Reviewerly/exHarmony huggingface dataset:
DF-Captcha: A Deepfake Captcha for Preventing Fake Calls

Social engineering (SE) is a form of deception that aims to trick people into giving access to data, information, networks and even money. For decades SE has been a key method for attackers to gain access to an organization, virtually skipping all lines of defense. Attackers also regularly use SE to scam innocent people by making threatening phone calls which impersonate an authority or by sending infected emails which look like they have been sent from a loved one. SE attacks will likely remain a top attack vector for criminals because humans are the weakest link in cyber security. Unfortunately, the threat will only get worse now that a new technology called deepfakes as arrived. A deepfake is believable media (e.g., videos) created by an AI. Although the technology has mostly been used to swap the faces of celebrities, it can also be used to `puppet' different personas. Recently, researchers have shown how this technology can be deployed in real-time to clone someone's voice in a phone call or reenact a face in a video call. Given that any novice user can download this technology to use it, it is no surprise that criminals have already begun to monetize it to perpetrate their SE attacks. In this paper, we propose a lightweight application which can protect organizations and individuals from deepfake SE attacks. Through a challenge and response approach, we leverage the technical and theoretical limitations of deepfake technologies to expose the attacker. Existing defence solutions are too heavy as an end-point solution and can be evaded by a dynamic attacker. In contrast, our approach is lightweight and breaks the reactive arms race, putting the attacker at a disadvantage.
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork

We develop an approach for text-to-image generation that embraces additional retrieval images, driven by a combination of implicit visual guidance loss and generative objectives. Unlike most existing text-to-image generation methods which merely take the text as input, our method dynamically feeds cross-modal search results into a unified training stage, hence improving the quality, controllability and diversity of generation results. We propose a novel hypernetwork modulated visual-text encoding scheme to predict the weight update of the encoding layer, enabling effective transfer from visual information (e.g. layout, content) into the corresponding latent domain. Experimental results show that our model guided with additional retrieval visual data outperforms existing GAN-based models. On COCO dataset, we achieve better FID of $9.13$ with up to $3.5 \times$ fewer generator parameters, compared with the state-of-the-art method.
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models

Nowadays, systems based on machine learning (ML) are widely used in different domains. Given their popularity, ML models have become targets for various attacks. As a result, research at the intersection of security/privacy and ML has flourished. Typically such work has focused on individual types of security/privacy concerns and mitigations thereof. However, in real-life deployments, an ML model will need to be protected against several concerns simultaneously. A protection mechanism optimal for one security or privacy concern may interact negatively with mechanisms intended to address other concerns. Despite its practical relevance, the potential for such conflicts has not been studied adequately. We first provide a framework for analyzing such "conflicting interactions". We then focus on systematically analyzing pairwise interactions between protection mechanisms for one concern, model and data ownership verification, with two other classes of ML protection mechanisms: differentially private training, and robustness against model evasion. We find that several pairwise interactions result in conflicts. We explore potential approaches for avoiding such conflicts. First, we study the effect of hyperparameter relaxations, finding that there is no sweet spot balancing the performance of both protection mechanisms. Second, we explore if modifying one type of protection mechanism (ownership verification) so as to decouple it from factors that may be impacted by a conflicting mechanism (differentially private training or robustness to model evasion) can avoid conflict. We show that this approach can avoid the conflict between ownership verification mechanisms when combined with differentially private training, but has no effect on robustness to model evasion. Finally, we identify the gaps in the landscape of studying interactions between other types of ML protection mechanisms.
EGCR: Explanation Generation for Conversational Recommendation

Growing attention has been paid in Conversational Recommendation System (CRS), which works as a conversation-based and recommendation task-oriented tool to provide items of interest and explore user preference. However, existing work in CRS fails to explicitly show the reasoning logic to users and the whole CRS still remains a black box. Therefore we propose a novel end-to-end framework named Explanation Generation for Conversational Recommendation (EGCR) based on generating explanations for conversational agents to explain why they make the action. EGCR incorporates user reviews to enhance the item representation and increase the informativeness of the whole conversation. To the best of our knowledge, this is the first framework for explainable conversational recommendation on real-world datasets. Moreover, we evaluate EGCR on one benchmark conversational recommendation datasets and achieve better performance on both recommendation accuracy and conversation quality than other state-of-the art models. Finally, extensive experiments demonstrate that generated explanations are not only having high quality and explainability, but also making CRS more trustworthy. We will make our code available to contribute to the CRS community
NECE: Narrative Event Chain Extraction Toolkit

To understand a narrative, it is essential to comprehend the temporal event flows, especially those associated with main characters; however, this can be challenging with lengthy and unstructured narrative texts. To address this, we introduce NECE, an open-access, document-level toolkit that automatically extracts and aligns narrative events in the temporal order of their occurrence. Through extensive evaluations, we show the high quality of the NECE toolkit and demonstrates its downstream application in analyzing narrative bias regarding gender. We also openly discuss the shortcomings of the current approach, and potential of leveraging generative models in future works. Lastly the NECE toolkit includes both a Python library and a user-friendly web interface, which offer equal access to professionals and layman audience alike, to visualize event chain, obtain narrative flows, or study narrative bias.
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks

Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application. The main causes are (a) non-detection and (b) false-detection phenomena: (a) The class activation maps refined from existing WSSS-IL methods still only represent partial regions for large-scale objects, and (b) for small-scale objects, over-activation causes them to deviate from the object edges. We propose RecurSeed, which alternately reduces non- and false detections through recursive iterations, thereby implicitly finding an optimal junction that minimizes both errors. We also propose a novel data augmentation (DA) approach called EdgePredictMix, which further expresses an object's edge by utilizing the probability difference information between adjacent pixels in combining the segmentation results, thereby compensating for the shortcomings when applying the existing DA methods to WSSS. We achieved new state-of-the-art performances on both the PASCAL VOC 2012 and MS COCO 2014 benchmarks (VOC val: 74.4%, COCO val: 46.4%). The code is available at https://github.com/shjo-april/RecurSeed_and_EdgePredictMix.
Momentum-Based Policy Gradient with Second-Order Information

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient method, called SHARP, which incorporates second-order information into stochastic gradient descent (SGD) using momentum with a time-varying learning rate. SHARP algorithm is parameter-free, achieving $\epsilon$-approximate first-order stationary point with $O(\epsilon^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration. Unlike most previous work, our proposed algorithm does not require importance sampling which can compromise the advantage of variance reduction process. Moreover, the variance of estimation error decays with the fast rate of $O(1/t^{2/3})$ where $t$ is the number of iterations. Our extensive experimental evaluations show the effectiveness of the proposed algorithm on various control tasks and its advantage over the state of the art in practice.
Learning-based Collision-free Planning on Arbitrary Optimization Criteria in the Latent Space through cGANs

We propose a new method for collision-free planning using Conditional Generative Adversarial Networks (cGANs) to transform between the robot's joint space and a latent space that captures only collision-free areas of the joint space, conditioned by an obstacle map. Generating multiple plausible trajectories is convenient in applications such as the manipulation of a robot arm by enabling the selection of trajectories that avoids collision with the robot or surrounding environment. In the proposed method, various trajectories that avoid obstacles can be generated by connecting the start and goal state with arbitrary line segments in this generated latent space. Our method provides this collision-free latent space, after which any planner, using any optimization conditions, can be used to generate the most suitable paths on the fly. We successfully verified this method with a simulated and actual UR5e 6-DoF robotic arm. We confirmed that different trajectories could be generated depending on optimization conditions.
Continual Adaptation of Semantic Segmentation using Complementary 2D-3D Data Representations

Semantic segmentation networks are usually pre-trained once and not updated during deployment. As a consequence, misclassifications commonly occur if the distribution of the training data deviates from the one encountered during the robot's operation. We propose to mitigate this problem by adapting the neural network to the robot's environment during deployment, without any need for external supervision. Leveraging complementary data representations, we generate a supervision signal, by probabilistically accumulating consecutive 2D semantic predictions in a volumetric 3D map. We then train the network on renderings of the accumulated semantic map, effectively resolving ambiguities and enforcing multi-view consistency through the 3D representation. In contrast to scene adaptation methods, we aim to retain the previously-learned knowledge, and therefore employ a continual learning experience replay strategy to adapt the network. Through extensive experimental evaluation, we show successful adaptation to real-world indoor scenes both on the ScanNet dataset and on in-house data recorded with an RGB-D sensor. Our method increases the segmentation accuracy on average by 9.9% compared to the fixed pre-trained neural network, while retaining knowledge from the pre-training dataset.
Pengembangan Modul Elektronik Menulis Teks Cerpen Berbasis Project Based Learning bagi Siswa Kelas XI SMA

This study aims to describe the process of developing an electronic module for learning to write short stories based on project-based learning that is valid, practical, and effective, which class XI high school students use. This research and development (R&amp;D) use a 4D development model, which includes definition, design, development, and deployment. The results of this study indicate that the electronic module improves student learning outcomes. It is evidenced by the results of the electronic module's validity, practicality, and effectiveness. The validity of the electronic module by experts is 95.1%, so the electronic module is categorized as very valid. The practicality of the electronic module by the teacher is 93.05% in the very practical category, and the practicality by students is 85.8% in the very practical category. Student learning activities are 97.5% in the very active category. The effectiveness of the electronic module is based on the results of the student knowledge test, the average score obtained is 87 with an A predicate. Based on the student skills test results, the average score obtained is 87 with an A predicate (very effective).
The Life of Nampo Geum Man-Young, A Literary Person of Honam In The 17th Century and Development of Yeomrakpung Poetry

This study aimed to examine the development aspect of Yeomrakpung poetry by Nampo Kim Man-Young, a literary person of Honam in the 17th century and the meaning and perspective of the poetry. Yeomrakpung is related to the poetical style of Confucian scholars in Song Dynasty. Kim Man-Young’s poetry was connected to Yeomrakpung because it was mentioned in the preface and epilogue of his collection of literary works and his records.&#x0D; Kim Man-Young is a local Confucian scholar who was born in Naju, Jeonnam in the 17th century and finished his life there. At that time, he was recommended for an official post by many people due to his learning and virtue, and good behaviors, but he was a local scholar from noble family who stayed out of the government service. He was also much respected by many local people as he lived a life as a Confucian scholar. Meanwhile, he was naturally engaged in writing poems while living a common life and approximately 530 poems with 460 themes were handed down. Of all the poems handed down, 85 poems with 73 themes were classified into Yeomrakpung poetry.&#x0D; It was suggested that the main themes of Yeomrakpung poetry by Kim Man-Young were moral mind, government property, nature and reading. Specifically, he expressed the will of moral mind training, acquired the exquisite principles through government property, admired nature, declared he would obey to nature, dealt with pleasure of reading and criticized Scriptures. Therefore, this study recognized that they were main themes of Yeomrakpung poetry and specifically examined the development aspect.&#x0D; In Chinese poetry history in Korea, Yeomrakpung poetry continued since Confucianism of Song Dynasty had been introduced. It is meaningful that Kim Man-Young’s Yeomrakpung poetry naturally reflected his academic world through poetry and revealed his peculiar characteristics. Kim Man Young witnessed difficulties of local people while living in local areas and expressed what he saw in his poetry. This study suggested that future studies should pay attention to it.
Automatic face recognition using normalized cross correlation (NCC) function with variable template size

Face detection is one of the most difficult image processing issues. In the many processing stage of face recognition, one of the most important parts is face detection. In the recent years, there has been much progress in face detection. Many face recognition system adopt or develop .We have proposed automatic face detection algorithm of an individual person through of group image by matching the extracted face of the individual person from group image based on Normalized Cross Correlation (NCC) functions and skin detection of variable size of individual person been has face detected . By observing the results, it is clear that Normalized Cross-Correlation (NCC) with skin detection is the best approach for face matching. It gives perfect face matching in the given target image. The maximum cross-correlation coefficient values indicate the perfect matching of extracted face with the target image. The algorithm is implemented in MATLAB. The experimental results show that developed algorithm is robust for similarity measure if the image was not affected by pose-variation.
Pengaruh Penggunaan Media Foto dan Video Animasi Karikatur terhadap Kemampuan Peserta Didik dalam Memahami Teks Anekdot Siswa Kelas X SMA

This study aims to determine the effect of using photos and videos of animated caricature media on students in understanding anecdotal texts for class X SMA. This research method is quantitative with a descriptive approach and associative analysis. Data collection techniques, namely questionnaires and anecdotal writing tests. The study's results on the use of photo media in class X high school students, on average, were at a high level. A total of 35 students (48%) in the high category, 20 students (27%) in the medium category, and 18 students (25%) in the low category. On average, the use of animated caricature videos in class X high school students is in the medium category. A total of 20 students (27%) in the high category, 41 students (56%) in the medium category, and 12 students (17%) in the low category. Research on media of photos and videos of animated caricatures with the ability to understand anecdotal texts for class X high school students, on average, is in the high category. A total of 30 students (41%) in the high category, 29 students (40%) in the medium category, and 14 students (19%) in the low category.
Detecting Personal Medication Intake in Twitter via Domain Attention-Based RNN with Multi-Level Features

Personal medication intake detection aims to automatically detect tweets that show clear evidence of personal medication consumption. It is a research topic that has attracted considerable attention to drug safety surveillance. This task is inevitably dependent on medical domain information, and the current main model for this task does not explicitly consider domain information. To tackle this problem, we propose a domain attention mechanism for recurrent neural networks, LSTMs, with a multi-level feature representation of Twitter data. Specifically, we utilize character-level CNN to capture morphological features at the word level. Subsequently, we feed them with word embeddings into a BiLSTM to get the hidden representation of a tweet. An attention mechanism is introduced over the hidden state of the BiLSTM to attend to special medical information. Finally, a classification is performed on the weighted hidden representation of tweets. Experiments over a publicly available benchmark dataset show that our model can exploit a domain attention mechanism to consider medical information to improve performance. For example, our approach achieves a precision score of 0.708, a recall score of 0.694, and a F1 score of 0.697, which is significantly outperforming multiple strong and relevant baselines.
Multicultural Career Guidance in the School Education System

Abstract: Career guidance is part of the activities in the school education system in Bulgaria to provide general support for students’ personal development. Career guidance programs include activities with students from I to XII grade as preparation for transition to higher education or for participation in the labor market. An important task of career guidance is counselling of students who are at risk of dropping out of school, as well as the reintegration of those who have dropped out of school and the development of skills in social inclusion and mobility. The fact that premature dropout from the educational system has its ethno-cultural specifics determines the need for special attention to the peculiarities of career guidance in a multicultural school environment. The article focuses on multicultural career guidance in the field of vocational education and training. The parameters of intercultural competence in the preparation of career guidance specialists for the school education system and in the preparation of mentors in a dual education system are outlined. Keywords: career guidance, counseling, multicultural environment, intercultural competence.
Manifestations of Pedagogical Communication: Virtual, Formal and Semi-formal

Abstract: The article presents the results of an analysis of the topic of education in a virtual environment, focusing on pedagogical communication and its manifestations in websites, virtual communities and the social network Facebook. An analysis is made of the opinions of users expressed in groups and sites that have been identified by their creators as engaged in presenting topics concerning education. Groups and sites that publicise topics related to innovation and the quality of education and where users’ opinions are presented in these groups are analysed. Cyberethnographic observation and content analysis methods were used to analyse communication on websites, forums, online groups, and social networks. A period of one year is covered: from 1 June 2021 to 1 June 2022. Significant educational topics for this period, presented formally and commented on professionally or informally by different groups and communities, were selected. Generalisations are made about the characteristics of virtual communication and virtual pedagogical communication on educational topics. Discussion questions are formulated that may be part of future research on virtual pedagogical communication. Keywords: pedagogical communication, virtual pedagogical communication, websites, virtual forums, online groups, Facebook, education.
VISION BASED FIRE FIGHTING ROBOT USING IOT

With the advancement in the field of mechanical technology, human interaction has become less And robots are being utilized for various works and for welfare of beings. Nowadays, fire mischances keep happening frequently, cause danger to human life &amp; property, also cause difficulties to fire fighters to save lives. In such case, a fire-fighting robot is utilized to protect human lives, wealth &amp; surroundings from the fire mis-happenings. This model is IOT based fire-fighting robot that detects the fire with vision technology. Afterbeing informed authorities can start visualizing the fire location and can communicate with people stuck with helpof an automatic receiver installed instructions can be given by human to the robot regarding its movement, turningon its water pump or flame sensor pump depending on fire type through long distances. The fire type is knownusing the sensors installed. The analysis also useful to further give information to safety authorities regarding amount of poisonous gases inhaled over a time period by the occupants of the affected area so they can takeappropriate actions to undo the harm.
Google Assistant In Teaching Pronunciation To Junior High School Students Post Pandemic Era

The objective of this study is to teach pronunciation using Google Assistant to junior high school students. This study uses a quantitative approach with a pre-experimental design. In this study the researcher uses one class as the sample with a total of students are 25 students. The data were collected bteststest thatwase pre-test, treatment, and post-test. The research results are explained based on the research findings. the findings showed that the students’ results progress. the findingsreveals that Google Assistant is effective as one of media in teaching pronunciation to junior high school students