Reviewerly/exHarmony huggingface dataset:
Smart Face Attendance System

In colleges, universities, organizations, schools, and offices, taking attendance is one of the most important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor
Expound: A Black-Box Approach for Generating Diversity-Driven Adversarial Examples

Deep neural networks (DNNs) have been increasingly used in safety-critical systems for decision-making (e.g., medical applications, autonomous vehicles, etc.). Early work with adversarial examples shows that they can hinder a DNN’s ability to correctly process inputs. These techniques largely focused on identifying individual causes of failure, emphasizing that minimal perturbations can cause a DNN to fail. Recent research suggests that diverse adversarial examples, those that cause different erroneous model behaviors, can better assess the robustness of DNNs. These techniques, however, use white-box approaches to exhaustively search for diverse DNN misbehaviors. This paper proposes a black-box, model and data-agnostic approach to generate diverse sets of adversarial examples, where each set corresponds to one type of model misbehavior (e.g., misclassification of image to a specific label), thus termed a failure category. Furthermore, each failure category comprises a diverse set of perturbations, all of which produce the same type of model misbehavior. As such, this work provides both breadth and depth-based information for addressing robustness with respect to multiple categories of failures due to adversarial examples. We illustrate our approach by applying it to popular image classification datasets using different image classification models.
Evaluating Explanations for Software Patches Generated by Large Language Models

Large language models (LLMs) have recently been integrated in a variety of applications including software engineering tasks. In this work, we study the use of LLMs to enhance the explainability of software patches. In particular, we evaluate the performance of GPT 3.5 in explaining patches generated by the search-based automated program repair system ARJA-e for 30 bugs from the popular Defects4J benchmark. We also investigate the performance achieved when explaining the corresponding patches written by software developers. We find that on average 84% of the LLM explanations for machine-generated patches were correct and 54% were complete for the studied categories in at least 1 out of 3 runs. Furthermore, we find that the LLM generates more accurate explanations for machine-generated patches than for human-written ones.
Enhancing Genetic Improvement Mutations Using Large Language Models

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to $$75\%$$ higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.
Tinjauan Kesulitan Belajar Karena Gangguan Perkembangan (Autism, ADHD, dan Down Syndrome)

This research aims to identify the types of learning difficulties or obstacles that occur among students in the learning process and ways to overcome them. In this study, we employed the library research method. Learning difficulties refer to a condition where learners are unable to achieve the expected learning goals. These difficulties may arise due to developmental disorders. Developmental disorders are conditions in which an individual cannot develop normally, such as Autism, ADHD, and Down Syndrome. Identifying the forms of learning difficulties is crucial to be done early on if there are signs that do not align with a child's development. This is expected to enable the effective resolution of these difficulties by experts.
A Multiscale Deep Encoder–Decoder with Phase Congruency Algorithm Based on Deep Learning for Improving Diagnostic Ultrasound Image Quality

Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant attention. This study proposes a multiscale deep encoder–decoder with phase congruency (MSDEPC) algorithm based on deep learning to improve the quality of diagnostic ultrasound images. The MSDEPC algorithm included low-resolution (LR) images and edges as inputs and constructed a multiscale convolution and deconvolution network. Simulations were conducted using the Field 2 program, and data from real experimental research were obtained using five clinical datasets containing images of the carotid artery, liver hemangiomas, breast malignancy, thyroid carcinomas, and obstetric nuchal translucency. LR images, bicubic interpolation, and super-resolution convolutional neural networks (SRCNNs) were modeled as comparison groups. Through visual assessment, the image processed using the MSDEPC was the clearest, and the lesions were clearly distinguished. The structural similarity index metric (SSIM) value of the simulated ultrasound image using the MSDEPC algorithm improved by approximately 38.84% compared to LR. In addition, the peak signal-to-noise ratio (PSNR) and SSIM values of clinical ultrasound images using the MSDEPC algorithm improved by approximately 2.33 times and 88.58%, respectively, compared to LR. In conclusion, the MSDEPC algorithm is expected to significantly improve the spatial resolution of ultrasound images.
Analysis of Artificial Intelligence Literacy in the Blended Learning Model in Higher Education

The purpose of the study was to determine the literacy of artificial intelligence in the blended learning model by looking at the influence of: (1) introduction to artificial intelligence (IAI) on data security and privacy (DSP), (2) Advantages and Disadvantages of Artificial Intelligence (ADAI) on DSP, (3) Implications of Artificial Intelligence (IAII) on DSP, (4) IAI on DSP moderated by Ethics and Laws of Artificial Intelligence (ELAI), (5) ADAI on DSP moderated by ELAI, (6) IAII on DSP moderated by ELAI, and (7) ELAI on DSP. The research design is expost de facto. The research sample is 4th semester 2021 students who have studied web programming courses in the department of informatics and computer engineering with a total of 156 students. Data analysis with partial least square (PLS) using the SmartPLS application. The results showed that: (1) IAI has a positive but insignificant effect on DSP, (2) ADAI has a positive and significant effect on DSP, (3) IAII has a positive and significant effect on DSP, (4) IAI has no positive and insignificant effect on DSP moderated by ELAI, (5) ADAI has a positive and significant effect on DSP moderated by ELAI, (6) IAII has no positive and insignificant effect on DSP moderated by ELAI, and (7) ELAI has a positive and significant effect on DSP. In addition, the analysis results show an acceptable level of variance of the lecturer trust model (68.8%). This means that there is 31.2% variance in the dependent variable explained by other factors.
Meningkatkan Hasil Belajar Menulis Narasi dengan Strategi Quantum Writing Bebantuan Media Picture and Picture bagi Siswa Kelas V SD

Initial observations were the initial foundation for carrying out this research. It was found that many of the fifth grade students at SDI al-Azhar Muhammadiyah still had difficulty writing narratives with evidence that their overall average completion rate had not yet reached the predetermined KKM namely 70. The research used namely PTK using two cycles, each cycles has for steps, namely planning, action, observation, and reflection. The subjects of this research were class V students at SDI al-Azhar Muhammadiyah consisting of 38 student. Then this research uses research isntruments in the from of observations and tests. The results of this research show that there is an increase in students learning from cycles I to cycles II yhe average overall students completion was 68,94. Then in cycles II there was a significant increase, namely with an overall average og 78,68. So it can be concluded that the application of the quantum writing strategy assisted by picture and picture media can solve problems in the classroom, namely it can provide solutions to the learning to write narratives for class V students at SDI al-Azhar Muhammadiyah.
Interactive Change-Aware Transformer Network for Remote Sensing Image Change Captioning

Remote sensing image change captioning (RSICC) aims to automatically generate sentences describing the difference in content in remote sensing bitemporal images. Recent works extract the changes between bitemporal features and employ a hierarchical approach to fuse multiple changes of interest, yielding change captions. However, these methods directly aggregate all features, potentially incorporating non-change-focused information from each encoder layer into the change caption decoder, adversely affecting the performance of change captioning. To address this problem, we proposed an Interactive Change-Aware Transformer Network (ICT-Net). ICT-Net is able to extract and incorporate the most critical changes of interest in each encoder layer to improve change description generation. It initially extracts bitemporal visual features from the CNN backbone and employs an Interactive Change-Aware Encoder (ICE) to capture the crucial difference between these features. Specifically, the ICE captures the most change-aware discriminative information between the paired bitemporal features interactively through difference and content attention encoding. A Multi-Layer Adaptive Fusion (MAF) module is proposed to adaptively aggregate the relevant change-aware features in the ICE layers while minimizing the impact of irrelevant visual features. Moreover, we extend the ICE to extract multi-scale changes and introduce a novel Cross Gated-Attention (CGA) module into the change caption decoder to select essential discriminative multi-scale features to improve the change captioning performance. We evaluate our method on two RSICC datasets (e.g., LEVIR-CC and LEVIRCCD), and the experimental results demonstrate that our method achieves a state-of-the-art performance.
Decentralized Federated Learning-Enabled Relation Aggregation for Anomaly Detection

Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance data security. In the financial insurance industry, enterprises tend to leverage the relation mining capabilities of knowledge graph embedding (KGE) for anomaly detection. However, auto insurance fraud labeling strongly relies on manual labeling by experts. The efficiency and cost issues of labeling make auto insurance fraud detection still a small-sample detection challenge. Existing schemes, such as migration learning and data augmentation methods, are susceptible to local characteristics, leading to their poor generalization performance. To improve its generalization, the recently emerging Decentralized Federated Learning (DFL) framework provides new ideas for mining more frauds through the joint cooperation of companies. Based on DFL, we propose a federated framework named DFLR for relation embedding aggregation. This framework trains the private KGE of auto insurance companies on the client locally and dynamically selects servers for relation aggregation with the aim of privacy protection. Finally, we validate the effectiveness of our proposed DFLR on a real auto insurance dataset. And the results show that the cooperative approach provided by DFLR improves the client’s ability to detect auto insurance fraud compared to single client training.
Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection

Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distribution (OOD) data and prompt human intervention, a critical competency. While several frameworks for OOD detection have been introduced and achieved remarkable results, most state-of-the-art (SOTA) models rely on supervised learning with annotated data for their training. However, acquiring labeled data can be a demanding, time-consuming or, in some cases, an infeasible task. Consequently, unsupervised learning has gained substantial traction and has made noteworthy advancements. It empowers models to undergo training solely on unlabeled data while still achieving comparable or even superior performance compared to supervised alternatives. Among the array of unsupervised methods, contrastive learning has asserted its effectiveness in feature extraction for a variety of downstream tasks. Conversely, auto-encoders are extensively employed to acquire indispensable representations that faithfully reconstruct input data. In this study, we introduce a novel approach that amalgamates contrastive learning with auto-encoders for OOD detection using unlabeled data. Contrastive learning diligently tightens the grouping of in-distribution data while meticulously segregating OOD data, and the auto-encoder augments the feature space with increased refinement. Within this framework, data undergo implicit classification into in-distribution and OOD categories with a notable degree of precision. Our experimental findings manifest that this method surpasses most of the existing detectors reliant on unlabeled data or even labeled data. By incorporating an auto-encoder into an unsupervised learning framework and training it on the CIFAR-100 dataset, our model enhances the detection rate of unsupervised learning methods by an average of 5.8%. Moreover, it outperforms the supervised-based OOD detector by an average margin of 11%.
Kemampuan Membaca Diftong pada Siswa Kelas III Sekolah Dasar Negeri 010 Desa Pengalihan Kecamatan Keritang Kabupaten Indragiri Hilir

The aim of this research is to determine the ability to read diphthongs in class III students at state elementary school 010, diversion village, Keritang subdistrict, Indragiri downstream district, academic year 2022/2023. This research is qualitative research using a case study approach. The research subjects were third grade students. Data collection used observation and in-depth interview methods. The researcher is the main instrument in the research with the help of observation guidelines and interview guidelines. The data analysis techniques used are data presentation, data reduction, and drawing conclusions. The triangulation used to test the validity of the data is source, time and technique triangulation. The results of the research showed that there were three class III students who were not able to read diphthongs well and correctly, in fact they could not read at all, these students were DI aged 9 years, RR aged 9 years, AS aged 9 years and three students in class III were research subject. This research shows that there are three cases of students who have not been able to read diphthongs properly and correctly.
SVMVC: A Refinement to classical MVC for enhancing the Performance of web applications

Web application architectures are established design patterns employed to structure the design of web applications. Typically, web application architecture is divided into distinct layers and components, each catering to various quality enhancement requirements. These design patterns are crucial for creating web applications that are scalable, maintainable, and well-organized. Among the most renowned and widely used design patterns for web applications is the Model-View-Controller (MVC) pattern. The MVC architecture's inherent attributes, such as reusability, testability, scalability, and separation of concerns, have played a pivotal role in its widespread adoption.&#x0D; As performance stands out as a prominent quality indicator for web applications, this paper introduces a novel refinement to the MVC architecture, with a primary focus on enhancing the performance of web applications.
Visualization modality for augmented reality guidance of in‐depth tumour enucleation procedures

Recent research studies reported that the employment of wearable augmented reality (AR) systems such as head-mounted displays for the in situ visualisation of ultrasound (US) images can improve the outcomes of US-guided biopsies through reduced procedure completion times and improved accuracy. Here, the authors continue in the direction of recent developments and present the first AR system for guiding an in-depth tumour enucleation procedure under US guidance. The system features an innovative visualisation modality with cutting trajectories that 'sink' into the tissue according to the depth reached by the electric scalpel, tracked in real-time, and a virtual-to-virtual alignment between the scalpel's tip and the trajectory. The system has high accuracy in estimating the scalpel's tip position (mean depth error of 0.4 mm and mean radial error of 1.34 mm). Furthermore, we demonstrated with a preliminary user study that our system allowed us to successfully guide an in-depth tumour enucleation procedure (i.e. preserving the safety margin around the lesion).
Distributed Cipher Hashing Cryptographic Technique For Secure Data Storage In Cloud Computing Using XOR-MECC

The user is enabled by means of Cloud Computing (CC) to store and access the data as of anywhere. To secure the data amassed on the Cloud Server (CS), CC security encompasses a collection of policies and technologies. Most existing methods bring about a lower-Security Level (SL), the higher computation time for encryption together with decryption, and also Memory Usage (MU) issues, et cetera. Here, to resolve such issues in the existent methods, an effectual modified cryptographic is proposed in favor of the secured Data Storage (DS) in CC. Firstly, secure authentication is done via the Alder32 hash algorithm. The user is authenticated via registration, login, together with verification. Subsequently, the authenticated user commences uploading the file. Prior to uploading, the inputted file is bifurcated into sub-files counting on the size, and the features are extracted as of the split file and distributed CS. Next, the Brownian Motion-based BAT algorithm (BM-BAT) selects the appropriate distributed CS. Then, the Modified Cryptographic (XOR-MECC) uploads the files securely into the CS. The experimentation's outcomes state that the proposed work attains better performance when weighed against the prevailing works.
Assessment of Face Image Quality using Machine Learning

Face recognition systems have gained significant attention in recent years due to their applications in various domains such as security, authentication and surveillance. However, the performance of these systems heavily relies on the quality of input face images. This paper presents a quality assessment technique for face images with the aim of enhancing the accuracy and reliability of face recognition systems. There are several methods used for Face Image Quality Assessment (FIQA), thus measuring the quality of images is likely to filter out poor-quality images, which can improve the performance of downstream tasks. Therefore, a novel FIQA framework is proposed to select the best facial image for better recognition. In our proposed methodology, firstly measures various image quality factors then apply fusion techniques to combine all quality factors for classifying the face quality. In addition, conducting a statistical test to check the level of confidence of proposed model. The technique is evaluated on the Color-Feret and KinectFace datasets. The correlation determine between the proposed system and human score is 0.95.
AI And Copyright

In the realm of AI and copyright, secondary data analysis reveals a landscape characterized by the burgeoning capabilities of artificial intelligence to autonomously generate creative content, challenging traditional copyright principles and raising questions about authorship and ownership. This analysis delves into the evolving debates surrounding copyright infringement, as AI's capacity to replicate and transform copyrighted materials blurs the lines between original works and derivative content. The nuanced concepts of "fair use" and "transformative works" are under scrutiny in the context of AI-generated content, leading to legal disputes and the need for sophisticated copyright infringement detection mechanisms. Additionally, secondary data underscores the complexities of licensing and royalty arrangements for AI-generated content and highlights the ethical and social concerns associated with the impact on human creativity and potential job displacement in creative industries. It emphasizes the importance of global considerations and harmonization of international copyright laws. Ultimately, this secondary data analysis illuminates the multifaceted nature of the AI-copyright intersection, necessitating ongoing research and informed policy decisions to navigate this evolving terrain.