Reviewerly/exHarmony huggingface dataset:
Students' Perception of Example-Based Learning in Software Modeling Education

Teaching software modeling is challenging because it involves understanding and applying complex theoretical concepts to design structural models. In this sense, empowering students with the knowledge and skills necessary for the software industry is a challenge, intensified by the majority adoption of traditional (expositive) lectures, where students are passive subjects in the learning process. Adopting methodologies where the student plays an active role during learning is necessary to deal with this challenge. Example-Based Learning (EBL) is an active teaching methodology that satisfies such a condition. It uses worked examples to demonstrate how to perform a given task or acquire a given skill. In this context, this work aims to investigate, from the student’s perspective, how EBL can contribute (or not) to learning class diagram modeling. To do so, an exploratory study was carried out in which instructors used a support portal whose objective was to guide them in applying EBL in their classes. The study was conducted with 40 volunteer students from three different Informatics and Computer Science classes at a Brazilian university. The results showed that students had a positive perception of EBL, reporting that it facilitated the understanding of theoretical concepts, improved the connection between theory and practice, helped to deal with errors, and promoted essential skills for software development. These results corroborate that EBL is an efficient approach to the software modeling learning process, which can contribute to encouraging instructors to adopt this type of methodology in teaching Software Modeling.
Teaching and Promoting Engagement with OSS: Yet Another Experience Report

OSS-based learning refers to using open source software (OSS) and their sociotechnical practices in the pedagogical context. Several educators reported its benefits and barriers within different contexts, goals, and areas of knowledge, bringing evidence that it is a feasible approach to address Software Engineering Education challenges. Some appealing factors to adopting OSS-based learning are the availability of the software source code and workflows, access to the OSS community, and information about its development and evolution, which may improve the learning of SE concepts and practices and foster students’ engagement in real-world projects. Observing students as they work towards hard and soft skills, understanding, engaging with, and eventually contributing to an OSS project is a rewarding part of the routine of educators who have recognized the benefits of OSS-based learning and adopted it in their classes. We report our experience after delivering a “hands-on” course to introduce graduate students to OSS projects and their sociotechnical practices. Some graduate students were higher education instructors in other institutions. We present the course design and details of its execution, followed by a reflection based on students’ feedback and our perceptions of gains and pains. Overall, students valued the course and were highly motivated to explore OSS, especially those who worked as educators. Most of the feedback concerning the course methodology was positive, but some students requested more information about the weekly lesson plans in advance. We hope this experience report helps to demystify OSS, inspire educators to adopt OSS projects in their courses, and foster instructors’ and students’ engagement with OSS projects.
Un-Gaze: a Unified Transformer for Joint Gaze-Location and Gaze-Object Detection

This paper proposes an efficient and effective method for joint gaze location detection (GL-D) and gaze object detection (GO-D), i.e., gaze following detection. Current approaches frame GL-D and GO-D as two separate tasks, employing a multi-stage framework where human head crops must first be detected and then be fed into a subsequent GL-D sub-network, which is further followed by an additional object detector for GO-D. In contrast, we reframe the gaze following detection task as detecting human head locations and their gaze followings simultaneously, aiming at jointly detect human gaze location and gaze object in a unified and single-stage pipeline. To this end, we propose GTR, short for Gaze following detection TRansformer, streamlining the gaze following detection pipeline by eliminating all additional components, leading to the first unified paradigm that unites GL-D and GO-D in a fully end-to-end manner. GTR enables an iterative interaction between holistic semantics and human head features through a hierarchical structure, inferring the relations of salient objects and human gaze from the global image context and resulting in an impressive accuracy. Concretely, GTR achieves a 12.1 mAP gain (25.1%) on GazeFollowing and a 18.2 mAP gain (43.3%) on VideoAttentionTarget for GL-D, as well as a 19 mAP improvement (45.2%) on GOO-Real for GO-D. Meanwhile, unlike existing systems detecting gaze following sequentially due to the need for a human head as input, GTR has the flexibility to comprehend any number of people’s gaze followings simultaneously, resulting in high efficiency. Specifically, GTR introduces over a ×9 improvement in FPS and the relative gap becomes more pronounced as the human number grows.
Robust Calibration Approach for Robot Base Coordinate System Based on RANSAC-SVD

Base coordinate system calibration is a critical issue when performing certain tasks in a uniform coordinate system especially in industrial manipulation. Conventional calibration approaches are susceptible to the rotation matrix normalization and measurement noise, which may influence the calibration accuracy and robustness greatly. To address this problem, a robust calibration approach based on random sample consensus (RANSAC)-singular value decomposition (SVD) is proposed in this article. Specifically, a closed chain configuration is firstly adopted to construct the pose relationship between the manipulator and external measuring equipment. The intermediate orthonormal rotation matrix is obtained using the SVD-based approach with four noncoplanar points. Then the candidate calibration solution is finally estimated based on RANSAC by alleviating the effects of measurement noise and using the robust iterative scheme. The proposed calibration approach possesses the advantage of not only reducing the impact of measurement noise on the calibration accuracy greatly, but also avoiding the non-orthogonalization problem when finding the solution of the rotation matrix. Then, the numerical simulations are conducted on the calibration accuracy and robustness in presence of different levels of noise. A research prototype of the calibration system consisting of industrial manipulator and binocular camera is developed. Experiments are carried out to validate that the RANSAC-SVD based calibration approach is superior to other calibration approaches in terms of calibration accuracy and robustness. The calibration approach can provide reference for other pose calibration problems.
Subject-Level Membership Inference Attack via Data Augmentation and Model Discrepancy

Federated learning (FL) models are vulnerable to membership inference attacks (MIAs), and the requirement of individual privacy motivates the protection of subjects where the individual data is distributed across multiple users in the cross-silo FL setting. In this paper, we propose a subject-level membership inference attack based on data augmentation and model discrepancy. It can effectively infer whether the data distribution of the target subject has been sampled and used for training by specific federated users, even if other users (also) may sample from the same subject and use it as part of their training set. Specifically, the adversary uses a generative adversarial network (GAN) to perform data augmentation on a small amount of priori federation-associated information known in advance. Subsequently, the adversary aggregates two different outputs from the global and tested user models using an optimal feature construction method. We simulate a controlled federation configuration and conduct extensive experiments on real datasets that include both image and categorical data. Results show that the area under the curve (AUC) is improved by 12.6% to 16.8% compared to the classical membership inference attack. This is at the expense of the test accuracy of the data augmented with GAN, which is at most 3.5% lower than the real test data. We also explore the degree of privacy leakage between overfitted models and well-generalized models in the cross-silo FL setting and conclude experimentally that the former is more likely to leak individual privacy with a subject-level degradation rate of up to 0.43. Finally, we present two possible defense mechanisms to attenuate this newly discovered privacy risk.
Construction of highly nonlinear permutations on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math> with differential uniformity at most 8

In this paper, we propose a construction of permutations on Z2n having high nonlinearity by taking two highly nonlinear permutations on Zn, where n is odd. For n being prime, we identify a subclass of permutations whose differential uniformity is bounded above by 8. We further explore and show that the differential uniformity of such permutations on Z2p is at most 7 if p≡2(mod15) or p≡8(mod15).
Content Recommender System Based on Users Reviews

The usage of recommender systems is widespread across many areas and has been demonstrated to be extremely important in several fields. The majority of conventional recommender systems rely on a user’s numerical rating of a consumed item to reflect that user’s opinion; however, these ratings are not always available. As a result, to make up for the absence of these evaluations, a new source of information represented by user-generated reviews is added to the recommendation process. This saves the user time from searching the Internet for movies among the thousands that are already available. The items that might be suggested to the user are described by content-based recommendation systems. It makes predictions about what content a user will enjoy based on a dataset and takes into account the qualities of the content they have already liked. The sentiment analysis field can be used to gather rich and extensive information from reviews about the entire item or a specific aspect. This publication provides a thorough introduction to assist researchers who wish to work with sentiment analysis and recommender systems. It provides background information on recommender systems, including their phases, techniques, and performance measures. After that, it talks about the idea of sentiment analysis and highlights its key components, such as level, approaches, and aspect-based sentiment analysis.
Environmental Perception Algorithm for Multi-Target Autonomous Driving Based on YOLOv7-CBAM

In this study, we proposed an improved YOLOv7 network for autonomous driving multi-target detection, YOLOv7-CBAM. Based on YOLOv7, a tiny target detection structure was added to the backbone network for detecting tiny-scale targets. The ResNet-CBAM structure was utilized to reconstruct the YOLOv7 backbone network for enhancing the image feature extraction capability of network model, thereby obtaining more nonlinear features information from different feature layers. Besides, the Kernel-means algorithm was used for obtain the anchor instead of the K-means. Experiments showed that the proposed YOLOv7-CBAM network demonstrates faster convergence and reaches a mean average precision value of 82.0% on the Udacity Self Driving Car dataset, superior to the original YOLOv7 model.
MetaEx-GAN: Meta Exploration to Improve Natural Language Generation via Generative Adversarial Networks

Generative Adversarial Networks (GANs) have been popularly researched in natural language generation, so-called Language GANs. Existing works adopt reinforcement learning (RL) based methods such as policy gradients for training Language GANs. The previous research of Language GANs usually focuses on stabilizing policy gradients or applying robust architectures (such as the large-scale pre-trained GPT-2) to achieve better performance. However, the quality and diversity of sampling are not guaranteed simultaneously. In this article, we propose a novel meta-learning-based generative adversarial network, Meta Exploration GAN (MetaEx-GAN), for ensuring the quality and diversity of sampling (sampling efficiency). In the proposed MetaEx-GAN, we develop an explorer trained by Meta Exploration to sample from the generated data to achieve better sampling efficiency. MetaEx-GAN employs MetaEx first applied to Language GANs to achieve better performance. We also propose a critical training method for MetaEx-GAN on the NLG task. According to our experimental results, MetaEx-GAN achieves state-of-the-art performance compared with existing Language GANs methods. Our experiments also demonstrate the generality of MetaEx-GAN with different architectures (involving GPT-2) and how MetaEx-GAN operates to improve Language GANs.
Adoption of Digital Payments in Emerging Economies: Challenges and Policy Responses

This paper presents a comprehensive overview of the emergence and significance of digital payments, focusing on their impact on competitiveness and the need for policy interventions. Additionally, it explores the design of policies that promote the adoption of digital payments, highlighting the benefits they offer to providers and users. It examines the technological advances that have driven the growth of digital payment systems. It identifies key requirements for successful adoption and discusses the associated risks, along with potential strategies to mitigate these risks. Our findings emphasize the importance of responsible implementation and safeguarding the well-being of end-users to fully realize the benefits of digital payment adoption. Understanding the inherent risks and establishing effective risk mitigation mechanisms are crucial. This necessitates the development of appropriate infrastructure to support the provision of digital payment services. More research is needed to gain deeper insights into how emerging global trends in financial technology should be analyzed and understood by policymakers, service providers, and users. The findings of this study can guide policymakers, private sector managers, and consumers in comprehending the effects of emerging digitalization trends and determining their adoption responses accordingly.
Implementation of Progressive Web App on Dropship Data Management Application to Anticipate Product Order Errors

Digital business trends during the COVID-19 pandemic have increased. One of the digital businesses that can be run during a pandemic, one of which is the dropship business, has a fairly low risk because it does not require large capital and infrastructure preparation. Dropship data management without a data management system can make it difficult for drop shippers to manage and view customer, product, and supplier data. Besides that, without a data management system, it is very likely that the drop shipper will make an error in ordering and shipping the product. This research will discuss the creation of a data management application for drop shipping to cover dropship data management problems. The developed application will be web-based using a Progressive Web App (PWA) so that it can have better performance with a cache and a display that resembles a mobile application.
Refinement-based Modeling and Formal Verification for Multiple Secure Partitions of TrustZone

PDF HTML XML Export Cite reminder Refinement-based Modeling and Formal Verification for Multiple Secure Partitions of TrustZone DOI: 10.21655/ijsi.1673-7288.00301 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:As a trusted execution environment technology on ARM processors, TrustZone provides an isolated and independent execution environment for security-sensitive programs and data on the device. However, running the trusted OS and all the trusted applications in the same environment may cause problems---The exploitation of vulnerabilities on any component may affect the others in the system. Although ARM proposed the S-EL2 virtualization technology, which supports multiple isolated partitions in the secure world to alleviate this problem, there may still be security threats such as information leakage between partitions in the real-world partition manager. Current secure partition manager designs and implementations lack rigorous mathematical proofs to guarantee the security of isolated partitions. This study analyzes the multiple secure partitions architecture of ARM TrustZone in detail, proposes a refinement-based modeling and security analysis method for multiple secure partitions of TrustZone, and completes the modeling and formal verification of the secure partition manager in the theorem prover Isabelle/HOL. First, we build a multiple secure partitions model named RMTEE based on refinement: an abstract state machine is used to describe the system running process and security policy requirements, forming the abstract model. Then the abstract model is instantiated into the concrete model, in which the event specification is implemented following the FF-A specification. Second, to address the problem that the existing partition manager design cannot meet the goal of information flow security verification, we design a DAC-based inter-partition communication access control and apply it to the modeling and verification of RMTEE. Lastly, we prove the refinement between the concrete model and the abstract model, and the correctness and security of the event specification in the concrete model. The formalization and verification consist of 137 definitions and 201 lemmas (more than 11,000 lines of Isabelle/HOL code). The results show that the model satisfies confidentiality and integrity, and can effectively defend against malicious attacks on partitions. Reference Related Cited by
Application of Naïve Bayes Classification to Analyze Performance Using Stopwords

Based on current data, there has been an increase in social media users, which shows that more and more people are using social media as a place to express themselves and their emotions. This will generate thousands of tweets within a day. The tweet data is processed so that it is useful for stakeholders who need it to help them make a decision. Because sentence structures on social media are often irregular, pre-processing is necessary to make tweet sentences normal. Stemming and Stopwords are pre-processing techniques that are widely used in sentiment analysis. In previous studies, there were indications that its use did not have a significant effect on accuracy. In this study, the authors divide it into four models: using stemming and stopwords and without using stemming and stopwords. Data using stemming gets the best results with an f1-score of 65%. These results indicate an increase in performance in the use of stemming and stopwords using Multi-class Naive Bayes
Real-time AI-based inference of people gender and age in highly crowded environments

Gender identity is one of the most fundamental aspects of life. Automatic gender identification is increasingly being used in areas such as security, marketing, and social robots. The objective of this paper is to address the challenges of gender and age identification in very crowded/noisy environments where faces are unclear and/or people are moving in relatively random directions. It presents an end-to-end real-time intelligent video analytics solution for instant people counting, gender and age estimation in crowded and open environments. The proposed solution includes a complete pipeline for training vision deep learning models and deploying them to edge devices connected to a distributed streaming analytics server. Our final Deep Learning architecture is an extended version of FairMOT, a multi-object tracking model, with two additional layers for multi-class gender classification and age regression. The training phase is performed using an enhanced and enriched version of the CrowdHuman dataset, a public dataset for human detection, with gender and age annotations added. The overall system has been validated for various movies and has shown state-of-the-art performance in terms of people tracking, gender and age inference. Our code, models, and data can be found at https://github.com/jasseur2017/people_gender_age.
OAGAN: an oversampling approach for imbalanced data problems

Imbalanced data problems are prevalent in our daily lives. When conducted on this kind of data, many classification models may encounter many difficulties and produce ineffective performance due to a high training bias towards the majority of class instances. In order to solve the imbalanced data problem, many effective oversampling methods have been proposed. However, few methods in previous work consider different weights for minority class samples. Therefore, we proposed an oversampling method for the minority class by Adaptive Synthetic Sampling with Conditional Tabular Generative Adversarial Network (OAGAN) in this study. Firstly, Adaptive Synthetic Sampling is used to assign different weights to each minority class sample, based on which new similar samples are generated by the Conditional Tabular Generative Adversarial Network. In other words, the weights of different minority class samples are considered when generating data using the Conditional Tabular Generative Adversarial Network, thus improving the method. The proposed OAGAN method was evaluated on ten imbalanced datasets and three standard oversampling algorithms, which achieved better learning performance.
A Deep Learning Model to Detect People and Monitor Social Distance

Social distancing appears to be a very effective method of slowing the spread of contagious lung disease caused by influenza viruses that contaminate the nose, throat, and sometimes the respiratory after suffering of COVID-19 in the global battle. People are advised to limit their physical contact to lessen the virus's transmission hazard by physical or close contact. Artificial intelligence and deep learning are used to analyze real-time scenarios captured by CCTV cameras and determine whether crowd safety measures such as wearing masks or maintaining social distance among themselves have been followed. Our proposed work focuses on how a deep learning framework can detect social distancing in shared spaces and workrooms. The proposed human proximity detector system tracks whether individuals are preserving a safe distance between themselves or not by processing video streams captured in crowded scenes that are captured in public areas and workplaces. The proposed system can be integrated into surveillance systems in workplaces, factories, or shops to monitor whether employees keep a safe distance from one another at work. The YOLO deep learning model is adopted for object detection. A pairwise distance measuring algorithm is used for distance calculation between pairs of objects to decide whether they are maintaining a safe distance. The system determines whether or not an individual follows the social-distancing law and is classified as safe or unsafe. Both a live stream and a video feed validate the findings. Results indicated that the YOLO model performed well. YOLO v3 is recorded with the most effective results with a balanced frame per second (FPS) score of 23 compared to other methods. The accuracy of the proposed model YOLO v3 is 91.0%.