Reviewerly/exHarmony huggingface dataset:
On the Cost of Suppressing Volume for Encrypted Multi-maps

Structured encryption (STE) schemes allow a client to store sensitive data on a semi-trusted remote server and query the data. STE schemes strike a balance between privacy and efficiency by leaking some information to the server. In particular, many STE schemes leak the volume pattern i.e., response lengths, and the query equality pattern i.e., if any queries are repeated. Recently discovered leakage-abuse attacks demonstrate that leaking the volume pattern can be unsafe; that is, the server can reconstruct parts of the database from this leakage. To address this leakage, Kamara and Moataz proposed a novel multi-map encryption scheme, AVLH, that hides query volumes by padding responses with parts of other responses (Eurocrypt 2019). AVLH was shown to be more storage-efficient than the naive approach to pad responses with dummy values to reach the maximum response length. Subsequently, Patel et al. provided an even more efficient volume-hiding multimap scheme, dprfMM (CCS 2019). Despite these advances, the costs of fully suppressing query volumes are still unclear. In this paper, we provide the first lower bounds on STE schemes for multi-maps that leak at most the query equality pattern. Surprisingly, we find that in many cases, such STE schemes cannot be more storage-efficient than naively padding to the maximum length.
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions

The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mechanism in the context of differential privacy (DP). Recent work [18–20, 24] has shown that PLD-based accounting allows for tighter (ε, δ)-DP guarantees for many popular mechanisms compared to other known methods. A key question in PLD-based accounting is how to approximate any (potentially continuous) PLD with a PLD over any specified discrete support. We present a novel approach to this problem. Our approach supports both pessimistic estimation, which overestimates the hockey-stick divergence (i.e., δ) for any value of ε, and optimistic estimation, which underestimates the hockey-stick divergence. Moreover, we show that our pessimistic estimate is the best possible among all pessimistic estimates. Experimental evaluation shows that our approach can work with much larger discretization intervals while keeping a similar error bound compared to previous approaches and yet give a better approximation than an existing method [24].
ATOM: Ad-network Tomography

Data sharing between online trackers and advertisers is a key component in online behavioral advertising. This sharing can be facilitated through a variety of processes, including those not observable to the user’s browser. The unobservability of these processes limits the ability of researchers and auditors seeking to verify compliance with recent regulations (e.g., CCPA and CDPA) which require complete disclosure of data sharing partners. Unfortunately, the applicability of existing techniques to make inferences about unobservable data sharing relationships is limited due to their dependence on protocol- or case-specific artifacts of the online behavioral advertising ecosystem (e.g., they work only when client-side header bidding is used for ad delivery or when advertisers perform ad retargeting). As behavioral advertising technologies continue to evolve rapidly, the availability of these artifacts and the effectiveness of transparency solutions dependent on them remain ephemeral. In this paper, we propose a generalizable technique, called ATOM, to infer data sharing relationships between online trackers and advertisers. ATOM is different from prior approaches in that it is universally applicable — i.e., independent of ad delivery protocols or availability of artifacts. ATOM leverages the insight that by the very nature of behavioral advertising, ad creatives themselves can be used to infer data sharing between trackers and advertisers — after all, the topics and brands showcased in an ad are dependent on the data available to the advertiser. Therefore, by selectively blocking trackers and monitoring changes in the characteristics of ad creatives delivered by advertisers, ATOM is able to identify data sharing relationships between trackers and advertisers. The relationships discovered by our implementation of ATOM include those not found using prior approaches and are validated by external sources.
Zswap: zk-SNARK Based Non-Interactive Multi-Asset Swaps

Privacy-oriented cryptocurrencies, like Zcash or Monero, provide fair transaction anonymity and confidentiality, but lack important features compared to fully public systems, like Ethereum. Specifically, supporting assets of multiple types and providing a mechanism to atomically exchange them, which is critical for e.g. decentralized finance (DeFi), is challenging in the private setting. By combining insights and security properties from Zcash and SwapCT (PETS 21, an atomic swap system for Monero), we present a simple zk-SNARKs based transaction scheme, called Zswap, which is carefully malleable to allow the merging of transactions, while preserving anonymity. Our protocol enables multiple assets and atomic exchanges by making use of sparse homomorphic commitments with aggregated open randomness, together with Zcash friendly simulation-extractable non-interactive zero-knowledge (NIZK) proofs. This results in a provably secure privacypreserving transaction protocol, with efficient swaps, and overall performance close to that of existing deployed private cryptocurrencies. It is similar to Zcash Sapling and benefits from existing code-bases and implementation expertise.
Performing musicology in the socio-cultural space of Ukraine of the XXI century: scientific and creative discourse

At the present stage, performing musicology plays an important role in the formation and development of professional musicians. In the socio-cultural space of Ukraine of the 21st century, it continues the historical traditions of research on the formation of performers, but, at the same time, it undergoes serious transformations. The aim of the research is to identify the key, fundamental principles of performance skill in the context of today’s realities, to determine the factors influencing the reformatting of the essential features of the creative activity of musicians. The methods of work were: analysis of scientific literature, systematization of methodological complex, method of analysis and synthesis, method of thematic analysis and critical approach to discretization of musical works. The results of the work allow us to assert about radical changes in the approach to art as such, as well as in the formation of professionals and their creative activity with a change in the basic parameters of the approach to music education. The institutionalization of art currently plays a leading role in the cultural socialization of masters, but today it is not decisive. With the development of progressive methods and approaches to education, modern technologies, globalization, the process of becoming a musician becomes easier, but access to the world of art without mediation opens wide horizons for promising musicians, and, at the same time, increases the level of competition for material and non-material incentives. The study offers a new perspective on performing musicology as a promising field for further scientific work, which will allow a better understanding of the peculiarities of the formation of musicians in the socio-cultural space of the 21st century.
Economic Globalisation and Inclusive Green Growth in Africa: Contingencies and Policy-Relevant Thresholds of Governance

Abstract This study employs macrodata for 23 African countries to examine whether good governance interacts with economic globalisation (EG) to foster inclusive green growth (IGG). First, the study finds that EG hampers IGG in Africa. Second, although unconditionally good governance promotes IGG, only government effectiveness interacts with EG to foster IGG. Across the social and environmental sustainability dimensions of IGG, however, the effects differ substantially. Notably, while the EG-governance pathways yield remarkable environmental sustainability net gains, a modest harmful effect was observed for socioeconomic sustainability. Evidence from our threshold analyses also suggests that while government effectiveness is critical for propelling EG to promote IGG, across the social and environmental perspectives of IGG, it is investments in building frameworks and structures for corruption control and the rule of law that are crucial. Our results shed new light on IGG and have several implications for Agenda 2030 and Agenda 2063. JEL Codes : F18; F4; F6; F63; F64; H1; O55; Q01; Q56
STUDENT EXPERIENCE OF ONLINE INTERNATIONAL DESIGN STUDIO PARTICIPATION

We argue that students participating in competitive project activities may facilitate development of their skills such as creative thinking, the formation of "vision" and stress resilience.In this paper, we will reflect on students' experiences who participated in the 10 weeklong creative Global Studio project, which was delivered online across five countries.The article describes features of the creative studio project, the tasks and goals that the participating students set for themselves, as well as their motivations.An important place in the structure of our exploration is the identification of the aspects in this creative studio project which students perceived to benefit their learning as well as the difficulties they experienced with the implementation of their projects.The article reports the results of a student survey and discusses the attitudes of students with different levels of preparation for creative studio projects.The article highlights the importance of professional projects and project work as an integral part of the educational process of students of creative specialties and notes that competitive projects carried out jointly with students from different countries of the world give a positive experience of combining the educational process.We conclude that in the modern world, virtually devoid of borders due to the development of technology, competitive project activity is a new educational trajectory that brings out the student's creativity and teaches them to adapt quickly to the changing stressful working conditions.
NEW COLLABORATIVE WORKFLOWS - IMMERSIVE CO-DESIGN FROM SKETCHING TO 3D CAD AND PRODUCTION

Digital technologies have enabled design sketching to expand into new applications and domains.Inevitably, these new forms of visualisation require re-evaluating how we use drawing to see, visualise, understand, and fabricate products and services in design education and the profession.This paper presents a selection of discoveries after the authors performed research, made presentations and mediated workshops when face-to-face collaborations and travel were impossible because of the Covid-19 epidemic restrictions.Findings add to work intending to build a modern taxonomy for design sketching and visual knowledge while accounting for immersive virtual collaboration and distributed workflows from sketching to 3D CAD and 3D printing.These are among the first indications of a drive towards synthesising historically demarked design process stages into a singularity of actions that merge and move simultaneously among ideation, design, and production.Participants in two international conference workshops shared ideas and discussed their local circumstances relating to the potential use and acceptance of new technologies already researched and adopted in other disciplines such as computer science and entertainment.A critical consensus was that the challenge of new technologies for our design education and profession is not as much about technology and its tools as the process and steps that enable change.Significantly, conversation pointed towards a strategy that enhances and augments habits in design education and the profession as the means to modify and transform culture and practice.
Determination of a Similar Anatomical Area on a Chest CT Image Using Traditional Image Feature Extraction Methods

The traditional image descriptor definition algorithms are considered, such as SIFT, ORB, LBP, GLSM. With the help of them, the searching task for a similar anatomical area on the CT images of the lungs is solved. The article proposes a methodology for performing a comparative traditional algorithms for determining images descriptors analysis and optimal anatomical features. Algorithms are tested when searching for a similar anatomical layer in the framework of the computer tomography images layers of of light patient, as part of the search for similar anatomical form on the layer among the computer tomography images of light two patients, and among the images of computed tomography of light hundred patients. As a result, it is determined that GLSM shows the best results when solving the task of classifying an image anatomical area (averaged error of determining the anatomical layer is 5 %). It is determined that the optimal signs on the lungs correspond to the presence of organs: heart, liver and top edge of the lung. Conclusions are fomulated about the need to use neural network methods to improve the error in determining the similar layer containing the necessary anatomical structure.
Arbitrary-Shaped Scene Text Detection with Scoring Mask Quality

Recently, segmentation based text detection methods have achieved outstanding results. However, due to the complexity of natural scenes and the diversity of text shapes, many instance segmentation based methods suffer from serious false positive problems. One of the main reasons is that these methods regard a proposal as text only depending on its classification score. But in practice, a highquality proposal should not only be classified accurately, but more importantly, be positioned accurately. To solve the problem, we propose an arbitrary-shaped text detection network with Mask Scoring quality (MSNet), which measures the quality of a proposal jointly considering its classification score and mask score, so as to filter out FP samples. Extensive experiments on 4 real-world datasets demonstrate that MSNet is able to achieve better results than some state-of-the-art methods.
Effect of Multimodal Teaching on Language Comprehensive Ability under the Background of Artificial Intelligence

Reading comprehension is an important language learning skill. In English class, we should pay attention to cultivating students’ reading comprehension ability to help students improve their reading comprehension ability. The application of language service products of artificial intelligence has brought great challenges and opportunities to language teaching. This research starts from the current situation of the teaching mode of English language and literature reading under the background of artificial intelligence. The following four questions are mainly studied. Can the multimodal oral teaching model improve students’ oral English performance? Has the students’ spoken English improved in the three aspects of language content, language accuracy, and pronunciation and intonation? Does multimodal oral teaching have a positive impact on students’ attitudes towards oral learning? How can students improve their English writing? This study uses two research methods, experimental research and questionnaire survey, to explore the above research questions through a 16-week multimodal oral teaching experiment. The subjects of the experiment were 61 non-English majors in two parallel classes in the first year of a university in Xi’an. The experiment is divided into three phases: pretest of oral test, implementation of multimodal oral teaching, and posttest of oral test. The experiment proved that most students (about 67.35%) did not show much interest in reading and writing in English. Only a few students (12.24%) like to read and write. It shows that after accepting the multimodal teaching experiment, the students’ oral English scores have been significantly improved, which is reflected in the language accuracy, pronunciation intonation, and language content. The students’ oral English learning attitude is positive after the multimodal oral teaching.
Application of Internet of Things in Smart Factories under the Background of Industry 4.0 and 5G Communication Technology

The key identification element of the smart factory is the interconnection between devices, which solves the production method and development dilemma of the factory under Industry 3.0 and previous models. The new article published by Xinhua News Agency on January 16, 2022, advocates the idea of industry+ Internet, and its main goal is to realize intelligent production. At present, smart factories have become the main development direction of industrial enterprises in the world. 5G technology has been rapidly deployed with the development of mobile communication, and its performance has also been greatly improved compared to previous communication technologies. After China put forward the smart manufacturing 2025 plan, although a large number of enterprises are still lingering in the process of research, learning, and exploration, the idea of Industry 4.0 has taken root in various intelligent manufacturing enterprises, and the development route of Chinese manufacturing enterprises in the future has been ahead of schedule. Direction. Based on the basic theory of Industry 4.0, 5G wireless communication, Internet of Things, and smart factories, this paper firstly distributed questionnaires to 20 enterprises with smart factories by means of network and interview and reanalyzed the collected questionnaires by regression analysis method and then used the questionnaire scale analysis method to analyze the reliability and validity of the questionnaire and combined with the results of the questionnaire analysis to analyze the problems existing in the current smart factory. Finally, based on the background of Industry 4.0 and 5G communication technology, combined with the Internet of Things technology, the development layout of the smart factory is designed; that is, based on the elliptic curve encryption algorithm, the signature mechanism of mutual trust of all electronic devices in the IoT smart factory is set to improve the smart factory.
MODELING OF APPROACHES TO TEACHING COMPUTER SCIENCE FOR VOCATIONAL STUDENTS BY CLUSTERING THEM INTO DIDACTIC GROUPS

Statement of the problem. The issues of finding ways to modernize the means and methods of teaching informatics to students in the system of secondary vocational education, which contribute to improving the effectiveness of the educational process, do not lose their relevance. It is of interest to study and substantiate the criteria for clustering the contingent of vocational students into didactic groups for the implementation of individualization and differentiation of the process of teaching computer science. The purpose of the article is to identify the characteristics of students in the system of secondary vocational education and, based on the analysis of the results obtained, to model approaches to teaching informatics for students in the system of secondary vocational education by clustering them into didactic groups. The research methodology consists of the analysis and generalization of legal documents in the field of secondary vocational education, research work of Russian scientists, and the experience of teaching informatics to students in various areas of vocational education. An anonymous survey method was used to identify the characteristics inherent to students of the system of secondary vocational education for subsequent clustering. Research results. On the basis of an anonymous survey of students in the system of secondary vocational education, their characteristic features, social, psychological and personal characteristics were revealed. They disclosed the need to cluster the contingent into three groups according to educational, cognitive and social criteria. At the same time, a differentiated approach to teaching computer science to students should be implemented through gamification, collective gaming teaching methods and the formation of a practice-oriented learning environment. Conclusion. The method proposed in the article for identifying characteristic features for clustering students in the system of secondary vocational education can become the basis for developing a suitable methodology for teaching them computer science. The distribution of students into three groups according to educational, cognitive and personal characteristics allows using a differentiated approach with gamification techniques and collective gaming methods of learning.
Fat Pointers for Temporal Memory Safety of C

Temporal memory safety bugs, especially use-after-free and double free bugs, pose a major security threat to C programs. Real-world exploits utilizing these bugs enable attackers to read and write arbitrary memory locations, causing disastrous violations of confidentiality, integrity, and availability. Many previous solutions retrofit temporal memory safety to C, but they all either incur high performance overhead and/or miss detecting certain types of temporal memory safety bugs. In this paper, we propose a temporal memory safety solution that is both efficient and comprehensive. Specifically, we extend Checked C, a spatially-safe extension to C, with temporally-safe pointers. These are implemented by combining two techniques: fat pointers and dynamic key-lock checks. We show that the fat-pointer solution significantly improves running time and memory overhead compared to the disjoint-metadata approach that provides the same level of protection. With empirical program data and hands-on experience porting real-world applications, we also show that our solution is practical in terms of backward compatibility -- one of the major complaints about fat pointers.
Interpretability Based Approach to Detect Fake Profiles in Instagram

The explosive rise of OSNs, as well as the vast quantity of personal data they hold, has attracted attackers and imposters looking to steal personal information, propagate fake news, and carry out destructive actions. Researchers, on the other hand, have begun to look at effective approaches for detecting anomalous behaviors and phony accounts using account characteristics and classification algorithms. Creating fake accounts, which is used to increase the popularity of an account in an immoral way, is one of the security challenges in these networks that has become a major concern for users. An attacker can affect the security and privacy of legitimate users by spreading spam, malware, and disinformation. Advertisers utilize these channels to reach out to a certain demographic. The number of false accounts is growing at an exponential rate, and in this study, we propose an architecture for detecting phony accounts in social media, particularly Instagram. We are utilizing Machine Learning techniques such as Bagging and Boosting in this study to make better predictions about detecting bogus accounts based on their profile information. We utilized the SMOTE technique to balance the two groups of data, which enables us to get the equal number of persons for each class. The approaches for understanding complicated Machine Learning Models to understand the reasoning behind a model choice, such as SHAP values and LIME, were also included in this article. The XGBoost and Random Forest models have a combined accuracy of 96%. An online fake detection method has been built to identify rogue accounts on Instagram, as shown below.