Reviewerly/exHarmony huggingface dataset:
Vulnerability of Open-Source Face Recognition Systems to Blackbox Attacks: A Case Study with InsightFace

This paper presents a comprehensive analysis of the security aspects of the InsightFace project (a popular open-source face recognition system) focusing on its susceptibility to three distinct black box attacks: Face Swap, Morphing, and Presentation. Open-source face recognition models are used in commercial applications, thereby motivating our security analysis. Our investigation entails a meticulous evaluation of the susceptibility of the project to false authentication when subjected to the three attacks. We observed from our experiments that InsightFace was not able to differentiate between legitimate images and manipulated images. The principal aim of this research is to draw attention to the security challenges inherent in open-source face recognition systems, often integrated into various public applications.
AlphaNet: Single Morphing Attack Detection Using Multiple Contributors

This paper proposes and explores a Single Morphing Attack Detection (S-MAD) method of morphed face images created from different numbers of contributors. We selected facial images from K subjects and their accomplices for developing morphing face images from K = 2, 4, 8 and 16 subjects. Morphing images with more contributors appear as smothered faces. We developed a multiclass Convolutional Neural Network (CNN) based on three levels of alpha filters applied to the RGB channels based on MobileNetV3 (AlphaNet). This AlphaNet was evaluated on intra-dataset and cross-dataset scenarios. Our method tested in synthetic images obtained an BPCER <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> of 4.41 % and BPCER <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</inf> of 4.56% on cross-dataset.
Reversing Deep Face Embeddings with Probable Privacy Protection

Generally, privacy-enhancing face recognition systems are designed to offer permanent protection of face embeddings. Recently, so-called soft-biometric privacy-enhancement approaches have been introduced with the aim of canceling soft-biometric attributes. These methods limit the amount of soft-biometric information (gender or skin-colour) that can be inferred from face embeddings. Previous work has underlined the need for research into rigorous evaluations and standardised evaluation protocols when assessing privacy protection capabilities. Motivated by this fact, this paper explores to what extent the non-invertibility requirement can be met by methods that claim to provide soft-biometric privacy protection. Additionally, a detailed vulnerability assessment of state-of-the-art face embedding extractors is analysed in terms of the transformation complexity used for privacy protection. In this context, a well-known state-of-the-art face image reconstruction approach has been evaluated on protected face embeddings to break soft biometric privacy protection. Experimental results show that biometric privacy-enhanced face embeddings can be reconstructed with an accuracy of up to approximately 98%, depending on the complexity of the protection algorithm.
MIPR: Automatic Segmentation Annotation of Medical Images via Generative model

Most state-of-the-art semantic segmentation methods in the medical domain rely on fully supervised deep learning. However, creating high-quality annotated datasets demands extensive labor and domain expertise, leading to significant time and cost investments. While previous approaches have explored semi-supervised and unsupervised learning to alleviate the scarcity of annotated data by leveraging unlabeled samples and achieving promising results, they still fall short of replicating the nuanced annotations provided by medical professionals. In this paper, drawing inspiration from self-training techniques in semi-supervised learning, we propose an innovative approach to address the issue of limited annotated data, called medical images pixel rearrangement. Our method combines image editing with the pseudo-label methodology to generate labeled image pairs. Our method supports iterative training, and with each iteration, the edited images progressively resemble the originals, and the resulting annotations align more closely with the annotations made by medical experts. This novel process allows us to derive labeled pairs of data directly from the pool of unlabeled data. The methodology is implemented through a purpose-built conditional generative model and a dedicated segmentation network. Experimental results on the ISIC18 dataset demonstrate that the annotation quality achieved by our segmentation method is not only comparable to, but potentially surpasses the quality of annotations provided by medical experts.
Sub-GMN: The Neural Subgraph Matching Network Model

As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry and natural language processing. Yet subgraph matching problem remains to be an NP-complete problem. This study proposes an end-to-end learning-based approximate method for subgraph matching task, called subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph representation learning to map nodes to node-level embedding. It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph. To test the performance of the proposed method, we applied our method on two databases. We used two existing methods, GNN and FGNN as baseline for comparison. Our experiment shows that, on dataset 1, on average the accuracy of Sub-GMN are 12.21% and 3.2% higher than that of GNN and FGNN respectively. On average running time Sub-GMN runs 20-40 times faster than FGNN. In addition, the average F1-score of Sub-GMN on all experiments with dataset 2 reached 0.95, which demonstrates that Sub-GMN outputs more correct node-to-node matches.Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage. Another advantage of our proposed Sub-GMN is that it can output a list of node-to-node matches, while most existing end-to-end GNNs based methods cannot provide the matched node pairs.
Robust Image Encryption Based on Improved Logistic Chaos and Cross Chaotic Diffusion

To address the problem of weak robustness of some current image encryption algorithms, this paper proposes a strong robust encryption algorithm. The Logistic chaotic system is improved to produce more random sequence distribution. The plaintext hash is used to construct the initial key of chaotic system and the spiral matrix transformation is used for the first scrambling. A novel chaotic sequence cross-diffusion method is constructed by introducing two values of plaintext correlation val1 and val2. Through plaintext transformation of chaotic sequences, it improves the plaintext sensitivity, which can resist the selective plaintext attack, and has strong robustness. In order to avoid the position of some pixels not changed in the first spiral matrix transformation, cross-scrambling is carried out again after cross-diffusion. Therefore a scrambling – diffusing - scrambling encryption framework is built. The experimental results show that the proposed algorithm can resist brute force attacks and selective plaintext attack. It has strong robustness and high security. Especially it has perfect performance in anti-shear attacks, which has significance for image security transmission and is very suitable for image encryption.
ERD-YOLOv5s: a Multi-Scale Feature Extraction Network for Object Detection in Driving Scenarios

Object detection is a fundamental task for autonomous driving, but accurately identifying objects within dense and complex target distributions remains challenging. To address this issue, we present a network named ERD-YOLOv5s for robust object detection across different autonomous driving scenarios. This network integrates Res2CSP layers within the backbone, EVC module in the neck and a decoupled head to enhance the ability of multi-scale feature extraction and dense object detection. To evaluate the effectiveness of our approach, we conducted experiments on two benchmark datasets: Cityscapes and BDD100K, which achieved 2.5% and 2.6% improvements of mAP@0.5 over YOLOv5s. Experiments demonstrate that the proposed approach is able to handle complex driving scenes more accurately.
Effective and Efficient Detection and Interpretation of Road Direction Signs

Road direction signs depict textual and directional information to convey instructions about how to reach a given destination. Detecting and interpreting road direction signs constitutes a key component in self-driving vehicles, therefore we propose a hybrid pipeline that combines deep learning with traditional handcrafted image processing, aiming for a combination of effectiveness and efficiency. Our solution includes a procedure for the identification of the orientation of arrows, generalizing on a wide variety of pictorial styles for direction signs across the globe. Our pipeline is evaluated in terms of accuracy and inference time of its individual steps, demonstrating excellent performance. Experiments are performed over two variants of the pipeline, assuming the availability of different levels of computational resources. We also test the system's dependence on annotated supervision by performing evaluation with a varying number of training instances.
Deep Learning Based Hate Speech Detection on Twitter

There have been growing worries about the effects of the widespread use of hate speech and harsh language on social media sites like Twitter. Effective strategies for recognising and reducing such dangerous material are necessary for resolving this problem. In this research, we give a detailed analysis of four deep learning models for identifying hate speech and inflammatory language on Twitter: the Long Short-Term Memory (LSTM), the Recurrent Neural Network (RNN), the Bidirectional LSTM (Bi-LSTM), and the Gated Recurrent Unit (GRU). We downloaded a large dataset from Kaggle that was curated for hate speech identification and used it in our experiment. We built each model after preprocessing and tokenization, then tweaked their hyperparameters for maximum efficiency. The models' abilities to detect hate speech were evaluated using standard measures including accuracy, precision, recall, and Fl-score. Our findings show that there is a wide range of effectiveness amongst models in terms of identifying hate speech and inflammatory language on Twitter. In terms of accuracy and Fl-scores, the Bi-LSTM and GRU models were superior to the LSTM and RNN. The results of this study imply that using bidirectional and gated processes may increase the models' capability of understanding the interdependencies and contexts of tweets, and hence, their classification accuracy.
Smart Functional Appliance - A New Approach to Improve Patient Compliance

Aim To assess the patient compliance to removable functional appliances using TheraMonreg Microsensor.Method The study sample included 15 patients requiring removable functional appliances. Microsensors were incorporated in each of them. After three weeks of appliance delivery microsensor was activated T0 and patients were not informed that they were being monitored. After the activation T0 patients were evaluated at T1 and T2 intervals at three and six week intervals respectively to gain insight into further compliance of the subjects included in the study after they were made aware of the presence of sensor during T1 appointment.Results The mean overall wear time during the aware period of 21 days was significantly higher 239.568plusmn74.79 as compared to the non-aware period 145.03plusmn58.69 and the mean difference was statistically significant P0.001. The overall mean wear time during the aware period of 21 days among female patients was relatively higher compared to male patients. However the mean difference between the gender was not statistically significant P0.25.Conclusion Patients wore the appliance for longer duration during the aware period than the non-aware period. There was no statistically significant difference between male and female patients regarding overall compliance.
Analysis of University Admission Procedures: Elements and Methods Focusing on the Case of a Christian University in Seoul

Today, integrative thinking and problem-solving skills are paramount. Yet, current college admissions remain anchored to traditional pedagogy. As global emphasis on tertiary education intensifies, it's essential to recalibrate admissions to align with universities' goals and standards. This study examined the documentation review of comprehensive student record admissions at four-year universities in Seoul and its metropolitan area. Using data from the “A” Christian university a correlation between document evaluation scores and student grades was identified. Candidates from the “Coram Deo Talent Selection” consistently showed exemplary academic performance. Furthermore, the document scores and academic capability factors significantly influenced student outcomes. Universities, as advanced academia pillars, must integrate admissions with primary to secondary education, endorsing judicious benchmarks. The student admission should evaluate candidates' comprehensive competencies beyond mere numbers. This research offers insights for a refined collegiate admissions strategy, fostering growth for institutions and students.
Validating the efficacy of ChatGPT based on its judgement of English grammaticality

The present study is aimed at validating the efficacy of ChatGPT based on its explanations of grammatical concepts. Given the fact that more and more English teachers refer to ChatGPT for the purpose of judging grammaticality, this study attempts to probe the potentials and challenges of ChatGPT for English education and assessment. ChatGPT’s explanations of 140 paired sentences from 11 grammatical categories including sentence types, verbs, verbals, passive voice, subjunctive mood, relatives, special structures, conjunctions, nouns-articles, adjectives-adverbs, and prepositions were collected and examined to identify the extent to which the information was valid. The results were then compared against such authoritative corpus resources as the Corpus of Contemporary American English (COCA) and Google Books Ngram Viewer (GBNV). The results revealed the average accuracy of the explanations provided by ChatGPT was approximately 73%. While some grammatical explanations of ChatGPT were accurate, 27% of explanations included misleading or even self-contradictory information. According to the survey, the majority of teachers think somewhat favorably of ChatGPT’s potential for teaching and testing, but they are aware of its inherent inaccurate information. Overall, the findings suggest that even ChatGPT’s state-of-the-art technology has yet to meet the adequacy of grammaticality for teaching and testing and that it be supplemented by authoritative English corpora.
The Effectiveness of Articulate Storyline 3 Application-Based Interactive Learning Media in Social Studies Learning for Elementary School

The research problem is students' low reading skills and social studies learning outcomes. This research aims to apply interactive media Articulate Storyline 3 to improve students' general map reading skills and learning outcomes in elementary school subjects in Makassar City. This study uses the ASSURE Research and Development (RD) research method (Analyze Learner Characteristics, State Objective Select Media, Utilize Media, Required learner participation Evaluate and revise) to develop learning media that can improve reading skills and learning outcomes for 28 elementary school students in Makassar city. The results showed that interactive learning media was declared effective because two indicators were achieved: the observations of student activities included in the very active category and the observations of teacher activities included in the very active category. Implications in learning can improve skills and student learning outcomes.
Techno-Educational Conditions in Mexico Before and During the Pandemic: Moving Towards the Necessary Consolidation of a Digital Education Agenda

This article provides a contextualization of the conditions prior to the pandemic in the Mexican educational system, in particular the programs and educational technologies implemented by the authorities to provide continuity of learning. With this, the actions and mechanisms of students and teachers in the use of information, communication, knowledge and digital learning technologies for the use of multimedia and interactive content are identified. From there, the strategies to provide schools and students with computers, laptops and tablets and Internet connectivity are considered. The results of the research provide a glimpse of those actions that are part of the necessary consolidation of a Digital Education Agenda.
EFL Students’ Strategies In Dealing With Speaking Anxiety In Post Pandemic Class

The purpose of the study was to find out the strategies that the EFL students use to reduce their speaking anxiety in post pandemic class. This research is a qualitative type of research that uses qualitative descriptive method to collect the necessary data in the hope of knowing the strategies used by students in dealing with their speaking anxiety. The participants of this study included students who were in the class of XI Bahasa at SMA Negeri 1 Pangkep. In this study, there were two instruments used, namely interviews which were carried out in two stages as the main instrument and an observation activity involving two observers. The observation was carried out in the classroom while the learning activity was conducted, and the interview sessions were carried out outside of their classroom. By using a purposive sampling technique, six student representatives were selected to be interviewed. The results of the study revealed that there were seventeen methods used by students to overcome their speaking anxiety which were then categorized into six different kinds of strategies. These six strategies according to the research finding are consisting of memory strategy, cognitive strategy, compensation strategy, metacognitive strategy, affective strategy and social strategy.
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The cover image is based on the Letter T owards bespoke optimizations of energy efficiency in HPC environments by Robert Tracey et al., https://doi.org/10.1002/ail2.87 . image
The Effect of Scientific-Based E-Worksheet on Hydrocarbon Material on Students’ Critical Thinking Ability at Vocational High Schools

The purpose of this reasearch was to know the effect of scientific-based e-worksheet on hydrocarbon material on student’s critical thinking ability at vocational high schools. The method used in this study was pre-experimental and the research design was one group pre-test and post-test with one experimental class and no control class. The population in this study were students of class X of a state vocational high school in Pekanbaru, while the number of samples were 30 students in class X RPL (Software Engineering), who were selected by purposive sampling technique. The instrument used to obtained data of students' critical thinking skills was multiple-choices (a, b, c, d and e) with total of 25 questions and each question has 4 points. Data of pre-test and post-test was analyzed with N-gain score. The results showed that pre-test was 44% and post-test was 88%. The average N-Gain score was found to be 0.7745 with high criteria. It could be stated that there was a significant difference between the pre-test and post-test scores on students' critical thinking skills. Furthermore, the average percentage of the N-Gain score was 77.4483% with effective criteria. It could be concluded that the scientific-based e-worksheet on hydrocarbon material was declared effective for training students' critical thinking skills at vocational high school.
Effectiveness of Digital Technology in Geography Syllabus in Tanzania's Ordinary Secondary Schools

Despite the pervasive of digital technology in all aspects of teaching and learning materials Geography syllabus remains ineffective in its execution in Tanzanian’s Ordinary secondary schools. This study aimed to assess the effectiveness of digital technology in the Geography syllabus in Tanzanian Ordinary secondary schools. The study focused on two objectives namely: To examine Geography topics that use digital technology instructional materials in Tanzanian’s Ordinary secondary schools and to compare the Geography topics that use digital technology instructional materials and the performance of the Certificate of Secondary Education Examination. The study used documentary analysis methods including analyzing the recent five years National Examination Reports; Candidate’s Item Response Analysis and current Geography syllabus. The data was analysed by using a quantitative approach where graphs; percentages and Spearman’s ranks order correlation coefficient techniques were applied. The findings revealed that the topics that used digital technology instructional materials in the Geography syllabus had satisfactory and moderate positive correlation coefficient between the topics that used digital technology instructional materials and the performance of the Certificate of Secondary Education Examination. The study recommended to unfold the Geography syllabus and then inculcate digital technology instructional materials for those topics that appeared to have inadequate digital instructional materials; to include all topics in the Geography Certificate of Secondary Education Examination particularly those using digital technology instructional materials and to use ‘Kiswahili’ as lingua franca language in digital technology instructional materials as teachers and students have pitfall in English language proficiency. The report concluded that, in order to pursue the utilization of digital technology in Geography syllabus educational philanthropists should be resilience to allocate sufficient funds in ordinary secondary schools in order to purchase digital facilities such as; computers, projectors, printers, television, radio, and smartphones and construct digital Geography classrooms with reliable in internet connectivity and electricity supply.