Reviewerly/exHarmony huggingface dataset:
Analyzing the Shuffle Model Through the Lens of Quantitative Information Flow

Local differential privacy (LDP) is a variant of differential privacy (DP) that avoids the necessity of a trusted central curator, at the expense of a worse trade-off between privacy and utility. The shuffle model has emerged as a way to provide greater anonymity to users by randomly permuting their messages, so that the direct link between users and their reported values is lost to the data collector. By combining an LDP mechanism with a shuffler, privacy can be improved at no cost for the accuracy of operations insensitive to permutations, thereby improving utility in many analytic tasks. However, the privacy implications of shuffling are not always immediately evident, and derivations of privacy bounds are made on a case-by-case basis. In this paper, we analyze the combination of LDP with shuffling in the rigorous framework of quantitative information flow (QIF), and reason about the resulting resilience to inference attacks. QIF naturally captures (combinations of) randomization mechanisms as information-theoretic channels, thus allowing for precise modeling of a variety of inference attacks in a natural way and for measuring the leakage of private information under these attacks. We exploit symmetries of k-RR mechanisms with the shuffle model to achieve closed formulas that express leakage exactly. We provide formulas that show how shuffling improves protection against leaks in the local model, and study how leakage behaves for various values of the privacy parameter of the LDP mechanism. In contrast to the strong adversary from differential privacy, who knows everyone's record in a dataset but the target's, we focus on an uninformed adversary, who does not know the value of any individual in the dataset. This adversary is often more realistic as a consumer of statistical datasets, and indeed we show that in some situations, mechanisms that are equivalent under the strong adversary can provide different privacy guarantees under the uninformed one. Finally, we also illustrate the application of our model to the typical strong adversary from DP.
Bayes Security: A Not So Average Metric

Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system's performance. In this paper, we study Bayes security, a security metric inspired by the cryptographic advantage. Similarly to DP, Bayes security i) is independent of an adversary's prior knowledge, ii) it captures the worst-case scenario for the two most vulnerable secrets (e.g., data records); and iii) it is easy to compose, facilitating security analyses. Additionally, Bayes security iv) can be consistently estimated in a black-box manner, contrary to DP, which is useful when a formal analysis is not feasible; and v) provides a better utility-security trade-off in high-security regimes because it quantifies the risk for a specific threat model as opposed to threat-agnostic metrics such as DP. We formulate a theory around Bayes security, and we provide a thorough comparison with respect to well-known metrics, identifying the scenarios where Bayes Security is advantageous for designers.
Desain Video Animasi Berbasis Studi Kasus Untuk Mata Kuliah Pengembangan E-Learning

Based on what happened in the field, it is known that the content developed in the video still has a weakness, namely the content developed is still conceptual. However, it is also expected that students can understand contextually. Contextual learning can make students more active because students try to learn concepts as well as apply and relate them to the real world. Making students more active in the learning process can occur if students are involved in the learning process. So, the purpose of designing this animated video is so that students feel firsthand the problems that occur in the field and students have a contextual understanding. In addition, so that this video media can be developed later. The research was conducted with a descriptive research design. The design of an animated video media is followed by media validation, then the media can be used in blended learning in e-learning development courses. The design activities carried out include 3, namely; design of video content in e-learning development courses; design of case study-based teaching material content; and designing animated videos with case study-based teaching material content. The design of case study-based animated video media by developing appropriate learning content can increase student involvement in the learning process and improve contextual understanding. From the results obtained, it can be concluded that the case study-based animation video media that has been designed is feasible to be used and developed in e-learning development courses
Impact of Financial Resources on Agricultural Growth in Sub-Saharan Africa

This study contributes to the literature on financial efficiency and growth. We show that banking development exerts a statistically significant and positive impact on local economic growth. We use the ARDL method to find the impact of institutional financial quality on agriculture sector growth in 14 Sub-Saharan African countries from 1990 to 2020. Our results show that land, rural population, and per capita agricultural income growth have long-run and significant (at 1% level) causal effects on the magnitude of agricultural value added as a percentage of GDP.&amp;nbsp;
Assessing the prospects for using artificial intelligence in higher education system

Introduction. The article deals with the problem of improving the quality of higher education in the context of its computerization. The purpose of the article is to describe the new structure of higher education, based on the principle of a neural network, as well as to identify the prospects of digital transformation for universities, when a wide range of administrative and educational functions might be performed by artificial intelligence. Materials and Methods. The study uses structural modeling in order to build a higher education system that functions as a neural network based on theoretical analysis and reviewing of scholarly literature on the methodology of teaching in high-ranking foreign universities. The author also employs the UTAUT (Unified theory of acceptance and use of technology) model to identify students’ attitudes towards the prospects for the introduction of artificial intelligence in higher education. Results. The paper proposes and describes a new intellectual structure of the higher education system. A distinctive feature of this structure is that employers should become the main evaluators of graduates’ education outcomes. Employers’ feedback is supposed to be provided for universities, adjusting the higher education system to continuously changing market requirements. The advantage of transforming the higher education system according to the principles of neural network functioning will bring a considerable increase in the quality of preparing top-level professionals, and therefore, real prospects for restructuring the national economy will be provided, when GDP growth is ensured not by increasing the amount of exporting raw materials, but by high-tech production. The results of students’ survey conducted and processed using the UTAUT model showed that the younger generation has a positive attitude towards the introduction of AI in the educational process: they are attracted by new prospects in obtaining knowledge and are not afraid of the risks associated with it. Conclusions. The paper concludes that Russian universities, by switching to the new model of higher education, based on a neural network, will be able to dramatically improve the quality of education and become world leaders in the field of preparing top-level professionals, as currently in foreign universities, artificial intelligence manages only a limited range of functions. A distinctive feature of the proposed model is complete digitalization and automation of all routine work at universities, decreasing methodological and reporting load for academic staff, as well as transferring the main teaching load from classrooms to laboratories for a deeper students’ involvement in research activities.
HyNet: A novel hybrid deep learning approach for efficient interior design texture retrieval

Abstract Interior designers are suffering from a lack of intelligent design methods. This study aims to enhance the accuracy and efficiency of retrieval textures for interior design, which is a crucial step toward intelligent design. Currently, interior designers rely on repetitive tasks to obtain textures from websites, which is ineffective as a interior design often requires hundreds of textures. To address this issue, this study proposes a hybrid deep learning approach, HyNet, which boosts retrieval efficiency by recommending similar textures instead of blindly searching. Additionally, a new indoor texture dataset is created to support the application of artificial intelligence in this field. The results demonstrate that the proposed method’s ten recommended images achieve a high accuracy rate of 91.41%. This is a significant improvement in efficiency, which can facilitate the design industry’s progression towards intelligence. Overall, this study offers a promising solution to the challenges facing interior designers, and it has the potential to significantly enhance the industry’s productivity and innovation.
UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?

Previous state-of-the-art models for lexical simplification consist of complex pipelines with several components, each of which requires deep technical knowledge and fine-tuned interaction to achieve its full potential. As an alternative, we describe a frustratingly simple pipeline based on prompted GPT-3 responses, beating competing approaches by a wide margin in settings with few training instances. Our best-performing submission to the English language track of the TSAR-2022 shared task consists of an “ensemble” of six different prompt templates with varying context levels. As a late-breaking result, we further detail a language transfer technique that allows simplification in languages other than English. Applied to the Spanish and Portuguese subset, we achieve state-of-the-art results with only minor modification to the original prompts. Aside from detailing the implementation and setup, we spend the remainder of this work discussing the particularities of prompting and implications for future work. Code for the experiments is available online at https://github.com/dennlinger/TSAR-2022-Shared-Task.
Construction of an S-Box Using Suppersingular Elliptic Curve over Finite Field

This research aims to develop an S-box using elliptic curve cryptography (ECC) that provides higher security than other cryptographic schemes with equivalent key size. In this paper, a supersingular elliptic curve has been taken of characteristic more than three. The generated S-box depends on the values of a and b used in the supersingular elliptic curve. The robustness of the recently constructed S-box is thoroughly evaluated against prevalent attacks, including linear, differential, and algebraic attacks, through an analysis of its nonlinearity, linear approximation, differential branch number, and other established properties. The achieved findings are additionally contrasted with some well-known S-boxes. Keywords: finite fields, supersingular elliptic curves, group law, rational points, substitution boxes. https://doi.org/10.55463/issn.1674-2974.50.7.5
SISTEM ABSENSI SISWA MENGGUNAKAN FINGERPRINT BERBASIS ARDUINO MEGA 2560

This study aims to design, create and test the attendance system of SMA Dharma Bhakti 4 Jambi City students using Arduino Mega 2560-based Fingerprint. This research was conducted at SMA Dharma Bhakti 4 Jambi City from January to April 2021.This research was conducted using the R and D (Research and Development) method which includes planning, designing, manufacturing and testing tools carried out by making a tool system with input in the form of a Fingerprint Module. Control system with Arduino Mega 2560. Output system in the form of LCD and SMS reports to parents of SMA Dharma Bhakti 4 students in Jambi City. The way this tool works is by identifying the fingerprint pattern of DB 4 High School students during registration and transfer using the Fingerprint Module. The Fingerprint module is activated by a fingerprint then identified. The fingerprint that is read will appear on the LCD and through serial communication with the Arduino, the fingerprint will automatically respond to send a message in the form of an SMS to the parents of students who have been registered in the Arduino module.The test results on the Module reached 100% successfully detecting fingerprints that can record attendance data for SMA Dharma Bhakti 4 Jambi City students at SD CARD and provide SMS reports to their respective parents.
PacketGame: Multi-Stream Packet Gating for Concurrent Video Inference at Scale

The resource efficiency of video analytics workloads is critical for large-scale deployments on edge nodes and cloud clusters. Recent advanced systems have benefited from techniques including video compression, frame filtering, and deep model acceleration. However, based on our year-long experience of operating a real-time video analytics system on more than 1000 cameras, we identified a previously overlooked bottleneck of end-to-end concurrency: video decoding. To support concurrent video inference at scale, in this work, we investigate a new task, named video packet gating, which selectively filters packets before running a decoder. We propose a novel multi-view embedding approach for video packets and present PacketGame that has both theoretical performance guarantee and practical system designs. Experiments on both public datasets and a real system show PacketGame saves 52.0--79.3% decoding costs and achieves 2.1--4.8× concurrency compared to original workloads. Comparisons with four state-of-the-art complementary methods show the superiority of PacketGame in end-to-end concurrency.
MahaEmoSen: Towards Emotion-aware Multimodal Marathi Sentiment Analysis

With the advent of the Internet, social media platforms have witnessed an enormous increase in user-generated textual and visual content. Microblogs on platforms such as Twitter are extremely useful for comprehending how individuals feel about a specific issue through their posted texts, images, and videos. Owing to the plethora of content generated, it is necessary to derive an insight of its emotional and sentimental inclination. Individuals express themselves in a variety of languages and, lately, the number of people preferring native languages has been consistently increasing. Marathi language is predominantly spoken in the Indian state of Maharashtra. However, sentiment analysis in Marathi has rarely been addressed. In light of the above, we propose an emotion-aware multimodal Marathi sentiment analysis method (MahaEmoSen). Unlike the existing studies, we leverage emotions embedded in tweets besides assimilating the content-based information from the textual and visual modalities of social media posts to perform a sentiment classification. We mitigate the problem of small training sets by implementing data augmentation techniques. A word-level attention mechanism is applied on the textual modality for contextual inference and filtering out noisy words from tweets. Experimental outcomes on real-world social media datasets demonstrate that our proposed method outperforms the existing methods for Marathi sentiment analysis in resource-constrained circumstances.
ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction

In cloud gaming, interactive latency is one of the most important factors in users' experience. Although the interactive latency can be reduced through typical network infrastructures like edge caching and congestion control, the interactive latency of current cloud-gaming platforms is still far from users' satisfaction.
Enhancement of UAV Data Security and Privacy Via Ethereum Blockchain Technology

Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions. The MetaMask wallet for Ethbalance is necessary for BCT transactions. The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods. This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries. The proposed platform’s security measures could mitigate privacy concerns, minimize cyber security risk, and enhance public safety, ultimately promoting the widespread adoption of UAVs. The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication, privacy preservation, confidentiality, integrity, and access control.
As far as my eyes can see: Generation Y consumers’ use of virtual reality glasses to determine tourist destinations

The study focused on the intention to adopt and actual use of virtual reality (VR) glasses to determine tourist destinations amongst Generation Y in South Africa. This study considers the direct influence of perceived usefulness, perceived ease of use and perceived credibility on attitudes towards use of virtual reality glasses, intention towards use of virtual reality glasses and actual use of virtual reality glasses. In addition, the possible moderating effect of perceived performance and perceived enjoyment on the nexus between the intention and actual use of virtual reality glasses is also examined. The study used a quantitative methodology and cross-sectional research methods to collect data from 550 Generation Y consumers. Data were then analyzed using the Smart PLS 4 computer software. The results indicate that perceived usefulness and ease of use had a positive and significant impact on the attitude towards using virtual reality glasses. Perceived credibility had an insignificant impact on attitude towards use of virtual reality glasses. In addition, attitude towards use of virtual reality glasses had a positive and a significant impact on intention towards the use of virtual reality glasses. There was also a positive association between intention towards use of virtual reality glasses and the actual use of virtual reality glasses. Moreover, perceived performance and perceived enjoyment proved to be the key moderators on the connection between the intention and actual use of virtual reality glasses.Although there are many studies on the use of virtual reality in tourism, studies that have examined the factors that influence how Gen Y consumers use virtual reality glasses to choose travel destinations have several shortcomings. As a result, this study seeks to complement existing tourism-related literature, particularly in the African context. Tourism organisation marketers could spend some time exploring the potential of using virtual reality glasses to increase awareness of destinations.
DEVELOPMENT A WEB-BASED STUDENT INTERNSHIP APPLICATION USING LARAVEL FRAMEWORK

In this research, the author will discuss the development of a student internship information system implemented using the Laravel framework. The aim is to provide convenience for students in registering/applying for internships, reporting daily activities, submitting final reports, and updating all other internship-related information, as well as facilitating institutions in managing internship data. The method used in this research is the information system development method, which includes literature review, needs analysis, system design, testing, and evaluation. The Laravel framework was chosen because of its good performance, ease of use, and many features that can facilitate the development of information systems. The student internship management information system designed has easy-to-use internship registration and reporting features that can be accessed from anywhere, as well as integrated internship data management features. The results of this research show that the development of a student internship information system using the Laravel framework can provide convenience in registering and reporting internship results for students, as well as help institutions in managing internship data. It is hoped that with the existence of this student internship information system, it can be a solution to simplify and accelerate the internship process and management for students and participating institutions.
A novel clustering method with consistent data in a three-dimensional graphical format over existing clustering mechanisms

With regard to scientific literature reviews, these papers are divided into three major aspects: traditional and novel clustering algorithms, applications, and problems. This study aimed to develop a novel clustering method to remove all the problems of existing clustering methods, such as unnecessary logical partitions, uncertain results, and increased time complexity. A novel clustering method is based on three major steps. It extracts data from the database via a heuristic mechanism and makes comprehensive logical partitions in the main memory. It transforms all logical partitions from main memory into a text file. Furthermore, it presents consistent data in all clusters in three-dimensional format via a Python program. It has few advantages over conventional clustering methods. Firstly, it is one of the systematic approaches to dealing with mixed data types, consistent data, and handling outliers. Secondly, when it uses attributes for the splitting process, it performs single-time database scanning. In other words, it introduces abstract and mathematical models as well as algorithms and metrics and gets significant results via a two-step cluster. Finally, it uses five datasets (freight, HR employee, patient treatment, medical store, and iris flower as raw data) to present consistent data in all clusters in a three-dimensional format.
Buffer-MIL: Robust Multi-instance Learning with a Buffer-Based Approach

Histopathological image analysis is a critical area of research with the potential to aid pathologists in faster and more accurate diagnoses. However, Whole-Slide Images (WSIs) present challenges for deep learning frameworks due to their large size and lack of pixel-level annotations. Multi-Instance Learning (MIL) is a popular approach that can be employed for handling WSIs, treating each slide as a bag composed of multiple patches or instances. In this work we propose Buffer-MIL, which aims at tackling the covariate shift and class imbalance characterizing most of the existing histopathological datasets. With this goal, a buffer containing the most representative instances of each disease-positive slide of the training set is incorporated into our model. An attention mechanism is then used to compare all the instances against the buffer, to find the most critical ones in a given slide. We evaluate Buffer-MIL on two publicly available WSI datasets, Camelyon16 and TCGA lung cancer, outperforming current state-of-the-art models by 2.2% of accuracy on Camelyon16.