Sketch Simplification [24],[25] that extracts the edge map from an image. We then introduce a Stochastic Region Abstraction (SRA), an approach to augmenting our dataset to improve the robustness of the SGLDM to handle arbitrarily abstract sketch inputs. The evaluation study shows that the SGLDM can synthesize high-quality face images with different expressions, facial accessories, and hairstyles from various sketches having different abstraction levels, and the code and model have been released on the project page. https://puckikk1202.github.io/difffacesketch2023/.
Optimizing With Low Budgets: A Comparison On the Black-Box Optimization Benchmarking Suite and OpenAI Gym

The growing ubiquity of machine learning (ML) has led it to enter various areas of computer science, including black-box optimization (BBO). Recent research is particularly concerned with Bayesian optimization (BO). BO-based algorithms are popular in the ML community, as they are used for hyperparameter optimization and more generally for algorithm configuration. However, their efficiency decreases as the dimensionality of the problem and the budget of evaluations increase. Meanwhile, derivative-free optimization methods have evolved independently in the optimization community. Therefore, we urge to understand whether cross-fertilization is possible between the two communities, ML and BBO, i.e., whether algorithms that are heavily used in ML also work well in BBO and vice versa. Comparative experiments often involve rather small benchmarks and show visible problems in the experimental setup, such as poor initialization of baselines, overfitting due to problem-specific setting of hyperparameters, and low statistical significance. With this paper, we update and extend a comparative study presented by Hutter et al. in 2013. We compare BBO tools for ML with more classical heuristics, first on the well-known BBOB benchmark suite from the COCO environment and then on Direct Policy Search for OpenAI Gym, a reinforcement learning benchmark. Our results confirm that BO-based optimizers perform well on both benchmarks when budgets are limited, albeit with a higher computational cost, while they are often outperformed by algorithms from other families when the evaluation budget becomes larger. We also show that some algorithms from the BBO community perform surprisingly well on ML tasks.
Teaching Engineering of AI-intensive Systems

With the increasing prevalence of AI, a key question is how to adequately prepare the next generation of software engineers to build AIintensive systems. This article presents our teaching experience for the “Engineering of AI-intensive Systems” course to postgraduate students. This course is tailored for computer science students, bridging the disciplines of software engineering (SE) and artificial intelligence (AI). The primary goal is to equip participants with the knowledge and skills to adeptly engineer AI-intensive systems with a strong foundation in conventional SE principles. The article delves into the course’s structure, teaching methods, and assessment techniques, underscoring the advantages inherent in this interdisciplinary educational approach.
Research, Implementation and Analysis of Source Code Metrics in Rust-Code-Analysis

The software industry is proliferating at an unprecedented pace, with a massive volume of software being released every day. Among the manifold challenges faced by software engineering researchers, one of the most significant is maintaining and enhancing software quality. Software metrics, designed to quantify various aspects of software, are essential in achieving this goal. They provide developers with a comprehensive snapshot of a codebase’s status throughout its evolution, thereby facilitating timely intervention and continual improvement. Tools like Rust-Code-Analysis (RCA), developed and maintained by Mozilla, serve as crucial aids in this endeavour. RCA is a static code analyser that scrutinises a source code without executing it and computes a series of source code metrics, which quantitatively assess code characteristics such as complexity, maintainability, and robustness. The present article seeks to contribute to this area by undertaking a threefold task. Firstly, we intend to explore new source code Java metrics that can be integrated into RCA. We have chosen Java language due to its not yet declined pervasiveness in many industrial software and world of smartphones. The metrics will be selected based on their potential to provide valuable insights into codebase status and facilitate optimisation. Once the new metrics have been identified, the second part of our task involves implementing these metrics within RCA’s library and also accessed through its CLI. This involves the coding and integration of the metrics using the modern Rust language, taking advantage of its unique features like memory safety without garbage collection, and data concurrency. Finally, to ascertain the effectiveness and reliability of metrics, we conduct an evaluation using diverse Java repositories. This involves studying the values generated by these metrics across repositories of varying sizes and levels of activity. From the smallest library to large-scale applications, our analysis spans various types of repositories, ensuring comprehensive coverage.
Mucha: Multi-channel based Code Change Representation Learning for Commit Message Generation

Commit messages provide a natural language description of the changes made to the code, enabling developers to swiftly comprehend the alterations without delving into the implementation complexities. Nevertheless, generating commit messages faces a considerable challenge due to the semantic and structural differences between code and natural language. Several researchers have put forward automated techniques aimed at generating commit messages. However, the full potential of code-related information is not yet fully harnessed. In this paper, we propose a Multi-channel based Code Change Representation Learning for Commit Message Generation(Mucha). We first compare the changed code and the corresponding AST before and after the change. Subsequently, we extract the altered information from various granularities and employ a multi-channel approach to capture the code changes, utilizing the extracted information as the basis for our analysis. In addition, we also use the query mechanism and attention mechanism to assist in learning the final code change representation. We build the experimental dataset, since there is still no publicly sufficient dataset for this task. The release of this dataset would serve as a valuable contribution towards advancing research in this particular field. We conduct a comprehensive experiment to assess the effectiveness of Mucha. The experimental evaluation demonstrates that our model outperforms the baseline model, which has significant improvements of at least 18.2%, 72.2%, and 10.5% against the baselines.
A Direction for Swarm Robotic Path Planning Technique Using Potential Field Concepts and Particle Swarm Optimization

Swarm robotics has emerged as a prominent research field in recent years. Managing a group of small robots with limited capabilities to achieve their goals poses a significant challenge for developers. Path planning, a crucial process for robotic systems, is essential for ensuring that all robots can reach their destinations while avoiding obstacles. However, path planning techniques commonly face a trade-off between speed and path quality. Achieving higher path quality often demands more iterations to find shorter and smoother paths, consequently increasing the processing time. This trade-off becomes even more intricate in multi-robot systems, where an escalation in the number of robots naturally amplifies the number of processes. This can lead to system failures due to the constrained processing capabilities of such systems. This paper presents a novel path planning approach for swarm robotic systems. The proposed system aims to effectively address the trade-off dilemma by integrating two techniques recognized for their swift execution: the potential field method and Particle Swarm Optimization (PSO). The potential field, generated using a unique objective function modeling method, enables robots to navigate toward the goal while avoiding obstacles. The objective function is composed using two terms, the Euclidean and the Gaussian term. The Euclidean term is responsible for finding the shortest path possible to the goal point while the Gaussian term is responsible for assisting the robot to avoid the obstacles. This objective function is later optimized using PSO algorithm. Simulation results involving three follower robots and a leader robot demonstrate the superior performance of the proposed technique compared to the widely used Probabilistic RoadMap (PRM) path planning technique. The proposed approach excels in terms of processing speed and path quality.
A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction

Software systems are getting larger and more complex than ever before. In order to improve software reliability, software defect prediction is applied to assist developers in bug discovery. The ranking-oriented software defect prediction aims to rank software modules according to the predicted defect counts. However, existing ranking-oriented defect prediction models are constructed based on traditional hand-crafted features, which might overlook the rich syntactic information buried inside the source codes. In this paper, we propose a universal deep learning-based framework called United Deep Network for ranking-oriented software defect prediction. This framework utilizes deep neural networks to automatically generate features from source code with the syntactic and structural information preserved, and it can combine extracted features with traditional hand-crafted features in order to take advantage of both kinds of features to construct prediction models. Experimental results over 29 sets of data show the good performance of the proposed framework for building ranking-oriented defect prediction models.
Implementation of the K-Nearest Neighbors Algorithm in the AgroWeather System for Plant Recommendations Based on Weather Predictions

This study aims to develop an accurate weather prediction model that can be applied using the K-Nearest Neighbor (KNN) method to make it easier for the community, especially farmers, to know the weather in the future and to match plants that must be planted in certain seasons in Bandung Regency. Rising temperatures and climate change occurring in Bandung, West Java, have a significant impact on rice cultivation and agricultural production. With reliable weather predictions, farmers can avoid the risks associated with growing crops that are not suited to the weather conditions, such as pest attacks, plant diseases, and crop failure. The KNN method is a useful approach to predicting weather conditions based on historical data. The results of this research can help farmers or agricultural experts determine suitable plants to plant based on weather forecasts. The targeted output is a weather prediction model that can be integrated into an agricultural information system in Bandung Regency. This model has an accuracy rate of 90& in providing farmers with relevant information for the coming season and updates regarding weather conditions. In addition, this model also provides plant type recommendations with an accuracy rate of 85&. These recommendations are based on an analysis of historical data on crops that have done well in each previous season.
Robust Detection of Adversarial Attacks for EEG-based Motor Imagery Classification using Hierarchical Deep Learning

Electroencephalography (EEG) signal finds extensive use in various medical diagnoses and non-invasive brain computer interface (BCI) applications. These applications include assisting individuals with disabilities, operating devices, and facilitating communication with their environments. Recent EEG studies have achieved successful decoding of neural activity using only time series data, surpassing the classification accuracy achieved by human experts. However, the decoding models are susceptible to adversarial examples that remain imperceptible to human evaluation. Thus, there is a current lack of a versatile architecture capable of simultaneously detecting adversarial examples and classifying EEG data. In this paper, we explore a hierarchical neural network-based classifier and introduce an adversarial training approach to enable the first classifier to learn from both clean and adversarial EEG data, enhancing its resilience to adversarial attacks. Subsequently, clean data have fed to EEGNET model to classify each EEG data point into its respective class. In the evaluation phase, we focused on the robustness of this approach against Fast Gradient Sign Method (FGSM) adversarial attacks using the BCI Competition IV-2a dataset, We assessed this approach and achieved an accuracy of 99.92% and a Kappa score of 0.9985.
Development of ORM Modules for Public Administration Service Request Business Processes Using Open ERP

The role of information technology is increasingly vital in transforming governance and public services in the era of globalization. It compels public organizations in developing countries to pursue optimal service through Internet applications. However, regional autonomy policies pose a significant challenge in integrating information technology. This study draws insights from Jayawijaya Regency, illustrating how local governments address autonomy challenges. The Population and Civil Registry Office is pivotal in citizen administration within this context. Encountered problems include inadequate data integration, and complex administrative processes, resulting in difficulties accessing and consolidating data. Administrative processes can be intricate and time-consuming, encompassing employee performance management and services. Consequently, public services and business processes are impeded. In response to challenges, adopting an enterprise resource planning (ERP) system emerges as a solution. While proprietary ERP systems solve issues, they have drawbacks, like high licensing costs and vendor dependency. Open ERP uses ORM for a flexible, efficient, and secure data approach. This research deploys ORM methods to develop public service and administration modules in the Population and Civil Registry Office of Jayawijaya, including Administration Mail Management, Service Submission, and Employee e-Performance, streamlining tasks like digital documentation, service submission, and incoming/outgoing mail. We test our application modules to assess the system functionality. The testing results demonstrate that 90.04% of the modules exhibit validity. Specifically, out of 231 test cases, 208 successfully passed without encountering any failures. However, a defect rate of 9.96% is observed, where 23 out of 231 test cases are identified as defective.
The Development of a Low-Cost Two-Axis CNC Laser Engraver with a Bluetooth Connection

This paper presents a compact multi-purpose computer numerical control (CNC) laser engraver design. The implementation of the CNC system becomes complex to cope with customers’ needs and requires a large amount of cost in the application of industries. This paper’s goal is to develop a CNC 2D Laser Engraver in a compact system with a Bluetooth connection. The board is equipped with 4 pinouts of the A4988 motor driver, Arduino with a CNC Shield controller, and a motor stepper driver. The CNC machine receives an order from Android with Bluetooth HC-05, and GRBL orders the laser to be on and the stepper motor will move respectively with coordinates determined by GRBL. The specialty of this system is that the users can easily change whether they want to perform operations with smartphones, microSD cards, or computers and produce 2D drawings and objects in different media types. The result from this paper shows that the engraving process of the Cilacap State of Polytechnic logo requires a laser intensity of 400 to 750 mW while engraving acrylic media requires a laser intensity of 400 - 600 mW depending on the speed of the stepper motor that we set at the beginning process.
How are user movie ratings predicted using collaborative filtering?

Recommender systems (RSs) are one of the most important tools for helping users decide what to buy, play, read, book, and watch. They become very effective tools for information filtering. The aim of this work is to forecast user ratings for a variety of movies, which is a current research topic in collaborative filtering (CF). Numerous CF models have been analysed and modeled in this paper for empirical-based comparison analysis. We have investigated several similarity metrics based on user-item rating predictions. We will then compare them by running simulations on the MovieLens dataset and we will use the root mean squared errors (RMSE), the precision, and the Spearman’s rank correlation coefficient as comparison statistics. A perfect correlation and RMSE of 0 were achieved through experimental analysis. A further advantage of the model is that it predicted the top 50 movie ratings with a precision of 100%.
Towards the Adoption and Adaptation of the AndroidX Library: An Empirical Study

AndroidX is an official Android library that enables backward compatibility for Android APIs used by various apps. It is the successor of the Android Support library since Android 9.0. Since then, many apps that originally relied on Android Support needed to be adapted to use AndroidX. However, for app developers, such a migration task can be challenging and error-prone. Yet, there is no systematic study on the migration status of real-world Android apps or the issues that may arise during the migration to AndroidX. To fill this knowledge gap, we conducted the first comprehensive study concerning the adoption and adaptation of the AndroidX library. In this study, we inspected 171 Stack Overflow posts about AndroidX and identified common categories of issues that can occur when adapting apps to use AndroidX, as well as the causes. We also examined the trend of these issues in recent five years to assess their impact over time. Then, we investigated the utilization status of both Android Support and AndroidX libraries in 15,334 top commercial apps and 2,470 open-source apps. Finally, we developed an algorithm that utilizes cosine similarity to identify Java class mappings between Android Support and AndroidX. The algorithm allows us to recover an additional mapping of 579 Java class pairs, which can supplement the official class mapping. Our study reveals the following key findings: (1) Improper API usage, misuse of dependencies, and incorrect build configurations are common challenges faced by developers during the adoption of AndroidX; (2) Around 13.3% commercial apps and 17% open-source apps still rely on the Android Support library; (3) Inconsistencies are observed between the API migration table in Android Studio and the API mapping table provided in AndroidX documentation. (4) On average, the migrated AndroidX classes achieve a 0.978 code similarity score with the original Android Support classes.
Commit Classification via Diff-Code GCN based on System Dependency Graph

Commit Classification, an automated process of classifying Diff-Code based on their purpose, plays a crucial role in enhancing comprehension and the quality of software. Some previous studies only used commit messages or code metrics to represent diff-code but lacked code context structure characterization. Alternatively, other studies have used Abstract Syntax Trees (ASTs) tokens to represent diff-code but did not consider contextual information like data dependency and control dependency. In this paper, we propose a new commit classification model called Diff-Code GCN (Graph Convolutional Network). Specifically, we firstly build a more detailed system dependency graph (SDG) of the commit, and secondly use program slicing to search the impact scope of diff-code. Thirdly, we extract the scope as a Change Impact Graph (CIG). We utilize GCN to extract contextual information from CIG and combine it with syntactic changed information of ASTs to represent the commit. Finally, we classify the commit into three maintenance categories (corrective, perfective, and adaptive). We evaluate our model based on commonly used datasets and compare our model with popular commit classification approaches. The experiment result well shows that both in within-project and cross-project prediction tasks, our model performs better than baseline models.
JITBoost: Boosting Just-In-Time Defect Prediction using Boolean Combination of Classifiers

Just-In-Time Software Defects Prediction (JIT-SDP) plays a critical role in software engineering by enabling the early identification of potential defects before they impact system performance. This study investigates the effectiveness of Boolean Combination of Classifiers (BCC) in building effective JIT-SDP models. We propose the JITBoost framework, which leverages three BCC algorithms, namely Brute-force Boolean Combination (BBC), Iterative Boolean Combination (IBC), and Weighted Pruning Iterative Boolean Combination (WPIBC). JITBoost combines the decisions of six traditional machine learning algorithms and one deep learning algorithm. When applied to 259K commits of 34 projects, we show that JITBoost models perform better than traditional machine learning and deep learning algorithms when used individually. Specifically, JITBoost-BBC, JITBoost-IBC, and JITBoost-WPIBC achieve mean AUCs of 0.891, 0.879, and 0.886, respectively, with cross-validation. With a time-aware data-splitting approach, they achieve mean AUCs of 0.863, 0.854, and 0.857, respectively. Overall, the findings suggest that combining machine learning models within the JITBoost framework can lead to improved performance in JIT-SDP models.