nils-herrmann/clean_scirepeval huggingface dataset:
Blurred boundaries: the therapeutics and politics of medical marijuana.

For 5 millennia, Cannabis sativa has been used throughout the world medically, recreationally, and spiritually. From the mid-19th century to the 1930s, American physicians prescribed it for a plethora of indications, until the federal government started imposing restrictions on its use, culminating in 1970 with the US Congress classifying it as a Schedule I substance, illegal, and without medical value. Simultaneous with this prohibition, marijuana became the United States' most widely used illicit recreational drug, a substance generally regarded as pleasurable and relaxing without the addictive dangers of opioids or stimulants. Meanwhile, cannabis never lost its cachet in alternative medicine circles, going mainstream in 1995 when California became the first of 16 states to date to legalize its medical use, despite the federal ban. Little about cannabis is straightforward. Its main active ingredient, δ-9-tetrahydrocannabinol, was not isolated until 1964, and not until the 1990s were the far-reaching modulatory activities of the endocannabinoid system in the human body appreciated. This system's elucidation raises the possibility of many promising pharmaceutical applications, even as draconian federal restrictions that hamstring research show no signs of softening. Recreational use continues unabated, despite growing evidence of marijuana's addictive potential, particularly in the young, and its propensity for inducing and exacerbating psychotic illness in the susceptible. Public approval drives medical marijuana legalization efforts without the scientific data normally required to justify a new medication's introduction. This article explores each of these controversies, with the intent of educating physicians to decide for themselves whether marijuana is panacea, scourge, or both. PubMed searches were conducted using the following keywords: medical marijuana, medical cannabis, endocannabinoid system, CB1 receptors, CB2 receptors, THC, cannabidiol, nabilone, dronabinol, nabiximols, rimonabant, marijuana legislation, marijuana abuse, marijuana dependence, and marijuana and schizophrenia. Bibliographies were hand searched for additional references relevant to clarifying the relationships between medical and recreational marijuana use and abuse.
Amharic-English Speech Translation in Tourism Domain

This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and AmharicEnglish Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model (AM). Likewise, SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4% of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4% word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42%. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95%) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.
Map-based navigation in mobile robots:: II. A review of map-learning and path-planning strategies

This article reviews map-learning and path-planning strategies within the context of mapbased navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, that must be tightly tailored to the specific robot that is used notably to the capacities and limitations of its sensory-motor equipment and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot’s way to its goal.
Quaternion Zernike moments and their invariants for color image analysis and object recognition

Moments and moment invariants have become a powerfu l tool in pattern recognition and image analysis. C onventional methods to deal with color images are based on RGB decomposition or graying, which may lose some signi ficant color information. In this paper, by using the algebra of quaternions, we introduce the quaternion Zernike m oments (QZMs) to deal with the color images in a holistic manner. It is s hown that the QZMs can be obtained from the convent ional Zernike moments of each channel. We also provide the theore tical framework to construct a set of combined inva riants with respect to rotation, scaling and translation (RST) transfor mation. Experimental results are provided to illust rate the efficiency of the proposed descriptors.
Research on data mining models for the internet of things

In this paper, we propose four data mining models for the Internet of Things, which are multi-layer data mining model, distributed data mining model, Grid based data mining model and data mining model from multi-technology integration perspective. Among them, multi-layer model includes four layers: 1) data collection layer, 2) data management layer, 3) event processing layer, and 4) data mining service layer. Distributed data mining model can solve problems from depositing data at different sites. Grid based data mining model allows Grid framework to realize the functions of data mining. Data mining model from multi-technology integration perspective describes the corresponding framework for the future Internet. Several key issues in data mining of IoT are also discussed.
MVC architecture driven restructuring to achieve client-side web page composition

This paper presents a restructuring approach to relocating web page composition from servers to browsers for Java web applications. The objective is to reduce redundant manipulation and transfer of code/data that are shared by web pages. The reduction is carried out through a restructuring algorithm, effectively keeping consistency between source and target applications from the perspective of the model-view-controller (MVC) architecture, because the problem requires the target application to preserve the observable behavior of its source application. Case studies show that our restructuring tool can efficiently support the restructuring process.
Conditional connectivity

For a noncomplete graph G , the traditional definition of its connectivity is the minimum number of points whose removal results in a disconnected subgraph with components H , , . . . , H k . The conditional connectivity of G with respect to some graphtheoretic property P is the smallest cardinality of a set S of points, if any, such that every component Hi of the disconnected graph G S has property P. A survey of promising properties P is presented. Questions for various P-connectivities are listed in analogy with known results on connectivity and line-connectivity.
Wound healing activity of the fruit skin of Punica granatum.

The skin of the fruit and the bark of Punica granatum are used as a traditional remedy against diarrhea, dysentery, and intestinal parasites. The fruit skin extract of P. granatum was tested for its wound healing activity in rats using an excision wound model. The animals were divided into three groups of six each. The experimental group of animals was topically treated with P. granatum at a dose of 100 mg/kg every day for 15 days, while the controls and standard group animals were treated with petroleum jelly and mupirocin ointment, respectively. Phytochemical analysis of the extract revealed the presence of saponins, triterpenes, tannins, alkaloids, flavonoids, and cardiac glycosides. Extract-treated animals exhibited 95% reduction in the wound area when compared with controls (84%), which was statistically significant (P<.01). The extract-treated wounds were found to epithelize faster compared with controls. The hydroxyproline content of extract-treated animals was significantly higher than controls (P<.05). The fruit skin extract did not show any antimicrobial activity against the microrganisms tested. P. granatum promotes significant wound healing in rats and further evaluation of this activity in humans is suggested.
2005: Tutorial on Agent-Based Modeling and Simulation

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agentbased applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.
The Art of Getting Deep Neural Networks in Shape

Training a deep neural network (DNN) involves selecting a set of hyperparameters that define the network topology and influence the accuracy of the resulting network. Often, the goal is to maximize prediction accuracy on a given dataset. However, non-functional requirements of the trained network -- such as inference speed, size, and energy consumption -- can be very important as well. In this article, we aim to automate the process of selecting an appropriate DNN topology that fulfills both functional and non-functional requirements of the application. Specifically, we focus on tuning two important hyperparameters, depth and width, which together define the shape of the resulting network and directly affect its accuracy, speed, size, and energy consumption. To reduce the time needed to search the design space, we train a fraction of DNNs and build a model to predict the performances of the remaining ones. We are able to produce tuned ResNets, which are up to 4.22 times faster than original depth-scaled ResNets on a batch of 128 images while matching their accuracy.
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction

Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction rely on supervised learning methods, which suffer from labeled data scarcity problem. The state-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which is Long-Short-Term-Memory network (LSTM). Deep neural networks, due to their large number of free parameters rely heavily on large annotated corpora for learning the end task. But in the real-world, it is hard to get large labeled data, mainly due to the heavy cost associated with the manual annotation. To this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction. In this study, we tackle the problem of labeled data scarcity for Adverse Drug Reaction mention extraction from social media and propose a novel semi-supervised learning based method which can leverage large unlabeled corpus available in abundance on the web. Through empirical study, we demonstrate that our proposed method outperforms fully supervised learning based baseline which relies on large manually annotated corpus for a good performance.
TUT database for acoustic scene classification and sound event detection

We introduce TUT Acoustic Scenes 2016 database for environmental sound research, consisting of binaural recordings from 15 different acoustic environments. A subset of this database, called TUT Sound Events 2016, contains annotations for individual sound events, specifically created for sound event detection. TUT Sound Events 2016 consists of residential area and home environments, and is manually annotated to mark onset, offset and label of sound events. In this paper we present the recording and annotation procedure, the database content, a recommended cross-validation setup and performance of supervised acoustic scene classification system and event detection baseline system using mel frequency cepstral coefficients and Gaussian mixture models. The database is publicly released to provide support for algorithm development and common ground for comparison of different techniques.
Student Privacy and Educational Data Mining: Perspectives from Industry

While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attention. The momentum behind and scrutiny of student privacy has made it increasingly difficult for EDM applications to transition from academia to industry. Based on experience as academic researchers transitioning into industry, we present three primary areas of concern related to student privacy in practice: policy, corporate social responsibility, and public opinion. Our discussion will describe the key challenges faced within these categories, strategies for overcoming them, and ways in which the academic EDM community can support the adoption of innovative technologies in large-scale production.
GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams

The real-time discovery of local events (e.g., protests, crimes, disasters) is of great importance to various applications, such as crime monitoring, disaster alarming, and activity recommendation. While this task was nearly impossible years ago due to the lack of timely and reliable data sources, the recent explosive growth in geo-tagged tweet data brings new opportunities to it. That said, how to extract quality local events from geo-tagged tweet streams in real time remains largely unsolved so far.
 We propose GeoBurst, a method that enables effective and real-time local event detection from geo-tagged tweet streams. With a novel authority measure that captures the geo-topic correlations among tweets, GeoBurst first identifies several pivots in the query window. Such pivots serve as representative tweets for potential local events and naturally attract similar tweets to form candidate events. To select truly interesting local events from the candidate list, GeoBurst further summarizes continuous tweet streams and compares the candidates against historical activities to obtain spatiotemporally bursty ones. Finally, GeoBurst also features an updating module that finds new pivots with little time cost when the query window shifts. As such, GeoBurst is capable of monitoring continuous streams in real time. We used crowdsourcing to evaluate GeoBurst on two real-life data sets that contain millions of geo-tagged tweets. The results demonstrate that GeoBurst significantly outperforms state-of-the-art methods in precision, and is orders of magnitude faster.
Geometric and Photometric Consistency in a Mixed Video and Galvanoscopic Scanning Laser Projection Mapping System

We present a geometric calibration method to accurately register a galvanoscopic scanning laser projection system (GLP) based on 2D vector input data onto an arbitrarily complex 3D-shaped projection surface. This method allows for accurate merging of 3D vertex data displayed on the laser projector with geometrically calibrated standard rasterization-based video projectors that are registered to the same geometry. Because laser projectors send out a laser light beam via galvanoscopic mirrors, a standard pinhole model calibration procedure that is normally used for pixel raster displays projecting structured light patterns, such as Gray codes, cannot be carried out directly with sufficient accuracy as the rays do not converge into a single point. To overcome the complications of accurately registering the GLP while still enabling a treatment equivalent to a standard pinhole device, an adapted version is applied to enable straightforward content generation. Besides the geometrical calibration, we also present a photometric calibration to unify the color appearance of GLPs and standard video projectors maximizing the advantages of the large color gamut of the GLP and optimizing its color appearance to smoothly fade into the significantly smaller gamut of the video projector. The proposed algorithms were evaluated on a prototypical mixed video projector and GLP projection mapping setup.
Consecutive Support: Better Be Close!

We propose a new measure of support (the number of occurrences of a pattern), in which instances are more important if they occur with a certain frequency and close after each other in the stream of transactions. We will explain this new consecutive support and discuss how patterns can be found faster by pruning the search space, for instance using so-called parent support recalculation. Both consecutiveness and the notion of hypercliques are incorporated into the Eclat algorithm. Synthetic examples show how interesting phenomena can now be discovered in the datasets. The new measure can be applied in many areas, ranging from bio-informatics to trade, supermarkets, and even law enforcement. E.g., in bio-informatics it is important to find patterns contained in many individuals, where patterns close together in one chromosome are more significant.
Overview of Current Development in Electrical Energy Storage Technologies and the Application Potential in Power System Operation

Electrical power generation is changing dramatically across the world because of the need to reduce greenhouse gas emissions and to introduce mixed energy sources. The power network faces great challenges in transmission and distribution to meet demand with unpredictable daily and seasonal variations. Electrical Energy Storage (EES) is recognized as underpinning technologies to have great potential in meeting these challenges, whereby energy is stored in a certain state, according to the technology used, and is converted to electrical energy when needed. However, the wide variety of options and complex characteristic matrices make it difficult to appraise a specific EES technology for a particular application. This paper intends to mitigate this problem by providing a comprehensive and clear picture of the state-of-the-art technologies available, and where they would be suited for integration into a power generation and distribution system. The paper starts with an overview of the operation principles, technical and economic performance features and the current research and development of important EES technologies, sorted into six main categories based on the types of energy stored. Following this, a comprehensive comparison and an application potential analysis of the reviewed technologies are presented.
Hybrid Client Side Phishing Websites Detection Approach

Phishing tricks to steal personal or credential information by entering victims into a forged website similar to the original site, and urging them to enter their information believing that this site is legitimate. The number of internet users who are becoming victims of phishing attacks is increasing beside that phishing attacks have become more sophisticated. In this paper we propose a client-side solution to protect against phishing attacks which is a Firefox extension integrated as a toolbar that is responsible for checking whether recipient website is trusted or not by inspecting URLs of each requested webpage. If the site is suspicious the toolbar is going to block it. Every URL is evaluated corresponding to features extracted from it. Three heuristics (primary domain, sub domain, and path) and Naïve Bayes classification using four lexical features combined with page ranking received from two different services (Alexa, and Google page rank) used to classify URL. The proposed method requires no server changes and will prevent internet users from fraudulent sites especially from phishing attacks based on deceptive URLs. Experimental results show that our approach can achieve 48% accuracy ratio using a test set of 246 URL, and 87.5% accuracy ratio by excluding NB addition tested over 162 URL. Phishing Attacks; Browser Plugin; Anti Phishing; Security; Firefox
A 0.21-V minimum input, 73.6% maximum efficiency, fully integrated voltage boost converter with MPPT for low-voltage energy harvesters

This paper proposes a fully integrated voltage boost converter with a maximum power point tracking (MPPT) circuit for ultra-low power energy harvesting. The converter is based on a conventional charge pump circuit and can deliver a wide range of load current by using nMOS and pMOS driver circuits for highly efficient switching operation. The MPPT circuit we propose dissipates nano-watt power to extract maximum power regardless of the harvester's power generation conditions and load current. The measurement results demonstrated that the circuit converted a 0.49-V input to a 1.46-V output with 73.6% power conversion efficiency when the output power was 348 μW. The circuit can operate at an extremely low input of 0.21 V.
Conceptualising user hedonic experience

For many years, the approach taken towards technology design focused on supporting the effective, efficient, and satisfying use of it. The way people use technology has shifted from merely using it to enjoying using it. This paper describes an early approach to understanding user experience in context of technologies (e.g. digital cameras, PDAs, and mobile phones), as well as in more general context such as physical activities e.g. exercising, orienteering, and walking, and in context of diaries. The focus of this paper is on hedonic user experience; that is pleasure, enjoyment, excitement, and fun in the context of technology. This study provides insights into factors contributing to and influencing such experiences and the relationships between them.