Reviewerly/exHarmony huggingface dataset:
Proposal of a Support System for Visualization of Acquired Skills Using iCD in Project-Based Learning for Software Development and How to Acquire its Data

There have been many practices using software development as a subject for Project-Based Learning (PBL). While PBL for software development has high educational effects, it is difficult for learners to realize many of its educational effects because they overly focus on the activities themselves. Therefore, this paper proposes a support system for visualization of acquired skills in PBL for software development using i-Competency Dictionary. We also describe the data acquisition method for the realization of the system.
How are Smart Phones Used by Students in Higher Education? Exploring the Intersection of Digital Learning Activities and Personal Technology

Students in higher education face a de facto expectation that they will supply the personal technology required to participate in learning activities. Although traditional computing devices are most commonly used for academic work, students report significant engagement in their studies via their mobile devices. Understanding the intersection of student behavior and the technology students choose to use could allow instructors and instructional designers to develop digital learning materials that meet students’ expectations. This study examines computer-based learning activities through the lens of several theoretical frameworks to analyze and quantify the types of academic work students are most likely to – and would like to be able to – perform on their smart phones. The results show that most students perform at least some academic tasks on their smart phones but are highly selective about which tasks they choose to carry out. However, it is demonstrated that younger students and students with higher levels of financial need are more dependent on their smart phones and use them to perform a much wider range of learning activities.
Design of a Graduate level Software Engineering Program with a Product Perspective and a Product Management Specialization

While Software Engineering practice in the industry had matured well over the second half of the twentieth century, the first two decades of this millennium have witnessed disruptive growth of the field arising out of the evolution of new problem domains (e.g., intelligent applications and data management) as well as diverse deployment patterns (e.g., cloud, mobile and embedded devices). More subtle and impactful for practitioners is the change in perspective and approach-from a project-driven lifecycle to a product-driven lifecycle of software. Academic programs have kept up with the former set of changes as evidenced in curricular aspects or components such as DevOps, Mobile Application Development, Cloud-Native Application Development, Data Engineering, Big Data Systems, Software Engineering for AI, etc. But there is a distinct lack of representation of the product perspective or approach and the emerging role of Product Management in typical University-level Software Engineering programs and curricula. We propose to address this gap by identifying-and then incorporating into a curriculum-the following:(i)requirements and features of a Software Product Life Cycle,(ii)practices of a product-driven approach to Software Engineering, and(iii)competencies of a Software Product Manager.We show how these aspects can be addressed by a graduate level Software Engineering program with an optional specialization in Software Product Management targeted at practicing Software Engineers. We present the design of such a program in sufficient detail along with validation from industry and comparison with other programs.
Development of a Transmissive LED Touch Display for Engineered Marble

We have developed a transmissive LED touch display for engineered marble that can be used for such as cooking support on a kitchen counter. This device has an LED matrix for display and a reflective infrared sensor matrix for touch detection, and both are placed on the back of the engineered marble. The dots displayed by the LED matrix are transmitted through the engineered marble, and the blurred image is displayed on its surface. The reflective infrared sensor matrix can detect the positions of objects existing on the surface of the engineered marble. By incorporating these two functions into one device, we were able to develop a unique device suitable for smart home applications.
Research on generating high-quality news headlines based on the GPT-2 model to replace artificial headlines

As headlines of Internet news become out of context. This project is expected to use the GPT-2 model and use Chinese news materials for training. And use the model to generate in-text headlines instead of exaggerated headlines. This research will use web crawlers to collect news data in Taiwan and use Pearson’s correlation coefficient to detect the correlation between manual ratings and machine ratings and verify whether this experimental model can produce headlines of sufficient quality through a variety of research tools.
Impact of Key Scrum Role Locations in Student Distributed Software Development Projects

Employing an agile development methodology, particularly Scrum, in a distributed student project setting is challenging for both teachers and involved students. Allowing distributed student teams to self-organize and assign key Scrum roles using various strategies, specifically regarding the locations of students taking on key roles, increases the complexity of such projects. In addition, the interaction of the Project Owner role with the project customer, which occurs outside the distributed student team, adds a new dimension to this problem. This paper investigates the impact of various key role assignment strategies, and their interactions, on the performance of distributed student projects. Furthermore, we investigate the intensity of collaboration within the distributed team and between key project roles, as well as their impact on project performance. We analyzed data collected on 37 distributed student projects conducted over the course of eight academic years. The results reveal that letting students assign key project roles regardless of their location in the distributed team has no significant impact on the quality of project outcomes. However, a deeper analysis uncovers that more educationally desirable assignments of those roles exist; favoring increased collaboration intensity within distributed student teams.
Is Online Teaching Dead After COVID-19? Student Preferences for Programming Courses

The COVID-19 pandemic precipitated an unprecedented paradigm shift in higher education, compelling swift adaptation to online teaching methods. Consequently, the merits of remote education, including increased flexibility and geographic independence, were emphasized. At the same time, however, the problems associated with distance education became apparent, such as the lack of networking, collaborative learning, and social interactions. This situation led to detrimental effects on student motivation and learning outcomes in team-oriented software engineering courses.To address the dichotomy of learning preferences, one potential solution proposed is the simultaneous offering of online and onsite instruction. However, such a proposition presents substantial logistical challenges, necessitating additional resources, labor, and organizational overhead. This research paper presents a case study conducted during an introductory programming course, which serves as a precursor to a comprehensive, practical software engineering course. Upon easing of COVID-19 related restrictions, the instructors offered both online and onsite versions of this course and obtained student feedback through interviews to draw a comparative analysis.The study outcomes provide crucial insights into students’ preferences with respect to learning modalities in higher education, particularly within the software engineering discipline. The results indicate a predominant preference for the onsite version of the introductory course. Reasons attributed to this preference include enhanced social interactions, greater enjoyment, and increased motivation, thus highlighting the irreplaceable value of face-to-face education.
Survival Game Assisted Tactical Helmets

The modern idea of gunplay has its origins in games and TV shows, and survival games have emerged as a new form of competitive sport. To assist users in survival games, a system has been developed to help them gather enemy intelligence and gain an advantage for their team during gameplay. The system is designed to detect the enemy's position and transmit the information to the user, enabling them to respond quickly to the intelligence and significantly reduce the risk of being ambushed from behind.YOLOv4, a powerful object detection model, has found wide application in various industries, such as manufacturing and facial recognition. It is used to detect product defects and ensure the quality of products, and for comparing facial features to facilitate tasks such as face recognition and criminal tracking. In the pandemic, YOLOv4 has been employed for mask detection and social distancing monitoring in manufacturing facilities. Additionally, it has been customized for traffic management in the "Smart City Traffic Flow Solution" developed by Academia Sinica and Yilong. The modified YOLOv4 system uses real-time vehicle detection at intersections to regulate traffic flow and enforce speed limits.
Envisioning software engineer training needs in the digital era through the SWEBOK V4 prism

Our world’s needs have evolved dramatically since the origins of software engineering in the 1960s. The future software engineer must be able to anticipate our needs and desires in an era where complex challenges continually emerge, and adaptive solutions must be delivered on the fly. This paper addresses the evolution of the IEEE Computer Society’s Guide to the Software Engineering Body of Knowledge (SWEBOK Guide) and its impact on software engineering higher education and professional training that should prepare engineers to fulfill their mission in this dynamic digital future.
Image-to-Action Translations Based Object Grasping Strategy without Depth Information and Robot Kinematics Analysis

The main objective of this paper is to utilize only RGB images to let a robotic arm grasp a target object without the related 3D position information. The advantages of the proposed method include image-to-action translations that apply to a class of general robotic arms, and the mathematical analysis of the inverse kinematics is not necessary. We employ the RGB images and the proximal policy optimization (PPO) algorithm to train the reinforcement learning network in the Gazebo simulated environment. Finally, an illustrative example shows how effective the proposed strategy is.
Augmented Reality for Real Object Detection

In the era of AI booming, object detection is an essential technology for computer vision tasks and widely adopted in autonomous driving. We propose a method to enhance object detection accuracy by adding virtual objects to real scenes through augmented reality, thereby quickly generating a large amount of data to facilitate model training. In addition, Augmented Reality (AR) can create data for rare scenarios in real worlds, such as a car flipping over on the road or a cargo overturned, which can alleviate the long-tail problem of AI models. Furthermore, our tool can generate both 2D and 3D bounding boxes directly. To verify our method, we performed transfer learning on YOLOv7 pre-trained model using 30,766 AR synthesized images of 4 traffic-related classes: Person, Car, Bicycle and Motorcycle. The new detector was evaluated on the COCO dataset. Experiments showed that our method can increase the detector accuracy as well its ability of detecting small objects.
A Spatio-temporal Graph Based Hybrid Encoder-Decoder Approach for PV Output Prediction

Photovoltaic (PV) is a clean and renewable energy source and is considered an effective way to solve energy problems and reduce emissions. However, photovoltaic output is significantly impacted by different weather conditions. To improve the reliability and stability of photovoltaic resource, in this paper, we propose a photovoltaic output prediction approach to accurately deduct the future photovoltaic output capability. First, we construct a weighted undirected spatio-temporal graph to characterize the relationship between distributed photovoltaic sites. Then, we utilize the LSTM network to study time-series features of each node in the spatio-temporal graph. After that, we jointly design the edge and feature decoding architecture to learn the spatio-temporal features of nodes, based on which a multi-layer fully connected network is used to effectively infer the photovoltaic output of on-site nodes. Experimental results show that our proposed model outperforms other state-of-the-art models.
An Efficient Light-Weight Federated Learning Framework Implemented on Kubernetes and Docker

Federated learning is a new machine learning paradigm that trains an algorithm across multiple edge devices without sharing data. However, traditional deployment of federated learning encounters difficulties for efficient environment setup, resource allocation and maintenance. To resolve this, in this study rebuilt the federated learning algorithm into a containerized software structure based on Kubernetes and Docker. This framework significantly reduced the construction time and improves the efficiency of resource utilization. Moreover, we proposed removing gradient computation of some fully connected layers in the backward pass to shorten the training time. Compared with the classic federated learning algorithm FedAvg initiated by Google, the proposed lightweight federated learning algorithm could reduce the overall training time by 50% or more for large batch size and heavier local training without sacrificing accuracy.
Design of Violence Event Detection System Based on CCTVs by Human Body Pose Recognition

Violent incidents have happened regularly and usually cause tragedies due to without rescued immediately. Therefore, we expect to study a violence detection and alarm system based on human body pose recognition of images captured from public CCTVs. The system uses OpenPose for real-time skeleton recognition to detect poses based on the abnormal angles or speeds of movement between the various joints of the limbs to determine whether it is violent behavior or not. If possible violent events are detected, the system will trigger an alarm and notify the police or security guard to deter the attacker. Victims will be able to get assistance as soon as possible through this recognition system even if they have obstacles for calling help by themselves. Experimental results show that the target violence behaviors can be detected successfully.
Workshop on Bad Practice Smells in Novice Software Engineering Education

This workshop aims to address the common bad practices and habits that novice software engineering students develop and how educators can detect them and guide students towards better practices before they become ingrained. The workshop explored this by soliciting contributions of bad practices educators have observed in teaching introductory software engineering. Contributions were workshopped to develop a compendium of bad practices, along with proposed remediations and subsequently shared with the software engineering education community. The workshop concluded with recommendations for using the compendium to improve software engineering education.
A Study on Text Generation of Kinmen Culture Vocabulary based on GPT-2

In this paper, we discuss the role of language as a critical tool for communication and preserving human culture. Despite sharing the same official language and writing system, Kinmen and Taiwan have different relationships between words and characters, cultural beliefs, writing habits, and commonly used terms due to their unique geographical environment and historical context. Although these unique usages and terms have gradually been forgotten or changed over time due to historical evolution, they still exist in literature and related books. Therefore, we propose to use transfer learning combined with the GPT-2 model to discover Kinmen's unique cultural vocabularies by training a neural network. The text generation of Kinmen culture vocabulary will not only assist Kinmen's article writing but also achieve the purpose of cultural preservation through the integration of technology.
Activate Your Face in Virtual Meeting Platform

In private communications or even in television broadcasting, we can sometimes just see a picture of the speaker but not the face video of the person. This may due to the inconvenience of the speaker to make appearance in front of the camera or limited channel capacity for sending out the video. This paper gives our deep learning approach and an attractive demonstration to make the face picture talk as a usual person in video, which potentially allows many industrial and consumer applications.
Detecting Pedestrian Spatial Behavior in City Spaces by Processing 360° Videos

This study developed a framework based on deep learning algorithms for processing 360° videos for detecting pedestrian spatial behavior in urban spaces. Information divergence is determined through the sampling and conversion of spatiotemporal behavior data for pedestrian flow analysis. Traditional videos, such as those captured by one-way security cameras, cannot be used to fully analyze the flow of pedestrians in cities. Therefore, a 360° camera is used to capture panoramic videos of city spaces over time. Subsequently, deep learning algorithms are used to process the videos and obtain pedestrian trajectory data for analyzing their spatial behavior and interactions. The results of real-world implementation indicate that the proposed method and analytical framework can be used to detect pedestrians and collect data related to pedestrians’ spatial behavior. However, the sampling rate and application of pedestrians’ trajectory data must be explored in future studies.
Red Light, Green Light Game System based on IoT Devices

With the progress of technology, most of the entertainment of the younger generation is related to electronic products. People gradually forget traditional games. Red Light, Green Light has ample liquidity that people worldwide know it. Therefore, to bring traditional games back to children’s childhood, we use IoT devices to develop a Red Light, Green Light Game System. This system comprises three modules: an identification module, a distance measurement module, and a notification module. In the identification module, we use the Raspberry Pi equipped with the MediaPipe Pose to detect 33 pose nodes of the human body. Then the model reads the camera image through OpenCV and marks the players’ skeleton position. Finally, we compare the players’ skeleton positions in the front and rear images to determine whether the player has moved. The ultrasonic sensor "HC-SR04" is used in the distance measurement module to measure the distance. The change of the front and rear distances is used to identify whether a player is passing through the finish line. The notification module places the "TM1637" four-digit display and the speaker. It will display the remaining time and play the sounds needed for the game during the gameplay. The proposed game system allows adults to relive their childhood and introduces traditional games to younger generations.
Using Shadow Models to Protect Private Data on Machine Unlearning

Machine unlearning is an act of eliminating a user’s data from a machine learning model. Machine unlearning, however, causes a new problem of privacy leakage because an attacker may infer the deleted data by comparing the difference between the original model and the unlearned model. In this study, we develop a new privacy-preserving mechanism for machine unlearning that is universal to all machine learning models. The shadow model, typically used to train attackers in a black-box scenario, is employed as a defense means to protect the deleted data in a white-box scenario. We produce a machine learning model by a linear combination of the unlearned model and shadow models. This approach confuses the attacker while retains the accuracy of the original model. The proposed system provides a flexible trade-off between the accuracies of the adversarial attack and the protected model. Experimental results show that the proposed method could reduce the accuracy of the attack by more than 13%.
The Daily Smirk: A Preliminary Prototype for Continuous Peer Assessment of Team-based Projects

Team-based projects are common in many software engineering classes, but come with a host of well-known problems such as social loafing, free-riding, and undesirable team dynamics. To counter these issues, many instructors use peer assessment. Unfortunately, current peer assessment approaches exhibit several limitations, including a typically time-consuming evaluation process, feedback being too infrequent or too late to make a difference for the project, and student ratings being unreliable. In this paper, we introduce the Daily Smirk, a preliminary prototype of a new peer assessment tool that is based on three primary design decisions: (1) peer assessment is continuous throughout the project, (2) assessment is through a lightweight smiley-based rating system, and (3) assessment is normalized around a neutral state so that additional feedback is only needed for outlier ratings. We elaborate on the design of the Daily Smirk and its primary functionality, and report on a preliminary evaluation in a software design course with three team-based projects.
Prediction of Shoot Events by Considering Spatio-temporal Relations of Multimodal Features

In this paper, we propose a prediction method for shoot events using multimodal features. Soccer event prediction has conventionally been conducted based on statistical analysis by using an enormous quantity of tracking data. In contrast, with the development of the machine learning technology, an approach that uses advanced semantic information extracted from various data such as video, audio, and text has achieved high accuracy in many tasks of soccer analysis. Therefore, it is expected to improve the prediction performance of shoot events by cooperatively using multiple modalities instead of tracking data. Specifically, the proposed method predicts shoot events by considering spatio-temporal relations of a graph constructed based on audio and visual features extracted from soccer videos. Through the experiments, we verify the effectiveness of our method.
Give Drivers a Safe Way Home:Early Warning of Car Accidents through Deep Learning

Among the causes of traffic accidents in Taiwan in 2021, "not paying attention to conditions ahead of the vehicle" ranked among the top two. It is a big challenge to assist drivers in judging road conditions in advance to reduce the occurrence of traffic accidents. This project uses the DR(eve)VE dataset to capture video data of driving conditions in different environments and applies the YOLO v4 algorithm to detect real-time road objects. The detection results are then imported into the Deep Sort algorithm to track the trajectory of the objects. Finally, the optimal safe distance between the vehicle and the detected objects is calculated. If the objects are within the safe distance, the driver will be alerted to avoid potential car accidents. By integrating the YOLO v4 and Deep Sort algorithms, this project aims to assist drivers in judging road conditions so that they can arrive home safely.