Reviewerly/exHarmony huggingface dataset:
Image Steganography Technique by Using MSUSAN Algorithm

Nowadays, the protection of secret data, which is being sent during transmission channel, has become one of the most important challenges in the information security.Therefore, the using of multimedia technology to provide data protection is needed.Hence, one of the most common data security methods used is image steganography.This paper applied "Modified Smallest Univalue Segment Assimilating Nucleus" (MSUSAN) algorithm to detect the interest points, which it are used to steganography, so that the embedding process in the area containing the edges or corners is less susceptible to discovery because the human eye is not sensitive to the change of edges.So when the nucleus is a candidate corner, the block detected as a rough block.Whereas the nucleus isn't a candidate corner, the block detected as smooth block.The least significant bit ("LSB") technique is used to hide 3 bits in the rough block while 2 bits are hidden in the smooth block.Therefore, this method is of high efficiency in terms of payload rate, "Peak Signal Noise Ratio"(PSNR) and "Mean Square Error"( MSE) which gives average PSNR of 54.25 and average MSE of 0.25 when hiding secret.imageof_size "128*128" (131,072 bits) in different cover image of size 512*512.In spite of hiding the largest number of secret bits up to 546506 bits, which is approximately secret image of size 261 * 261 , the PSNR is still above 46 and the MSE is still less than 1.4 .This shows that the quality of the proposed technique in this paper is very good
A non-invasive social monitoring application for danger situations based on a edge-based Machine Learning solution

In this article, we analyze the use of non-intrusive monitoring sensors to detect dangerous situations in social events, a typical example being sexual harassment situations during parties. Non invasive sensors can be defined as devices that help measuring some specific parameter of interest remaining hidden or un-perceived by the user.We propose a solution based on a social application that can, in a non-intrusive way, detect sexual harassment situations and generate alerts automatically to the close by people so that detection of these situations can become quick and independent from the person being attacked. Our results are still preliminary but show that the project can certainly be developed into a working system that can help prevent control crimes from happening at public gatherings.
Are CryptoPunks Copyrightable?

tl;dr: It’s unclear, and that might be a problem for Larva Labs, but might also be an opportunity.Larva Labs’s CryptoPunks NFTs are iconic. Created in 2017, they were among the first NFTs on the Ethereum blockchain. Four years later, they are among the most valuable, selling for anywhere from $200,000 to millions of dollars.The CryptoPunks collection consists of 10,000 NFTs, each of which is associated with a unique CryptoPunks image. Everyone knows who owns each CryptoPunks NFT. The Ethereum blockchain provides indelible proof. But people disagree about who owns - and who should own - the copyright in the CryptoPunks images. Most CryptoPunks NFT owners believe they should own the copyright in the image associated with their NFT, or at least have the right to use it. Larva Labs believes it owns the copyright in all of the images, and entered a licensing deal with United Talent Agency based on its ownership of the CryptoPunks brand and copyrights.Color me skeptical. I’m not sure anyone owns the copyright in the CryptoPunks images, because I’m not sure they’re copyrightable in the first place. And I suspect Larva Labs is also worried about the copyrightability of the CryptoPunks images. After all, they complain about copyright infringement, but don’t file infringement actions.In this essay, I explain how copyright works, what it protects, and why. I analyze the copyrightability of the CryptoPunks images. And I reflect on what it means for the CryptoPhunks and V1 Punks NFTs, as well as the NFT market in general.
Seven Lessons Learned From Automotive Software Supplier Collaborations

There are challenges when collaborating with automotive software suppliers. Seven lessons that alleviate these challenges are summed up in this article.
INTELLIGENT STYLE TRANSFER OF FILM IMAGE BASED ON CYCLEGAN

With the development and application of deep learning, image style transfer has achieved an important breakthrough.It has been able to intelligently generate stylized natural, realistic and high-quality images, which can be applied to film screens to reduce the cost of artificial effects.This paper proposes an intelligent style transfer method of film image style based on CycleGAN.In view of the high spatial resolution, large size and rich details of high-tech format film images, we have modified the input layer of the network so that the network can better process film images, and replace the original GAN loss with WGAN loss to achieve a more stable training effect.As a result, style features can be better transformed between images, and at the same time, SSIM loss is added to the cycle consistency loss to enhance the recovery of images similarity to the original image and improve the quality of the generated image.Experiments show that this method is effective in processing the style transfer of film images, and can intelligently generate high-quality and natural realistic style transfer images.
BLOCKCHAIN BASED CROSS ORGANIZATIONAL WORKFLOW MANAGEMENT SYSTEM

Currently most of the workflows in enterprise business processes are implemented and managed as information system.However, workflows between organizations are still processed manually based on paper document because it is difficult for the organizations to trust the process and the result of workflow in the system which is provided by one of the organizations or a third-party organization.In this paper, a system architecture for cross organizational workflow management utilizing blockchain technology is proposed.It eliminates every Single Point of Trust (SPoT) which causes falsification risk of the process and data.Our proposed system architecture accommodates two types of smart contracts for workflow definition and workflow processing, which validates related data in every transaction.By this architecture, each organization can be aware of the data falsification when the data on one of the organization nodes is falsified by a malicious workflow administrator or a system administrator.Through evaluation of the falsification risks based on the attack scenarios along workflow life cycle, it is confirmed that all the risks are avoided by the proposed system architecture.
CODE SWITCHING USED BY ENGLISH TEACHER IN TEACHING ENGLISH AT THE SECOND GRADE OF SMAN 1 AEK NATAS

The aims of this study were to find out the types of code switching used by English teacher and to investigate the reasons why the English teacher used code switching in Teaching English. This study was conducted by using descriptive qualitative method. The data were collected through observing, interview, and recording. The analysis of code switching was based on Poplack’s theory to know the types of code switching and Hoffman’s theory to ivestigate the reasons why the teacher used code switching. Based on the analysis, it was found that there were 106 utterances that has been categorized as code swutching consisting of 46 intra-sentential, 28 inter-sentential, and 32 tag switching. The reasons why teacher used code switching in teaching English process were Quoting somebody else, Interjection and Repetition used for clarification, there were two reasons set by the teacher, there were to make the students understand about the explanation given by the teacher and to develop student’s English skill.Keywords: Code Switching, English Teacher, Teaching English
GAM-APT: A SERIOUS GAME ATTRIBUTE TAXONOMY FOR ADVANCED PERSISTENT THREATS

Serious games find immense applications in the domain of education, information systems and information security due to convenience of time, instant feedback, attractiveness and the ease of simulating novel and multiple attack scenarios.Initially created for military purposes, it emerged as a promising approach to self-train computer users in changing their profile from a 'weakest link' to a 'strong link' in the network chain.In this respect, training computer users on advanced persistent threats (APT) vectors which deploys simple social engineering yet complex technical methods to exploit internetworked computer user and subsequent vulnerabilities is a challenging task.While commercial and academic efforts in technical cyber security tools and models along with awareness and training has contributed to its abatement to some extent, the increase in successful attacks pose serious questions on the effectiveness of existing user awareness and training methods.While serious games and models have been proposed by researchers in cyber security domain, attributes for building a serious gaming model for APT is wanting in both academic and practitioner domains.In this respect, we propose a serious game taxonomy GAM-APT by integrating the serious gaming model components namely the 'player', 'game' and 'knowledge' dimensions with Gagne's nine events using the serious gaming taxonomy.
Identificação e Controle de um Sistema de Troca de Calor entre Vapor e Líquido Saturado: uma Abordagem Híbrida

The present work proposes i) the extraction of the dynamics of a liquid-saturated steam heat exchanger through a black-box approach, considering multiobjective optimization of an Echo State Network (ESN) hyperparameters based on performance and complexity metrics and ii) the application of a fuzzy control on the ESN obtained to follow a temperature trajectory. Results showed that the generated model can be controlled and, when compared with a model obtained in an approach with only performance optimization, a similar performance is achieved but with a significant model complexity reduction.
SEGMENTATION AND RECOGNITION OF CONNECTED HANDWRITTEN CHARACTERS

Optical Character Recognition is still an open problem in computer science.This paper explores segmentation of connected (touching/overlapping) handwritten characters through implicit segmentation and explicit segmentation.Explicit segmentation is achieved through rules based on background thinning and analyzing the contour hierarchy.Implicit segmentation is achieved through generating a dataset of connected characters and training a CNN.This research was inspired by the need to recognize math documents containing algebraic equations.Recognition of 72 characters (alphanumeric and math symbols) are achieved through the proposed methods.The scope of the implemented OCR is limited to maximum of two connected characters.
REGIONAL GROSS DOMESTIC PRODUCT PREDICTION USING TWITTER DEEP LEARNING REPRESENTATIONS

This work presents a method to predict the regional Gross Domestic Product (GDP) using the textual information stored in tweets.In particular, we propose the use of a hybrid autoencoder to predict the GDP of the Valencian Community (Spain) using the tweets written by the most influential economists, politicians, newspapers, and institutions in the region.The proposed method uses an autoencoder that is trained to simultaneously minimize the reconstruction error of the textual information of tweets along with their corresponding GDP value.In this way, we ensure that the latent space of the autoencoder represents the important characteristics of tweets that relate them to the GDP.This system allows to obtain a representation of new tweets that is related to a GDP prediction and also to analyze them to create clusters that represent the agreement or disagreement of the opinions.The results show that the proposed approach captures the GDP growth trend with a small margin of error.This method was also compared with other methods from the state of the art showing a better result in prediction accuracy.
Assessing Atmospheric Instability over the Bay of Bengal during October and November Months between 2007 – 2018

An attempt has been made in this research to delineate a pattern for atmospheric instability during the months of October and November from 2007 to 2018. The results show an alarming trend of increasing instability throughout the study period. The average temperature at 2-meters height around the Bay of Bengal has changed significantly over time. Most notably, the gap between monthly average highest and monthly average lowest temperatures (at 2-meters height) is more or less increasing from 2007 to 2018 – ranging from 3.0 degrees to 11.0 degrees Celsius. Similar tendencies are observed for CAPE (Convective Available Potential Energy) as well – with the highest value of more than 3000 J/Kg. The findings of this research will help in understanding the prevailing conditions over the Bay, and also enable better preparedness for any potential severe convective activities. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(2), 2020, P 45-54
Teens Evaluating Tag Lines of Foods: The Relevance of Seemingly ‘Throw Away’ Messages Revealed by Mind Genomics Cartography

and what was then considered a 'throw-away' space filler, but one which was oriented towards the fun of eating.This was 'Silo C' , the 'tagline' .The research proceeded by mixing together these messages according to an underlying set of recipes, the so-called experimental design (REF).Figure 1 shows an example of the stimulus.The respondent did not see the boxes at the left, but simply saw combinations of elements, the messages, shown in the center of Figure 1.The respondent's task was simply to read the vignette, and assign a rating.The task required the respondent to read and evaluate 60 different combinations, a task which took 10-12 minutes.Although the array of combinations, the so-called 'vignettes' seems like a set of randomly constructed combinations, the reality was and remains that in these Mind Genomics experiments great care is taken to create systematic combinations, each combination or set of 60 vignettes different from every other set, and each vignette comprising precisely the correct set of combinations to allow analysis by OLS (ordinary least-squares) analysis, even at the level of the individual respondent [5].This design is called a permuted experimental design.To the respondent, the test combinations may appear to be haphazard combinations of messages the respondent was simply asked to read the combination, and rate the combination.The rating was 'How intense is your craving for this FOOD NAME HERE IN CAPS).Most respondents looking at Figure 1 (without the call outs, but simply with the material present on the screen) begin by
A Hybrid Recommendation System Based on Correlation and Co-Occurrence Within Social Learning Network

Social learning is one of the most prevalent disciplines in terms of e-learning. To handle learning resources within social environments, recommendation systems are gaining tremendous prominence based on a series of criteria such as the rate of learner interaction with the learning environment. On the basis of this, we highlight an overriding issue focusing on the influence of the rate of learner interaction on the calculated recommendations. In other words, to what extent considering the events carried out by the learners and the existing links between them will lead to more relevant and reliable recommendations. To emphasize this point and to support current recommendation systems, we are evaluating our recommendation approach that integrates a set of learner activities based on correlation and co-occurrence. We then compare the performance of the hybrid system to the following two recommendation systems: the recommendation system based uniquely on correlation and the recommendation system based solely on co-occurrence.
Features of choreography teacher activities in COVID-19 pandemic conditions and distance learning

The aim of the study in highlight the practical experience of organizing distance learning of choreographers at the Kyiv Municipal Ukrainian Academy of Dance named after Serge Lifar. Practical Significance lies in using the research results in the educational process in teaching disciplines of the choreographic cycle “Theory and methods of working with the choreographic team”, “History of choreographic art”, “Theory and methods of teaching choreographic disciplines at higher educational institutions” for students of the educational program “Choreography" at the Kyiv Municipal Ukrainian Academy of Dance named after Serge Lifar. The approbation of modern forms of organizing distance learning and its daily analysis in conditions dictated by real events makes it possible to state that it is convenient to work with students-choreographers in practical classes using video communication in the mobile applications Viber, Messenger on Facebook and Instagram, as well as in the mode of video conferences in the Zoom program.
Reimagining Higher Education in India leveraging Blockchain Technology A study to categorize challenges and opportunities

Blockchain makes debut as cryptocurrency in the world. Later, it starts influencing other areas too. Although its use cases are growing day by day, it will be very exciting to try a different perspective to address the problems in the higher education sector of India. And the potential it inherits can revolutionize the Indian higher education system. Besides a remarkable stature of Blockchain, its real-time implementation, adoption, scaling comprises challenges and opportunities too. This study tries to categorize them all to know better what the higher education system may expect from Blockchain technology and up to what extent.
The Enforcement of Intellectual Property Rights in a Digital Era

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Aggregation and Adjustment mechanisms for disaster relief task allocation with uneven distribution

The use of unmanned aerial vehicles (UAVs) to perform disaster relief task is becoming more and more popular.Generally, UAVs for rescuing are required to be heterogeneous, since the types of rescue missions are diverse.There are many researches on the task allocation of heterogeneous UAVs, in which they setup tasks to be randomly distributed when simulation.However, in most cases the distribution of tasks is regular rather than random.Therefore, we setup a set of special scenarios to simulate real disaster relief tasks, where most tasks concentrate in a few small areas and others are distributed randomly.LAL algorithm is an appropriate alternative to deal with assignment problems in scenarios where tasks are completely randomly distributed, but with a bit poor performance in the scenarios above mentioned.In order to obtain better results, this paper proposes three algorithms based on LAL algorithm in such scenarios.They are the Aggregation-LAL (A-LAL) using aggregation mechanism, Dynamic Adjustment-LAL (DA-LAL) using dynamic adjustment mechanism and Aggregation and Adjustment (AA-LAL) algorithms.Through the experiments in the NetLogo simulation environment, the proposed algorithms are evaluated.The result shows improvements, which are average 5% obtained by AA-LAL, of task allocation in our special scenarios compared with existing algorithms.
Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data

Generative modelling for temporal data has seen a paradigm shift from autoregressive to adversarial models. Adversarial generation algorithms have proven to be more efficient in capturing the complex temporal correlations that the simplistic autoregressive model could not. Albeit, high-fidelity remains a concern even for adversarial modelling. The generation of high-fidelity data requires the model to have three strengths: capture feature correlations, model long-term dependencies, and scalability in dimensions. This paper analyzes these strengths on the existing methods of adversarial temporal generation regarding the fidelity of synthetic data. Towards this, we evaluate different algorithms for adversarial temporal generation on five different datasets of varying dynamics (long-term vs. short-term dependency) and dimensionality. We conclude by discussing gaps in the literature and future directions for high fidelity temporal data generation through adversarial methods.
Deep Inelastic Electron–Nucleon Scattering

The Standard Model of particle physics is an amazingly successful theory describing the fundamental particles and forces of nature. This text, written for a two-semester graduate course on the Standard Model, develops a practical understanding of the theoretical concepts it's built upon, to prepare students to enter research. The author takes a historical approach to demonstrate to students the process of discovery which is often overlooked in other textbooks, presenting quantum field theory and symmetries as the necessary tools for describing and understanding the Standard Model. He develops these tools using a basic understanding of quantum mechanics and classical field theory, such as Maxwell's electrodynamics, before discussing the important role that Noether's theorem and conserved charges play in the theory. Worked examples feature throughout the text, while homework exercises are included for the first five parts, with solutions available online for instructors. Inspired by the author's own teaching experience, suggestions for independent research topics have been provided for the second-half of the course, which students can then present to the rest of the class.
PENGARUH PENGGUNAAN E-MODUL PENGAYAAN ISOLASI DAN KARAKTERISASI BAKTERI UNTUK MENINGKATKAN KEMANDIRIAN BELAJAR SISWA SMA

This study aims to determine the effect of the use of enrichment e-modules in increasing the learning independence of high school students. This research is a quasi experimental study with nonequivalent pretest posttest control group design. The sample of the study is Jetis 1 High School students Bantul consisting of 2 classes. This sample was taken based on purposive sampling technique, class X MIPA 5 as a control class and class X MIPA 1 as an experimental class. The instruments used were questionnaires and observation sheets for students' learning independence. Data were analyzed through Independent Sample T-Test and Normalized Gain Score (N-Gain Score). The results showed that the E-module developed had a significant effect on student learning independence as evidenced by the significance value of 0,000 &lt;α 0.05 and the category of moderate improvement from the calculation of N-Gain Score of 0.307.&#x0D; &#x0D; Keywords : E-modul, enhancement, bacteria, self regulation learning
Diagnosis prediction based on similarity of patients physiological parameters

Medical staff can be considerably supported in patient healthcare delivery thanks to the adoption of machine learning and deep learning methods by enhancing clinicians decisions and analysis with targeted clinical knowledge, patient information, and other health data. This paper proposes a learning methodology that, on the basis of the current patient health status, clinical history, diagnostic and laboratory results, provides insights for clinicians in the diagnosis and therapy decision processes. The approach relies on the concept that patients with similar vital signs patterns are, in all probability, affected by the same or very similar health problems. Thus, they can have the same or very similar diagnoses. Patients physiological signals are modeled as time series and the similarity among them is exploited. The method is formulated as a classification problem in which an ad-hoc multi-label k-nearest neighbor approach is combined with similarity concepts based on word embedding. Experimental results on real-world clinical data have shown that the proposed approach allows detecting diagnoses with a precision up to about 75%.