nils-herrmann/clean_scirepeval huggingface dataset:
Lactose intolerance and gastrointestinal cow’s milk allergy in infants and children – common misconceptions revisited

Lactose is the main carbohydrate in human and mammalian milk. Lactose requires enzymatic hydrolysis by lactase into D-glucose and D-galactose before it can be absorbed. Term infants express sufficient lactase to digest about one liter of breast milk daily. Physiological lactose malabsorption in infancy confers beneficial prebiotic effects, including the establishment of Bifidobacterium-rich fecal microbiota. In many populations, lactase levels decline after weaning (lactase non-persistence; LNP). LNP affects about 70% of the world's population and is the physiological basis for primary lactose intolerance (LI). Persistence of lactase beyond infancy is linked to several single nucleotide polymorphisms in the lactase gene promoter region on chromosome 2. Primary LI generally does not manifest clinically before 5 years of age. LI in young children is typically caused by underlying gut conditions, such as viral gastroenteritis, giardiasis, cow's milk enteropathy, celiac disease or Crohn's disease. Therefore, LI in childhood is mostly transient and improves with resolution of the underlying pathology. There is ongoing confusion between LI and cow's milk allergy (CMA) which still leads to misdiagnosis and inappropriate dietary management. In addition, perceived LI may cause unnecessary milk restriction and adverse nutritional outcomes. The treatment of LI involves the reduction, but not complete elimination, of lactose-containing foods. By contrast, breastfed infants with suspected CMA should undergo a trial of a strict cow's milk protein-free maternal elimination diet. If the infant is not breastfed, an extensively hydrolyzed or amino acid-based formula and strict cow's milk avoidance are the standard treatment for CMA. The majority of infants with CMA can tolerate lactose, except when an enteropathy with secondary lactase deficiency is present.
Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers, and data sources for the attacks, giving structure to the fuzzy term “smart city.” Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities.
Scaling Academic Writing Instruction: Evaluation of a Scaffolding Tool (Thesis Writer)

No thesis - no graduation. Academic writing poses manifold challenges to students, instructors and institutions alike. High labor costs, increasing student numbers, and the Bologna Process (which has reduced the period after which undergraduates in Europe submit their first thesis and thus the time available to focus on writing skills) all pose a threat to students’ academic writing abilities. This situation gave rise to the practical goal of this study: to determine if, and to what extent, academic writing and its instruction can be scaled (i.e., designed more efficiently) using a technological solution, in this case Thesis Writer (TW), a domain-specific, online learning environment for the scaffolding of student academic writing, combined with an online editor optimized for producing academic text. Compared to existing automated essay scoring and writing evaluation tools, TW is not focusing on feedback but on instruction, planning, and genre mastery. While most US-based tools, particularly those also used in secondary education, are targeting on the essay genre, TW is tailored to the needs of theses and research article writing (IMRD scheme). This mixed-methods paper reports data of a test run with a first-year course of 102 business administration students. A technology adoption model served as a frame of reference for the research design. From a student’s perspective, problems posed by the task of writing a research proposal as well as the use, usability, and usefulness of TW were studied through an online survey and focus groups (explanatory sequential design). Results seen were positive to highly positive – TW is being used, and has been deemed supportive by students. In particular, it supports the scaling of writing instruction in group assignment settings.
Differential Privacy for Bayesian Inference through Posterior Sampling

Differential privacy formalises privacy-preserving mechanisms that provide access to a database. Can Bayesian inference be used directly to provide private access to data? The answer is yes: under certain conditions on the prior, sampling from the posterior distribution can lead to a desired level of privacy and utility. For a uniform treatment, we define differential privacy over arbitrary data set metrics, outcome spaces and distribution families. This allows us to also deal with non-i.i.d or non-tabular data sets. We then prove bounds on the sensitivity of the posterior to the data, which delivers a measure of robustness. We also show how to use posterior sampling to provide differentially private responses to queries, within a decision-theoretic framework. Finally, we provide bounds on the utility of answers to queries and on the ability of an adversary to distinguish between data sets. The latter are complemented by a novel use of Le Cam’s method to obtain lower bounds on distinguishability. Our results hold for arbitrary metrics, including those for the common definition of differential privacy. For specific choices of the metric, we give a number of examples satisfying our assumptions.  2014). This version corrects proofs, constant factors in the upper bounds and introduces new material on utility analysis, lower bounds and examples.
MasterPrint: Exploring the Vulnerability of Partial Fingerprint-Based Authentication Systems

This paper investigates the security of partial fingerprint-based authentication systems, especially when multiple fingerprints of a user are enrolled. A number of consumer electronic devices, such as smartphones, are beginning to incorporate fingerprint sensors for user authentication. The sensors embedded in these devices are generally small and the resulting images are, therefore, limited in size. To compensate for the limited size, these devices often acquire multiple partial impressions of a single finger during enrollment to ensure that at least one of them will successfully match with the image obtained from the user during authentication. Furthermore, in some cases, the user is allowed to enroll multiple fingers, and the impressions pertaining to multiple partial fingers are associated with the same identity (i.e., one user). A user is said to be successfully authenticated if the partial fingerprint obtained during authentication matches any one of the stored templates. This paper investigates the possibility of generating a “MasterPrint,” a synthetic or real partial fingerprint that serendipitously matches one or more of the stored templates for a significant number of users. Our preliminary results on an optical fingerprint data set and a capacitive fingerprint data set indicate that it is indeed possible to locate or generate partial fingerprints that can be used to impersonate a large number of users. In this regard, we expose a potential vulnerability of partial fingerprint-based authentication systems, especially when multiple impressions are enrolled per finger.
Bodily moral disgust: what it is, how it is different from anger, and why it is an unreasoned emotion.

With the recent upswing in research interest on the moral implications of disgust, there has been uncertainty about what kind of situations elicit moral disgust and whether disgust is a rational or irrational player in moral decision making. We first outline the benefits of distinguishing between bodily violations (e.g., sexual taboos, such as pedophilia and incest) and nonbodily violations (e.g., deception or betrayal) when examining moral disgust. We review findings from our lab and others' showing that, although many existing studies do not control for anger when studying disgust, disgust at nonbodily violations is often associated with anger and hard to separate from it, while bodily violations more consistently predict disgust independently of anger. Building on this distinction, we present further empirical evidence that moral disgust, in the context of bodily violations, is a relatively primitively appraised moral emotion compared to others such as anger, and also that it is less flexible and less prone to external justifications. Our review and results underscore the need to distinguish between the different consequences of moral emotions.
Audio-Visual Synchronization and Fusion using Canonical Correlation Analysis

It is well-known that early integration (also called data fusion) is effective when the modalities are cor related, and late integration (also called decision or opin ion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification using a combination of early and late integration following canonical correlation analysis (CCA) of speech and lip texture features. We also propose a method for high precision synchronization of the speech and lip features using CCA prior to the proposed fusion. Experimental results show that i) the proposed fusion strategy yields the best equal error rates (EER), which are used to quantify the performance of the fusion strategy for open-set speaker identification, an d ii) precise synchronization prior to fusion improves the EER; hence, the best EER is obtained when the proposed synchronization scheme is employed together with the proposed fusion strategy. We note that the proposed fusion strategy outperforms others because the features used in the late integration are truly uncorrelated, since they are output of the CCA analysis.
Adversarial Inference for Multi-Sentence Video Description

While significant progress has been made in the image captioning task, video description is still comparatively in its infancy, due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video. Recently, reinforcement and adversarial learning based methods have been explored to improve the image captioning models; however, both types of methods suffer from a number of issues, e.g. poor readability and high redundancy for RL and stability issues for GANs. In this work, we instead propose to apply adversarial techniques during inference, designing a discriminator which encourages better multi-sentence video description. In addition, we find that a multi-discriminator “hybrid” design, where each discriminator targets one aspect of a description, leads to the best results. Specifically, we decouple the discriminator to evaluate on three criteria: 1) visual relevance to the video, 2) language diversity and fluency, and 3) coherence across sentences. Our approach results in more accurate, diverse and coherent multi-sentence video descriptions, as shown by automatic as well as human evaluation on the popular ActivityNet Captions dataset.
Offline A/B Testing for Recommender Systems

Online A/B testing evaluates the impact of a new technology by running it in a real production environment and testing its performance on a subset of the users of the platform. It is a well-known practice to run a preliminary offline evaluation on historical data to iterate faster on new ideas, and to detect poor policies in order to avoid losing money or breaking the system. For such offline evaluations, we are interested in methods that can compute offline an estimate of the potential uplift of performance generated by a new technology. Offline performance can be measured using estimators known as counterfactual or off-policy estimators. Traditional counterfactual estimators, such as capped importance sampling or normalised importance sampling, exhibit unsatisfying bias-variance compromises when experimenting on personalized product recommendation systems. To overcome this issue, we model the bias incurred by these estimators rather than bound it in the worst case, which leads us to propose a new counterfactual estimator. We provide a benchmark of the different estimators showing their correlation with business metrics observed by running online A/B tests on a large-scale commercial recommender system.
Synthesis Focusing the Meaning ( s ) of Resilience : Resilience as a Descriptive Concept and a Boundary Object

This article reviews the variety of definitions proposed for “resilience” within sustainability science and suggests a typology according to the specific degree of normativity. There is a tension between the original descriptive concept of resilience first defined in ecological science and a more recent, vague, and malleable notion of resilience used as an approach or boundary object by different scientific disciplines. Even though increased conceptual vagueness can be valuable to foster communication across disciplines and between science and practice, both conceptual clarity and practical relevance of the concept of resilience are critically in danger. The fundamental question is what conceptual structure we want resilience to have. This article argues that a clearly specified, descriptive concept of resilience is critical in providing a counterbalance to the use of resilience as a vague boundary object. A clear descriptive concept provides the basis for operationalization and application of resilience within ecological science.
Student Involvement : A Developmental Theory for Higher Education

Even a casual reading of the extensive literature on student development in higher education can create confusion and perplexity. One finds not only that the problems being studied are highly diverse but also that investigators who claim to be studying the same problem frequently do not look at the same variables or employ the same methodologies. And even when they are investigating the same variables, different investigators may use completely different terms to describe and discuss these variables. My own interest in articulating a theory of student development is partly practical—I would like to bring some order into the chaos of the literature—and partly self-protective. I and increasingly bewildered by the muddle of f indings that have emerged from my own research in student development, research that I have been engaged in for more than 20 years. The theory of student involvement that I describe in this article appeals to me for several reasons. First, it is simple: I have not needed to draw a maze consisting of dozens of boxes interconnected by two-headed arrows to explain the basic elements of the theory to others. Second, the theory can explain most of the empirical knowledge about environmental influences on student development that researchers have gained over the years. Third, it is capable of embracing principles from such widely divergent sources as psychoanalysis and classical learning theory. Finally, this theory of student involvement can be used both by researchers to guide their investigation of student development—and by college administrators and
Design of Low Cost Stair Climbing Robot Using Arduino

Since the invention of the wheel, Man has sought to reduce effort to get things done easily. Ultimately, it has resulted in the invention of the Robot, an Engineering Marvel. Up until now, the biggest factor that hampers wide proliferation of robots is locomotion and maneuverability. They are not dynamic enough to conform even to the most commonplace terrain such as stairs. To overcome this, we are proposing a stair climbing robot that looks a lot like the human leg and can adjust itself according to the height of the step. But, we are currently developing a unit to carry payload of about 4 Kg. The automatic adjustment in the robot according to the height of the stair is done by connecting an Android device that has an application programmed in OpenCV with an Arduino in Host mode. The Android Device uses it camera to calculate the height of the stair and sends it to the Arduino for further calculation. This design employs an Arduino Mega ADK 2560 board to control the robot and other home fabricated custom PCB to interface it with the Arduino Board. The bot is powered by Li-Ion batteries and Servo motors.
Barriers and benefits of quality management in the construction industry : An empirical study

In this paper we discuss the problems of defining quality in the construction industry, examine possible benefits of implementing quality, and look at barriers to quality implementation in construction. We use data collected during interviews with contractors and data from questionnaire surveys. Results show that contractors do understand the potential benefits of quality implementation but that there are also many barriers to implementation. We describe recent developments that might help to overcome the barriers. However, the different actors in construction need to understand that change is a slow and often painful process and that much effort is required to implement quality in construction industry.
Collaboration and Creativity : The Small World Problem

Small world networks have received disproportionate notice in fields as diverse as sociology, physics, epidemiology, neurology, and the popular arts because of their suspected affect on system dynamics, including creativity, robustness, and stratification. In this paper, we analyzed the effects of a small world on the creativity of the artists who made original Broadway musicals from 1945 to 1989. Based on original arguments, new methods for analyzing bipartite-affiliation graphs, and tests of construct validity, we found that the varying “small world” properties of the creative artist network did significantly impact creativity in terms of both the financial and artistic success of an individual musical as well as the percentage of hits and flops and raves and pans produced each season. Using several criterion variables, different estimation techniques, and multiple levels of analysis, we found that a small world network had a parabolic effect that at first increased performance up to a threshold after which point the positive effects reversed. These effects were in agreement with and extended historical analyses of the Broadway industry and held up for both individual productions and for the system as a whole.
Game development software engineering process life cycle: a systematic review

Software game is a kind of application that is used not only for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Multidisciplinary nature of the game development processes that combine sound, art, control systems, artificial intelligence (AI), and human factors, makes the software game development practice different from traditional software development. However, the underline software engineering techniques help game development to achieve maintainability, flexibility, lower effort and cost, and better design. The purpose of this study is to assesses the state of the art research on the game development software engineering process and highlight areas that need further consideration by researchers. In the study, we used a systematic literature review methodology based on well-known digital libraries. The largest number of studies have been reported in the production phase of the game development software engineering process life cycle, followed by the pre-production phase. By contrast, the post-production phase has received much less research activity than the pre-production and production phases. The results of this study suggest that the game development software engineering process has many aspects that need further attention from researchers; that especially includes the postproduction phase.
A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.
Exploration of Attention in Question Answering

Question answering is a complex and valuable task in natural language processing and artificial intelligence. Several deep learning models having already been proposed to solve it. In this work, we propose a deep learning model with an attention mechanism that is based on a previous work and a decoder that incorporates a wide summary of the context and question. That summary includes a condensed representation of the question, a context paragraph representation previous created by the model, as well as positional question summaries created by the attention mechanism. We demonstrate that a strong attention layer allows a deep learning model to do well even on long questions and context paragraphs in addition to contributing significantly to model performance.
Multitask learning for semantic sequence prediction under varying data conditions

Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, because significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.