nils-herrmann/clean_scirepeval huggingface dataset:
Metabolic and physiologic effects from consuming a hunter-gatherer (Paleolithic)-type diet in type 2 diabetes

The contemporary American diet figures centrally in the pathogenesis of numerous chronic diseases– 'diseases of civilization'–such as obesity and diabetes. We investigated in type 2 diabetes whether a diet similar to that consumed by our pre-agricultural hunter-gatherer ancestors ('Paleolithic' type diet) confers health benefits.We performed an outpatient, metabolically controlled diet study in type 2 diabetes patients. We compared the findings in 14 participants consuming a Paleo diet comprising lean meat, fruits, vegetables and nuts, and excluding added salt, and non-Paleolithic-type foods comprising cereal grains, dairy or legumes, with 10 participants on a diet based on recommendations by the American Diabetes Association (ADA) containing moderate salt intake, low-fat dairy, whole grains and legumes. There were three ramp-up diets for 7 days, then 14 days of the test diet. Outcomes included the following: mean arterial blood pressure; 24-h urine electrolytes; hemoglobin A1c and fructosamine levels; insulin resistance by euglycemic hyperinsulinemic clamp and lipid levels.Both groups had improvements in metabolic measures, but the Paleo diet group had greater benefits on glucose control and lipid profiles. Also, on the Paleo diet, the most insulin-resistant subjects had a significant improvement in insulin sensitivity (r=0.40, P=0.02), but no such effect was seen in the most insulin-resistant subjects on the ADA diet (r= 0.39, Even short-term consumption of a Paleolithic-type diet improved glucose control and lipid profiles in people with type 2 diabetes compared with a conventional diet containing moderate salt intake, low-fat dairy, whole grains and legumes.
Study on the classification of data streams with concept drift

Data streams mining has become a novel research topic of growing interest in knowledge discovery. Because of the high speed and huge size of data set in data streams, the traditional classification technologies are no longer applicable. In recent years a great deal of research has been done on this problem, most intends to efficiently solve the data streams mining problem with concept drift. This paper presents the state-of-the-art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a critical summary of existing approaches to the problem, including Stagger, FLORA, MetaL(B), MetaL(IB), CD3, CD4, CD5, OLIN, CVFDT and different ensemble classifiers. At last, this paper explores the challenges and future work in this field.
UrbanMatch - linking and improving Smart Cities Data

Urban-related data and geographic information are becoming mainstream in the Linked Data community due also to the popularity of Location-based Services. In this paper, we introduce the UrbanMatch game, a mobile gaming application that joins data linkage and data quality/trustworthiness assessment in an urban environment. By putting together Linked Data and Human Computation, we create a new interaction paradigm to consume and produce location-specific linked data by involving and engaging the final user. The UrbanMatch game is also offered as an example of value proposition and business model of a new family of linked data applications based on gaming in Smart Cities.
Improve Face Recognition Rate Using Different Image Pre-Processing Techniques

Face recognition become one of the most important and fastest growing area during the last several years and become the most successful application of image analysis and broadly used in security system. It has been a challenging, interesting, and fast growing area in real time applications. The propose method is tested using a benchmark ORL database that contains 400 images of 40 persons. Pre-Processing technique are applied on the ORL database to increase the recognition rate. The best recognition rate is 97.5% when tested using 9 training images and 1 testing image. Increasing image database brightness is efficient and will increase the recognition rate. Resizing images using 0.3 scale is also efficient and will increase the recognition rate. PCA is used for feature extraction and dimension reduction. Euclidean distance is used for matching process.
A Case Study on Legal Case Annotation

The paper reports the outcomes of a study with law school students to annotate a corpus of legal cases for a variety of annotation types, e.g. citation indices, legal facts, rationale, judgement, cause of action, and others. An online tool is used by a group of annotators that results in an annotated corpus. Differences amongst the annotations are curated, producing a gold standard corpus of annotated texts. The annotations can be extracted with semantic searches of complex queries. There would be many such uses for the development and analysis of such a corpus for both legal education and legal research.
Reaching for the clouds: contextually enhancing smartphones for energy efficiency

Energy efficiency has gradually become a compulsory need in mobile computing as the processing requirements for smartphones have increased exponentially. Moreover, the current demand is stretching beyond the extents of modern battery technology. In this sense, we introduce a novel collaboration solution for mobile devices based on a contextual search entitled Hybrid Contextual Cloud for Ubiquitous Platforms comprising of Smartphones (HYCCUPS). HYCCUPS takes advantage of the pervasive nature of smartphones and of current wireless communication technologies as to offer offloading execution of mobile applications in an opportunistic on-the-fly hybrid computing cloud. We design an adaptive contextual search algorithm for schedulling mobile code execution in smartphone communities based on predicting the availability and mobility of devices in the near vicinity. We emulate the HYCCUPS framework based on real user traces and we prove that it improves battery health, maximizes power save, minimizes overall execution time of mobile applications and it preserves or even enhances user experience.
Synthesis of Behavioral Models from Scenarios

Scenario-based specifications such as Message Sequence Charts (MSCs) are useful as part of a requirements specification. A scenario is a partial story, describing how system components, the environment, and users work concurrently and interact in order to provide system level functionality. Scenarios need to be combined to provide a more complete description of system behavior. Consequently, scenario synthesis is central to the effective use of scenario descriptions. How should a set of scenarios be interpreted? How do they relate to one another? What is the underlying semantics? What assumptions are made when synthesizing behavior models from multiple scenarios? In this paper, we present an approach to scenario synthesis based on a clear sound semantics, which can support and integrate many of the existing approaches to scenario synthesis. The contributions of the paper are threefold. We first define an MSC language with sound abstract semantics in terms of labeled transition systems and parallel composition. The language integrates existing approaches based on scenario composition by using high-level MSCs (hMSCs) and those based on state identification by introducing explicit component state labeling. This combination allows stakeholders to break up scenario specifications into manageable parts and reuse scenarios using hMCSs; it also allows them to introduce additional domainspecific information and general assumptions explicitly into the scenario specification using state labels. Second, we provide a sound synthesis algorithm which translates scenarios into a behavioral specification in the form of Finite Sequential Processes. This specification can be analyzed with the Labeled Transition System Analyzer using model checking and animation. Finally, we demonstrate how many of the assumptions embedded in existing synthesis approaches can be made explicit and modeled in our approach. Thus, we provide the basis for a common approach to scenario-based specification, synthesis, and analysis.
Successful reduction in catheter-associated urinary tract infections: focus on nurse-directed catheter removal.

Despite using sterile technique for catheter insertion, closed drainage systems, and structured daily care plans, catheter-associated urinary tract infections (CAUTIs) regularly occur in acute care hospitals. We believe that meaningful reduction in CAUTI rates can only be achieved by reducing urinary catheter use.



We used an interventional study of a hospital-wide, multidisciplinary program to reduce urinary catheter use and CAUTIs on all patient care units in a 300-bed, community teaching hospital in Connecticut. Our primary focus was the implementation of a nurse-directed urinary catheter removal protocol. This protocol was linked to the physician's catheter insertion order. Three additional elements included physician documentation of catheter insertion criteria, a device-specific charting module added to physician electronic progress notes, and biweekly unit-specific feedback on catheter use rates and CAUTI rates in a multidisciplinary forum.



We achieved a 50% hospital-wide reduction in catheter use and a 70% reduction in CAUTIs over a 36-month period, although there was wide variation from unit to unit in catheter reduction efforts, ranging from 4% (maternity) to 74% (telemetry).



Urinary catheter use, and ultimately CAUTI rates, can be effectively reduced by the diligent application of relatively few evidence-based interventions. Aggressive implementation of the nurse-directed catheter removal protocol was associated with lower catheter use rates and reduced infection rates.
Towards a resolution of the personality triad : Persons , situations , and behaviors

The issue of the variant vs. invariant in personality often arises in diVerent forms of the “person– situation” debate, which is based on a false dichotomy between the personal and situational determination of behavior. Previously reported data are summarized that demonstrate how behavior can vary as a function of subtle situational changes while individual consistency is maintained. Further discussion considers the personal source of behavioral invariance, the situational source of behavioral variation, the person–situation interaction, the nature of behavior, and the “personality triad” of persons, situations, and behaviors, in which each element is understood and predicted in terms of the other two. An important goal for future research is further development of theories and methods for conceptualizing and measuring the functional aspects of situations and of behaviors. One reason for the persistence of the person situation debate may be that it serves as a proxy for a deeper, implicit debate over values such as equality vs. individuality, determinism vs. free will, and Xexibility vs. consistency. However, these value dichotomies may be as false as the person–situation debate that they implicitly drive.
Towards Neural Theorem Proving at Scale Anonymous

Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their computational complexity, which renders them unusable on real world datasets. We focus on the Neural Theorem Prover (NTP) model proposed by Rocktäschel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their embedding representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results – this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. This enables inference and learning on previously impracticable KBs.
A Strong RSA-based and Certificateless-based Signature Scheme

The certificateless-based signature system allows people to verify the signature without the certificate. For this reason, we do not need the certificate authority (CA) to store and manage users’ certificates and public keys. Certificateless-based signature can also overcome the certificate management problem and the key escrow problem of the traditional signature system. In 2012, Zhang and Mao first designed the certificateless-based signature scheme based on RSA operations; however, their scheme still has latent vulnerabilities. To overcome these shortcomings, we propose an improved version to make the RSA-based certificateless scheme stronger and more secure. Besides, we reduce the computational cost to make our scheme more efficient.
Deep Forward and Inverse Perceptual Models for Tracking and Prediction

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.
How Do RIM-BPs Link Voltage-Gated Ca2+ Channels to Evoked Neurotransmitter Release?

Coupling between voltage-gated Ca(2+) influx and synaptic vesicle exocytosis is essential for rapid evoked neurotransmission. Acuna et al. show that the knockout of RIM-BPs, which are key structural components of this coupling, decreases the reliability of evoked neurotransmitter release.
How we forget may depend on how we remember

Recent developments reveal that memories relying on the hippocampus are relatively resistant to interference, but sensitive to decay. The hippocampus is vital to recollection, a form of memory involving reinstatement of a studied item within its spatial-temporal context. An additional form of memory known as familiarity does not involve contextual reinstatement, but a feeling of acquaintance with the studied items. Familiarity depends more on extrahippocampal structures that do not have the properties promoting resistance to interference. These notions led to the novel hypothesis that the causes of forgetting depend on the memories' nature: memories depending on recollection are more vulnerable to decay than interference, whereas for memories depending on familiarity, the reverse is true. This review provides comprehensive evidence for this hypothesis.
Incorporating Chinese Characters of Words for Lexical Sememe Prediction

word frequency 6 50 51– 100 101 – 1,000 1,001 – 5,000 5,001 – 10,000 10,001 – 30,000 >30,000 occurrences 8537 4868 3236 2036 663 753 686 SPWE 0.312 0.437 0.481 0.558 0.549 0.556 0.509 SPSE 0.187 0.273 0.339 0.409 0.407 0.424 0.386 SPWE + SPSE 0.284 0.414 0.478 0.556 0.548 0.554 0.511 SPWCF 0.456 0.414 0.400 0.443 0.462 0.463 0.479 SPCSE 0.309 0.291 0.286 0.312 0.339 0.353 0.342 SPWCF + SPCSE 0.467 0.437 0.418 0.456 0.477 0.477 0.494 SPWE + fastText 0.495 0.472 0.462 0.520 0.508 0.499 0.490 CSP 0.527 0.555 0.555 0.626 0.632 0.641 0.624
Biologic width dimensions--a systematic review.

Consideration of the biologic width in restorative dentistry seems to be important for maintaining periodontal health.



To evaluate the dimensions of the biologic width in humans.



A systematic literature search was performed for publications published by 28 September 2012 using five different electronic databases; this search was complemented by a manual search. Two reviewers conducted the study selection, data collection, and validity assessment. The PRISMA criteria were applied. From 615 titles identified by the search strategy, 14 publications were included and six were suitable for meta-analyses.



Included studies were published from the years 1924 to 2012. They differed with regard to measurements of the biologic width. Mean values of the biologic width obtained from two meta-analyses ranged from 2.15 to 2.30 mm, but large intra- and inter-individual variances (subject sample range: 0.2 - 6.73 mm) were observed. The tooth type and site, the presence of a restoration and periodontal diseases/surgery affected the dimensions of the biologic width. Pronounced heterogeneity among studies regarding methods and outcome measures exists.



No universal dimension of the biologic width appears to exist. Establishment of periodontal health is suggested prior to the assessment of the biologic width within reconstructive dentistry.
Comparisons Between Data Clustering Algorithms

Clustering is a division of data into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar compared to objects of other groups.  The algorithms under investigation are: k-means algorithm, hierarchical clustering algorithm, self-organizing maps algorithm, and expectation maximization clustering algorithm. All these algorithms are compared according to the following factors: size of dataset, number of clusters, type of dataset and type of software used. Some conclusions that are extracted belong to the performance, quality, and accuracy of the clustering algorithms.
Enhancing My Keepon robot: A simple and low-cost solution for robot platform in Human-Robot Interaction studies

Many robots capable of performing social behaviors have recently been developed for Human-Robot Interaction (HRI) studies. These social robots are applied in various domains such as education, entertainment, medicine, and collaboration. Besides the undisputed advantages, a major difficulty in HRI studies with social robots is that the robot platforms are typically expensive and/or not open-source. It burdens researchers to broaden experiments to a larger scale or apply study results in practice. This paper describes a method to modify My Keepon, a toy version of Keepon robot, to be a programmable platform for HRI studies, especially for robot-assisted therapies. With an Arduino microcontroller board and an open-source Microsoft Visual C# software, users are able to fully control the sounds and motions of My Keepon, and configure the robot to the needs of their research. Peripherals can be added for advanced studies (e.g., mouse, keyboard, buttons, PlayStation2 console, Emotiv neuroheadset, Kinect). Our psychological experiment results show that My Keepon modification is a useful and low-cost platform for several HRI studies.
Low Power Five Stage Current Starved Voltage Controlled Oscillator in 0 . 18 μ m CMOS Technology towards Green Electronics

This paper presents a five stage current starved Voltage Controlled Oscillator (CMOS VCO) for low power Phase Lock Loop (PLL). The implemented design used a standard 0.18μm CMOS Technology with simulation CAD software mentor graphics tool and uses two models of P-channel and N-channel Mosfets Model I and II. The Model I and II of P-channel MOSFET have two parameters, wide and length of 10, 0.5 and 8, 0.2 respectively, while Model I and II of N-channel MOSFET with similar parameters have wide and length of 10, 0.5 and 8, 0.2 respectively. The experimental results presented suggests that the design exhibits VCO frequency ranging from 21MHz to 315.34 MHz at low power. The designed circuit is simulated using 180nm technology, and the results shows that the voltage drawn is around 5V supplied at VDD, and the produced current and voltage approximate the power consumption is 105.3mW. The proposed design is suitable for PLL as a frequency multiplier based on its features as presented.  VCO, CMOS Low Power, PLL.
Innate and adaptive immunity in teleost fish: a review.

The immune system of fish is very similar to vertebrates, although there are some important differences. Fish are free-living organisms from the embryonic stage of life in their aquatic environment. They have mechanisms to protect themselves from a wide variety of microorganisms. Consequently, fish rely on their innate immune system for an extended period of time, beginning at the early stages of embryogenesis. The components of the innate immune response are divided into physical, cellular and humoral factors and include humoral and cellular receptor molecules that are soluble in plasma and other body fluids. The lymphoid organs found in fish include the thymus, spleen and kidney. Immunoglobulins are the principal components of the immune response against pathogenic organisms. Immunomodulatory products, including nucleotides, glucans and probiotics, are increasingly used in aquaculture production. The use of these products reduces the need for therapeutic treatments, enhances the effects of vaccines and, in turn, improves the indicators of production. The aim of this review is to provide a review of the immune system in fish, including the ontogeny, mechanisms of unspecific and acquired immunity and the action of some immunomodulators.
Pattern Analysis in Dermoscopic Images

In this chapter an extensive review of algorithmic methods that automatically detect patterns in dermoscopic images of pigmented lesions is presented. Pattern Analysis seeks to identify specific patterns, which may be global and local. It is the method most commonly used for providing diagnostic accuracy for cutaneous melanoma. In this chapter, a description of global and local patterns identified by pattern analysis is presented as well as a brief explanation of algorithmic methods that carry out the detection and classification of these patterns. Although the 7-Point Checklist method corresponds to a different diagnostic technique than pattern analysis, it can be considered as a simplification of it as it classifies seven features related with local patterns. For this reason, the main techniques to automatically assess the 7-Point Checklist are briefly explained in this chapter.
Mining infrequent patterns in data stream

In recent years researches are focused towards mining infrequent patterns rather than frequent patterns. Mining infrequent pattern plays vital role in detecting any abnormal event. In this paper, an algorithm named Infrequent Pattern Miner for Data Streams (IPM-DS) is proposed for mining nonzero infrequent patterns from data streams. The proposed algorithm adopts the FP-growth based approach for generating all infrequent patterns. The proposed algorithm (IPM-DS) is evaluated using health data set collected from wearable physiological sensors that measure vital parameters such as Heart Rate (HR), Breathing Rate (BR), Oxygen Saturation (SPO2) and Blood pressure (BP) and also with two publically available data sets such as e-coli and Wine from UCI repository. The experimental results show that the proposed algorithm generates all possible infrequent patterns in less time.
Humanoid arm with the integrated serial elastic actuator

This paper proposes the design and development of an integrated serial elastic actuator (SEA) which is used as the elbow joint in the soft humanoid arm. First, requirements of SEA are illustrated. And six different types of elastic elements (e.g. the discoid element, the cylindrical) are analyzed using the design of experiment (DOE) method. And then, a discoid elastic element with larger flexibility is developed. Based on this, an integrated SEA containing the position sensors and the torque sensor is developed. And a 7-degree-of-freedom humanoid arm with joint flexibilities is designed. Finally, experiments of SEA demonstrate the approximation of the stiffness between the desired SEA and the prototype.