Reviewerly/exHarmony huggingface dataset:
The Effect of Quantum Learning Model on Learning Outcomes of Massage Course and Character of FOK Undiksha Students

This study aims to analyze (1) differences in learning outcomes of massage courses and characters between groups of students who study with the quantum learning model and groups of students who study with Direct Instruction (DI) learning models, (2) differences in learning outcomes of massage courses between groups.Students who study with the quantum learning model and groups of students who learn with the Direct Instruction (DI) learning model, and (3) the difference in character between the group of students who study with the quantum learning model and the group of students who learn with the Direct Instruction (DI) learning model.This type of research is a quasi-experimental research (quasi-experimental) with Posttest Only Non-Equivalent Control Group Design.The population of this study were all third semester FOK Undiksha a total population of 91 students.The samples with a total of 64 students selected by simple random sampling technique.The data collected in this study were in the form of learning outcomes for massage and character courses.Data on massage course learning outcomes were collected by means of a massage course study result test and student character was collected by observing students during the learning process.The results showed that, (1) there were differences in the learning outcomes of massage and character courses between the group of students who studied with the quantum learning model and the group of students who studied with the Direct Instruction learning model (F = 80,935; p < 0.05), (2) there are differences in the learning outcomes of massage courses between groups of students who study with the quantum learning model and groups of students who study with the Direct Instruction learning model (F = 39,256; p < 0.05), and (3) there are differences in character between students who study by using a quantum learning model with students who study with a direct instruction learning model (F = 132,667; p < 0.05).
The Influence of M-Learning on Students’ Japanese Learning Outcomes

This study aims to determine the influence of M-Learning learning media on the learning outcomes of Japanese students at SLA Tompaso.This research uses quantitative research methods with experimental approach methods.The research design used is one group pre-test design, with the research instrument being the test.The total population taken was 210 students studying Japanese and a sample of 35 students.Basic contribution as areference in the field of Japanese language education.This research is an important thing that Japanese language learners must know, in order to know the influence of the use of M-Learning on Japanese learning outcomes.This research will produce Japanese language learning software that is made accessible to Japanese language students in particular and can also be used for japanese language lovers, both in other high schools and other agencies.This research is to add to national literature and as material for international publications.The main contribution to science is the field of education, especially Japanese language education.By using M-Learning-based media, it affects and improves student learning outcomes from an initial average score of 34.07%(pretest) rising to 74.11% (posttest).The use of M-Learning is very effectively used as a learning medium for students both as the main medium in learning and as a supporting medium that can be done both at school and at home, because it is effective and efficient in saving time costs and energy.
Text News in Japanese: The Study of the Relationship Between Elements of Text

The Japanese news text is interesting to study to find out and describe the relationship between the elements that make up a coherent and logical text.This is also what underlies the author to examine the relationship between the elements that make up news texts in Japanese, which aims to explain and describe the relationship between the elements that make up news texts in Japanese.The research data were analyzed using content analysis with the results of the research (1) British English text as shown in paragraphs (1), ( 2) and (3); news writers only use the name of the institution, the name of the area, and the name of the person.References are not used, (2) the word seifu is substituted by sushou'prime minister'.Nichi'the 19th' is substituted by jitsu'on the same day', the word chiji'governor' is substituted by osakafu'Osaka government', (3) there are the sentences Tokyo and Hyogo ni tsuite no yosei o kanto.The conclusion in this study is that the specificity of the news text in Japanese is a reference that refers to the name of a person, but the name of the person, the name of the object, and the name of the institution are not used.
Analysis of Socioeconomic Status, Parent’s Income Towards the Interest in Continuing Studies in Students of Tompaso Baru State High School South Minahasa Regency

Families have a major and very important role in shaping students' interest in determining the steps to be taken.The family is responsible for providing funds for educational needs.The average low income of parents is only enough to pay for daily life such as clothing and food which are basic needs, while it is very difficult to pay for their children's education to college because their income is not sufficient.The purpose of this study was to analyze the effect of Parents' Socio-Economic Status on Interest in Continuing Education in Students of Tompaso Baru State High School and secondly to analyze the effect of Parents' Income on Interest in Continuing Studies in Students of Tompaso Baru State High School, the population in this study was all students of class III (three) who became the population in this study were 134 students.The sample in this study was 100 people.Based on the test results, the influence of X1's Socio-Economic Status on Interest in Continuing Education to Higher Education Y is 0.319.Based on the test results above, the value of t arithmetic = 5.742 > t table = 1.983 so accept H1 and reject H0 then there is a significant influence of Socio-Economic Status on Interest in Continuing Education to Higher Education.That is, if the Socio-Economic Status increases, it will be followed by an increase in interest in continuing their education to higher education.Next, based on the test, the influence of X2's Parents' Income on Interest in Continuing Education to Higher Education Y is 0.427.Based on the test results above the value of t arithmetic = 4.419 > t table = 1.983 so Accept H1 and reject H0 then there is a significant influence of Parent's Income on Interest in Continuing Education to Higher Education.That is, if there is an increase in Parents' Income, the Interest in Continuing Education to Higher Education will increase.
On the Implementation of the Principle of Dualism of Intellectual Law in Co-authorship

The relevance of the conducted research is conditioned by the absence of legislative stipulation of the procedure and criteria of evaluation of the creative contribution of co-authors in the single result of their creative activity, as well as by the inconsistency of some cited court arguments, including the impossibility to create a photograph by several authors. In fact, the legislative definition of the notion of co-authorship, given in art. 1258 of the Civil Code of Russian Federation, is not substantial and functional: it lacks the essential and distinctive features and does not reflect the essence of this phenomenon, which makes it difficult to determine the contribution of each author and to evaluate it. In this paper we explore the notion of co-authorship, both in terms of its semantic meaning and in terms of the creative process. Co-authorship is examined using the example of the creation of a photograph as an object of copyright. In fact, the subject of the study is the relationship of established co-creation in photography and exclusive rights to it. The methods of analysis, inductive and comparison with the legislation of foreign countries were used. The main attention is paid to the analysis of judicial practice in the settlement of disputes on the violation of exclusive rights to photographic images, judicial explanation concerning the co-authorship and other similar in nature relationships arising in the process of creating an intellectual creative product (photography). Conclusions are drawn on the relationship between court-established infringement of exclusive rights and the recognition of copyright, legal dualism as the relationship between exclusive and copyright in the court's recognition of co-authorship or lack thereof.
New Data on Ore Minerals of the Konder Deposit

The article presents and analyzes the results of microscopic and microprobe studies of polished sections of the Konder deposit. We have identified ore minerals of the sulfosalt group, and provide data on their chemical composition, spatio-temporal relationships. The joint finding of sulfosalts of lead, silver, antimony, tin, nickel and copper, representing geochemically contrasting groups, allows us to continue the discussion of the history of the formation of the deposit and to clarify some of the features and originality of the named parageneses.
Universal, Transferable Adversarial Perturbations for Visual Object Trackers

In recent years, Siamese networks have led to great progress in visual object tracking. While these methods were shown to be vulnerable to adversarial attacks, the existing attack strategies do not truly pose great practical threats. They either are too expensive to be performed online, require computing image-dependent perturbations, lead to unrealistic trajectories, or suffer from weak transferability to other black-box trackers. In this paper, we address the above limitations by showing the existence of a universal perturbation that is image agnostic and fools black-box trackers at virtually no cost of perturbation. Furthermore, we show that our framework can be extended to the challenging targeted attack setting that forces the tracker to follow any given trajectory by using diverse directional universal perturbations. At the core of our framework, we propose to learn to generate a single perturbation from the object template only, that can be added to every search image and still successfully fool the tracker for the entire video. As a consequence, the resulting generator outputs perturbations that are quasi-independent of the template, thereby making them universal perturbations. Our extensive experiments on four benchmarks datasets, i.e., OTB100, VOT2019, UAV123, and LaSOT, demonstrate that our universal transferable perturbations (computed on SiamRPN++) are highly effective when transferred to other state-of-the-art trackers, such as SiamBAN, SiamCAR, DiMP, and Ocean online.
On-the-Go Reflectance Transformation Imaging with Ordinary Smartphones

Reflectance Transformation Imaging (RTI) is a popular technique that allows the recovery of per-pixel reflectance information by capturing an object under different light conditions. This can be later used to reveal surface details and interactively relight the subject. Such process, however, typically requires dedicated hardware setups to recover the light direction from multiple locations, making the process tedious when performed outside the lab. We propose a novel RTI method that can be carried out by recording videos with two ordinary smartphones. The flash led-light of one device is used to illuminate the subject while the other captures the reflectance. Since the led is mounted close to the camera lenses, we can infer the light direction for thousands of images by freely moving the illuminating device while observing a fiducial marker surrounding the subject. To deal with such amount of data, we propose a neural relighting model that reconstructs object appearance for arbitrary light directions from extremely compact reflectance distribution data compressed via Principal Components Analysis (PCA). Experiments shows that the proposed technique can be easily performed on the field with a resulting RTI model that can outperform state-of-the-art approaches involving dedicated hardware setups.
Attribution-Based Confidence Metric for Detection of Adversarial Attacks on Breast Histopathological Images

In this paper, we develop attribution-based confidence (ABC) metric to detect black-box adversarial attacks in breast histopathology images. Due to the lack of data for this problem, we subjected histopathological images to adversarial attacks using the state-of-the-art technique Meta-Learning the Search Distribution (Meta-RS) and generated a new dataset. We adopt the Sobol Attribution Method to the problem of cancer detection. The output helps the user to understand those parts of the images that determine the output of a classification model. The ABC metric characterizes whether the output of a deep learning network can be trusted. We can accurately identify whether an image is adversarial or original with the proposed approach. The proposed approach is validated with eight different deep learning-based classifiers. The ABC metric for all original images is greater or equal to 0.8 and less for adversarial images. To the best of our knowledge, this is the first work to detect attacks on medical systems for breast cancer detection based on histopathological images using the ABC metric.
Why Is the Video Analytics Accuracy Fluctuating, and What Can We Do About It?

It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this "leap of faith" that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a video camera is viewing a scene that is not changing in any human-perceptible way, and we control for external factors like video compression and environment (lighting), the accuracy of video analytics application fluctuates noticeably. These fluctuations occur because successive frames produced by the video camera may look similar visually, but are perceived quite differently by the video analytics applications. We observed that the root cause for these fluctuations is the dynamic camera parameter changes that a video camera automatically makes in order to capture and produce a visually pleasing video. The camera inadvertently acts as an "unintentional adversary" because these slight changes in the image pixel values in consecutive frames, as we show, have a noticeably adverse impact on the accuracy of insights from video analytics tasks that re-use image-trained deep learning models. To address this inadvertent adversarial effect from the camera, we explore the use of transfer learning techniques to improve learning in video analytics tasks through the transfer of knowledge from learning on image analytics tasks. Our experiments with a number of different cameras, and a variety of different video analytics tasks, show that the inadvertent adversarial effect from the camera can be noticeably offset by quickly re-training the deep learning models using transfer learning. In particular, we show that our newly trained Yolov5 model reduces fluctuation in object detection across frames, which leads to better tracking of objects ( $$\sim $$ 40% fewer mistakes in tracking). Our paper also provides new directions and techniques to mitigate the camera's adversarial effect on deep learning models used for video analytics applications.
Elimination and Restoring Deduplicated Storage for Multilevel Integrated Approach with Cost Estimation

Data handling is an important task in cloud computing because the data arrive at a high rate with a large volume. These data are stored in the cloud storage for consistent performance. Cloud offers services to the customer by allocating the virtual machine (VM) for requesting tasks. Cloud provider always satisfies the customer at anytime and anywhere manner. The services depend upon the resources at the data center through virtualization. Cloud follows the strategy called demand-based and pay-as-you-go basis. The customer must pay for the resource consumed using metering services. Customers store large volumes of data which occupies more space in storage. It leads to expensive problems for the customer because of excess payments paid to the cloud provider. This type of storage suffers a high volume with redundancy in the uploaded data. This problem is overcome by using the deduplication technique for keeping only one copy of data in the cloud storage. This achieves less storage, so the cost of services reduces drastically. The integrated data elimination with the cost estimation method has been introduced in this study to achieve better efficiency and availability. This process has been carried out by using two levels of elimination techniques such as local based and global based. This data are integrated into datasets without any redundancy in a data-centric manner. Integration of eliminated datasets is combined as a global level of elimination process by handling newly generated data. Finally, the data are stored in the cloud storage without any repetition in data.
Joint Prediction of Amodal and Visible Semantic Segmentation for Automated Driving

Amodal perception is the ability to hallucinate full shapes of (partially) occluded objects. While natural to humans, learning-based perception methods often only focus on the visible parts of scenes. This constraint is critical for safe automated driving since detection capabilities of perception methods are limited when faced with (partial) occlusions. Moreover, corner cases can emerge from occlusions while the perception method is oblivious. In this work, we investigate the possibilities of joint prediction of amodal and visible semantic segmentation masks. More precisely, we investigate whether both perception tasks benefit from a joint training approach. We report our findings on both the Cityscapes and the Amodal Cityscapes dataset. The proposed joint training outperforms the separately trained networks in terms of mean intersection over union in amodal areas of the masks by $$6.84\%$$ absolute, while even slightly improving the visible segmentation performance.
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images

Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected from the Mars rovers Curiosity and Perseverance, and from the Mars Reconnaissance Orbiter. Such methods are appealing since the vast majority of Mars images are unlabeled as manual annotation is labor intensive and requires extensive domain knowledge. Contrastive learning, however, assumes that any given pair of distinct images contain distinct semantic content. This is an issue for Mars image datasets, as any two pairs of Mars images are far more likely to be semantically similar due to the lack of visual diversity on the planet’s surface. Making the assumption that pairs of images will be in visual contrast — when they are in fact not — results in pairs that are falsely considered as negatives, impacting training performance. In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset. Both cases reduce the rate of false negative pairs, thus minimizing the rate in which the model is incorrectly penalized during contrastive training. These modified approaches remain fully unsupervised end-to-end. To evaluate their performance, we add a single linear layer trained to generate class predictions based on these contrastively-learned features and demonstrate increased performance compared to supervised models; observing an improvement in classification accuracy of $$3.06\%$$ using only $$10\%$$ of the labeled data.
A Creative Domain of Blockchain Application: NFTs

Blockchain has many applications in the digital world one of which is non-fungible token (NFT). The NFT originated from Ethereum and it is gaining the attention of researchers over the years. Increasing researchers’ attention toward NFTs has played a most important role in growing the digital market size. NFT is a creative new art type for artists, manufacturers, and musicians. The digital items can be exhibited, sold, stored, and purchased in virtual galleries through NFT. Blockchain provides permanent evidence of trust, rights, and acquaintances to these digital items. In this paper, the NFT market framework is designed and developed. Smart contracts for the NFT market are written in solidity language.
SIM2E: Benchmarking the Group Equivariant Capability of Correspondence Matching Algorithms

Correspondence matching is a fundamental problem in computer vision and robotics applications. Solving correspondence matching problems using neural networks has been on the rise recently. Rotation-equivariance and scale-equivariance are both critical in correspondence matching applications. Classical correspondence matching approaches are designed to withstand scaling and rotation transformations. However, the features extracted using convolutional neural networks (CNNs) are only translation-equivariant to a certain extent. Recently, researchers have strived to improve the rotation-equivariance of CNNs based on group theories. Sim(2) is the group of similarity transformations in the 2D plane. This paper presents a specialized dataset dedicated to evaluating sim(2)-equivariant correspondence matching algorithms. We compare the performance of 16 state-of-the-art (SoTA) correspondence matching approaches. The experimental results demonstrate the importance of group equivariant algorithms for correspondence matching on various sim(2) transformation conditions. Since the subpixel accuracy achieved by CNN-based correspondence matching approaches is unsatisfactory, this specific area requires more attention in future works. Our dataset is publicly available at: mias.group/SIM2E .
Automatic Detection and Monitoring of Hate Speech in Online Multi-social Media

Nowadays, we all want to be a part of social media networks in the internet world. Social media has played a critical role in human interaction in the last decade. Every day people use social media huge in numbers and many unfiltered messages are also being posted on multi-social media. Many hate speeches in these messages target an individual or group. In this context, many government and non-government organizations are concerned about these messages and taking some necessary steps to prevent their impact. In this chapter, we have created an intelligent system named “HateDetector-a recursive system” for monitoring and generating alerts on hate speech text for preventive measures on multi-social media with the help of an LSTM-CNN automatic detection model. We have also compared the performance of our LSTM-CNN model with classical machine learning methods in terms of F1 Score, Precision, Recall and, Accuracy.