Reviewerly/exHarmony huggingface dataset:
Squeeze-Loss: A Utility-Free Defense Against Membership Inference Attacks

Membership inference attacks can infer whether a data sample exists in the training set of the target model based on limited adversary knowledge, which results in serious leakage of privacy. A large number of recent studies have shown that model overfitting is one of the main reasons why membership inference attacks can be executed successfully. Therefore, some classic methods to solve model overfitting are used to defend against membership inference attacks, such as dropout, spatial dropout, and differential privacy. However, it is difficult for these defense methods to achieve an available trade-off in defense success rate and model utility. In this paper, we focus on the impact of model training loss on model overfitting, and we design a Squeeze-Loss strategy to dynamically find the training loss that achieves the best balance between model utility and privacy. Extensive experimental results show that our strategy can limit the success rate of membership inference attacks to the level of random guesses with almost no loss of model utility, which always outperforms other defense methods.
Adaptive Kalman filter state-of-charge design for unmanned aerial vehicle battery monitoring based on altitude and current measurement

A battery management system is essential for unmanned aerial vehicles (UAVs) to determine the ability to return home or continue the flight mission. State-of-charge (SoC) is the primary parameter for battery management which is traditionally estimated using Kalman filter based on current and voltage measurement of battery. This paper estimate the SoC of UAV batteries using adaptive Kalman filter based on current and altitude measurements. We also compare two adaptive Kalman filter methods, namely Werries-Dollan and Sage-Husa. Root-mean-square error is used as a comparison parameter. The simulation results show Werries-Dollan is better than Sage-Husa.
Gamification as a challenging response to motivate classes in secondary education in the context of COVID-19

The objective was to determine the correlation between gamification and motivation. It was developed through quantitative, non-experimental and correlational research. We worked with a census population of 253 students of regular basic education. To obtain data, the Gamification Observation Guide by García (2020) and the MSLQ Motivation Questionnaire by T. García et al. (1988). The results indicated that there is a very low, significant relationship of 0.025 (p value = 0.0694&gt; 0.05). It is concluded that gamification is not related to the motivation of the students, most of the teachers, in the development of their classes, do not use digital tools - a necessary element in the delivery of classes at a distance and virtually due to the difficult situation caused by COVID-19. Likewise, teachers suffer from inexperience in handling social networks, videoconferences and online forms.
Development of Blockchain Social Network

Social Media has become an important and prevalent part of people’s life as billions of people use social networks every day. But this has given rise to concerns regarding security, privacy, censorship and management issues. The current social networks are centralized, which allows the big companies to observe and track the activities and behaviour of their user base. These big companies tend to user data to third parties keeping huge profits without users&amp;#39; permission. Involvement of the blockchain technology has started to challenge this status quo and pushed for better privacy and security standards. Distributed social networks can help solve these privacy issues but are yet to reach mainstream adoption. This paper researched the blockchain technology and proposes a social network built using the same to take advantages of both the traditional social network and a decentralized social network. The Inter Planetary File System (IPFS) is used as a distributed storage system to reduce backend operation cost and increase data availability.
A Pilot Study on the AR Interface Design for People with Intellectual Disabilities

We have identified two problems for people with intellectual disabilities when using Augmented Reality (AR) devices. One is not noticing the presented information and the other is the difficulty in pressing buttons in the AR space. To solve the first problem, we propose two methods to attract a user's attention, one is to place the information window near the user's gaze point, and dynamically adapt the window based on eye-tracking data. The other is to blink the information window. As a solution to the second problem, we propose a method that detects the user's button-pressing gesture, and when the gesture is detected, the system presses the button on behalf of the user. The effectiveness of the proposed methods was validated through the user studies with 7 participants of varying degrees of intellectual disability involved.
Lagrange’s Mathematical Equations in the Sports Training of College Students

Abstract Because the current sports group competition middle school students’ motion video recognition is complicated, the article uses Lagrangian mathematical equations to classify the motion videos. Based on the spatial smoothness of the flicker parameter function, this paper proposes a Lagrangian mathematical method for detecting local motion regions. The research results show that the motion differential equation and numerical calculation method of the video derived in the article are correct.
A Real-time Object Volume Measurement Method Based on Line Laser Scanning

Volume measurement of objects has numerous applications in industry and transportation, especially non-contact volume measurement is required in many scenarios. Laser scanning technology is widely used in non-contact volume measurement, but the current volume measurement methods based on laser scanning usually treat the point cloud of the object as a whole structure, which leads to the inability to measure the volume in real-time and the increase of measurement error. Based on the above, we designed a real-time object volume measurement system based on line laser scanning. The principle is: After irradiating the object with a beam of line laser, it will form an undulating curve according to the shape of the surface of the object, and then a single frame image can be obtained by shooting from the side with an industrial camera. The frame image is processed by the Rosenfeld image thinning algorithm of fusion burr trimming to obtain the contour lines of the object. And then the outline is processed into a line of outline points by the mapping of the 2D image to the 3D space. Multiple lines of outline points can be obtained during the movement of the object, splicing it to obtain a point cloud that can represent the surface of the object. Since the ordered point cloud obtained by the above method naturally has the condition of integration by the differential method, the volume of the object is calculated in the process of obtaining the ordered point cloud in a point-by-point, line-by-line integration way, which achieves the effect of calculating the volume of the object in real-time during the scanning process.
A Secure Multiparty Computation Round Optimization Scheme Based on Standard Assumption

Many distributed interaction scenarios exist in big data networks, which require joint computation in different environments. In the traditional secure multiparty computation(SMPC), there exist constant rounds or even more rounds of interaction, which greatly increases the communication overhead. In this paper, we address the problem of optimizing the complexity of rounds by constructing a secure computation protocol that achieves semi-honest static security based on a polynomial puzzle assumption. The multiparty interactive computation is performed using multilinear maps operation, and the obtained results are transmitted over the broadcast channel. The security analysis shows that the protocol achieves static security.
Our Brains Beguil’d: Copyright Protection for AI Created Works

In October of 2019, the USPTO (the Office) published a notice in the Federal Register asking for comments on the viability of changing copyright law to allow protection for Artificial Intelligence (AI) created content. Advancements in AI technology have moved the needle in the creative works arena to the point where creative works made as a result of AI intervention are now a reality. With the creation of works whose authorship falls to AI there is a vacuum in the law to address where the ownership of that creative output should fall. The current law considers authorship by a non-human author to lack the creative spark and therefore to be unworthy of protection because Writings are defined as being founded in the creative powers of the ming and as the fruits of intellectual labor. As AI technology advances, however, it is very likely that there will come a point in which that creative spark worthy of authorship may be produced by a non-human author. The Office asked 13 specific questions covering the issue of authorship that ranged from what happens if AI creates an infringing work to the legality of allowing AI use of protected works to create new content. Most of the questions asked by the Office have no concrete answers in current US copyright law but the debate they generate could have far reaching outcomes to the future of copyright law. Not surprisingly, most of the answers given by different stakeholders to the notice for comment from the Office focus on the current state of the law and how that reality should be applied to the problem. The primary purpose of this article is to focus on the six copyright specific question in the thirteen general questions and offer potential outcomes for the possibility of protecting AI created content. The answers in this article will look beyond the current law and focus on a possible world where AI has evolved beyond its current generative role to a creative role and how to best fit that evolution into a legal definition.
Graduate studies in acoustics and wave physics at Institut d’Acoustique - Graduate School, Le Mans, France

This poster presents the graduate studies in Acoustics at Le Mans (France) offered at the “Institut d’Acoustique - Graduate School”. Graduate studies in Acoustics at Le Mans University have been awarded in 2018 the excellence label “École Universitaire de Recherche” among 28 other reference centers for all fields of Science, through a highly selective national call. Our master and engineering school programs range from physical acoustics, environmental acoustics, acoustics and vibrations to international masters on electro-acoustics and on wave physics. The education through research is carried out at the LAUM, UMR CNRS, one of the largest acoustics laboratory in the world. Several details, objectives, student feedbacks and contact informations on the graduates studies will be given on the poster.
Blockchain Enabled Voting

The following paper proposes a blockchain-enabled online voting system with existing technologies. Blockchain technologies enable a plethora of applications that take advantage of distributed economies. The proposed model is a web application built with AngularJS that uses Firebase for security and authentication. The Ethereum Blockchain is used by the project to deploy the smart contract. A smart contract is constructed for each poll to keep track of vote tallies, and when the vote is cast, a transaction is triggered. A voter can see the live status of the poll after they cast their vote.
Prediction of Immune and Stromal Cell Population Abundance from Hepatocellular Carcinoma Whole Slide Images Using Weakly Supervised Learning

Weakly supervised multiple instance learning has been proven to have the ability to predict gene signatures related to immunotherapy sensitivity from hepatocellular carcinoma whole slide images. By examination of the most predictive patches highlighted by the algorithm, specific immune cell populations were enriched in three interferon-gamma-related gene signatures. We proposed to use the algorithm to predict the abundance of various immune and stromal cell populations. The correlation and colocalization with each cell population and gene signature were investigated. It was observed that specific immune cells had positive correlations to interferon-gamma and inflammation gene signatures while stromal cells had negative or trivial correlations. Our models of specific gene signatures and cell populations are promising to develop a novel image-based biomarker for immunotherapy.
New Realities of Educational Practices. The Example of Libraries Activity in the Krasnoyarsk Region

Federal Law No. 85-FZ of 05.04.2021 “On Amendments to the Federal Law ‘On Education in the Russian Federation’” defines the significance of educational activities. The article presents the forms of the modern process of education carried out by the commonwealth of cultural subjects, formerly called cultural and educational, including libraries. Currently, there are used technologies of traditional cultural practices, creative technologies, as well as fragments of mass culture technologies. The technological arsenal of cultural institutions in education is complemented by electronic resources and opportunities provided in the online environment. These issues affect the variety of cultural institutions, but library systems will be in the lead. Communication interaction will not only remain the proven tool of the educational process in libraries, but will also strengthen. The authors supplement this thesis with examples from the activities of libraries in the Krasnoyarsk Region. The kind of catalyst for creative processes in libraries is the activity of employees of municipal libraries in the design and implementation of justification for inclusion in prestigious large-scale modernization projects (National project “Culture” for the creation of model libraries, the Krasnoyarsk Regional network project “Libraries of the Future”). The authors identify the possible risks of intensification of this area of activity, which can be overcome by using both traditional and innovative methods of work.
Parallel Data and Information Visualization

We present a visualization tool developed for parallel data and information visualization in the domain of healthcare. The tool was used to get an insight into patient's current state in a limited time during clinic visits by different clinicians. Based on this tool, a new generic interactive visualization platform is proposed. The platform will use a specific knowledge model to identify suitable visualizations based on a chosen domain, previous cases, and available data.
A Novel Self-supervised Affine-invariant Feature Learning Method for Remote Sensing Image Matching

Robust image matching is a prerequisite for many common tasks in the field of remote sensing. Because of the lack of human-labeled feature matching datasets, it is a challenging task to develop techniques for accurate and robust image feature matching in remote sensing. For this problem, this paper proposes a self-supervised affine-invariant feature learning algorithm for remote sensing imagery. The algorithm mainly consists of three steps, firstly the pretrained deep convolution neural network is trained to obtain high-level semantic features from remote sensing imagery pairs, next affine-invariant features are extracted by means of self-attentional graph neural network, which is trained using a self-supervised paradigm. Finally, feature matching is achieved utilizing mutual nearest neighbor criteria. The experimental results demonstrate that the proposed affine-invariant feature learning method is able to achieve satisfied performance and robust to significant geometric distortion of images.
Merge Nature: a tool to support research about merge conflicts

The modern software development model requires concurrent programming to create more complex programs. As such, existing approaches are able to track and merge the changes made on a code repository, allowing more developers to work collaboratively. Despite these solutions, 10% to 20% of all merges result in conflicts, such as when changes are made on the same file region. Existing studies have analyzed, prevented, predicted, and even automatically resolved merge conflicts. However, they are usually focused on a specific language or on language-independent information from the source-code lines involved in the conflict. This situation hinders the generalization of results. To enrich the information to be used by further studies about merges, we present the Merge Nature Tool, which recreates all project merges and extracts characteristics from conflicts, such as the resolutions adopted by developers, and the language constructs involved in the conflict for Java, C++, and Python files. The tool was evaluated regarding reproducibility and generalization. It was able to reproduce 87.75% of the results of a previous study. The differences can be explained by newly introduced features. On the other hand, regarding generalization, the tool was used to analyze a set of 100 merges from Python projects. The merges were analyzed manually to ensure the reliability of the results. We found that 100% of the data matched the expected result. We expect this tool to facilitate future work regarding merge conflicts. For instance, it may allow comparing the characteristics of merge conflicts from different programming languages.
A MEMETIC ALGORITHM FOR OPTIMIZING THE ASSIGNMENT OF TABLES ON RELATIONAL DATABASE STORAGE UNITS

Optimizing the storage of large volumes of information in relational databases has become an increasingly common need in modern organizations.Database management systems and load balancers aim to accomplish this and employ often inefficient or empirical table mapping protocols.Artificial Intelligence, through bio-inspired algorithms, has come to the aid of this optimization process and the present paper seeks to propose a memetic algorithm to perform the best allocations to tables in relational database storage units, seeking best configurations and accelerating the access to such data.This paper proposes the use of a Memetic Algorithm in the table assignment problem that works on two levels.First, optimizing the result of the best local search algorithm found in the literature, such as the Taboo Search Algorithm; and, secondly, the minimization of exchange processes between tables was achieved, as no other algorithm in the reviewed literature presents.
Towards Massive Parallel Corpus Creation for Hausa-to-English Machine Translation

This chapter reports on the creation of a massive parallel corpus—towards the development of a Hausa (Afroasiatic)-to-English machine translation (MT) system. We discuss the progress so far made in MT and the rapid evolution of commercial MT systems. We found that none of these systems has satisfactorily addressed MT resources for low-resourced languages. To support this course, therefore, we demonstrate our experience in a collaborative research “SCRIPT: Speech Synthesis for Spoken Content Production”—a 3-year (2017–2020) research and innovation project aimed at developing synthetic voices for low-resourced languages, and hosted at The Centre for Speech Technology Research (CSTR)—University of Edinburgh, UK. The current MT resource creation is supported by a limited funding from the Tertiary Education Trust Fund (TETFUND), Nigeria; and is expected to evolve (in the long run) into a massive parallel translation corpus. We target the following resource types: (1) audio/video recordings, and (2) freely available texts, books, including authors-generated materials. We present the methods for achieving our first parallel corpus, for the “Ibadan 400 Wordlist”—a set of basic English words selected from common (everyday) activities. A larger project funding/grant to actualize the desired goal of the present research is expected. This research is certain to revolutionize MT research in Nigeria and unleash the numerous potentials of multilingual MT for low-resource languages.
Data Hiding by Unsupervised Machine Learning Using Clustering K-mean Technique

Steganography includes hiding text, image, or any sentient information inside another image, video, or audio. It aims to increase individuals’ use of social media, the internet and web networks to securely transmit information between sender and receiver and an attacker will not be able to detect its information. The current article deals with steganography that can be used as machine learning method, it suggests a new method to hide data by using unsupervised machine learning (clustering k-mean algorithm). This system uses hidden data into the cover image, it consists of three steps: the first step divides the cover image into three clusterings that more contrast by using k-means cluster, the selects a text or image to be converted to binary by using ASCII code, the third step hides a binary message or binary image in the cover image by using sequential LSB method. After that, the system is implemented. The objective of the suggested system is obtained, using Unsupervised Machine Learning (K-mean technique) to securely send sensitive information without worrying about man-in-the-middle attack was proposed. Such a method is characterized by high security and capacity. Through evaluation, the system uses PSNR, MSE, Entropy, and Histogram to hide the secret message and secret image in the spatial domain in the cover image. Index Terms— Steganography, (LSB), K-mean, Cluster, Machine learning.
Perubahan Semantik Kosakata Serapan Bahasa Asing dalam Bahasa Indonesia

Abstract. This study aims to describe the changes in meaning that occur in foreign language absorption vocabulary in Indonesian. The data source in this study is Indonesian vocabulary absorbed from Foreign languages, namely Dutch and English. The data in this study is a dictionary of foreign absorption words in Indonesian by J.S Badudu. Data collection techniques using document review techniques. This research uses qualitative approach and research type using descriptive method. The results of this study researchers found the words absorption that experienced a change in meaning, as for the word absorption that experienced changes in meaning in the form of expansion, narrowing and total changes. The expansion of meaning is a process that occurs in a word that at first only has a meaning, but then because various factors become have other meanings. The Dutch example of the word "paniek" means panic. The narrowing of meaning is a process that occurs in a word that initially has a broad enough meaning, then changes to be limited to a meaning only. Examples of such emphasis words in English are pressure and heavy points, while the meaning in Indonesian is the pressure on syllables or on words and the emphasis on importance (something). The total change of meaning is a process of changing the meaning that has now been much different from the meaning of the original word. Examples of the word "morgem (BB)" are absorbed in The Indonesian language into besuk. The change in meaning that occurs because of the shift in meaning is the word besuk means making a visit to the hospital to visit people who are sick
Use of digital instruments and services in the system of children’s additional education

The study examines digital instruments and services that can be used in the system of additional education. Being able to use digital instruments and services in professional activity and being ready to account for modern trends in the development of digital technology is important for a teacher in additional education. The organizational foundation of additional education is a digital learning platform. Digital technologies require new approaches to the development of educational and methodological materials, types of activities, methods, means, and organizational forms of learning to ensure the high quality of the educational process. The authors explore the use of Google digital services, resources for creating infographics, and MindMeister, a service for creating mind maps, in the system of additional education.