Reviewerly/exHarmony huggingface dataset:
Defense against membership inference attack in graph neural networks through graph perturbation

Graph neural networks have demonstrated remarkable performance in learning node or graph representations for various graph-related tasks. However, learning with graph data or its embedded representations may induce privacy issues when the node representations contain sensitive or private user information. Although many machine learning models or techniques have been proposed for privacy preservation of traditional non-graph structured data, there is limited work to address graph privacy concerns. In this paper, we investigate the privacy problem of embedding representations of nodes, in which an adversary can infer the user’s privacy by designing an inference attack algorithm. To address this problem, we develop a defense algorithm against white-box membership inference attacks, based on perturbation injection on the graph. In particular, we employ a graph reconstruction model and inject a certain size of noise into the intermediate output of the model, i.e., the latent representations of the nodes. The experimental results obtained on real-world datasets, along with reasonable usability and privacy metrics, demonstrate that our proposed approach can effectively resist membership inference attacks. Meanwhile, based on our method, the trade-off between usability and privacy brought by defense measures can be observed intuitively, which provides a reference for subsequent research in the field of graph privacy protection.
Construction of Internet Game Development Environment based on OpenGL and Augmented Reality

Based on OpenGL and augmented reality, this paper studies the construction of Internet game development environment, and proposes an algorithm based on quadtree to solve the problem of the number of triangles in OpenGL largescale terrain rendering. And using sky sphere technology, sunglasses lens halo technology and bulletin board technology, etc., under the premise of ensuring the rendering speed, it effectively improves the visual reality of the scene. This article discusses the settings of the augmented reality technology for the server data structure in detail, and proposes to use the navigation speculation method to solve the long message processing delay, effectively solve the load balance, and increase the security of data communication by 6.5%.
The Users’ Behavioral Intention to use Mobile Health-Tech Application to Prevent the Spreading of Coronavirus

Abstract The emergence of mobile health applications (MH-Apps) has enhanced the healthcare field’s services, particularly in the treatment, diagnosis, and follow-up. AMAN Mobile Health Application (AMAN MH-App) is one of the health-tech solutions used to fight the Coronavirus pandemic. It has a built-in feature to track users’ activities to protect users from contacting an infected person. However, the acceptance of AMAN MH-App in Jordan is still in an early stage, and the number of users has reached 15% of the country’s population. Therefore, this study aims to assess the use of AMAN MH-App among young people using the quantitative method. A total of (450) valid samples participated in the study after removing 33 invalid samples. Smart-PLS 3.2.7 was used for data analysis. The findings showed that all independent variables (Perceived usefulness, Perceived ease of use value, Subjective norms, Perceived behavioral control, Information Credibility, and Optimism) positively impact on the dependent variable (Behavioural Intention to use AMAN MH-App). We believe that AMAN MH-App’s information’s credibility (i.e. providing up-to-date, authoritative, accurate, and trustworthy information) will increase the number of the App’s users. The results of this research can be applied to similar context and applications in different countries.
Speak Like a Dog: Human to Non-human creature Voice Conversion

This paper proposes a new voice conversion (VC) task from human speech to dog-like speech while preserving linguistic information as an example of human to non-human creature voice conversion (H2NH-VC) tasks. Although most VC studies deal with human to human VC, H2NH-VC aims to convert human speech into non-human creature-like speech. Non-parallel VC allows us to develop H2NH-VC, because we cannot collect a parallel dataset that non-human creatures speak human language. In this study, we propose to use dogs as an example of a non-human creature target domain and define the “speak like a dog” task. To clarify the possibilities and characteristics of the “speak like a dog” task, we conducted a comparative experiment using existing representative non-parallel VC methods in acoustic features (Mel-cepstral coefficients and Mel-spectrograms), network architectures (five different kernel-size settings), and training criteria (variational autoencoder (VAE)-based and generative adversarial network (GAN)-based). Finally, the converted voices were evaluated using mean opinion scores: dog-likeness, sound quality and intelligibility, and character error rate (CER). The experiment showed that the employment of the Mel-spectrogram improved the dog-likeness of the converted speech, while it is challenging to preserve linguistic information. Challenges and limitations of the current VC methods for H2NH-VC are highlighted.
Comparison of Data Normalization for Wine Classification Using K-NN Algorithm

The range of values that are not balanced on each attribute can affect the quality of data mining results. For this reason, it is necessary to pre-process the data. This preprocessing is expected to increase the accuracy of the results from the wine dataset classification. The preprocessing method used is data transformation with normalization. There are three ways to do data transformation with normalization, namely min-max normalization, z-score normalization, and decimal scaling. Data that has been processed from each normalization method will be compared to see the results of the best classification accuracy using the K-NN algorithm. The K used in the comparisons were 1, 3, 5, 7, 9, 11. Before classifying the normalized wine dataset, it was divided into test data and training data with k-fold cross validation. The division of the data using k is equal to 10. The results of the classification test with the K-NN algorithm show that the best accuracy lies in the wine dataset which has been normalized using the min-max normalization method with K = 1 of 65.92%. The average obtained is 59.68%.
“Oh no, the forest is burning!” cultural differences in the complex problem-solving process only under high uncertainty

What do people in different cultures do when they encounter complex problems? Whereas some cross-cultural research exists about complex problem-solving predictors and performance, the process has rarely been studied. We presented participants from Brazil, Germany, the Philippines, and the United States with two computer-simulated dynamic problems, one where quick action was required - the WinFire simulation - and one where cautious action was required - the Coldstore simulation. Participants were asked to think aloud in their native language while working on these two tasks. These think-aloud protocols were digitally recorded, transcribed, and coded by coders in each country in terms of the steps involved in complex problem solving and dynamic decision making. For the current study, we developed a program to calculate transition frequencies from one problem solving step to another and analyzed only those protocols with more than 15 transitions. For WinFire, these were 256 think-aloud protocols from the four countries with a total of 12,542 statement, for Coldstore, these were 247 participants with a total of 15,237 statements. Based on previous, limited cross-cultural research, we predicted that after identifying a problem, Brazilians would make emotional and self-related statements, Germans would engage primarily in planning, Filipinos would gather additional information, and Americans would primarily state solutions. Results of latent transition analysis partially support these hypotheses, but only in the highly uncertain Coldstore situation and not in the more transparent WinFire situation. Transition frequencies were then also analyzed regarding community clusters using the spinglass algorithm in R, igraph. Results highlight the importance of process analyses in different tasks and show how cultural background guides people's decisions under uncertainty.
Sharp Estimates on Random Hyperplane Tessellations

We study the problem of generating a hyperplane tessellation of an arbitrary set in , ensuring that the Euclidean distance between any two points corresponds to the fraction of hyperplanes separating them up to a prespecified error . We focus on random Gaussian tessellations with uniformly distributed shifts and derive sharp bounds on the number of hyperplanes that are required. This result has natural applications in data dimension reduction—it yields a binary version of the Johnson–Lindenstrauss embedding—and signal processing under coarse quantization. Surprisingly, our lower estimates falsify the conjecture that , where is the Gaussian width of , is optimal. This conjecture is the natural analogue of a conjecture by Plan and Vershynin on random tessellations of subsets of the Euclidean sphere. As it turns out, the true optimal rate is larger by an order of magnitude in the accuracy parameter and depends in an intricate way on the geometry of the set. In particular, we give an explicit example where is required. To the best of our knowledge, the fact that the optimal error decay rate depends on the geometry of the set is a new phenomenon in the general context of random embeddings.
Trade Secret Protection in the Law of the Republic of Serbia

The subject of this paper is analysis of trade secret protection in Serbian law, which is organized and implemented at two levels: within the business company and the proceedings before the court. By applying the dogmatic method of research, the rules of current and relevant domestic sources of law are analysed, while the comparative method is used for comparing the Serbian system of trade secret legal protection with the analogous legal system in the European Union. The starting hypothesis is that the protection of trade secrets is a strategic issue that requires thoughtful planning and organization as well as constant improvement to preserve trade secrets and use their commercial potential for as long as possible. The aim of this work is to reach the conclusions that are practically usable both for trade secrets holders and for everyone else who participates in various roles in judicial procedures for trade secret protection.
Computation Graph

Abstract A computation graph is a basic theoretical tool that underlines modern deep learning libraries. It is also an important component in Owl. This chapter first gives a bird’s-eye view on the computation graph in Owl and its importance in computing. We then demonstrate how to use it in Owl with some examples. Then we will continue to cover the design and implementation details of the computation graph module and how it is fitted into Owl’s functor stack.
Disk-Graph Probabilistic Roadmap: Biased Distance Sampling for Path Planning in a Partially Unknown Environment

In this paper, we propose a new sampling-based path planning approach, focusing on the challenges linked to autonomous exploration. Our method relies on the definition of a disk graph of free-space bubbles, from which we derive a biased sampling function that expands the graph towards known free space for maximal navigability and frontiers discovery. The proposed method demonstrates an exploratory behavior similar to Rapidly-exploring Random Trees, while retaining the connectivity and flexibility of a graph-based planner. We demonstrate the interest of our method by first comparing its path planning capabilities against state-of-the-art approaches, before discussing exploration-specific aspects, namely replanning capabilities and incremental construction of the graph. A simple frontiers-driven exploration controller derived from our planning method is also demonstrated using the Pioneer platform.
User-Generated Content Extraction: A Bibliometric Analysis of the Research Literature (2007–2022)

Scientific studies on user-generated content extraction began in 2007. User-generated content (UGC), which is all forms of content created by someone, is widely available on social media and can influence customer desire to shop. This study aims to systematically map research trends in the field of UGC extraction over the last 15 years using metadata taken from the Scopus database. Thus, novelties and opportunities will be found that will serve as a resource for researchers conducting research and determining the research theme. Bibliometric review analysis was carried out in this study by analyzing literature from year 2007 until 2022. The search using keywords related to UGC extraction resulted in 382 papers related to the specified keywords. The main findings of this study are 1) Research in the field of UGC extraction has emerged and has grown since 2007, 2) Research in this field has been conducted by researchers from various countries, mostly from China, followed by the United States, India, Italy, Germany, Spain, etc., 3) Several keywords were discussed in this field, which include UGC, sentiment analysis, opinion mining, social media, and information extraction. This bibliometric analysis has provided information on research opportunities/directions related to UGC extraction in the future. The originality of this study is that a bibliometric analysis was performed for the research trends in UGC with a focus on technical extraction. This topic is interesting to raise because mining and extracting knowledge from UGC is quite an expensive and labor-intensive undertaking.
How Literate are Prospective Science Teachers in this digital era? Profile of Digital Literacy Skills of Preservice Science Teachers in South Sulawesi, Indonesia

There is a lack of information about the digital literacy skill of prospective science teachers in Indonesia, specifically in South Sulawesi. The focus will be placed on their digital literacy skills in three dimensions: technical, cognitive, and social emotional. The population was undergraduate students from educational science program at three universities in South Sulawesi namely Universitas Negeri Makassar (State University of Makassar), Universitas Bosowa (Bosowa University), and IAIN Parepare (State Islamic Institute of Parepare). Sample consisted of 313 students. Results show that the average digital literacy skill is in the medium category.
Enhanced Preprocessing Technique for Degraded Printed Marathi Characters

Designing a dynamic and proficient OCR entity is an interesting and captivating region in image processing. The OCR organism purposes to decipher text in images to a machine comprehensible text. A successful degraded document has numerous applications like preservation of history, persistent documents, and many more. For the same, copious policies occur for numerous scripts, languages, and so far for virtuous class papers. Contrariwise, unique restricted versions have been explored for degraded printed Marathi characters. The OCR structure comprises stages like data collection, preprocessing, feature extraction, segmentation, classification, and recognition. For this work, we have developed our dataset size of 4900 images for 49 isolated Marathi characters. Among these, preprocessing phase plays a significant role, especially in the case of degraded characters. This paper focuses work on preprocessing of degraded Marathi characters. Mean Square Error, Mutual information, and Peak signal to noise ratio assessment factors are used for the evaluation of the enriched image. The proposed approach’s effects are attained in MATLAB R2015a. Many researchers have made efforts for developing OCR for degraded printed documents for various languages, whereas very less attempts made for degraded Marathi character recognition. In the case of the degraded Marathi character recognition problem, the recognition accuracy is largely dependent on the preprocessing techniques and how the character information and features are retained. To overcome the limitations of earlier reported systems of loss of information and features retention, a novel and enhanced preprocessing technique is developed specifically for degraded Marathi character recognition. The proposed methodology is giving promising results as compared to other methods which are 35.14. Similarly, the average mutual information value of the proposed technique is 2.71 which is greater than others. The projected practices offered less value of average mean square error and response time. The proposed method for preprocessing will improve the recognition accuracy for degraded isolated Marathi characters.
LSDStrategy: A Lightweight Software-Driven Strategy for Addressing Big Data Variety of Multimedia Streaming

Many devices, users, and applications stream an irregular amount of varied data every second. This rapid generation of data continues at an enormous rate, constructing the big data that increase the need for solutions, despite resource constraints, to analyze and manipulate data. Current methods allocate cloud resources according to the characteristics of the data. Resource allocation requires a comprehensive view of the workload requirements. However, the data characteristics in big data streams are uncertain due to the random nature of data generation. Choosing and allocating the right resources to this stream is challenging. With the variety of big data streams, the stochastic nature of the stream led to unpredictable requirements and specifications. The critical issue is forecasting the workload to avoid the over-provisioning and under-provisioning of resources. Such forecasting needs an adequate dataset to describe the history logs of the incoming workload. A fast release for such a dataset provides a high chance of deploying forecasting at the right time. This paper addresses this issue with a novel strategy named LSDStrategy that analyzes the received multimedia stream based on its binary content using machine learning techniques with artificial and real datasets. LSDStrategy utilizes an evaluating voting technique to select the optimum classifier to trade off accuracy and prediction time as metrics. Multi-classifiers that have been built and tested include Decision Tree (DT), K-Nearest Neighbor (K-NN), and Random Forest (RF) over multi-content-based features. Experiments evaluated the performance of the adopted models and the selected features. According to experimental analysis, the DT approach provides a consistent performance for both artificial and real-world datasets for 85% and 81.3%, respectively. We deploy and evaluate the LSDStrategy efficiency on a regular specification server through a set of experiments using a synthetic stream. The experiments prove the LSDStrategy agility and adaptivity in identifying the multimedia-based workload type utilizing small chunks of load.
High-Resolution Tiled Clothes Generation from a Model

Many image translation methods based on conditional generative adversarial networks can transform images from one domain to another, but the results of many methods are at a low resolution. We present a modified pix2pixHD model, which generates high-resolution tiled clothing from a model wearing clothes. We choose a single Markovian discriminator instead of a multi-scale discriminator for a faster training speed, added a perceptual loss term, and improved the feature matching loss. Deeper feature maps have lower weights when calculating losses. A dataset was specifically built for this improved model, which contains over 20,000 paired high-quality tiled clothing. The experimental results demonstrate the feasibility of our improved method and can be extended to other fields.
Circuit Analysis of Goerges Phenomenon in a Three-Phase Induction Machine

Unbalanced rotor impedance may lead to an induction motor operating at half of synchronous speed. This phenomenon was discovered by Hans Goerges in 1896. A 1950 paper by Garbarino and Gross gives an equivalent circuit for this unbalanced system. The circuit analysis results were validated by hardware experiments. The current paper is a tribute to Garbarino and Gross for the elegant analysis. We give a detailed explanation on how the equivalent circuit can be derived step by step using symmetric component theory along with an understanding of machines. We also present the torque computing equation which was not presented in the 1950 paper. For validation, we rely on electromagnetic transient (EMT) simulation, to design experiments, produce time-domain data, carry out fast Fourier transform (FFT) analysis for harmonic analysis, and produce torque-speed curves to verify the analysis results.
Subjectivity of Explainable Artificial Intelligence

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals.
Multilingual Toxic Text Classification Model Based On Deep Learning

The nature of comments usually has an important impact on the network environment. Polite and gentle comments can not only promote communication between users, but also maintain the stability of the network platform. On the contrary, rude and toxic comments will make the communication environment unacceptable. Therefore, we need to impose certain restrictions on comments. This article is based on the XLM-RoBERTa model to achieve the classification of multilingual toxic comments. We first use training and verification data to train and optimize the model, and then use the test data to get the final classification results. In addition, our model is compared with models such as LSTM and RNN. Experiments show that the model proposed in this paper has better classification performance.