bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differentially Private Simple Linear Regression
Authors: Alabi Daniel , McMillan Audra , Sarathy Jayshree , Smith Adam , Vadhan Salil 
Published: March 2022

Abstract
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individual information. We study regression algorithms that satisfy differential privacy, a constraint which guarantees that an algorithm’s output reveals little about any individual input data record, even to an attacker with side information about the dataset. Motivated by the Opportunity Atlas, a high-profile, small-area analysis tool in economics research, we perform a thorough experimental evaluation of differentially private algorithms for simple linear regression on small datasets with tens to hundreds of records—a particularly challenging regime for differential privacy. In contrast, prior work on differentially private linear regression focused on multivariate linear regression on large datasets or asymptotic analysis. Through a range of experiments, we identify key factors that affect the relative performance of the algorithms. We find that algorithms based on robust estimators—in particular, the median-based estimator of Theil and Sen—perform best on small datasets (e.g., hundreds of datapoints), while algorithms based on Ordinary Least Squares or Gradient Descent perform better for large datasets. However, we also discuss regimes in which this general finding does not hold. Notably, the differentially private analogues of Theil–Sen (one of which was suggested in a theoretical work of Dwork and Lei) have not been studied in any prior experimental work on differentially private linear regression.
Privacy-preserving training of tree ensembles over continuous data
Authors: Adams Samuel , Choudhary Chaitali , de Cock Martine , Dowsley Rafael , Melanson David , Nascimento Anderson , Railsback Davis , Shen Jianwei 
Published: March 2022

Abstract
Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In real-life applications, features are often numerical. The standard “in the clear” algorithm to grow decision trees on data with continuous values requires sorting of training examples for each feature in the quest for an optimal cut-point in the range of feature values in each node. Sorting is an expensive operation in MPC, hence finding secure protocols that avoid such an expensive step is a relevant problem in privacy-preserving machine learning. In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (“extra-trees”) on the original data. Approaches (2) and (3) both involve randomizing feature choices. In addition, in approach (3) cut-points are chosen randomly as well, thereby alleviating the need to sort or to discretize the data up front. We implemented all proposed solutions in the semi-honest setting with additive secret sharing based MPC. In addition to mathematically proving that all proposed approaches are correct and secure, we experimentally evaluated and compared them in terms of classification accuracy and runtime. We privately train tree ensembles over data sets with thousands of instances or features in a few minutes, with accuracies that are at par with those obtained in the clear. This makes our solution more efficient than the existing approaches, which are based on oblivious sorting.
Checking Websites’ GDPR Consent Compliance for Marketing Emails
Authors: Kubíček Karel , Merane Jakob , Cotrini Carlos , Stremitzer Alexander , Bechtold Stefan , Basin David 
Published: March 2022

Abstract
The sending of marketing emails is regulated to protect users from unsolicited emails. For instance, the European Union’s ePrivacy Directive states that marketers must obtain users’ prior consent, and the General Data Protection Regulation (GDPR) specifies further that such consent must be freely given, specific, informed, and unambiguous.
Based on these requirements, we design a labeling of legal characteristics for websites and emails. This leads to a simple decision procedure that detects potential legal violations. Using our procedure, we evaluated 1000 websites and the 5000 emails resulting from registering to these websites. Both datasets and evaluations are available upon request. We find that 21.9% of the websites contain potential violations of privacy and unfair competition rules, either in the registration process (17.3%) or email communication (17.7%). We demonstrate with a statistical analysis the possibility of automatically detecting such potential violations.
Knowledge Cross-Distillation for Membership Privacy
Authors: Chourasia Rishav , Enkhtaivan Batnyam , Ito Kunihiro , Mori Junki , Teranishi Isamu , Tsuchida Hikaru 
Published: March 2022

AbstractAmembership inference attack (MIA)poses privacy risks for the training data of a machine learning model. With an MIA, an attacker guesses if the target data are a member of the training dataset. The state-of-the-art defense against MIAs, distillation for membership privacy (DMP), requires not only private data for protection but a large amount of unlabeled public data. However, in certain privacy-sensitive domains, such as medicine and finance, the availability of public data is not guaranteed. Moreover, a trivial method for generating public data by using generative adversarial networks significantly decreases the model accuracy, as reported by the authors of DMP. To overcome this problem, we propose a novel defense against MIAs that uses knowledge distillation without requiring public data. Our experiments show that the privacy protection and accuracy of our defense are comparable to those of DMP for the benchmark tabular datasets used in MIA research, Purchase100 and Texas100, and our defense has a much better privacy-utility trade-off than those of the existing defenses that also do not use public data for the image dataset CIFAR10.
Updatable Private Set Intersection
Authors: Badrinarayanan Saikrishna , Miao Peihan , Xie Tiancheng 
Published: March 2022

Abstract
Private set intersection (PSI) allows two mutually distrusting parties each with a set as input, to learn the intersection of both their sets without revealing anything more about their respective input sets. Traditionally, PSI studies the static setting where the computation is performed only once on both parties’ input sets. We initiate the study of updatable private set intersection (UPSI), which allows parties to compute the intersection of their private sets on a regular basis with sets that also constantly get updated. We consider two specific settings. In the first setting called UPSI with addition, parties can add new elements to their old sets. We construct two protocols in this setting, one allowing both parties to learn the output and the other only allowing one party to learn the output. In the second setting called UPSI with weak deletion, parties can additionally delete their old elements every t days. We present a protocol for this setting allowing both parties to learn the output. All our protocols are secure against semi-honest adversaries and have the guarantee that both the computational and communication complexity only grow with the set updates instead of the entire sets. Finally, we implement our UPSI with addition protocols and compare with the state-of-the-art PSI protocols. Our protocols compare favorably when the total set size is sufficiently large, the new updates are sufficiently small, or in networks with low bandwidth.
d3p - A Python Package for Differentially-Private Probabilistic Programming
Authors: Prediger Lukas , Loppi Niki , Kaski Samuel , Honkela Antti 
Published: March 2022

Abstract
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general applicability to a wide range of probabilistic modelling problems by implementing the differentially private variational inference algorithm, allowing users to fit any parametric probabilistic model with a differentiable density function. d3p adopts the probabilistic programming paradigm as a powerful way for the user to flexibly define such models. We demonstrate the use of our software on a hierarchical logistic regression example, showing the expressiveness of the modelling approach as well as the ease of running the parameter inference. We also perform an empirical evaluation of the runtime of the private inference on a complex model and find a ~10 fold speed-up compared to an implementation using TensorFlow Privacy.
Nutritional Composition of Plant-Based Texture: Modified Foods for Dysphagia Patients
Authors: Kampuse Solvita , Ozola Liene , Krūma Zanda , Kļava Dace , Galoburda Ruta , Straumīte Evita , Siksna Inese 
Published: March 2022

Abstract
There are approximately 590 million people in the world with swallowing disorders (dysphagia), who need texture-modified foods to ensure nutritional intake on a daily basis. The aim of this research was to create new texture-modified products and to evaluate their bioactive and mineral compound concentration. Nine new products (five desserts and four puree-soups) were developed from plant-based ingredients with addition of a source of protein, canola oil, ascorbic acid, and other ingredients. Obtained samples were vacuum-cooked, sterilised at 115 °C for 5 min and tested for their bioactive compound concentration (total carotene and phenol concentration, and ABTS and DPPH radical scavenging activity), and concentration of seven vitamins and thirteen minerals important in human nutrition. The obtained data showed that bioactive compound concentration varied from sample to sample, which was expected due to the varied ingredients of each product. Mineral compound concentration analysis showed low levels of iodine, molybdenum, and selenium. The vitamin analysis showed traces of vitamin D3 and low levels of B group vitamins. Overall, the analysed product composition indicated that the natural background of these products is insufficient for ensuring the human body with all of the necessary vitamins and minerals, and the best solution could be the supplementation of these products with complexes of vitamins and minerals.
Changes in Health Behaviour and Emotional Well-Being of Pregnant Women During the COVID-19 Pandemic in Latvia
Authors: Rezgale Roberta , Cauce Vinita , Bule Violeta , Piskurjova Anna , Meija Laila 
Published: March 2022

Abstract
Pregnancy is a vulnerable period of life for changes in physical and mental health, which could be exacerbated by COVID-19. The aim of this study was to assess the impact of COVID-19 on health behaviour and emotional well-being among pregnant women in Latvia. The cross-sectional study included 269 women: pregnant women in the third trimester and women until the 7th day post-partum. The study was conducted between July and October 2020, performed by a trained interviewer. Information regarding demographic, anthropometric data, self-assessment of changes in nutritional habits, daily physical activities, and emotional well-being due to the COVID-19 pandemic was collected. 31.6% reported cooking at home more often and only 10.4% reported eating more often and/or larger portions, 23.2% were going on walks outdoors more often, 32.9% were feeling anxious, insecure about health of themselves and relatives, and 24.8% were lacking communication with friends and family. Latvian women were emotionally more affected than other nationalities (p = 0.015). Overall, women 30+ years of age were more often affected then women aged up to 30 years (p = 0.014). These results suggest that the COVID-19 pandemic had notable impact on health behaviour and emotional well-being in pregnant women in Latvia, especially on women 30+ years of age, who may need more careful counselling.
Daily Time-Restricted Feeding And Alternative Day Fasting For Weight Management: Comparative Analysis
Authors: Lukstiņa Zane , Bodžs Bogdans , Ozoliņa-Moll Līga 
Published: March 2022

Abstract
The approach of limiting nutritional intake frequency, but without a large reduction of the amount of calories consumed, the so-called intermittent fasting, has gained growing popularity. Intermittent fasting is an umbrella term for meal timing schedules that cycle between voluntary fasting (or vastly reduced calories intake) and non-fasting over a defined period. In our study, we compared two of the most popular intermittent fasting regimens — 16/8 time-restricted feeding and 5/2 alternate day fasting. A total of 16 healthy young women (under 30 years of age) participated in the study, eight in each intermittent fasting group, for six consecutive weeks. Among anthropometric parameters, height, weight, umbilical waist circumference, sagittal abdominal diameter, and skinfold thickness were measured and body mass index and relative body fat were calculated. Differences between the initial values and the values after the six-week intermittent fasting period were calculated and compared between the two groups. We found no statistically significant difference (p > 0.05) between 16/8 time-restricted feeding and 5/2 alternate day fasting regimens in differences over the six-week period for the tested parameter, including weight loss. As there were no significant differences for overall changes in parameter values in the six-week period between the two intermittent fasting regimens, we concluded that both were equally suitable for weight loss programmes.
Polyprenol Liposomes Improve the Efficiency of Oxygen Usage in Well-Trained Amateur Athletes
Authors: Ušacka Laila , Plakane Līga , Vanaga Ilona , Klētnieks Kārlis , Jansone Baiba , Muceniece Ruta , Klētnieks Uģis , Pahirko Leonora 
Published: March 2022

Abstract
Polyprenols are natural substances that are known for their role as cell membrane protectors from peroxidation. However, there is a lack of information about their effect on the performance of athletes. Therefore, we studied the effects of polyprenol on athlete performance. A total of 30 male floorball players participated in this study, 16 of whom were taking 30 mg polyprenol liposome caps and 14 of whom were not. All of the participants underwent VO2max exhaustive incremental cycling testing, before and after the use of polyprenols. VO2peak and other cardiorespiratory variables were measured to evaluate cardiorespiratory adaptation. For the polyprenol users (PU) the oxygen pulse (O2/HR) increased by 0.9 ml/beat, while in the control group (C) decreased by 0.4 ml/beat from before to after the use of polyprenols (p < 0.05). The diastolic pressure (Pdia) decreased by 4 mmHg for the PU, while it increased by 10 mmHg in the C (p < 0.05). The stroke volume (SVc) in the PU increased by 2.9 ml, while that in the C decreased by 7.4 ml (p < 0.05). In conclusion, polyprenols improved oxygen consumption efficiency by increasing SVc and O2/HR.
Effect of Storage Conditions on Biologically Active Compounds in Purple-Fleshed Potatoes
Authors: Zariņš Reinis , Krūma Zanda , Skrabule Ilze 
Published: March 2022

Abstract
Potatoes (Solanum tuberosum L.) are an important crop. They are a source of natural phenolic compounds with high biological activity. Stress factors may decrease phenolic concentration and increase concentration of the toxin glycoalkaloid as a result of plant self-protection against stress. The aim of the current study was to evaluate the effect of storage under light on the concentration of biologically active compounds in purple-fleshed potatoes. The purple-fleshed potato variety ‘Blue Congo’ was harvested from test fields of the Institute of Agricultural Resources and Economics in Latvia. Storage under light was performed in a climate chamber ICH110 (Memmert GmbH + Co. KG, Germany) with constant pre-set relative air humidity 85%, temperature +22 °C, and full-spectrum light (Eecoo, 1200 W, LED) intensity of 2000 lux and total time seven days. Total phenolic concentration, antioxidant activity and total glycoalkaloid concentration was measured using spectrophotometry, and moisture was determined according to standard LVS 272:2000. Storage under artificial light of purple-fleshed variety ‘Blue Congo’ potatoes caused decrease of total phenolic concentration and antioxidant activity, no change in moisture level, and increase of total glycoalkaloid concentration.
Changes in Phenolic Profile and Antioxidant Activity of Horseradish Roots During Freezing and Frozen Storage
Authors: Tomsone Lolita , Galoburda Ruta , Krūma Zanda , Cinkmanis Ingmārs 
Published: March 2022

Abstract
Freezing is one of the ways to preserve plant products, because it allows inhibiting natural degradation and transformation processes of the bioactive compounds. The aim of this study was to evaluate the effect of freezing on bioactive compounds of horseradish roots and their dynamics in long-term frozen storage. Horseradish roots were frozen at two different conditions (–18±2°C and –40 ± 2 °C) and further stored at –18 ± 2 °C for 12 months. Total phenolic content (TPC), total flavonoid content (TFC), total flavonol content (TFlC), total flavan-3-ol content (TF3C), total phenolic acid content (TPAC), and radical scavenging activity (RedPow, DPPHÿ, ABTSÿ+) were determined spectrophotometrically. Individual phenolic compounds were deremined by liquid chromatography. The dominant individual phenolic compounds were phenolic acids (gallic and sinapic) and flavonoids (kaempferol, luteolin, and rutin). Because of freezing, TPC, TFC, and DPPHÿ as well as RedPow increased in horseradish roots. During storage, the content of analysed bioactive compounds mainly decreased. After 12-month storage, it was not possible to say unambiguously which of the freezing methods turned out to be better in general.
Sprouted Hulless Barley Grains and their Application Possibilities for the Functional Sweet Snacks Development
Authors: Šterna Vita , Segliņa Dalija , Krasnova Inta , Ķince Tatjana , Jansone Zaiga , Bleidere Māra 
Published: March 2022

Abstract
Currently, consumption of confectionery continues to grow, and there is a tendency to supplement snacks with ingredients that can be considered as functional products. The aim of this study was to evaluate the benefits of sprouted barley grains and their potential use in barley-fruit-vegetable snack production. Several samples of sweet bars with hulless barley variety ‘Kornelija’ flour and sprouted, crushed barley grain, dried fruit and vegetables were prepared. The chemical composition including the concentration of biologically active compounds (total concentration of phenols, flavonoids and tannins, as well as antioxidant activity) of untreated and sprouted grains was determined. The results showed that protein concentration of barley-fruit-vegetable bars varied from 8.65% to 10.85% and ß-glucans concentration varied from 0.77% to 2.19%. Their nutritional value varied from 1079.33 to 1430.41 kJ. Total fibre concentration of unsprouted, 24 h and 36 h sprouted grains was 26.30%, 25.80%, and 24.60%, respectively. Total phenol concentration of unsprouted barley grains was on average 273.14 mg·100 g−1 and flavonoid concentration was 290.25 mg·100 g−1, and for sprouted grains — 258.98 mg·100 g−1 and 256.19 mg·100 g−1, respectively. The evaluators preferred bars made from ground sprouted hulless barley grains ‘Kornelija’ — 7.4, according to the sensory analysis — hedonic scale.
Physicochemical and Rheological Properties of Non-Fat Ice Cream
Authors: Silantjeva Karīna , Zagorska Jeļena , Galoburda Ruta 
Published: March 2022

AbstractIce cream is a popular dessert in Latvia. At present, however, as consumers are watching their calorie intake, ice cream is starting to lose its leading position due to rich fat content. Therefore, development of low-fat or non-fat ice cream can provide a good alternative for consumers. At the same time, reduced fat is a challenge to a producer because it strongly affects the texture and sensory properties of ice cream. The aim of the current study was to assess the physicochemical and rheological properties of whey-based non-fat ice cream, depending on the ingredients used in the formulation. In total, 11 ice cream formulations were developed and evaluated, with differing proportions of the main ingredients. Chemical composition, hardness (after6hof hardening and subsequent two-week storage), overrun and rheological properties were analysed. Higher amounts of pumpkin puree, sugar and whey concentrate decreased the hardness of ice cream, and increased the overrun. Thus, higher proportions of pumpkin puree and whey concentrate significantly influenced product formulation by stabilising and strengthening air cells. Both an increased proportion of pumpkin puree and gelatine increased complex viscosity. There was a positive correlation between carbohydrate content and ice cream hardness after two-week storage, as well as between protein content and complex viscosity.
Disappearing Anomaly di Bursa Efek Indonesia
Authors: Sukawati A.A. Rai Niti Darmika , Wirama Dewa Gede 
Published: March 2022

The purpose of this research is to analyze whether there is a disappearing anomaly on the Indonesia Stock Exchange, especially for the Monday effect and January effect. The analysis was carried out by comparing the two anomalies in two periods, namely 2011 to 2015 and 2016 to 2020. The study used a sample of companies that were consistently listed on the Kompas100 Index during the observation period, with a total sample consisting of 42 companies. The analysis technique used is a different test with independent sample t-test. The test results show that the Monday effect in the 2016-2020 period is significantly lower than the Monday effect for the 2011-2015 period, but there is no significant difference in the January effect in the two periods.
Keywords: Disappearing Anomaly, January effect, Monday effect.
Kualitas Laporan Keberlanjutan: Eksistensi dari Media dan Industri
Authors: Tizmi Salsabila , Luthan Elvira , Rahman Annisaa 
Published: March 2022

The purpose of this study is to determine the effect of media and industry to the quality of sustainability report disclosure. Media is identified by online and social media. The amount of unfavorable news stories in Kompas and Detik is used to quantify online media. The amount of likes, comments, and tweets on Facebook, Twitter, and Instagram is used to quantify social media. The term industry refers to the sort of industry. GRI G4 Index is used to assess the sustainability report's disclosure. This study employs 37 firms as a sample that have gone public and are listed on the BEI in 2017-2020. Purposive sampling is used to determine the sample size. The data analysis technique in this study uses multiple linear regression. The study's findings indicate that online media, social media, and industry type all have a major impact on the quality of sustainability report disclosure.
Keywords: Sustainability Report Disclosure; Online Media; Social Media; Type Of Industry.