Reviewerly/exHarmony huggingface dataset:
Improved Polynomial Secret-Sharing Schemes

Despite active research on secret-sharing schemes for arbitrary access structures for more than 35 years, we do not understand their share size – the best known upper bound for an arbitrary n-party access structure is $$2^{O(n)}$$ , while the best known lower bound is $$\varOmega (n/\log (n))$$ . Consistent with our knowledge, the share size can be anywhere between these bounds. To better understand this question, one can study specific families of secret-sharing schemes. For example, linear secret-sharing schemes, in which the sharing and reconstruction functions are linear mappings, have been studied in many papers, e.g., it is known that they require shares of size at least $$2^{0.5n}$$ . Secret-sharing schemes in which the sharing and/or reconstruction are computed by low-degree polynomials have been recently studied by Paskin-Cherniavsky and Radune [ITC 2020] and by Beimel, Othman, and Peter [CRYPTO 2021]. It was shown that secret-sharing schemes with sharing and reconstruction computed by polynomials of degree 2 are more efficient than linear schemes (i.e., schemes in which the sharing and reconstruction are computed by polynomials of degree one). Prior to our work, it was not known if using polynomials of higher degree can reduce the share size. We show that this is indeed the case, i.e., we construct secret-sharing schemes for arbitrary access structures with reconstruction by degree-d polynomials, where as the reconstruction degree d increases, the share size decreases. As a step in our construction, we construct conditional disclosure of secrets (CDS) protocols. For example, we construct 2-server CDS protocols for functions $$f:[N]\times [N] \rightarrow {\{}0,1{\}}$$ with reconstruction computed by degree-d polynomials with message size $$N^{O(\log \log d/\log d)}$$ . Combining our results with a lower bound of Beimel et al. [CRYPTO 2021], we show that increasing the degree of the reconstruction function in CDS protocols provably reduces the message size. To construct our schemes, we define sparse matching vectors, show constructions of such vectors, and design CDS protocols and secret-sharing schemes with degree-d reconstruction from sparse matching vectors.
Combinatorially Homomorphic Encryption

Homomorphic encryption enables public computation over encrypted data. In the past few decades, homomorphic encryption has become a staple of both the theory and practice of cryptography. Nevertheless, while there is a general loose understanding of what it means for a scheme to be homomorphic, to date there is no single unifying minimal definition that captures all schemes. In this work, we propose a new definition, which we refer to as combinatorially homomorphic encryption, which attempts to give a broad base that captures the intuitive meaning of homomorphic encryption. Our notion relates the ability to accomplish some task when given a ciphertext, to accomplishing the same task without the ciphertext, in the context of communication complexity. Thus, we say that a scheme is combinatorially homomorphic if there exists a communication complexity problem f(x, y) (where x is Alice’s input and y is Bob’s input) which requires communication c, but can be solved with communication less than c when Alice is given in addition also an encryption $$E_k(y)$$ of Bob’s input (using Bob’s key k). We show that this definition indeed captures pre-existing notions of homomorphic encryption and (suitable variants are) sufficiently strong to derive prior known implications of homomorphic encryption in a conceptually appealing way. These include constructions of (lossy) public-key encryption from homomorphic private-key encryption, as well as collision-resistant hash functions and private information retrieval schemes.
Taming Adaptivity in YOSO Protocols: The Modular Way

YOSO-style MPC protocols (Gentry et al., Crypto’21), are a promising framework where the overall computation is partitioned into small, short-lived pieces, delegated to subsets of one-time stateless parties. Such protocols enable gaining from the security benefits provided by using a large community of participants where “mass corruption” of a large fraction of participants is considered unlikely, while keeping the computational and communication costs manageable. However, fully realizing and analyzing YOSO-style protocols has proven to be challenging: While different components have been defined and realized in various works, there is a dearth of protocols that have reasonable efficiency and enjoy full end to end security against adaptive adversaries. The YOSO model separates the protocol design, specifying the short-lived responsibilities, from the mechanisms assigning these responsibilities to machines participating in the computation. These protocol designs must then be translated to run directly on the machines, while preserving security guarantees. We provide a versatile and modular framework for analyzing the security of YOSO-style protocols, and show how to use it to compile any protocol design that is secure against static corruptions of t out of c parties, into protocols that withstand adaptive corruption of T out of N machines (where T/N is closely related to t/c, specifically when $$t/c<0.5$$ , we tolerate $$T/N \le 0.29$$ ) at overall communication cost that is comparable to that of the traditional protocol even when $$c << N$$ . Furthermore, we demonstrate how to minimize the use of costly non-committing encryption, thereby keeping the computational and communication overhead manageable even in practical terms, while still providing end to end security analysis. Combined with existing approaches for transforming stateful protocols into stateless ones while preserving static security (e.g. Gentry et al. 21, Kolby et al. 22), we obtain end to end security.
Network Agnostic MPC with Statistical Security

In this work, we initiate the study of network agnostic MPC protocols with statistical security. Network agnostic MPC protocols give the best possible security guarantees, irrespective of the behaviour of the underlying network. While network agnostic MPC protocols have been designed earlier with perfect and computational security, nothing is known in the literature regarding their possibility with statistical security. We consider the general-adversary model, where the adversary is characterized by an adversary structure which enumerates all possible candidate subsets of corrupt parties. Known statistically-secure synchronous MPC (SMPC) and asynchronous MPC (AMPC) protocols are secure against adversary structures satisfying the $$\mathbb {Q}^{(2)}$$ and $$\mathbb {Q}^{(3)}$$ conditions respectively, meaning that the union of no two and three subsets from the adversary structure cover the entire set of parties. Fix adversary structures $$\mathcal {Z}_s$$ and $$\mathcal {Z}_a$$ , satisfying the $$\mathbb {Q}^{(2)}$$ and $$\mathbb {Q}^{(3)}$$ conditions respectively, where $$\mathcal {Z}_a \subset \mathcal {Z}_s$$ . Then given an unconditionally-secure PKI, we ask whether it is possible to design a statistically-secure MPC protocol, which is resilient against $$\mathcal {Z}_s$$ and $$\mathcal {Z}_a$$ in a synchronous and an asynchronous network respectively, even if the parties are unaware of the network type. We show that this is possible iff $$\mathcal {Z}_s$$ and $$\mathcal {Z}_a$$ satisfy the $$\mathbb {Q}^{(2, 1)}$$ condition, meaning that the union of any two subsets from $$\mathcal {Z}_s$$ and any one subset from $$\mathcal {Z}_a$$ is a proper subset of the set of parties. The complexity of our protocol is polynomial in $$|\mathcal {Z}_s|$$ .
Statistical characterization of microcellular polyurethane foams microstructure based on 2D and 3D image analysis

This paper presents protocols developed to quantitatively characterize the cellular microstructure of microcellular polyurethane foams, from scanning electron microscopy (2D) and X-ray micro-computed tomography (2D and 3D) data. The objectives are to provide, for both techniques: (i) a detailed description of the analysis steps based on open source Python algorithms; (ii) a method for automatic, robust and objective detection of the cells to limit user’s biases; (iii) a statistical description of fraction, size, shape and spatial distribution of cells. The study considers 12 samples with densities ranging from about 400 to 600 kg m −3 and pore sizes from a few micrometers to several hundred micrometers. In addition, the database obtained is used to investigate the reliability of 2D measurements to describe the cellular microstructure statistics.
871. Clinical Detection of Intestinal Protozoa in Digitized Modified Trichrome-Stained Stool Specimens using a Deep Convolutional Neural Network Assisted Workflow

Abstract Background Deep convolutional neural networks (DNN) have been successfully applied to the detection and segmentation of objects in gigapixel digital slides. In this study, trained laboratory technologists utilized a DNN-assisted digital workflow (Techcyte, Inc.; Orem, UT) to evaluate clinical stool specimens for the presence and identification of protozoan parasites. Methods Slides were prepared from 142 stool specimens by spreading a thin layer of concentrated stool onto a slide, staining with modified-trichrome, and coverslipping with permanent mounting medium. Slides were then scanned using a NanoZoomer 360 digital slide scanner (Hamamatsu Photonics K.K., Japan) at 40x apparent magnification and prospectively analyzed by the Techcyte DNN. Objects of interest were identified and classified by the DNN (protozoa, leukocytes, erythrocytes) and presented to a laboratory technologist for interpretation. (See example image below.) DNN-assisted results were compared to those from conventional microscopic examination. To determine analytical sensitivity, slides were prepared from serial, 2-fold dilutions of 3 unique stool specimens (6 slides at each dilution/specimen) and analyzed by the DNN-assisted method and conventional microscopy. Example of Techcyte DNN analysis review screen Objects of interest most likely to be significant are presented to the technologist for review in an intuitive web-based format. Parasites are confirmed as present or absent by a trained technologist after reviewing these images. Results Digital analysis was highly reproducible, showing 100% slide level agreement (positive/negative) within and between runs using positive and negative slides. There was 98.7% and 98.3% positive and negative agreement, respectively, between conventional and DNN-assisted results. Of the 142 slides conventional microscopy detected parasites in 82, while the DNN-assisted method detected 83. The DNN-assisted method also detected more slides with leukocytes (29 vs 24) and erythrocytes (12 vs 8). All discrepant results were confirmed by repeat conventional microscopy. Analytical sensitivity of the DNN-assisted method was equivalent or superior to conventional microscopy review, detecting down to 1:64 dilutions in 6/6 slides per specimen (conventional = 1:32 to 1:64), extending for some slides to detection at dilutions of 1:2048. Conclusion The DNN-assisted method facilitates rapid screening of negative specimens, while providing sensitive and accurate detection and classification of protozoa. Disclosures All Authors: No reported disclosures
Functional Data Regression Based on KLRFSIR and Bayesian Model Averaging

This paper focuses on an important direction in modern data analysis: functional data regression. Functional data is typically infinite-dimensional, and this paper specifically examines the case where the predictor variable is functional data and the response variable is a continuous scalar. Based on this, the paper propose a regression method that combines kernel localized and regularized functional sliced inverse regression with Bayesian model averaging. The innovation of this method lies in two aspects: firstly, compared to traditional sliced inverse regression, it captures more detailed local information through kernel clustering, providing better estimates of the reduced subspace when the number of slices is small; secondly, model averaging effectively balances the bias and variance of the prediction model, avoiding overfitting or underfitting issues. Empirical results show that the proposed method has smaller errors in terms of mean squared error and absolute error compared to some classical methods, and it demonstrates a certain level of robustness. Finally, this paper explore the potential application of the kernel clustering concept in other dimension reduction approaches based on covariance.
Demonstrasi Eco Enzyme melalui Media Sosial dan Modul dalam Pemanfaatan Sampah Organik di Desa Kuok

Kuok is one of the villages in Kampar Regency that has yet to utilize its household waste, both organic and inorganic waste. This activity aims to educate on processing and utilizing household organic waste into a multi-purpose eco-enzyme solution through social media demonstrations and modules in Kuok Village. The method of this activity is based on the YouTube channel of KKN-T IPB June-August 2021 Kamparkab02. The target training participants are the community, community organizations and leaders of Kuok Village, 100 participants with cell phone devices and YouTube, Instagram and WhatsApp applications. The theory part of the video is 12 minutes long, and the practical part of making eco enzymes is 3 minutes long. During seven days, there were 158 views out of the target of 100 views, with 23 likes out of the target of 50 likes, and the number of comments was 15 out of 30 comments. As many as 50% of the viewers are homemakers, 10% are village officials, and the rest are youth from Kuok Village. As many as 62.5% of people stated that the eco enzyme processing video was more effective than the module. Both the video and the module have positively impacted knowledge, attitudes and actions and have enabled people to process their organic waste into eco enzymes.
A Random Forest-Based Word Difficulty Prediction Model

The aim of this paper is to analyse the relationship between the word of the day and the corresponding distribution of the number of attempts in the Wordle game and to give a prediction method for the proportional distribution of word attempts. Firstly, the paper preprocesses the data provided by Question C of the 2023 American Collegiate Mathematical Modelling Competition. By constructing a model, this paper quantifies the word information entropy and people's preference for choosing common letters. Considering the above features and the influence of previous attempts on the follow-up, this paper constructs a regression model to verify the correlation between the word composition features and the distribution of the number of attempts. Meanwhile, considering the subjectivity of feature selection, this paper constructs a random forest model for further analysis. Comparing the results of the model analysis, the random forest model fits better, and the proportion of word EERIE attempts from 1 to 7 is 0%, 1.97%, 15.99%, 36.31%, 29.83%, 13.34%, and 2.24% respectively . This paper provides a theoretical basis for predicting the number of attempts of the corresponding words, which helps Wordle to optimally adjust the lexicon.
TEXT MINING FOR PUBLIC PERCEPTION ANALYSIS IN THE TELECOMMUNICATION COMPANIES IN RWANDA: CASE OF MTN RWANDA

The utilization of social media platforms has become indispensable for a significant number of individuals as a means to articulate their viewpoints and emotions. Organizations are increasingly recognizing social media platforms as a crucial instrument for obtaining pertinent information pertaining to various subjects and policies. Social media has had a significant rise in global usage, with a multitude of individuals utilizing these platforms to express their viewpoints on various aspects of social life, legislation, and politics. Twitter is a widely utilized social media platform for individuals to voice their viewpoints. The significant rise in information dissemination on Twitter throughout the year has positioned it as a prominent data source and a preferred option for conducting research on customer opinions and sentiment analysis. MTN Rwanda Plc has employed the use of questionnaires as a means of conducting customer opinion surveys for about 24 years since its establishment in Rwanda. This study aligned with the MTN project aimed at facilitating MTN Rwanda's access to consumer sentiments regarding newly announced products and services. It achieves this by analyzing the polarity of customer tweets on MTN Rwanda's Twitter accounts. The study examined the Twitter tweets of MTN Rwanda with the aim of doing sentiment analysis. The objective was to extract subjective sentiments, emotions, and public audience thoughts and opinions. The findings served as input to the MTN Rwanda customer experience department. The researcher utilized natural language processing and text mining methodologies to analyze and evaluate the public perceptions and expectations of MTN Rwanda's Twitter popularity rate. This involved employing text mining and analytics techniques. The findings indicated that there is a prevailing positive public impression and sentiment towards the tweets of MTN Rwanda. Moreover, the artificial neural network classifier predicts that this good sentiment is likely to persist in the future. The practical applications of the study were also discussed. It was expected that the study findings would benefit bilingual business entities on a global scale. Key Words: Text Mining; Text Analytics; Social Media; Sentiment Analysis CITATION : Murwanashyaka, J. M., &amp; Uwimana, A. (2023). Text mining for public perception analysis in the telecommunication companies in Rwanda: case of MTN Rwanda. The Strategic Journal of Business &amp; Change Management, 10 (4), 1167 – 1174. http://dx.doi.org/10.61426/sjbcm.v10i4.2811
AN EXAMINATION OF THE EFFECTS OF CYBER SECURITY IN ENHANCING PERFORMANCE OF THE PUBLIC SECTOR INSTITUTIONS

The anticipated outcomes of this study was to gain a deeper understanding of the current state of cyber security adoption by the public sector institutions, especially in Kenya. The study reviewed on different literature on the status of cyber security adoption in regard to the enhancement on the performance of these institutions, identified challenges that the institutions faced in adopting and implementing such cyber security technologies, and the strategies needed to improve on cyber security applications. The review also pointed out cyber security measures that can lead to improved service delivery, increased operational efficiency, and more secure digital environment for the country's citizens. Thus, by identifying the challenges and barriers faced by these institutions, the study could help inform policy and decision-making processes, guiding future investments in cyber security and other related technologies. Further, the findings of this research could contribute to the enhancement of cyber security measures and their implementation in Kenya's public sector institutions. This, in turn, could lead to improved service delivery, increased operational efficiency, and a more secure digital environment for the country's citizens. By identifying the challenges and barriers faced by these institutions, the study could also help inform policy and decision-making processes, guiding future investments in cyber security technologies.
Challenges and Remedies for Research Investment in Tertiary Institutions: Insights from Nigeria

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; In this study, we investigate the factors contributing to the inadequate investment in research programs within Nigerian tertiary institutions. Employing secondary data from both printed and online sources, we identify key issues such as the lack of political commitment in implementing research funding policies, declining national revenue, corruption, security concerns, limited private sector involvement, competition from other economic sectors, revenue source limitations, and high debt servicing as primary drivers of this problem. To address these challenges, our recommendations include the implementation of research funding policies, economic diversification, the establishment of effective monitoring and evaluation mechanisms for research funds, the creation of additional sources of research funding, and greater private sector engagement in supporting research endeavors across the nation. This study provides valuable insights into the challenges faced by the Nigerian higher education sector in fostering research excellence and offers practical solutions to enhance research investment for sustainable development.&#x0D; Highlights:&#x0D; &#x0D; Inadequate Funding: Insufficient financial support hinders research program growth.&#x0D; Political Will: Lack of commitment from policymakers affects research funding policy implementation.&#x0D; Private Sector Engagement: Greater involvement of private industries can boost research investment in Nigeria's tertiary institutions.&#x0D; &#x0D; Keywords: Research Investment, Tertiary Institutions, Nigeria, Funding Policy, Private Sector Participation&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Cost Distortion and Profitability: Evaluating Activity-Based Costing Systems

&#x0D; &#x0D; &#x0D; This study aimed to investigate the implementation of Activity Based Costing System (ABC) on cost distortion and profitability in PT. Bina Graha Sejahtera. The research method used was a quantitative descriptive study with field research techniques. The research object was PT. Bina Graha Sejahtera, and the subject was the Cost of Goods Manufactured Report for the year 2019. The results showed that the implementation of ABC resulted in lower cost calculations for Plate Joint Spun Pile 400 mm and 500 mm products, which were Rp. 133,266 and Rp. 160,290, respectively. The profitability level of the company also increased to 8% for both products. The implication of this study is that the implementation of ABC can reduce cost distortion and increase profitability in companies.&#x0D; Highlight :&#x0D; &#x0D; &#x0D; Resume keywords and phrases are specific abilities, skills, expertise, and traits recruiters and hiring managers look for in a candidate.&#x0D; Keyword research can provide insights into current marketing trends and help center content on relevant customer acquisition.&#x0D; Keywords in a manuscript should convey the main topics of the study, highlight the importance of the research, be concise, and attract readers.&#x0D; &#x0D; Keywords: resume keywords, keyword research, manuscript keywords, Python keywords, bullet points.&#x0D; &#x0D; &#x0D; &#x0D;
Enhancing Arabic Vocabulary Mastery in Primary Students through Powtoon-Based Web Media

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study investigates the impact of utilizing Powtoon-based web media on the mastery of Arabic vocabulary (mufrodaat) among fourth-grade students at SD Muhammadiyah 09 Ngaban. Twenty students participated as research subjects, and quantitative data analysis techniques were employed. Data collection involved pretests, posttests, observations, and documentation. The study revealed a noteworthy average N-Gain score of 0.515, indicating a moderate effect of Powtoon media on students' Arabic vocabulary mastery. These findings underscore the potential of web-based media in augmenting language acquisition and offer practical insights for educators aiming to optimize vocabulary instruction.&#x0D; Highlight:&#x0D; &#x0D; This study assesses the impact of Powtoon-based web media on Arabic vocabulary acquisition in fourth-grade students.&#x0D; Quantitative data analysis and various assessment methods were employed to measure the effectiveness of the intervention.&#x0D; The research highlights the potential of web-based media in enhancing language learning outcomes, providing valuable insights for educators in vocabulary instruction optimization.&#x0D; &#x0D; Keyword: Arabic Vocabulary Mastery, Powtoon-Based Web Media, Language Acquisition, Educational Impact, Vocabulary Instruction Enhancement&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;