nils-herrmann/clean_scirepeval huggingface dataset:
Systems biology of personalized nutrition

Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person's health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology-based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of "systems flexibility" is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed.
Sibling Gender Composition and Participation in STEM Education

This paper studies the causal impact of sibling gender composition on participation in Science, Technology, Engineering, and Mathematics (STEM) education. I focus on a sample of first-born children who all have a younger biological sibling, using rich administrative data on the total Danish population. The randomness of the secondborn siblings’ gender allows me to estimate the causal effect of having an opposite sex sibling relative to a same sex sibling. The results are robust to family size and show that having a second-born opposite sex sibling makes first-born men more and women less likely to enroll in a STEM program. Although sibling gender composition has no impact on men’s probability of actually completing a STEM degree, it has a powerful effect on women’s success within these fields: women with a younger brother are eleven percent less likely to complete any field-specific STEM education relative to women with a sister. I provide evidence that parents of mixed sex children gender-specialize their parenting more than parents of same sex children. These findings indicate that the family environment plays in important role for shaping interests in STEM fields.
ALEF: From Application to Platform for Adaptive Collaborative Learning

Web 2.0 has had a tremendous impact on education. It facilitates access and availability of learning content in variety of new formats, content creation, learning tailored to students’ individual preferences, and collaboration. The range of Web 2.0 tools and features is constantly evolving, with focus on users and ways that enable users to socialize, share and work together on (user-generated) content. In this chapter we present ALEF – Adaptive Learning Framework that responds to the challenges posed on educational systems in Web 2.0 era. Besides its base functionality – to deliver educational content – ALEF particularly focuses on making the learning process more efficient by delivering tailored learning experience via personalized recommendation, and enabling learners to collaborate and actively participate in learning via interactive educational components. Our existing and successfully utilized solution serves as the medium for presenting key concepts that enable realizing Web 2.0 principles in education, namely lightweight models, and three components of framework infrastructure important for constant evolution and inclusion of students directly into the educational process – annotation framework, feedback infrastructure and widgets. These make possible to devise and implement various mechanisms for recommendation and collaboration – we also present selected methods for personalized recommendation and collaboration together with their evaluation in ALEF.
Big Data and Predictive Analytics: Recalibrating Expectations.

With the routine use of electronic health records (EHRs) in hospitals, health systems, and physician practices, there has been rapid growth in the availability of health care data over the last decade. In addition to the structured data in EHRs, new methods such as natural language processing can derive meaning from unstructured data, permitting the capture of substantial clinical information embedded in clinical notes. Furthermore, the growth in the availability of registries and claims data and the linkages between all these data sources have created a big data platform in health care, vast in both size and scope. Concurrently, new computational machine learning approaches promise ever-more-accurate prediction. The marvel of Google and of Watson, the inexorability of Moore’s law (ie, computing power doubles every 2 years for the same cost), suggest a future in which medicine will be transformed into an information science, and each clinical decision may be optimized based on a forecasting of outcomes under alternative treatment options, beyond the knowledge and understanding of the individual physician. Yet despite these innovations and those to come, quantitative risk prediction in medicine has been available for several decades, based on more classical
GERSE: Guia de Elicitação de Requisitos para Sistemas Embarcados

The projects with embedded systems are used for many different purposes, being a major challenge for the community of developers of such systems. As we benefit from technological advances the complexity of designing an embedded system increases significantly. This paper presents GERSE, a guideline to requirements elicitation for embedded systems. Despite of advances in the area of embedded systems, there is a shortage of requirements elicitation techniques that meet the particularities of this area. The contribution of GERSE is to improve the capture process and organization of the embedded systems requirements.
Evaluation of Production Serverless Computing Environments

Serverless computing provides a small runtime container to execute lines of codes without infrastructure management which is similar to Platform as a Service (PaaS) but a functional level. Amazon started the event-driven compute named Lambda functions in 2014 with a 25 concurrent limitation, but it now supports at least a thousand of concurrent invocation to process event messages generated by resources like databases, storage and system logs. Other providers, i.e., Google, Microsoft, and IBM offer a dynamic scaling manager to handle parallel requests of stateless functions in which additional containers are provisioning on new compute nodes for distribution. However, while functions are often developed for microservices and lightweight workload, they are associated with distributed data processing using the concurrent invocations. We claim that the current serverless computing environments can support dynamic applications in parallel when a partitioned task is executable on a small function instance. We present results of throughput, network bandwidth, a file I/O and compute performance regarding the concurrent invocations. We deployed a series of functions for distributed data processing to address the elasticity and then demonstrated the differences between serverless computing and virtual machines for cost efficiency and resource utilization.
Mindfulness-Based Interventions in Context : Past , Present , and Future

Baer's review (2003; this issue) suggests that mindfulness-based interventions are clinically efficacious, but that better designed studies are now needed to substantiate the field and place it on a firm foundation for future growth. Her review, coupled with other lines of evidence, suggests that interest in incorporating mindfulness into clinical interventions in medicine and psychology is growing. It is thus important that professionals coming to this field understand some of the unique factors associated with the delivery of mindfulness-based interventions and the potential conceptual and practical pitfalls of not recognizing the features of this broadly unfamiliar landscape. This commentary highlights and contextualizes (1) what exactly mindfulness is, (2) where it came from, (3) how it came to be introduced into medicine and health care, (4) issues of cross-cultural sensitivity and understanding in the study of meditative practices stemming from other cultures and in applications of them in novel settings, (5) why it is important for people who are teaching mind-fulness to practice themselves, (6) results from 3 recent Health Care, and Society not reviewed by Baer but which raise a number of key questions about clinical applicability , study design, and mechanism of action, and (7) current opportunities for professional training and development in mindfulness and its clinical applications. Iappreciate the opportunity to comment on Baer's (2003; this issue) review of mindfulness training as clinical intervention and to add my own reflections on the emergence of mindfulness in a clinical context, especially in a journal explicitly devoted to both science and practice. The universe of mindfulness 1 brings with it a whole new meaning and thrust to the word practice, one which I believe has the potential to contribute profoundly to the further development of the field of clinical psychology and its allied disciplines , behavioral medicine, psychosomatic medicine, and health psychology, through both a broadening of research approaches to mind/body interactions and the development of new classes of clinical interventions. I find the Baer review to be evenhanded, cogent, and perceptive in its description and evaluation of the work that has been published through the middle of 2001, work that features mindfulness training as the primary element in various clinical interventions. It complements nicely the recent review by Bishop (2002), which to my mind ignores some of the most important, if difficult to define, features of such interventions in its emphasis on the perceived need
Cyber-Security-Aware Network Design of Industrial Control Systems

The pervasive adoption of traditional information and communication technologies hardware and software in industrial control systems (ICS) has given birth to a unique technological ecosystem encapsulating a variety of objects ranging from sensors and actuators to video surveillance cameras and generic PCs. Despite their invaluable advantages, these advanced ICS create new design challenges, which expose them to significant cyber threats. To address these challenges, an innovative ICS network design technique is proposed in this paper to harmonize the traditional ICS design requirements pertaining to strong architectural determinism and real-time data transfer with security recommendations outlined in the ISA-62443.03.02 standard. The proposed technique accommodates security requirements by partitioning the network into security zones and by provisioning critical communication channels, known as security conduits, between two or more security zones. The ICS network design is formulated as an integer linear programming (ILP) problem that minimizes the cost of the installation. Real-time data transfer limitations and security requirements are included as constraints imposing the selection of specific traffic paths, the selection of routing nodes, and the provisioning of security zones and conduits. The security requirements of cyber assets denoted by traffic and communication endpoints are determined by a cyber attack impact assessment technique proposed in this paper. The sensitivity of the proposed techniques to different parameters is evaluated in a first scenario involving the IEEE 14-bus model and in a second scenario involving a large network topology based on generated data. Experimental results demonstrate the efficiency and scalability of the ILP model.
Blockchain for Configuration Management in IoT

Until now, most systems for Internet of Things (IoT) management, have been designed in a Cloud-centric manner, getting benefits from the unified platform that the Cloud offers. However, a Cloud-centric infrastructure mainly achieves static sensor and data streaming systems, which do not support the direct configuration management of IoT components. To address this issue, a virtualization of IoT components (Virtual Resources) is introduced at the edge of the IoT network. This research also introduces permission-based Blockchain protocols to handle the provisioning of Virtual Resources directly onto edge devices. The architecture presented by this research focuses on the use of Virtual Resources and Blockchain protocols as management tools to distribute configuration tasks towards the edge of the IoT network. Results from lab experiments demonstrate the successful deployment and communication performance (response time in milliseconds) of Virtual Resources on two edge platforms, Raspberry Pi and Edison board. This work also provides performance evaluations of two permission-based blockchain protocol approaches. The first blockchain approach is a Blockchain as a Service (BaaS) in the Cloud, Bluemix. The second blockchain approach is a private cluster hosted in a Fog network, Multichain.
Experience Replay for Continual Learning

Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions. This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills. We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence. Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience. While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution – that of using experience replay buffers for all past events – with a mixture of onand off-policy learning, leveraging behavioral cloning. We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities. When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.
Empirical study of particle swarm optimization

In this paper, we empirically study the performance of the particle swarm optimizer (PSO). Four different benchmark functions with asymmetric initial range settings are selected as testing functions. The experimental results illustrate the advantages and disadvantages of the PSO. Under all the testing cases, the PSO always converges very quickly towards the optimal positions but may slow its convergence speed when it is near a minimum. Nevertheless, the experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO’s performance near the optima, such as using an adaptive inertia weight.
A Survey about Consensus Algorithms Used in Blockchain

Thanks to its potential in many applications, Blockchain has recently been nominated as one of the technologies exciting intense attention. Blockchain has solved the problem of changing the original low-trust centralized ledger held by a single third-party, to a high-trust decentralized form held by different entities, or in other words, verifying nodes. The key contribution of the work of Blockchain is the consensus algorithm, which decides how agreement is made to append a new block between all nodes in the verifying network. Blockchain algorithms can be categorized into two main groups. The first group is proof-based consensus, which requires the nodes joining the verifying network to show that they are more qualified than the others to do the appending work. The second group is voting-based consensus, which requires nodes in the network to exchange their results of verifying a new block or transaction, before making the final decision. In this paper, we present a review of the Blockchain consensus algorithms that have been researched and that are being applied in some well-known applications at this time.
Use of a free radical method to evaluate antioxidant activity

Tile antiradical activities of various antioxidants were determined using the free radical, 2.2-Diphenyl-l-pict3,1hydrazyl (DPPI-I°). In its radical form, DPPI-I ° has an absorption band at 515 nm which disappears upon reduction by an antiradical compound. Twenty compounds were reacted with the DPPI-I ° and shown to follow one of three possible reaction kinetic types. Ascorbie acid, isoascorbic acid and isoeugenol reacted quickly with the DPPI-I ° reaching a steady state immediately. Rosmarinic acid and 6-tocopherol reacted a little slower and reached a steady state within 30 rain. The remaining compounds reacted more progressively with the DPPH ° reaching a steady state from I to 6 h. Caffeic acid, gentisic acid and gallic acid showed the highest antiradical activities with a stoichiometo, of 4 to 6 reduced DPPH ° molecules pet" molecule of antioxidant. Vanillin, phenol, y-resort3'lic acid and vanillic acid were found to be poor antiradical compounds. The stoichiometry, for the other 13 phenolic compounds varied from one to three reduced DPPH ° molecules pet" molecule of antioxidant. Possible mechanisms are proposed to explain the e.werimental results.
Silhouette Analysis-Based Gait Recognition for Human Identification

Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on Principal Component Analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost.
On Evaluating Embedding Models for Knowledge Base Completion

Knowledge bases contribute to many web search and mining tasks, yet they are often incomplete. To add missing facts to a given knowledge base, various embedding models have been proposed in the recent literature. Perhaps surprisingly, relatively simple models with limited expressiveness often performed remarkably well under today’s most commonly used evaluation protocols. In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion. We show that when focusing on a different prediction task for evaluating knowledge base completion, the performance of current embedding models is unsatisfactory even on datasets previously thought to be too easy. This is especially true when embedding models are compared against a simple rule-based baseline. This work indicates the need for more research into the embedding models and evaluation protocols for knowledge base completion.
A novel type of transient luminous event produced by terrestrial gamma-ray flashes

Terrestrial Gamma-ray Flashes (TGFs), discovered in 1994 by the Compton Gamma-Ray Observatory, are high-energy photon bursts originating in the Earth’s atmosphere in association with thunderstorms. In this paper, we demonstrate theoretically that, while TGFs pass through the atmosphere, the large quantities of energetic electrons knocked out by collisions between photons and air molecules generate excited species of neutral and ionized molecules, leading to a significant amount of optical emissions. These emissions represent a novel type of transient luminous events in the vicinity of the cloud tops. We show that this predicted phenomenon illuminates a region with a size notably larger than the TGF source and has detectable levels of brightness. Since the spectroscopic, morphological, and temporal features of this luminous event are closely related with TGFs, corresponding measurements would provide a novel perspective for investigation of TGFs, as well as lightning discharges that produce them.
Consensus guidelines for diagnosis, treatment and follow-up of patients with pancreatic cancer in Spain

The management of patients with pancreatic cancer has advanced over the last few years. We convey a multidisciplinary group of experts in an attempt to stablish practical guidelines for the diagnoses, staging and management of these patients. This paper summarizes the main conclusions of the working group. Patients with suspected pancreatic ductal adenocarcinoma should be rapidly evaluated and referred to high-volume centers. Multidisciplinary supervision is critical for proper diagnoses, staging and to frame a treatment plan. Surgical resection together with chemotherapy offers the highest chance for cure in early stage disease. Patients with advanced disease should be classified in treatment groups to guide systemic treatment. New chemotherapeutic regimens have resulted in improved survival. Symptomatic management is critical in this disease. Enrollment in a clinical trial is, in general, recommended.