nils-herrmann/clean_scirepeval huggingface dataset:
Quality Traits of “Cannabidiol Oils”: Cannabinoids Content, Terpene Fingerprint and Oxidation Stability of European Commercially Available Preparations

Cannabidiol (CBD)-based oil preparations are becoming extremely popular, as CBD has been shown to have beneficial effects on human health. CBD-based oil preparations are not unambiguously regulated under the European legislation, as CBD is not considered as a controlled substance. This means that companies can produce and distribute CBD products derived from non-psychoactive hemp varieties, providing an easy access to this extremely advantageous cannabinoid. This leaves consumers with no legal quality guarantees. The objective of this project was to assess the quality of 14 CBD oils commercially available in European countries. An in-depth chemical profiling of cannabinoids, terpenes and oxidation products was conducted by means of GC-MS and HPLC-Q-Exactive-Orbitrap-MS in order to improve knowledge regarding the characteristics of CBD oils. Nine out of the 14 samples studied had concentrations that differed notably from the declared amount, while the remaining five preserved CBD within optimal limits. Our results highlighted a wide variability in cannabinoids profile that justifies the need for strict and standardized regulations. In addition, the terpenes fingerprint may serve as an indicator of the quality of hemp varieties, while the lipid oxidation products profile could contribute in evaluation of the stability of the oil used as milieu for CBD rich extracts.
Combining Naive Bayes and n-Gram Language Models for Text Classification

We augment the naive Bayes model with an n-gram language model to address two shortcomings of naive Bayes text classifiers. The chain augmented naive Bayes classifiers we propose have two advantages over standard naive Bayes classifiers. First, a chain augmented naive Bayes model relaxes some of the independence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, smoothing techniques from statistical language modeling can be used to recover better estimates than the Laplace smoothing techniques usually used in naive Bayes classification. Our experimental results on three real world data sets show that we achieve substantial improvements over standard naive Bayes classification, while also achieving state of the art performance that competes with the best known methods in these cases.
Palmprint Recognition Using Deep Scattering Convolutional Network

Palmprint recognition has drawn a lot of attention during the recent years. Many algorithms have been proposed for palmprint recognition in the past, majority of them being based on features extracted from the transform domain. Many of these transform domain features are not translation or rotation invariant, and therefore a great deal of preprocessing is needed to align the images. In this paper, a powerful image representation, called scattering network/transform, is used for palmprint recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering network captures similar features to SIFT descriptors and the higher-layer features capture higher-frequency content of the signal which are lost in SIFT and other similar descriptors. After extraction of the scattering features, their dimensionality is reduced by applying principal component analysis (PCA) which reduces the computational complexity of the recognition task. Two different classifiers are used for recognition: multi-class SVM and minimum-distance classifier. The proposed scheme has been tested on a well-known palmprint database and achieved accuracy rate of 99.95% and 100% using minimum distance classifier and SVM respectively.
Subjectivity and Sentiment Analysis of Modern Standard Arabic

Although Subjectivity and Sentiment Analysis (SSA) has been witnessing a flurry of novel research, there are few attempts to build SSA systems for Morphologically-Rich Languages (MRL). In the current study, we report efforts to partially fill this gap. We present a newly developed manually annotated corpus of Modern Standard Arabic (MSA) together with a new polarity lexicon.The corpus is a collection of newswire documents annotated on the sentence level. We also describe an automatic SSA tagging system that exploits the annotated data. We investigate the impact of different levels of preprocessing settings on the SSA classification task. We show that by explicitly accounting for the rich morphology the system is able to achieve significantly higher levels of performance.
A Machine Learning Approach for Material Classification in MMW Imaging Systems based on Frequency Spectra

In this paper, a new approach toward material detection and classification, based on the spectral analysis of millimeter-wave images, using machine learning technique is proposed. The focus of this paper is to detect concealed dangerous materials. It is shown that by using adequate number of training data captured from different materials of interest, the trained machine could detect concealed dangerous materials with an acceptable accuracy. The training phase is performed with materials of varying thickness, shape, background, covering layers and distance. The training data is collected with laboratory experiments in the frequency range of 27–31 GHz with 51 frequency samples. The results show more than 92 percent accuracy in differentiating various materials based on the true-alarm-rate (TAR) metric.
A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line

Various new national advanced manufacturing strategies, such as Industry 4.0, Industrial Internet, and Made in China 2025, are issued to achieve smart manufacturing, resulting in the increasing number of newly designed production lines in both developed and developing countries. Under the individualized designing demands, more realistic virtual models mirroring the real worlds of production lines are essential to bridge the gap between design and operation. This paper presents a digital twin-based approach for rapid individualized designing of the hollow glass production line. The digital twin merges physics-based system modeling and distributed real-time process data to generate an authoritative digital design of the system at pre-production phase. A digital twin-based analytical decoupling framework is also developed to provide engineering analysis capabilities and support the decision-making over the system designing and solution evaluation. Three key enabling techniques as well as a case study in hollow glass production line are addressed to validate the proposed approach.
Challenges for Database Management in the Internet of Things

This article discusses the challenges for Database Management in the Internet of Things. We provide scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.
Algorithms for multi-armed bandit problems

The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as -greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a significant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. These properties are not described by current theory, even though they can be exploited in practice in the design of heuristics. Thirdly, the algorithms’ performance relative each to other is affected only by the number of bandit arms and the variance of the rewards. This finding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We find that an adaptive trial would have successfully treated at least 50% more patients, while significantly reducing the number of adverse effects and increasing patient retention. At the end of the trial, the best treatment could have still been identified with a high level of statistical confidence. Our findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies.
Depth From Defocus in Presence of Partial Self Occlusion

Contrary to the normal belief we show that selfocclusion is present in any real aperture image and we present a method on how we can take care of the occlusion while recovering the depth using the defocus as the cue. The spacevariant blur is modeled as an MRF and the MAP estimates are obtained f o r both the depth map and the everywhere f o cused intensity image. The blur kernel is adjusted in the regions where occlusion is present, particularly at the regions of discontinuities in the scene. The performance of the proposed algorithm is tested over synthetic data and the estimates are found to be better than the earlier schemes where such subtle effects were ignored.
Prevention of Relay Attack Using NFC

Near Field Communication (NFC) is one of the emerging and promising technological developments for mobile phones and other contactless devices. NFC technologies allow two active devices embedded with chip transmit small pieces of data between each other via short range wireless connection and at low speed depending on the configurations. It offers low friction process because of the close range that the two NFC enabled devices can setup a connection. The combination of NFC with smart devices has led to development and range of NFC that includes data exchange, service discovery, connection, e-payment, and ticketing. With the help of a NFC enabled phone and card reader device, contactless card transaction can be performed. Security problems related to relay attack were analyzed and identified a proper solution to prevent the attack. In the proposed system, a Frame wait integer is used to check and verify data manipulation, by attaching the transacted data with a signed integer.
Multilayer networks: an architecture framework

We present an architecture framework for the control and management of multilayer networks and associated advanced network services. This material is identified as an "architecture framework" to emphasize its role in providing guidance and structure to our subsequent detailed architecture, design, and implementation activities. Our work is motivated by requirements from the Department of Energy science application community for real-time on-demand science-domain-specific network services and resource provisioning. We also summarize the current state of deployments and use of network services based on this multilayer network architecture framework.
Causal Forest vs. Naive Causal Forest in Detecting Personalization: An Empirical Study in ASSISTments

It is widely understood that students learn in a variety of different ways and what is beneficial for one student may not necessarily help another. This work observes the effectiveness of Causal Forests as they compare to a new method we present called Näıve Causal Forests. This new method, aimed to be a simpler, more intuitive approach to identifying heterogeneous effects, is developed to better understand the strengths and limitations of the Causal Forest method. We apply these techniques to real student data on three RCTs run within the ASSISTments online learning platform.
Bitcoin : An Innovative Alternative Digital Currency : An Exploratory Study

“Mining”  in the world of Bitcoin is transaction processing, in which transaction records of current Bitcoin public ledger, known as blocks, are added to the record of past transactions, known as the block chain. Bitcoin provides a reward in exchange for the mining services provided by several “miners” throughout the world. This is a necessary incentive, as without the miners, the Bitcoin technology wouldn’t be able to operate. For being an effective miner, specialized & high-performance equipment is used. For even more efficient use of resources, mining “pools” are created. A mining pool is basically a number of miners mining on blocks as a group. Even though the blocks are mined faster, the rewards have to be divided in proportion to the efforts put in by individual miners as compared to the entire group.
Antidepressant effects of sleep deprivation require astrocyte-dependent adenosine mediated signaling

Major depressive disorder is a debilitating condition with a lifetime risk of ten percent. Most treatments take several weeks to achieve clinical efficacy, limiting the ability to bring instant relief needed in psychiatric emergencies. One intervention that rapidly alleviates depressive symptoms is sleep deprivation; however, its mechanism of action is unknown. Astrocytes regulate responses to sleep deprivation, raising the possibility that glial signaling mediates antidepressive-like actions of sleep deprivation. Here, we found that astrocytic signaling to adenosine (A1) receptors was required for the robust reduction of depressive-like behaviors following 12 hours of sleep deprivation. As sleep deprivation activates synaptic A1 receptors, we mimicked the effect of sleep deprivation on depression phenotypes by administration of the A1 agonist CCPA. These results provide the first mechanistic insight into how sleep deprivation impacts mood, and provide a novel pathway for rapid antidepressant development by modulation of glial signaling in the brain.
Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.

To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP).



A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers.



The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects.



The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.
Long-Legged Hexapod Giacometti Robot Using Thin Soft McKibben Actuator

This letter introduces a lightweight hexapod robot, Giacometti robot, made with long and narrow legs following the Alberto Giacometti's sculpture conception. The goal is achieved by, first, using multiple links with thin and soft McKibben actuators, and second, choosing a leg design which is narrow in comparison to its body's length and height, unlike conventional robot design. By such design characteristic, the leg will exhibit elastic deformations due to the low stiffness property of the thin link structure. Then, we model the leg structure and conduct the deflection analysis to confirm the capability of the leg to perform walking motion. The high force to weight ratio characteristics of the actuator provided the ability to drive the system, as shown by a static model and further validated experimentally. To compensate for the high elastic structural flexibility of the legs, two walking gaits namely customized Wave gait and Giacometti gait were introduced. The robot could walk successfully with both gaits at maximum speed of 0.005 and 0.05 m/s, respectively. It is envisaged that the lightweight Giacometti robot design can be very useful in legged robotic exploration.
Context-aware support for computer-supported ubiquitous learning

This paper describes a computer supported ubiquitous learning environment for language learning. This paper proposes two systems. The first is context-aware language-learning support system for Japanese polite expressions learning, which is called JAPELAS (Japanese polite expressions learning assisting system). This system provides learner the appropriate polite expressions deriving the learner's situation and personal information. The second system is called TANGO (Tag Added learNinG Objects) system, which detects the objects around learner using RFID tags, and provides the learner the educational information. This paper describes the preliminary evaluation of those two systems.
Predictive coding as a model of cognition

Previous work has shown that predictive coding can provide a detailed explanation of a very wide range of low-level perceptual processes. It is also widely believed that predictive coding can account for high-level, cognitive, abilities. This article provides support for this view by showing that predictive coding can simulate phenomena such as categorisation, the influence of abstract knowledge on perception, recall and reasoning about conceptual knowledge, context-dependent behavioural control, and naive physics. The particular implementation of predictive coding used here (PC/BC-DIM) has previously been used to simulate low-level perceptual behaviour and the neural mechanisms that underlie them. This algorithm thus provides a single framework for modelling both perceptual and cognitive brain function.
Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease

The prediction of survival of Coronary heart disease (CHD) has been a challenging research problem for medical society. The goal of this paper is to develop data mining algorithms for predicting survival of CHD patients based on 1000 cases .We carry out a clinical observation and a 6-month follow up to include 1000 CHD cases. The survival information of each case is obtained via follow up. Based on the data, we employed three popular data mining algorithms to develop the prediction models using the 502 cases. We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results indicated that the SVM is the best predictor with 92.1 % accuracy on the holdout sample artificial neural networks came out to be the second with91.0% accuracy and the decision trees models came out to be the worst of the three with 89.6% accuracy. The comparative study of multiple prediction models for survival of CHD patients along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data.
An Ontology-Based Architecture for Natural Language Access to Relational Databases

Natural language (NL) access to databases is a problem that has interested researchers for many years. We demonstrate that an ontology-based approach is technically feasible to handle some of the challenges facing NL query processing for database access. This paper presents the architecture, algorithms and results from the prototype thereof which indicate a domain and language independent architecture with high precision and recall rates. Studies are conducted for each of English and Swahili queries, both for same language and cross-lingual retrieval, from which we demonstrate promising precision and recall rates, language and domain independence, and that for language pairs it is sufficient to incorporate a machine translation system at the gazetteer level.
Discussions in the comments section: Factors influencing participation and interactivity in online newspapers' reader comments

Posting comments on the news is one of the most popular forms of user participation in online newspapers, and there is great potential for public discourse that is associated with this form of user communication. However, this potential arises only when several users participate in commenting and when their communication becomes interactive. Based on an adaption of Galtung and Ruge’s theory of newsworthiness, we hypothesized that a news article’s news factors affect both participation levels and interactivity in a news item’s comments section. The data from an online content analysis of political news are consistent with the hypotheses. This article explores the ways in which news factors affect participation levels and interactivity, and it discusses the theoretical, normative, and practical implications of those findings.
The effects of introducing Tabata interval training and stability exercises to school children as a school-based intervention program.

Physical activities during leisure time as well as school hours have changed over the past few years, with adolescents being less physically active and adopting a sedentary lifestyle.  The overall objective of this mixed-methods study was to evaluate the feasibility of introducing a 4-min Tabata interval training into a lower secondary school context. A further aim was to evaluate the possible effects on: coordination, balance, and strength.  The study was conducted as an intervention study with a mixed-method approach. Forty-three children, aged 7-9 years, participated in the intervention group. Additionally, 13 children were recruited as a control group. The intervention itself was delivered by the teachers and was performed for 4-min every day in a classroom setting. All participants performed physical tests before and after the intervention period to evaluate the Tabata training. After the completion of the 6-week Tabata interval training, the four teachers were interviewed.  The push-ups (p = 0.004), kneeling push-ups (p = 0.03), and standing long jump (p = 0.01) improved in the intervention group after 6 weeks. No differences were observed between the genders. The teachers experienced that it worked well to integrate the Tabata interval training in the classroom setting.  After 6 weeks, a school-based Tabata intervention program improved physical performance. The teachers saw no obstacles in including the Tabata intervention program in a classroom setting and pointed out several positive aspects such as an increased energy level and development in the children's movement patterns.