nils-herrmann/clean_scirepeval huggingface dataset:
EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation

We present EzPC: a secure two-party computation (2PC) framework that generates efficient 2PC protocols from high-level, easyto-write programs. EzPC provides formal correctness and security guarantees while maintaining performance and scalability. Previous language frameworks, such as CBMC-GC, ObliVM, SMCL, and Wysteria, generate protocols that use either arithmetic or boolean circuits exclusively. Our compiler is the first to generate protocols that combine both arithmetic sharing and garbled circuits for better performance. We empirically demonstrate that the protocols generated by our framework match or outperform (up to 19x) recent works that provide hand-crafted protocols for various functionalities such as secure prediction and matrix factorization.
Analysis of Three IoT-Based Wireless Sensors for Environmental Monitoring

The recent changes in climate have increased the importance of environmental monitoring, making it a topical and highly active research area. This field is based on remote sensing and on wireless sensor networks for gathering data about the environment. Recent advancements, such as the vision of the Internet of Things (IoT), the cloud computing model, and cyber-physical systems, provide support for the transmission and management of huge amounts of data regarding the trends observed in environmental parameters. In this context, the current work presents three different IoT-based wireless sensors for environmental and ambient monitoring: one employing User Datagram Protocol (UDP)-based Wi-Fi communication, one communicating through Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one using Bluetooth Smart. All of the presented systems provide the possibility of recording data at remote locations and of visualizing them from every device with an Internet connection, enabling the monitoring of geographically large areas. The development details of these systems are described, along with the major differences and similarities between them. The feasibility of the three developed systems for implementing monitoring applications, taking into account their energy autonomy, ease of use, solution complexity, and Internet connectivity facility, was analyzed, and revealed that they make good candidates for IoT-based solutions.
An efficient denoising algorithm for global illumination

We propose a hybrid ray-tracing/rasterization strategy for realtime rendering enabled by a fast new denoising method. We factor global illumination into direct light at rasterized primary surfaces and two indirect lighting terms, each estimated with one path-traced sample per pixel. Our factorization enables efficient (biased) reconstruction by denoising light without blurring materials. We demonstrate denoising in under 10 ms per 1280×720 frame, compare results against the leading offline denoising methods, and include a supplement with source code, video, and data.
Serum folate, vitamin B-12 and cognitive function in middle and older age: The HAPIEE study

Nutrient status of B vitamins, particularly folate and vitamin B-12, may be related to cognitive ageing but epidemiological evidence remains inconclusive.



The aim of this study was to estimate the association of serum folate and vitamin B-12 concentrations with cognitive function in middle-aged and older adults from three Central and Eastern European populations.



Men and women aged 45-69 at baseline participating in the Health, Alcohol and Psychosocial factors in Eastern Europe (HAPIEE) study were recruited in Krakow (Poland), Kaunas (Lithuania) and six urban centres in the Czech Republic. Tests of immediate and delayed recall, verbal fluency and letter search were administered at baseline and repeated in 2006-2008. Serum concentrations of biomarkers at baseline were measured in a sub-sample of participants. Associations of vitamin quartiles with baseline (n=4166) and follow-up (n=2739) cognitive domain-specific z-scores were estimated using multiple linear regression.



After adjusting for confounders, folate was positively associated with letter search and vitamin B-12 with word recall in cross-sectional analyses. In prospective analyses, participants in the highest quartile of folate had higher verbal fluency (p<0.01) and immediate recall (p<0.05) scores compared to those in the bottom quartile. In addition, participants in the highest quartile of vitamin B-12 had significantly higher verbal fluency scores (β=0.12; 95% CI=0.02, 0.21).



Folate and vitamin B-12 were positively associated with performance in some but not all cognitive domains in older Central and Eastern Europeans. These findings do not lend unequivocal support to potential importance of folate and vitamin B-12 status for cognitive function in older age. Long-term longitudinal studies and randomised trials are required before drawing conclusions on the role of these vitamins in cognitive decline.
Targeting inflammation in the treatment of type 2 diabetes: time to start

The role of inflammation in the pathogenesis of type 2 diabetes and associated complications is now well established. Several conditions that are driven by inflammatory processes are also associated with diabetes, including rheumatoid arthritis, gout, psoriasis and Crohn's disease, and various anti-inflammatory drugs have been approved or are in late stages of development for the treatment of these conditions. This Review discusses the rationale for the use of some of these anti-inflammatory treatments in patients with diabetes and what we could expect from their use. Future immunomodulatory treatments may not target a specific disease, but could instead act on a dysfunctional pathway that causes several conditions associated with the metabolic syndrome.
Visualization and Measurement of Source Code

The GRASP (Graphical Representations of Algorithms, Structures, and Processes) project, which has successfully prototyped a new algorithmic-level graphical representation for software—the control structure diagram (CSD)—is currently focused on the generation of a new fine-grained complexity metric called the complexity profile graph (CPG). The primary impetus for creation and refinement of the CSD and the CPG is to improve the comprehension efficiency of software and, as a result, improve reliability and reduce costs. The current GRASP release provides automatic CSD generation for Ada 95, C, C++, Java, and Very High-Speed Integrated Circuit Hardware Description Language (VHDL) source code, and CPG generation for Ada 95 source code. The examples and discussion in this article are based on using GRASP with Ada 95.
Laplacian Score for Feature Selection

In supervised learning scenarios, feature selection has be en studied widely in the literature. Selecting features in unsupervis ed learning scenarios is a much harder problem, due to the absence of class la bel that would guide the search for relevant information. And, almos t all of previous unsupervised feature selection methods are “wrapper ” techniques that require a learning algorithm to evaluate the candidate fe ture subsets. In this paper, we propose a “filter” method for feature select ion which is independent of any learning algorithm. Our method can be per formed in either supervised or unsupervised fashion. The proposed me thod is based on the observation that, in many real world classification pr oblems, data from the same class are often close to each other. The importa nce of a feature is evaluated by its power of locality preserving, or , Laplacian Score. We compare our method with data variance (unsupervised) an d Fisher score (supervised) on two data sets. Experimental re sults demonstrate the effectiveness and efficiency of our algorithm.
The impact of health information on the internet on the physician-patient relationship: patient perceptions.

Use of the Internet for health information continues to grow rapidly, but its impact on health care is unclear. Concerns include whether patients' access to large volumes of information will improve their health; whether the variable quality of the information will have a deleterious effect; the effect on health disparities; and whether the physician-patient relationship will be improved as patients become more equal partners, or be damaged if physicians have difficulty adjusting to a new role.



Telephone survey of nationally representative sample of the American public, with oversample of people in poor health.



Of the 3209 respondents, 31% had looked for health information on the Internet in the past 12 months, 16% had found health information relevant to themselves and 8% had taken information from the Internet to their physician. Looking for information on the Internet showed a strong digital divide; however, once information had been looked for, socioeconomic factors did not predict other outcomes. Most (71%) people who took information to the physician wanted the physician's opinion, rather than a specific intervention. The effect of taking information to the physician on the physician-patient relationship was likely to be positive as long as the physician had adequate communication skills, and did not appear challenged by the patient bringing in information.



For health information on the Internet to achieve its potential as a force for equity and patient well-being, actions are required to overcome the digital divide; assist the public in developing searching and appraisal skills; and ensure physicians have adequate communication skills.
Cross-Coupling in Coaxial Cavity Filters — A Tutorial Overview

This paper presents a tutorial overview of the use of coupling between nonadjacent resonators to produce transmission zeros at real frequencies in microwave filters. Multipath coupling diagrams are constructed and the relative phase shifts of multiple paths are observed to produce the known responses of the cascaded triplet and quadruplet sections. The same technique is also used to explore less common nested cross-coupling structures and to predict their behavior. A discussion of the effects of nonzero electrical length coupling elements is presented. Finally, a brief categorization of the various synthesis and implementation techniques available for these types of filters is given.
Tangential excision of pigmented nail matrix lesions responsible for longitudinal melanonychia: evaluation of the technique on a series of 30 patients.

We sought to assess the shave biopsy technique, which is a new surgical procedure for complete removal of longitudinal melanonychia. We evaluated the quality of the specimen submitted for pathological examination, assessed the postoperative outcome, and ascertained its indication between the other types of matrix biopsies.



This was a retrospective study performed at the dermatologic departments of the Universities of Liège and Brussels, Belgium, of 30 patients with longitudinal or total melanonychia.



Pathological diagnosis was made in all cases; 23 patients were followed up during a period of 6 to 40 months. Seventeen patients had no postoperative nail plate dystrophy (74%) but 16 patients had recurrence of pigmentation (70%).



This was a retrospective study.



Shave biopsy is an effective technique for dealing with nail matrix lesions that cause longitudinal melanonychia over 4 mm wide. Recurrence of pigmentation is the main drawback of the procedure.
Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.
Research on anti-money laundering based on core decision tree algorithm

This paper presents a core decision tree algorithm to identify money laundering activities. The clustering algorithm is the combination of BIRCH and K-means. In this method, decision tree of data mining technology is applied to anti-money-laundering filed after research of money laundering features. We select an appropriate identifying strategy to discover typical money laundering patterns and money laundering rules. Consequently, with the core decision tree algorithm, we can identify abnormal transaction data more effectively.
Double-ended tuning fork resonator in 0.35um CMOS technology for RF applications

A double-ended tuning fork (DETF) fabricated in a 0.35 um commercial CMOS technology is presented. Resonator performance for the application of this device in a RF front-end is measured using electrical test. DEFT offers a higher isolation between ports than clamped -clamped beams and the possibility to create a band-pass for frequency filtering or mixing using a single resonator. Discrepancies between expected and obtained results are studied using FEM mechanical simulations.
Low level of knowledge about neonatal danger signs and its associated factors among postnatal mothers attending at Woldia general hospital, Ethiopia

Neonatal mortality has persisted high in Ethiopia in spite of many efforts being applied to decrease this adverse trend. Early detection of neonatal illness is an important step towards improving newborn survival. Toward this end, there is a need for the mothers to be able to identify signs in neonates that signify severe illnesses. The aim of this study was to assess knowledge about neonatal danger signs and its associated factors among postnatal mothers attending at Woldia general hospital, Ethiopian.



Institutional based cross-sectional study design was conducted from January-May, 2017. The hospital that provides antenatal care (ANC), delivery, and postnatal services was purposively sampled. Structured interviewer managed questionnaire was administered to postnatal mothers attending Woldia general hospital. Frequencies, bivariate and multivariate logistic regression were determined using the SPSS software (Version 20).



During the study period 197 mothers attending postnatal care (PNC) service at Woldia general hospital were interviewed. Information on different neonatal danger signs was not provided to 92(46.7%) postnatal mothers during their antenatal clinic attendance by the healthcare providers. The majority of mothers, 174(88.3%) identified less than six neonatal danger signs. The hotness of the body of neonates was the commonly recognized danger sign by 106(53.8%) postnatal mothers. Of the total mothers, 67(34%), 60(30.5%), 56(28.4%), 44(22.3%) recognized unable to breastfeeding, convulsion, lethargy, difficulty in breathing as newly born danger signs, respectively. Out of 197 mothers, 32(16.2%) were giving birth at home. Mother's age(AOR = 1.33, 95% CI: 1.99-3.08), marital status(AOR = 2.50, 95% CI: 0.29-4.31), mother's education status(AOR = 3.48, 95% CI:1.57-8.72), husband's education(AOR = 4.92, 95% CI: 1.29-12.81), attending ANC (AOR = 2.88, 95% CI: 1.15, 4.85), mother's residence(AOR = 0.78, 95% CI: 0.47-1.65), information about neonatal danger signs(AOR = 3.48, 95% CI 1.40-9.49) had positive association with maternal level of knowledge to identify different neonatal danger signs.



Maternal knowledge level about neonatal danger signs was very low. Therefore, intervention modalities that focus on increasing level of parental education, access to ANC and PNC service are needed.
GILBO: One Metric to Measure Them All

We propose a simple, tractable lower bound on the mutual information contained in the joint generative density of any latent variable generative model: the GILBO (Generative Information Lower BOund). It offers a data-independent measure of the complexity of the learned latent variable description, giving the log of the effective description length. It is well-defined for both VAEs and GANs. We compute the GILBO for 800 GANs and VAEs each trained on four datasets (MNIST, FashionMNIST, CIFAR-10 and CelebA) and discuss the results.
3 Monitoring Cold Chain Logistics by Means of RFID

Every day, millions of tons of temperature sensitive goods are produced, transported, stored or distributed worldwide. For all these products the control of temperature is essential. The term “cold chain” describes the series of interdependent equipment and processes employed to ensure the temperature preservation of perishables and other temperaturecontrolled products from the production to the consumption end in a safe, wholesome, and good quality state (Zhang, 2007). In other words, it is a supply chain of temperature sensitive products. So temperature-control is the key point in cold chain operation and the most important factor when prolonging the practical shelf life of produce. Thus, the major challenge is to ensure a continuous ‘cold chain’ from producer to consumer in order to guaranty prime condition of goods (Ruiz-Garcia et al., 2007).These products can be perishable items like fruit, vegetables, flowers, fish, meat and dairy products or medical products like drugs, blood, vaccines, organs, plasma and tissues. All of them can have their properties affected by temperature changes. Also some chemicals and electronic components like microchips are temperature sensitive.
Obesity in Saudi Arabia.

Obesity and overweight are well known risk factors for coronary artery disease (CAD), and are expected to be increasing in the Kingdom of Saudi Arabia (KSA) particularly among females. Therefore, we designed this study with the objective to determine the prevalence of obesity and overweight among Saudis of both gender, between the ages of 30-70 years in rural as well as in urban communities. This work is part of a major national project called Coronary Artery Disease in Saudis Study (CADISS) that is designed to look at CAD and its risk factors in Saudi population.



This study is a community-based national epidemiological health survey, conducted by examining Saudi subjects in the age group of 30-70 years of selected households over a 5-year period between 1995 and 2000 in KSA. Data were obtained from body mass index (BMI) and were analyzed to classify individuals with overweight (BMI = 25-29.9 kg/m2), obesity (BMI >/=30 kg/m2) and severe (gross) obesity (BMI >/=40 kg/m2) to provide the prevalence of overweight and obesity in KSA.



Data were obtained by examining 17,232 Saudi subjects from selected households who participated in the study. The prevalence of overweight was 36.9%. Overweight is significantly more prevalent in males (42.4%) compared to 31.8% of females (p<0.0001). The age-adjusted prevalence of obesity was 35.5% in KSA with an overall prevalence of 35.6% [95% CI: 34.9-36.3], while severe (gross) obesity was 3.2%. Females are significantly more obese with a prevalence of 44% than males 26.4% (p<0.0001).



Obesity and overweight are increasing in KSA with an overall obesity prevalence of 35.5%. Reduction in overweight and obesity are of considerable importance to public health. Therefore, we recommend a national obesity prevention program at community level to be implemented sooner to promote leaner and consequently healthier community.
CPDScorer: Modeling and Evaluating Developer Programming Ability across Software Communities

Since developer ability is recognized as a determinant of better software project performance, it is a critical step to model and evaluate the programming ability of developers. However, most existing approaches require manual assessment, like 360 degree performance evaluation. With the emergence of social networking sites such as StackOverflow and Github, a vast amount of developer information is created on a daily basis. Such personal and social context data has huge potential to support automatic and effective developer ability evaluation. In this paper, we propose CPDScorer, a novel approach to modeling and scoring the programming ability of developer through mining heterogeneous information from both Community Question Answering (CQA) sites and Open-Source Software (OSS) communities. CPDScorer analyzes the answers posted in CQA sites and evaluates the projects submitted in OSS communities to assign expertise scores to developers, considering both the quantitative and qualitative factors. When modeling the programming ability of developer, a programming ability term extraction algorithm is also designed based on set covering. We have conducted experiments on StackOverflow and Github to measure the effectiveness of CPDScorer. The results show that our approach is feasible and practical in user programming ability modeling. In particular, the precision of our approach reaches 80%.
A Dataset for StarCraft AI & an Example of Armies Clustering

This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games’ state (not only player’s orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strategic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions’ mixtures components.